research paper topics on machine

Exploring 250+ Machine Learning Research Topics

In recent years, machine learning has become super popular and grown very quickly. This happened because technology got better, and there’s a lot more data available. Because of this, we’ve seen lots of new and amazing things happen in different areas. Machine learning research is what makes all these cool things possible. In this blog, we’ll talk about machine learning research topics, why they’re important, how you can pick one, what areas are popular to study, what’s new and exciting, the tough problems, and where you can find help if you want to be a researcher.

Whether you’re delving into popular areas or tackling tough problems, our ‘ ‘ service is here to support your research journey.”

Why Does Machine Learning Research Matter?

Table of Contents

Machine learning research is at the heart of the AI revolution. It underpins the development of intelligent systems capable of making predictions, automating tasks, and improving decision-making across industries. The importance of this research can be summarized as follows:

Advancements in Technology

The growth of machine learning research has led to the development of powerful algorithms, tools, and frameworks. Numerous industries, including healthcare, banking, autonomous cars, and natural language processing, have found use for these technology.

As researchers continue to push the boundaries of what’s possible, we can expect even more transformative technologies to emerge.

Real-world Applications

Machine learning research has brought about tangible changes in our daily lives. Voice assistants like Siri and Alexa, recommendation systems on streaming platforms, and personalized healthcare diagnostics are just a few examples of how this research impacts our world.

By working on new research topics, scientists can further refine these applications and create new ones.

Economic and Industrial Impacts

The economic implications of machine learning research are substantial. Companies that harness the power of machine learning gain a competitive edge in the market.

This creates a demand for skilled machine learning researchers, driving job opportunities and contributing to economic growth.

How to Choose the Machine Learning Research Topics?

Selecting the right machine learning research topics is crucial for your success as a machine learning researcher. Here’s a guide to help you make an informed decision:

Understanding Your Interests

Start by considering your personal interests. Machine learning is a broad field with applications in virtually every sector. By choosing a topic that aligns with your passions, you’ll stay motivated and engaged throughout your research journey.

Reviewing Current Trends

Stay updated on the latest trends in machine learning. Attend conferences, read research papers, and engage with the community to identify emerging research topics. Current trends often lead to exciting breakthroughs.

Identifying Gaps in Existing Research

Sometimes, the most promising research topics involve addressing gaps in existing knowledge. These gaps may become evident through your own experiences, discussions with peers, or in the course of your studies.

Collaborating with Experts

Collaboration is key in research. Working with experts in the field can help you refine your research topic and gain valuable insights. Seek mentors and collaborators who can guide you.

250+ Machine Learning Research Topics: Category-wise

Supervised learning.

Explainable AI for Decision Support
Few-shot Learning Methods
Time Series Forecasting with Deep Learning
Handling Imbalanced Datasets in Classification
Regression Techniques for Non-linear Data
Transfer Learning in Supervised Settings
Multi-label Classification Strategies
Semi-Supervised Learning Approaches
Novel Feature Selection Methods
Anomaly Detection in Supervised Scenarios
Federated Learning for Distributed Supervised Models
Ensemble Learning for Improved Accuracy
Automated Hyperparameter Tuning
Ethical Implications in Supervised Models
Interpretability of Deep Neural Networks.

Unsupervised Learning

Unsupervised Clustering of High-dimensional Data
Semi-Supervised Clustering Approaches
Density Estimation in Unsupervised Learning
Anomaly Detection in Unsupervised Settings
Transfer Learning for Unsupervised Tasks
Representation Learning in Unsupervised Learning
Outlier Detection Techniques
Generative Models for Data Synthesis
Manifold Learning in High-dimensional Spaces
Unsupervised Feature Selection
Privacy-Preserving Unsupervised Learning
Community Detection in Complex Networks
Clustering Interpretability and Visualization
Unsupervised Learning for Image Segmentation
Autoencoders for Dimensionality Reduction.

Reinforcement Learning

Deep Reinforcement Learning in Real-world Applications
Safe Reinforcement Learning for Autonomous Systems
Transfer Learning in Reinforcement Learning
Imitation Learning and Apprenticeship Learning
Multi-agent Reinforcement Learning
Explainable Reinforcement Learning Policies
Hierarchical Reinforcement Learning
Model-based Reinforcement Learning
Curriculum Learning in Reinforcement Learning
Reinforcement Learning in Robotics
Exploration vs. Exploitation Strategies
Reward Function Design and Ethical Considerations
Reinforcement Learning in Healthcare
Continuous Action Spaces in RL
Reinforcement Learning for Resource Management.

Natural Language Processing (NLP)

Multilingual and Cross-lingual NLP
Contextualized Word Embeddings
Bias Detection and Mitigation in NLP
Named Entity Recognition for Low-resource Languages
Sentiment Analysis in Social Media Text
Dialogue Systems for Improved Customer Service
Text Summarization for News Articles
Low-resource Machine Translation
Explainable NLP Models
Coreference Resolution in NLP
Question Answering in Specific Domains
Detecting Fake News and Misinformation
NLP for Healthcare: Clinical Document Understanding
Emotion Analysis in Text
Text Generation with Controlled Attributes.

Computer Vision

Video Action Recognition and Event Detection
Object Detection in Challenging Conditions (e.g., low light)
Explainable Computer Vision Models
Image Captioning for Accessibility
Large-scale Image Retrieval
Domain Adaptation in Computer Vision
Fine-grained Image Classification
Facial Expression Recognition
Visual Question Answering
Self-supervised Learning for Visual Representations
Weakly Supervised Object Localization
Human Pose Estimation in 3D
Scene Understanding in Autonomous Vehicles
Image Super-resolution
Gaze Estimation for Human-Computer Interaction.

Deep Learning

Neural Architecture Search for Efficient Models
Self-attention Mechanisms and Transformers
Interpretability in Deep Learning Models
Robustness of Deep Neural Networks
Generative Adversarial Networks (GANs) for Data Augmentation
Neural Style Transfer in Art and Design
Adversarial Attacks and Defenses
Neural Networks for Audio and Speech Processing
Explainable AI for Healthcare Diagnosis
Automated Machine Learning (AutoML)
Reinforcement Learning with Deep Neural Networks
Model Compression and Quantization
Lifelong Learning with Deep Learning Models
Multimodal Learning with Vision and Language
Federated Learning for Privacy-preserving Deep Learning.

Explainable AI

Visualizing Model Decision Boundaries
Saliency Maps and Feature Attribution
Rule-based Explanations for Black-box Models
Contrastive Explanations for Model Interpretability
Counterfactual Explanations and What-if Analysis
Human-centered AI for Explainable Healthcare
Ethics and Fairness in Explainable AI
Explanation Generation for Natural Language Processing
Explainable AI in Financial Risk Assessment
User-friendly Interfaces for Model Interpretability
Scalability and Efficiency in Explainable Models
Hybrid Models for Combined Accuracy and Explainability
Post-hoc vs. Intrinsic Explanations
Evaluation Metrics for Explanation Quality
Explainable AI for Autonomous Vehicles.

Transfer Learning

Zero-shot Learning and Few-shot Learning
Cross-domain Transfer Learning
Domain Adaptation for Improved Generalization
Multilingual Transfer Learning in NLP
Pretraining and Fine-tuning Techniques
Lifelong Learning and Continual Learning
Domain-specific Transfer Learning Applications
Model Distillation for Knowledge Transfer
Contrastive Learning for Transfer Learning
Self-training and Pseudo-labeling
Dynamic Adaption of Pretrained Models
Privacy-Preserving Transfer Learning
Unsupervised Domain Adaptation
Negative Transfer Avoidance in Transfer Learning.

Federated Learning

Secure Aggregation in Federated Learning
Communication-efficient Federated Learning
Privacy-preserving Techniques in Federated Learning
Federated Transfer Learning
Heterogeneous Federated Learning
Real-world Applications of Federated Learning
Federated Learning for Edge Devices
Federated Learning for Healthcare Data
Differential Privacy in Federated Learning
Byzantine-robust Federated Learning
Federated Learning with Non-IID Data
Model Selection in Federated Learning
Scalable Federated Learning for Large Datasets
Client Selection and Sampling Strategies
Global Model Update Synchronization in Federated Learning.

Quantum Machine Learning

Quantum Neural Networks and Quantum Circuit Learning
Quantum-enhanced Optimization for Machine Learning
Quantum Data Compression and Quantum Principal Component Analysis
Quantum Kernels and Quantum Feature Maps
Quantum Variational Autoencoders
Quantum Transfer Learning
Quantum-inspired Classical Algorithms for ML
Hybrid Quantum-Classical Models
Quantum Machine Learning on Near-term Quantum Devices
Quantum-inspired Reinforcement Learning
Quantum Computing for Quantum Chemistry and Drug Discovery
Quantum Machine Learning for Finance
Quantum Data Structures and Quantum Databases
Quantum-enhanced Cryptography in Machine Learning
Quantum Generative Models and Quantum GANs.

Ethical AI and Bias Mitigation

Fairness-aware Machine Learning Algorithms
Bias Detection and Mitigation in Real-world Data
Explainable AI for Ethical Decision Support
Algorithmic Accountability and Transparency
Privacy-preserving AI and Data Governance
Ethical Considerations in AI for Healthcare
Fairness in Recommender Systems
Bias and Fairness in NLP Models
Auditing AI Systems for Bias
Societal Implications of AI in Criminal Justice
Ethical AI Education and Training
Bias Mitigation in Autonomous Vehicles
Fair AI in Financial and Hiring Decisions
Case Studies in Ethical AI Failures
Legal and Policy Frameworks for Ethical AI.

Meta-Learning and AutoML

Neural Architecture Search (NAS) for Efficient Models
Transfer Learning in NAS
Reinforcement Learning for NAS
Multi-objective NAS
Automated Data Augmentation
Neural Architecture Optimization for Edge Devices
Bayesian Optimization for AutoML
Model Compression and Quantization in AutoML
AutoML for Federated Learning
AutoML in Healthcare Diagnostics
Explainable AutoML
Cost-sensitive Learning in AutoML
AutoML for Small Data
Human-in-the-Loop AutoML.

AI for Healthcare and Medicine

Disease Prediction and Early Diagnosis
Medical Image Analysis with Deep Learning
Drug Discovery and Molecular Modeling
Electronic Health Record Analysis
Predictive Analytics in Healthcare
Personalized Treatment Planning
Healthcare Fraud Detection
Telemedicine and Remote Patient Monitoring
AI in Radiology and Pathology
AI in Drug Repurposing
AI for Medical Robotics and Surgery
Genomic Data Analysis
AI-powered Mental Health Assessment
Explainable AI in Healthcare Decision Support
AI in Epidemiology and Outbreak Prediction.

AI in Finance and Investment

Algorithmic Trading and High-frequency Trading
Credit Scoring and Risk Assessment
Fraud Detection and Anti-money Laundering
Portfolio Optimization with AI
Financial Market Prediction
Sentiment Analysis in Financial News
Explainable AI in Financial Decision-making
Algorithmic Pricing and Dynamic Pricing Strategies
AI in Cryptocurrency and Blockchain
Customer Behavior Analysis in Banking
Explainable AI in Credit Decisioning
AI in Regulatory Compliance
Ethical AI in Financial Services
AI for Real Estate Investment
Automated Financial Reporting.

AI in Climate Change and Sustainability

Climate Modeling and Prediction
Renewable Energy Forecasting
Smart Grid Optimization
Energy Consumption Forecasting
Carbon Emission Reduction with AI
Ecosystem Monitoring and Preservation
Precision Agriculture with AI
AI for Wildlife Conservation
Natural Disaster Prediction and Management
Water Resource Management with AI
Sustainable Transportation and Urban Planning
Climate Change Mitigation Strategies with AI
Environmental Impact Assessment with Machine Learning
Eco-friendly Supply Chain Optimization
Ethical AI in Climate-related Decision Support.

Data Privacy and Security

Differential Privacy Mechanisms
Federated Learning for Privacy-preserving AI
Secure Multi-Party Computation
Privacy-enhancing Technologies in Machine Learning
Homomorphic Encryption for Machine Learning
Ethical Considerations in Data Privacy
Privacy-preserving AI in Healthcare
AI for Secure Authentication and Access Control
Blockchain and AI for Data Security
Explainable Privacy in Machine Learning
Privacy-preserving AI in Government and Public Services
Privacy-compliant AI for IoT and Edge Devices
Secure AI Models Sharing and Deployment
Privacy-preserving AI in Financial Transactions
AI in the Legal Frameworks of Data Privacy.

Global Collaboration in Research

International Research Partnerships and Collaboration Models
Multilingual and Cross-cultural AI Research
Addressing Global Healthcare Challenges with AI
Ethical Considerations in International AI Collaborations
Interdisciplinary AI Research in Global Challenges
AI Ethics and Human Rights in Global Research
Data Sharing and Data Access in Global AI Research
Cross-border Research Regulations and Compliance
AI Innovation Hubs and International Research Centers
AI Education and Training for Global Communities
Humanitarian AI and AI for Sustainable Development Goals
AI for Cultural Preservation and Heritage Protection
Collaboration in AI-related Global Crises
AI in Cross-cultural Communication and Understanding
Global AI for Environmental Sustainability and Conservation.

Emerging Trends and Hot Topics in Machine Learning Research

The landscape of machine learning research topics is constantly evolving. Here are some of the emerging trends and hot topics that are shaping the field:

As AI systems become more prevalent, addressing ethical concerns and mitigating bias in algorithms are critical research areas.

Interpretable and Explainable Models

Understanding why machine learning models make specific decisions is crucial for their adoption in sensitive areas, such as healthcare and finance.

Meta-learning algorithms are designed to enable machines to learn how to learn, while AutoML aims to automate the machine learning process itself.

Machine learning is revolutionizing the healthcare sector, from diagnostic tools to drug discovery and patient care.

Algorithmic trading, risk assessment, and fraud detection are just a few applications of AI in finance, creating a wealth of research opportunities.

Machine learning research is crucial in analyzing and mitigating the impacts of climate change and promoting sustainable practices.

Challenges and Future Directions

While machine learning research has made tremendous strides, it also faces several challenges:

Data Privacy and Security: As machine learning models require vast amounts of data, protecting individual privacy and data security are paramount concerns.
Scalability and Efficiency: Developing efficient algorithms that can handle increasingly large datasets and complex computations remains a challenge.
Ensuring Fairness and Transparency: Addressing bias in machine learning models and making their decisions transparent is essential for equitable AI systems.
Quantum Computing and Machine Learning: The integration of quantum computing and machine learning has the potential to revolutionize the field, but it also presents unique challenges.
Global Collaboration in Research: Machine learning research benefits from collaboration on a global scale. Ensuring that researchers from diverse backgrounds work together is vital for progress.

Resources for Machine Learning Researchers

If you’re looking to embark on a journey in machine learning research topics, there are various resources at your disposal:

Journals and Conferences

Journals such as the “Journal of Machine Learning Research” and conferences like NeurIPS and ICML provide a platform for publishing and discussing research findings.

Online Communities and Forums

Platforms like Stack Overflow, GitHub, and dedicated forums for machine learning provide spaces for collaboration and problem-solving.

Datasets and Tools

Open-source datasets and tools like TensorFlow and PyTorch simplify the research process by providing access to data and pre-built models.

Research Grants and Funding Opportunities

Many organizations and government agencies offer research grants and funding for machine learning projects. Seek out these opportunities to support your research.

Machine learning research is like a superhero in the world of technology. To be a part of this exciting journey, it’s important to choose the right machine learning research topics and keep up with the latest trends.

Machine learning research makes our lives better. It powers things like smart assistants and life-saving medical tools. It’s like the force driving the future of technology and society.

But, there are challenges too. We need to work together and be ethical in our research. Everyone should benefit from this technology. The future of machine learning research is incredibly bright. If you want to be a part of it, get ready for an exciting adventure. You can help create new solutions and make a big impact on the world.

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Text Mining and Text Classification

Text mining (also referred to as text analytics) is an artificial intelligence (AI) technology that uses natural language processing (NLP) to transform the free (unstructured) text in documents and databases into normalized, structured data suitable for analysis or to drive machine learning (ML) algorithms. Text classification tools categorize text by understanding its overall meaning, without predefined categories being explicitly present within the text. This is one of the best research and thesis topics for ML projects.

Image-Based Applications

An image-based test consists of a sequence of operations on UI elements in your tested application: clicks (for desktop and web applications), touches (for mobile applications), drag and drop operations, checkpoints, and so on. In image applications, one must first get familiar with masks, convolution, edge, and corner detection to be able to extract useful information from images and further use them for applications like image segmentation, keypoints extraction, and more.

Machine Vision

Using machine learning -based/mathematical techniques to enable machines to do specific tasks. For example, watermarking, face identification from datasets of images with rotation and different camera angles, criminals identification from surveillance cameras (video and series of images), handwriting and personal signature classification, object detection/recognition.

Clustering or cluster analysis is a machine learning technique, which groups the unlabeled dataset. It can be defined as "A way of grouping the data points into different clusters, consisting of similar data points. For example Graph clustering, data clustering, density-based clustering, and more. Clustering is one of the best research and thesis topics for ML projects.

Optimization

A) Population-based optimization inspired from a natural mechanism: Black-box optimization, multi/many-objective optimization, evolutionary methods (Genetic Algorithm, Genetic Programming, Memetic Programming), Metaheuristics (e.g., PSO, ABC, SA)

B) Exact/Mathematical Models: Convex optimization, Bi-Convex, and Semi-Convex optimization, Gradient Descent, Block Coordinate Descent, Manifold Optimization, and Algebraic Models

Voice Classification

Voice classification or sound classification can be referred to as the process of analyzing audio recordings. Voice and Speech Recognition, Signal Processing, Message Embedding, Message Extraction from Voice Encoded, and more are the best research and thesis topics for ML projects.

Sentiment Analysis

Sentiment analysis is one of the best Machine Learning projects well-known to uncover emotions in the text. By analyzing movie reviews, customer feedback, support tickets, companies may discover many interesting things. So learning how to build sentiment analysis models is quite a practical skill. There is no need to collect the data yourself. To train and test your model, use the biggest open-source database for sentiment analysis created by IMDb.

Recommendation Framework Project

This a rich dataset assortment containing a different scope of datasets accumulated from famous sites like Goodreads book audits, Amazon item surveys, online media, and so forth You will probably fabricate a recommendation engine (like the ones utilized by Amazon and Netflix) that can create customized recommendations for items, films, music, and so on, because of client inclinations, needs, and online conduct.

Mall Customers' Project

As the name suggests, the mall customers' dataset includes the records of people who visited the mall, such as gender, age, customer ID, annual income, spending score, etc. You will build a model that will use this data to segment the customers into different groups based on their behavior patterns. Such customer segmentation is a highly useful marketing tactic used by brands and marketers to boost sales and revenue while also increasing customer satisfaction.

Object Detection with Deep Learning

Object Detection with Deep Learning is one of the interesting machine learning projects to create. When it comes to image classification, Deep Neural Networks (DNNs) should be your go-to choice. While DNNs are already used in many real-world image classification applications, it is one of the best ML projects that aims to crank it up a notch. In this Machine Learning project, you will solve the problem of object detection by leveraging DNNs.

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Research Topics & Ideas

Artifical Intelligence (AI) and Machine Learning (ML)

Research topics and ideas about AI and machine learning

If you’re just starting out exploring AI-related research topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from past studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

AI-Related Research Topics & Ideas

Below you’ll find a list of AI and machine learning-related research topics ideas. These are intentionally broad and generic , so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

Developing AI algorithms for early detection of chronic diseases using patient data.
The use of deep learning in enhancing the accuracy of weather prediction models.
Machine learning techniques for real-time language translation in social media platforms.
AI-driven approaches to improve cybersecurity in financial transactions.
The role of AI in optimizing supply chain logistics for e-commerce.
Investigating the impact of machine learning in personalized education systems.
The use of AI in predictive maintenance for industrial machinery.
Developing ethical frameworks for AI decision-making in healthcare.
The application of ML algorithms in autonomous vehicle navigation systems.
AI in agricultural technology: Optimizing crop yield predictions.
Machine learning techniques for enhancing image recognition in security systems.
AI-powered chatbots: Improving customer service efficiency in retail.
The impact of AI on enhancing energy efficiency in smart buildings.
Deep learning in drug discovery and pharmaceutical research.
The use of AI in detecting and combating online misinformation.
Machine learning models for real-time traffic prediction and management.
AI applications in facial recognition: Privacy and ethical considerations.
The effectiveness of ML in financial market prediction and analysis.
Developing AI tools for real-time monitoring of environmental pollution.
Machine learning for automated content moderation on social platforms.
The role of AI in enhancing the accuracy of medical diagnostics.
AI in space exploration: Automated data analysis and interpretation.
Machine learning techniques in identifying genetic markers for diseases.
AI-driven personal finance management tools.
The use of AI in developing adaptive learning technologies for disabled students.

AI & ML Research Topic Ideas (Continued)

Machine learning in cybersecurity threat detection and response.
AI applications in virtual reality and augmented reality experiences.
Developing ethical AI systems for recruitment and hiring processes.
Machine learning for sentiment analysis in customer feedback.
AI in sports analytics for performance enhancement and injury prevention.
The role of AI in improving urban planning and smart city initiatives.
Machine learning models for predicting consumer behaviour trends.
AI and ML in artistic creation: Music, visual arts, and literature.
The use of AI in automated drone navigation for delivery services.
Developing AI algorithms for effective waste management and recycling.
Machine learning in seismology for earthquake prediction.
AI-powered tools for enhancing online privacy and data protection.
The application of ML in enhancing speech recognition technologies.
Investigating the role of AI in mental health assessment and therapy.
Machine learning for optimization of renewable energy systems.
AI in fashion: Predicting trends and personalizing customer experiences.
The impact of AI on legal research and case analysis.
Developing AI systems for real-time language interpretation for the deaf and hard of hearing.
Machine learning in genomic data analysis for personalized medicine.
AI-driven algorithms for credit scoring in microfinance.
The use of AI in enhancing public safety and emergency response systems.
Machine learning for improving water quality monitoring and management.
AI applications in wildlife conservation and habitat monitoring.
The role of AI in streamlining manufacturing processes.
Investigating the use of AI in enhancing the accessibility of digital content for visually impaired users.

Recent AI & ML-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic in AI, they are fairly generic and non-specific. So, it helps to look at actual studies in the AI and machine learning space to see how this all comes together in practice.

Below, we’ve included a selection of AI-related studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases (Sheng et al., 2022)
HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW (Patel, 2022)
Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications (Zheng et al., 2022)
Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities, and Challenges (Mukhamediev et al., 2022)
Will digitization, big data, and artificial intelligence – and deep learning–based algorithm govern the practice of medicine? (Goh, 2022)
Flower Classifier Web App Using Ml & Flask Web Framework (Singh et al., 2022)
Object-based Classification of Natural Scenes Using Machine Learning Methods (Jasim & Younis, 2023)
Automated Training Data Construction using Measurements for High-Level Learning-Based FPGA Power Modeling (Richa et al., 2022)
Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (Manickam et al., 2022)
Critical Review of Air Quality Prediction using Machine Learning Techniques (Sharma et al., 2022)
Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging (Salucci et al., 2022)
Machine learning alternative to systems biology should not solely depend on data (Yeo & Selvarajoo, 2022)
Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests (García et al., 2022).
Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls (Patil et al., 2022).
Automated Machine Learning on High Dimensional Big Data for Prediction Tasks (Jayanthi & Devi, 2022)
Breakdown of Machine Learning Algorithms (Meena & Sehrawat, 2022)
Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device (Carolan et al., 2021)
Machine Learning in Tourism (Rugge, 2022)
Towards a training data model for artificial intelligence in earth observation (Yue et al., 2022)
Classification of Music Generality using ANN, CNN and RNN-LSTM (Tripathy & Patel, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

Research Topic Kickstarter - Need Help Finding A Research Topic?

can one come up with their own tppic and get a search

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Machine learning articles from across Nature Portfolio

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.

Machine learning trims the peptide drug design process to a sweet spot

Drugs that target peptide hormone receptors are of great interest in the treatment of type 2 diabetes. In spite of limited data and vast design spaces, a bespoke computational pipeline has designed peptides that target two receptors with high potency.

Chloe E. Markey
Daniel Reker

A virtual rat tests theories of motor control

How the brain controls complex movements has been a mystery. Advances in artificial intelligence now make it possible to simulate this process in virtual animals. Comparing activations in artificial control networks with brain activity in real animals enables long-standing theories of motor control at the level of neural circuits to be probed.

Weather and climate predicted accurately — without using a supercomputer

A cutting-edge global model of the atmosphere combines machine learning with a numerical model based on the laws of physics. This ‘hybrid’ system accurately predicts the weather — and even shows promise for climate simulations.

Oliver Watt-Meyer

Latest Research and Reviews

A many-objective evolutionary algorithm based on three states for solving many-objective optimization problem

Huijie Zhang
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Clinical decision making in prostate cancer care—evaluation of EAU-guidelines use and novel decision support software

C. Engesser
C. Wetterauer

Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network

cell–cell communication (CCC) is crucial for understanding biological processes. Here, authors present DeepTalk, which combines single-cell RNA sequencing and spatial transcriptomics data to infer cell–cell communication at single-cell resolution, revealing intricate intercellular dynamics within tissues.

Pingping Wang
Qinghua Jiang

TEMPRO: nanobody melting temperature estimation model using protein embeddings

Jerome Anthony E. Alvarez
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Focal liver lesion diagnosis with deep learning and multistage CT imaging

The distinction of liver lesions is critical for the accurate diagnosis and treatment of liver cancer. Here, the authors develop LiLNet, a deep learning-based system to identify focal liver lesions as well as benign and malignant liver tumours from CT images with high accuracy across multiple patient cohorts.

A bioactivity foundation model using pairwise meta-learning

Traditional machine learning methods for drug development struggle with bioactivity prediction due to the limited number of compounds in each assay and assay incompatibilities. Feng et al. developed ActFound, a bioactivity foundation model trained by pairwise learning and meta-learning, that improves the accuracy and generalization of bioactivity prediction.

News and Comment

Five ways the brain can age: 50,000 scans reveal possible patterns of damage

Results raise hopes that methods could be developed to detect the earliest stages of neurodegenerative disease.

Michael Eisenstein

Chatbots in science: What can ChatGPT do for you?

Milton Pividori spent a year and a half studying how best to use ChatGPT in research. Here, he highlights three key lessons.

Milton Pividori

Has your paper been used to train an AI model? Almost certainly

Artificial-intelligence developers are buying access to valuable data sets that contain research papers — raising uncomfortable questions about copyright.

Elizabeth Gibney

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Artificial intelligence and machine learning research: towards digital transformation at a global scale

Published: 17 April 2021
Volume 13 , pages 3319–3321, ( 2022 )

Cite this article

Akila Sarirete 1 ,
Zain Balfagih 1 ,
Tayeb Brahimi 1 ,
Miltiadis D. Lytras 1 , 2 &
Anna Visvizi 3 , 4

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Artificial intelligence (AI) is reshaping how we live, learn, and work. Until recently, AI used to be a fanciful concept, more closely associated with science fiction rather than with anything else. However, driven by unprecedented advances in sophisticated information and communication technology (ICT), AI today is synonymous technological progress already attained and the one yet to come in all spheres of our lives (Chui et al. 2018 ; Lytras et al. 2018 , 2019 ).

Considering that Machine Learning (ML) and AI are apt to reach unforeseen levels of accuracy and efficiency, this special issue sought to promote research on AI and ML seen as functions of data-driven innovation and digital transformation. The combination of expanding ICT-driven capabilities and capacities identifiable across our socio-economic systems along with growing consumer expectations vis-a-vis technology and its value-added for our societies, requires multidisciplinary research and research agenda on AI and ML (Lytras et al. 2021 ; Visvizi et al. 2020 ; Chui et al. 2020 ). Such a research agenda should oscilate around the following five defining issues (Fig. 1 ):

Source: The Authors

An AI-Driven Digital Transformation in all aspects of human activity/

Integration of diverse data-warehouses to unified ecosystems of AI and ML value-based services

Deployment of robust AI and ML processing capabilities for enhanced decision making and generation of value our of data.

Design of innovative novel AI and ML applications for predictive and analytical capabilities

Design of sophisticated AI and ML-enabled intelligence components with critical social impact

Promotion of the Digital Transformation in all the aspects of human activity including business, healthcare, government, commerce, social intelligence etc.

Such development will also have a critical impact on government, policies, regulations and initiatives aiming to interpret the value of the AI-driven digital transformation to the sustainable economic development of our planet. Additionally the disruptive character of AI and ML technology and research will required further research on business models and management of innovation capabilities.

This special issue is based on submissions invited from the 17th Annual Learning and Technology Conference 2019 that was held at Effat University and open call jointly. Several very good submissions were received. All of them were subjected a rigorous peer review process specific to the Ambient Intelligence and Humanized Computing Journal.

A variety of innovative topics are included in the agenda of the published papers in this special issue including topics such as:

Stock market Prediction using Machine learning

Detection of Apple Diseases and Pests based on Multi-Model LSTM-based Convolutional Neural Networks

ML for Searching

Machine Learning for Learning Automata

Entity recognition & Relation Extraction

Intelligent Surveillance Systems

Activity Recognition and K-Means Clustering

Distributed Mobility Management

Review Rating Prediction with Deep Learning

Cybersecurity: Botnet detection with Deep learning

Self-Training methods

Neuro-Fuzzy Inference systems

Fuzzy Controllers

Monarch Butterfly Optimized Control with Robustness Analysis

GMM methods for speaker age and gender classification

Regression methods for Permeability Prediction of Petroleum Reservoirs

Surface EMG Signal Classification

Pattern Mining

Human Activity Recognition in Smart Environments

Teaching–Learning based Optimization Algorithm

Big Data Analytics

Diagnosis based on Event-Driven Processing and Machine Learning for Mobile Healthcare

Over a decade ago, Effat University envisioned a timely platform that brings together educators, researchers and tech enthusiasts under one roof and functions as a fount for creativity and innovation. It was a dream that such platform bridges the existing gap and becomes a leading hub for innovators across disciplines to share their knowledge and exchange novel ideas. It was in 2003 that this dream was realized and the first Learning & Technology Conference was held. Up until today, the conference has covered a variety of cutting-edge themes such as Digital Literacy, Cyber Citizenship, Edutainment, Massive Open Online Courses, and many, many others. The conference has also attracted key, prominent figures in the fields of sciences and technology such as Farouq El Baz from NASA, Queen Rania Al-Abdullah of Jordan, and many others who addressed large, eager-to-learn audiences and inspired many with unique stories.

While emerging innovations, such as Artificial Intelligence technologies, are seen today as promising instruments that could pave our way to the future, these were also the focal points around which fruitful discussions have always taken place here at the L&T. The (AI) was selected for this conference due to its great impact. The Saudi government realized this impact of AI and already started actual steps to invest in AI. It is stated in the Kingdome Vision 2030: "In technology, we will increase our investments in, and lead, the digital economy." Dr. Ahmed Al Theneyan, Deputy Minister of Technology, Industry and Digital Capabilities, stated that: "The Government has invested around USD 3 billion in building the infrastructure so that the country is AI-ready and can become a leader in AI use." Vision 2030 programs also promote innovation in technologies. Another great step that our country made is establishing NEOM city (the model smart city).

Effat University realized this ambition and started working to make it a reality by offering academic programs that support the different sectors needed in such projects. For example, the master program in Energy Engineering was launched four years ago to support the energy sector. Also, the bachelor program of Computer Science has tracks in Artificial Intelligence and Cyber Security which was launched in Fall 2020 semester. Additionally, Energy & Technology and Smart Building Research Centers were established to support innovation in the technology and energy sectors. In general, Effat University works effectively in supporting the KSA to achieve its vision in this time of national transformation by graduating skilled citizen in different fields of technology.

The guest editors would like to take this opportunity to thank all the authors for the efforts they put in the preparation of their manuscripts and for their valuable contributions. We wish to express our deepest gratitude to the referees, who provided instrumental and constructive feedback to the authors. We also extend our sincere thanks and appreciation for the organizing team under the leadership of the Chair of L&T 2019 Conference Steering Committee, Dr. Haifa Jamal Al-Lail, University President, for her support and dedication.

Our sincere thanks go to the Editor-in-Chief for his kind help and support.

Chui KT, Lytras MD, Visvizi A (2018) Energy sustainability in smart cities: artificial intelligence, smart monitoring, and optimization of energy consumption. Energies 11(11):2869

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Chui KT, Fung DCL, Lytras MD, Lam TM (2020) Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Comput Human Behav 107:105584

Lytras MD, Visvizi A, Daniela L, Sarirete A, De Pablos PO (2018) Social networks research for sustainable smart education. Sustainability 10(9):2974

Lytras MD, Visvizi A, Sarirete A (2019) Clustering smart city services: perceptions, expectations, responses. Sustainability 11(6):1669

Lytras MD, Visvizi A, Chopdar PK, Sarirete A, Alhalabi W (2021) Information management in smart cities: turning end users’ views into multi-item scale development, validation, and policy-making recommendations. Int J Inf Manag 56:102146

Visvizi A, Jussila J, Lytras MD, Ijäs M (2020) Tweeting and mining OECD-related microcontent in the post-truth era: A cloud-based app. Comput Human Behav 107:105958

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Effat College of Engineering, Effat Energy and Technology Research Center, Effat University, P.O. Box 34689, Jeddah, Saudi Arabia

Akila Sarirete, Zain Balfagih, Tayeb Brahimi & Miltiadis D. Lytras

King Abdulaziz University, Jeddah, 21589, Saudi Arabia

Miltiadis D. Lytras

Effat College of Business, Effat University, P.O. Box 34689, Jeddah, Saudi Arabia

Anna Visvizi

Institute of International Studies (ISM), SGH Warsaw School of Economics, Aleja Niepodległości 162, 02-554, Warsaw, Poland

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Sarirete, A., Balfagih, Z., Brahimi, T. et al. Artificial intelligence and machine learning research: towards digital transformation at a global scale. J Ambient Intell Human Comput 13 , 3319–3321 (2022). https://doi.org/10.1007/s12652-021-03168-y

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37 Research Topics In Data Science To Stay On Top Of

February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on https://arxiv.org/ every single day!

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.

20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.

24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.

30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.

32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.

34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the Terminator warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

Computer Science > Machine Learning

Title: the top 10 topics in machine learning revisited: a quantitative meta-study.

Abstract: Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research.

Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
Cite as:	[cs.LG]
	(or [cs.LG] for this version)
	Focus to learn more arXiv-issued DOI via DataCite
Journal reference:	Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017)

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In AI Origins & Evolution

Top Machine Learning Research Papers Released In 2021

Published on November 18, 2021
by Dr. Nivash Jeevanandam

Advances in machine learning and deep learning research are reshaping our technology. Machine learning and deep learning have accomplished various astounding feats this year in 2021, and key research articles have resulted in technical advances used by billions of people. The research in this sector is advancing at a breakneck pace and assisting you to keep up. Here is a collection of the most important recent scientific study papers.

Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training

The authors of this work examined why ACGAN training becomes unstable as the number of classes in the dataset grows. The researchers revealed that the unstable training occurs due to a gradient explosion problem caused by the unboundedness of the input feature vectors and the classifier’s poor classification capabilities during the early training stage. The researchers presented the Data-to-Data Cross-Entropy loss (D2D-CE) and the Rebooted Auxiliary Classifier Generative Adversarial Network to alleviate the instability and reinforce ACGAN (ReACGAN). Additionally, extensive tests of ReACGAN demonstrate that it is resistant to hyperparameter selection and is compatible with a variety of architectures and differentiable augmentations.

This article is ranked #1 on CIFAR-10 for Conditional Image Generation.

For the research paper, read here .

For code, see here .

Dense Unsupervised Learning for Video Segmentation

The authors presented a straightforward and computationally fast unsupervised strategy for learning dense spacetime representations from unlabeled films in this study. The approach demonstrates rapid convergence of training and a high degree of data efficiency. Furthermore, the researchers obtain VOS accuracy superior to previous results despite employing a fraction of the previously necessary training data. The researchers acknowledge that the research findings may be utilised maliciously, such as for unlawful surveillance, and that they are excited to investigate how this skill might be used to better learn a broader spectrum of invariances by exploiting larger temporal windows in movies with complex (ego-)motion, which is more prone to disocclusions.

This study is ranked #1 on DAVIS 2017 for Unsupervised Video Object Segmentation (val).

Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases

The authors offer an atlas-based technique for producing unsupervised temporally consistent surface reconstructions by requiring a point on the canonical shape representation to translate to metrically consistent 3D locations on the reconstructed surfaces. Finally, the researchers envisage a plethora of potential applications for the method. For example, by substituting an image-based loss for the Chamfer distance, one may apply the method to RGB video sequences, which the researchers feel will spur development in video-based 3D reconstruction.

This article is ranked #1 on ANIM in the category of Surface Reconstruction.

EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow

The researchers propose a revolutionary interactive architecture called EdgeFlow that uses user interaction data without resorting to post-processing or iterative optimisation. The suggested technique achieves state-of-the-art performance on common benchmarks due to its coarse-to-fine network design. Additionally, the researchers create an effective interactive segmentation tool that enables the user to improve the segmentation result through flexible options incrementally.

This paper is ranked #1 on Interactive Segmentation on PASCAL VOC

Learning Transferable Visual Models From Natural Language Supervision

The authors of this work examined whether it is possible to transfer the success of task-agnostic web-scale pre-training in natural language processing to another domain. The findings indicate that adopting this formula resulted in the emergence of similar behaviours in the field of computer vision, and the authors examine the social ramifications of this line of research. CLIP models learn to accomplish a range of tasks during pre-training to optimise their training objective. Using natural language prompting, CLIP can then use this task learning to enable zero-shot transfer to many existing datasets. When applied at a large scale, this technique can compete with task-specific supervised models, while there is still much space for improvement.

This research is ranked #1 on Zero-Shot Transfer Image Classification on SUN

CoAtNet: Marrying Convolution and Attention for All Data Sizes

The researchers in this article conduct a thorough examination of the features of convolutions and transformers, resulting in a principled approach for combining them into a new family of models dubbed CoAtNet. Extensive experiments demonstrate that CoAtNet combines the advantages of ConvNets and Transformers, achieving state-of-the-art performance across a range of data sizes and compute budgets. Take note that this article is currently concentrating on ImageNet classification for model construction. However, the researchers believe their approach is relevant to a broader range of applications, such as object detection and semantic segmentation.

This paper is ranked #1 on Image Classification on ImageNet (using extra training data).

SwinIR: Image Restoration Using Swin Transformer

The authors of this article suggest the SwinIR image restoration model, which is based on the Swin Transformer . The model comprises three modules: shallow feature extraction, deep feature extraction, and human-recognition reconstruction. For deep feature extraction, the researchers employ a stack of residual Swin Transformer blocks (RSTB), each formed of Swin Transformer layers, a convolution layer, and a residual connection.

This research article is ranked #1 on Image Super-Resolution on Manga109 – 4x upscaling.

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Dr. Nivash Jeevanandam

Classification , Deep Learning , Generative Adversarial Network , gradient boosting , Machine Learning , RGB , supervised learning , Unsupervised Learning

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The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

A comprehensive guide for crafting an original and innovative thesis in the field of ai..

By Aarafat Islam on 2023-01-11

“The beauty of machine learning is that it can be applied to any problem you want to solve, as long as you can provide the computer with enough examples.” — Andrew Ng

This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an introduction , which presents a brief overview of the topic and the research objectives . The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of the potential contributions and outcomes of the proposed research. The article also emphasized the importance of originality and the need for proper citation in order to avoid plagiarism.

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging: A deep learning approach to improve the accuracy of medical diagnoses.

Introduction: Medical imaging is an important tool in the diagnosis and treatment of various medical conditions. However, accurately interpreting medical images can be challenging, especially for less experienced doctors. This thesis aims to explore the use of GANs in medical imaging, in order to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

Introduction: Natural language generation is an important field in natural language processing (NLP) that deals with creating human-like text automatically. Deep learning has shown promising results in NLP tasks such as machine translation, sentiment analysis, and question-answering. This thesis aims to explore the use of deep learning in NLG and analyze the current state-of-the-art models, as well as potential future developments.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

Introduction: Robotic navigation and control are challenging tasks, which require a high degree of intelligence and adaptability. Deep RL has shown promising results in various robotics tasks, such as robotic arm control, autonomous navigation, and manipulation. This thesis aims to develop and evaluate a deep RL-based approach for robotic navigation and control and evaluate its performance in various environments and tasks.

4. Investigating the use of deep learning for drug discovery and development.

Introduction: Drug discovery and development is a time-consuming and expensive process, which often involves high failure rates. Deep learning has been used to improve various tasks in bioinformatics and biotechnology, such as protein structure prediction and gene expression analysis. This thesis aims to investigate the use of deep learning for drug discovery and development and examine its potential to improve the efficiency and accuracy of the drug development process.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

Introduction: Anomaly detection in time series data is a challenging task, which is important in various fields such as finance, healthcare, and manufacturing. Deep learning methods have been used to improve anomaly detection in time series data, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for anomaly detection in time series data and examine their respective strengths and weaknesses.

Photo by Joanna Kosinska on Unsplash

6. Use of deep transfer learning in speech recognition and synthesis.

Introduction: Speech recognition and synthesis are areas of natural language processing that focus on converting spoken language to text and vice versa. Transfer learning has been widely used in deep learning-based speech recognition and synthesis systems to improve their performance by reusing the features learned from other tasks. This thesis aims to investigate the use of transfer learning in speech recognition and synthesis and how it improves the performance of the system in comparison to traditional methods.

7. The use of deep learning for financial prediction.

Introduction: Financial prediction is a challenging task that requires a high degree of intelligence and adaptability, especially in the field of stock market prediction. Deep learning has shown promising results in various financial prediction tasks, such as stock price prediction and credit risk analysis. This thesis aims to investigate the use of deep learning for financial prediction and examine its potential to improve the accuracy of financial forecasting.

8. Investigating the use of deep learning for computer vision in agriculture.

Introduction: Computer vision has the potential to revolutionize the field of agriculture by improving crop monitoring, precision farming, and yield prediction. Deep learning has been used to improve various computer vision tasks, such as object detection, semantic segmentation, and image classification. This thesis aims to investigate the use of deep learning for computer vision in agriculture and examine its potential to improve the efficiency and accuracy of crop monitoring and precision farming.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

Introduction: Generative design is a powerful tool in engineering and architecture that can help optimize designs and reduce human error. Deep learning has been used to improve various generative design tasks, such as design optimization and form generation. This thesis aims to develop and evaluate deep learning models for generative design in engineering and architecture and examine their potential to improve the efficiency and accuracy of the design process.

10. Investigating the use of deep learning for natural language understanding.

Introduction: Natural language understanding is a complex task of natural language processing that involves extracting meaning from text. Deep learning has been used to improve various NLP tasks, such as machine translation, sentiment analysis, and question-answering. This thesis aims to investigate the use of deep learning for natural language understanding and examine its potential to improve the efficiency and accuracy of natural language understanding systems.

Photo by UX Indonesia on Unsplash

11. Comparing deep learning and traditional machine learning methods for image compression.

Introduction: Image compression is an important task in image processing and computer vision. It enables faster data transmission and storage of image files. Deep learning methods have been used to improve image compression, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for image compression and examine their respective strengths and weaknesses.

12. Using deep learning for sentiment analysis in social media.

Introduction: Sentiment analysis in social media is an important task that can help businesses and organizations understand their customers’ opinions and feedback. Deep learning has been used to improve sentiment analysis in social media, by training models on large datasets of social media text. This thesis aims to use deep learning for sentiment analysis in social media, and evaluate its performance against traditional machine learning methods.

13. Investigating the use of deep learning for image generation.

Introduction: Image generation is a task in computer vision that involves creating new images from scratch or modifying existing images. Deep learning has been used to improve various image generation tasks, such as super-resolution, style transfer, and face generation. This thesis aims to investigate the use of deep learning for image generation and examine its potential to improve the quality and diversity of generated images.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

Introduction: Anomaly detection in cybersecurity is an important task that can help detect and prevent cyber-attacks. Deep learning has been used to improve various anomaly detection tasks, such as intrusion detection and malware detection. This thesis aims to develop and evaluate deep learning models for anomaly detection in cybersecurity and examine their potential to improve the efficiency and accuracy of cybersecurity systems.

15. Investigating the use of deep learning for natural language summarization.

Introduction: Natural language summarization is an important task in natural language processing that involves creating a condensed version of a text that preserves its main meaning. Deep learning has been used to improve various natural language summarization tasks, such as document summarization and headline generation. This thesis aims to investigate the use of deep learning for natural language summarization and examine its potential to improve the efficiency and accuracy of natural language summarization systems.

Photo by Windows on Unsplash

16. Development and evaluation of deep learning models for facial expression recognition.

Introduction: Facial expression recognition is an important task in computer vision and has many practical applications, such as human-computer interaction, emotion recognition, and psychological studies. Deep learning has been used to improve facial expression recognition, by training models on large datasets of images. This thesis aims to develop and evaluate deep learning models for facial expression recognition and examine their performance against traditional machine learning methods.

17. Investigating the use of deep learning for generative models in music and audio.

Introduction: Music and audio synthesis is an important task in audio processing, which has many practical applications, such as music generation and speech synthesis. Deep learning has been used to improve generative models for music and audio, by training models on large datasets of audio data. This thesis aims to investigate the use of deep learning for generative models in music and audio and examine its potential to improve the quality and diversity of generated audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

Introduction: Anomaly detection in network traffic is an important task that can help detect and prevent cyber-attacks. Deep learning models have been used for this task, and traditional methods such as clustering and rule-based systems are widely used as well. This thesis aims to compare deep learning models with traditional algorithms for anomaly detection in network traffic and analyze the trade-offs between the models in terms of accuracy and scalability.

19. Investigating the use of deep learning for improving recommender systems.

Introduction: Recommender systems are widely used in many applications such as online shopping, music streaming, and movie streaming. Deep learning has been used to improve the performance of recommender systems, by training models on large datasets of user-item interactions. This thesis aims to investigate the use of deep learning for improving recommender systems and compare its performance with traditional content-based and collaborative filtering approaches.

20. Development and evaluation of deep learning models for multi-modal data analysis.

Introduction: Multi-modal data analysis is the task of analyzing and understanding data from multiple sources such as text, images, and audio. Deep learning has been used to improve multi-modal data analysis, by training models on large datasets of multi-modal data. This thesis aims to develop and evaluate deep learning models for multi-modal data analysis and analyze their potential to improve performance in comparison to single-modal models.

I hope that this article has provided you with a useful guide for your thesis research in machine learning and deep learning. Remember to conduct a thorough literature review and to include proper citations in your work, as well as to be original in your research to avoid plagiarism. I wish you all the best of luck with your thesis and your research endeavors!

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177 Great Artificial Intelligence Research Paper Topics to Use

In this top-notch post, we will look at the definition of artificial intelligence, its applications, and writing tips on how to come up with AI topics. Finally, we shall lock at top artificial intelligence research topics for your inspiration.

What Is Artificial Intelligence?

It refers to intelligence as demonstrated by machines, unlike that which animals and humans display. The latter involves emotionality and consciousness. The field of AI has gained proliferation in recent days, with many scientists investing their time and effort in research.

How To Develop Topics in Artificial Intelligence

Developing AI topics is a critical thinking process that also incorporates a lot of creativity. Due to the ever-dynamic nature of the discipline, most students find it hard to develop impressive topics in artificial intelligence. However, here are some general rules to get you started:

Read widely on the subject of artificial intelligence Have an interest in news and other current updates about AI Consult your supervisor

Once you are ready with these steps, nothing is holding you from developing top-rated topics in artificial intelligence. Now let’s look at what the pros have in store for you.

Artificial Intelligence Research Paper Topics

The role of artificial intelligence in evolving the workforce
Are there tasks that require unique human abilities apart from machines?
The transformative economic impact of artificial intelligence
Managing a global autonomous arms race in the face of AI
The legal and ethical boundaries of artificial intelligence
Is the destructive role of AI more than its constructive role in society?
How to build AI algorithms to achieve the far-reaching goals of humans
How privacy gets compromised with the everyday collection of data
How businesses and governments can suffer at the hands of AI
Is it possible for AI to devolve into social oppression?
Augmentation of the work humans do through artificial intelligence
The role of AI in monitoring and diagnosing capabilities

Artificial Intelligence Topics For Presentation

How AI helps to uncover criminal activity and solve serial crimes
The place of facial recognition technologies in security systems
How to use AI without crossing an individual’s privacy
What are the disadvantages of using a computer-controlled robot in performing tasks?
How to develop systems endowed with intellectual processes
The challenge of programming computers to perform complex tasks
Discuss some of the mathematical theorems for artificial intelligence systems
The role of computer processing speed and memory capacity in AI
Can computer machines achieve the performance levels of human experts?
Discuss the application of artificial intelligence in handwriting recognition
A case study of the key people involved in developing AI systems
Computational aesthetics when developing artificial intelligence systems

Topics in AI For Tip-Top Grades

Describe the necessities for artificial programming language
The impact of American companies possessing about 2/3 of investments in AI
The relationship between human neural networks and A.I
The role of psychologists in developing human intelligence
How to apply past experiences to analogous new situations
How machine learning helps in achieving artificial intelligence
The role of discernment and human intelligence in developing AI systems
Discuss the various methods and goals in artificial intelligence
What is the relationship between applied AI, strong AI, and cognitive simulation
Discuss the implications of the first AI programs
Logical reasoning and problem-solving in artificial intelligence
Challenges involved in controlled learning environments

AI Research Topics For High School Students

How quantum computing is affecting artificial intelligence
The role of the Internet of Things in advancing artificial intelligence
Using Artificial intelligence to enable machines to perform programming tasks
Why do machines learn automatically without human hand holding
Implementing decisions based on data processing in the human mind
Describe the web-like structure of artificial neural networks
Machine learning algorithms for optimal functions through trial and error
A case study of Google’s AlphaGo computer program
How robots solve problems in an intelligent manner
Evaluate the significant role of M.I.T.’s artificial intelligence lab
A case study of Robonaut developed by NASA to work with astronauts in space
Discuss natural language processing where machines analyze language and speech

Argument Debate Topics on AI

How chatbots use ML and N.L.P. to interact with the users
How do computers use and understand images?
The impact of genetic engineering on the life of man
Why are micro-chips not recommended in human body systems?
Can humans work alongside robots in a workplace system?
Have computers contributed to the intrusion of privacy for many?
Why artificial intelligence systems should not be made accessible to children
How artificial intelligence systems are contributing to healthcare problems
Does artificial intelligence alleviate human problems or add to them?
Why governments should put more stringent measures for AI inventions
How artificial intelligence is affecting the character traits of children born
Is virtual reality taking people out of the real-world situation?

Quality AI Topics For Research Paper

The use of recommender systems in choosing movies and series
Collaborative filtering in designing systems
How do developers arrive at a content-based recommendation
Creation of systems that can emulate human tasks
How IoT devices generate a lot of data
Artificial intelligence algorithms convert data to useful, actionable results.
How AI is progressing rapidly with the 5G technology
How to develop robots with human-like characteristics
Developing Google search algorithms
The role of artificial intelligence in developing autonomous weapons
Discuss the long-term goal of artificial intelligence
Will artificial intelligence outperform humans at every cognitive task?

Computer Science AI Topics

Computational intelligence magazine in computer science
Swarm and evolutionary computation procedures for college students
Discuss computational transactions on intelligent transportation systems
The structure and function of knowledge-based systems
A review of the artificial intelligence systems in developing systems
Conduct a review of the expert systems with applications
Critique the various foundations and trends in information retrieval
The role of specialized systems in transactions on knowledge and data engineering
An analysis of a journal on ambient intelligence and humanized computing
Discuss the various computer transactions on cognitive communications and networking
What is the role of artificial intelligence in medicine?
Computer engineering applications of artificial intelligence

AI Ethics Topics

How the automation of jobs is going to make many jobless
Discuss inequality challenges in distributing wealth created by machines
The impact of machines on human behavior and interactions
How artificial intelligence is going to affect how we act accordingly
The process of eliminating bias in Artificial intelligence: A case of racist robots
Measures that can keep artificial intelligence safe from adversaries
Protecting artificial intelligence discoveries from unintended consequences
How a man can stay in control despite the complex, intelligent systems
Robot rights: A case of how man is mistreating and misusing robots
The balance between mitigating suffering and interfering with set ethics
The role of artificial intelligence in negative outcomes: Is it worth it?
How to ethically use artificial intelligence for bettering lives

Advanced AI Topics

Discuss how long it will take until machines greatly supersede human intelligence
Is it possible to achieve superhuman artificial intelligence in this century?
The impact of techno-skeptic prediction on the performance of A.I
The role of quarks and electrons in the human brain
The impact of artificial intelligence safety research institutes
Will robots be disastrous for humanity shortly?
Robots: A concern about consciousness and evil
Discuss whether a self-driving car has a subjective experience or not
Should humans worry about machines turning evil in the end?
Discuss how machines exhibit goal-oriented behavior in their functions
Should man continue to develop lethal autonomous weapons?
What is the implication of machine-produced wealth?

AI Essay Topics Technology

Discuss the implication of the fourth technological revelation in cloud computing
Big database technologies used in sensors
The combination of technologies typical of the technological revolution
Key determinants of the civilization process of industry 4.0
Discuss some of the concepts of technological management
Evaluate the creation of internet-based companies in the U.S.
The most dominant scientific research in the field of artificial intelligence
Discuss the application of artificial intelligence in the literature
How enterprises use artificial intelligence in blockchain business operations
Discuss the various immersive experiences as a result of digital AI
Elaborate on various enterprise architects and technology innovations
Mega-trends that are future impacts on business operations

Interesting Topics in AI

The role of the industrial revolution of the 18 th century in A.I
The electricity era of the late 19 th century and its contribution to the development of robots
How the widespread use of the internet contributes to the AI revolution
The short-term economic crisis as a result of artificial intelligence business technologies
Designing and creating artificial intelligence production processes
Analyzing large collections of information for technological solutions
How biotechnology is transforming the field of agriculture
Innovative business projects that work using artificial intelligence systems
Process and marketing innovations in the 21 st century
Medical intelligence in the era of smart cities
Advanced data processing technologies in developed nations
Discuss the development of stelliform technologies

Good Research Topics For AI

Development of new technological solutions in I.T
Innovative organizational solutions that develop machine learning
How to develop branches of a knowledge-based economy
Discuss the implications of advanced computerized neural network systems
How to solve complex problems with the help of algorithms
Why artificial intelligence systems are predominating over their creator
How to determine artificial emotional intelligence
Discuss the negative and positive aspects of technological advancement
How internet technology companies like Facebook are managing large social media portals
The application of analytical business intelligence systems
How artificial intelligence improves business management systems
Strategic and ongoing management of artificial intelligence systems

Graduate AI NLP Research Topics

Morphological segmentation in artificial intelligence
Sentiment analysis and breaking machine language
Discuss input utterance for language interpretation
Festival speech synthesis system for natural language processing
Discuss the role of the Google language translator
Evaluate the various analysis methodologies in N.L.P.
Native language identification procedure for deep analytics
Modular audio recognition framework
Deep linguistic processing techniques
Fact recognition and extraction techniques
Dialogue and text-based applications
Speaker verification and identification systems

Controversial Topics in AI

Ethical implication of AI in movies: A case study of The Terminator
Will machines take over the world and enslave humanity?
Does human intelligence paint a dark future for humanity?
Ethical and practical issues of artificial intelligence
The impact of mimicking human cognitive functions
Why the integration of AI technologies into society should be limited
Should robots get paid hourly?
What if AI is a mistake?
Why did Microsoft shut down chatbots immediately?
Should there be AI systems for killing?
Should machines be created to do what they want?
Is the computerized gun ethical?

Hot AI Topics

Why predator drones should not exist
Do the U.S. laws restrict meaningful innovations in AI
Why did the campaign to stop killer robots fail in the end?
Fully autonomous weapons and human safety
How to deal with rogues artificial intelligence systems in the United States
Is it okay to have a monopoly and control over artificial intelligence innovations?
Should robots have human rights or citizenship?
Biases when detecting people’s gender using Artificial intelligence
Considerations for the adoption of a particular artificial intelligence technology

Are you a university student seeking research paper writing services or dissertation proposal help ? We offer custom help for college students in any field of artificial intelligence.

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100 Machine Learning Topics in 2025

1. core machine learning algorithms and techniques.

Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning
Transfer Learning
Federated Learning
Meta-Learning
Self-Supervised Learning
Active Learning
Few-Shot Learning
Zero-Shot Learning
Multi-Task Learning
Ensemble Learning
Bayesian Networks
Graph Neural Networks
Attention Mechanisms
Transformers
Neural Architecture Search
Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs)

2. Advanced Machine Learning Models

Capsule Networks
Spiking Neural Networks
Quantum Machine Learning
Neural Turing Machines
Self-Organizing Maps
Echo State Networks
Long Short-Term Memory Networks (LSTMs)
Gated Recurrent Units (GRUs)
Convolutional Neural Networks (CNNs)
Residual Networks (ResNets)

3. Machine Learning Applications in Various Domains

Natural Language Processing (NLP)
Computer Vision
Speech Recognition
Healthcare and Medical Diagnostics
Autonomous Vehicles
Finance and Trading
Cybersecurity
Recommender Systems
Smart Cities

4. Emerging Machine Learning Fields

Explainable AI (XAI)
AI Ethics and Fairness
AI for Social Good
AI in Climate Change
Neuro-Symbolic AI
AI in Education
AI in Art and Creativity
AI in Agriculture
AI in Gaming
AI for Accessibility

5. Machine Learning in Business and Industry

Predictive Analytics
Customer Segmentation
Sentiment Analysis
Supply Chain Optimization
Fraud Detection
Churn Prediction
HR Analytics
Inventory Management
Market Basket Analysis
Product Recommendation

6. Advanced Data Techniques

Big Data Analytics
Data Augmentation
Data Imputation
Synthetic Data Generation
Anomaly Detection
Time Series Forecasting
Spatial Data Analysis
Causal Inference
Feature Engineering
Hyperparameter Optimization

7. Tools and Frameworks

Scikit-Learn
Jupyter Notebooks
Apache Spark
Hugging Face Transformers

8. Trends and Future Directions

AI and IoT Integration
AI-Driven Automation
Augmented Reality (AR) and AI
AI in 5G Networks
Synthetic Biology and AI
AI for Mental Health
AI in Legal Tech
AI-Driven Personalization
AI in Space Exploration

9. Machine Learning Research Challenges

Scalability
Robustness and Reliability
Bias and Fairness
Interpretable Models
Energy Efficiency
Privacy and Security
Generalization to Unseen Data
Integration with Existing Systems
Cross-Disciplinary Research
Real-Time Processing

Machine Learning Research Topics & Ideas

1. image processing.

Machine learning can make processes of recognizing and classifying images faster and more accurate. Even if you’re not interested in your photographs, the time and effort that can be saved with an application like Photoshop is considerable.

Start by using a Google tool for machine learning, Vision, to take a quick image reading test and build the image recognition algorithm you’re most interested in.

Where to look: Sponsored posts are content that has been produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked.

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Projects based on Image processing are getting huge attention.

2. Data Visualization

Data visualization is the process of presenting information in a visually appealing way. This can be done by using graphics, charts, and maps, among other visualization tools. Challenge: Data visualization is a valuable tool for raising awareness of the data that’s necessary for machine learning .

If you have a data visualization website, create a gallery of useful data visualizations to present the topic. Instead of creating a cartographic map of your location to represent your body size, develop a globe so that it can represent how your body would be distributed across different areas of the world. The best way to make this easier is to visualize your body by drawing a human skeleton.

3. ML Topics in Predictive Maintenance

Predictive maintenance is the process of predicting when components or systems will fail by using machine learning to capture historical data and apply statistical analysis. Predictive maintenance can improve reliability and decrease repair costs for applications that are sensitive to changing conditions, such as data centers and power grids.

Also, Predictive maintenance could be used for airplane components, mining equipment, or even car models. Predictive maintenance can also be used to predict possible delays or delays in purchasing decisions by predicting the price of similar products.

Considerations and Benefits of Machine Learning Machine learning enable computers to do tasks without being explicitly programmed.

4. Social Media Analysis

Facebook’s “trending topics” let users see what topics are trending. If you’re really into knowing what’s happening in the news, you can simply start analyzing the trending topics on Facebook.

Do you know how many countries are talking about each topic, and how many users are talking about each topic? What’s the sentiment behind each conversation?

You can generate similar metrics on Twitter. Make sure to use a powerful machine-learning algorithm to interpret this data.

Polling For example, if you’re a local food or hotel chain, you can send SMS polling to every member in the country to know what their experience with your brand is. You can also apply machine learning to analyze the responses.

5. Natural Language Processing

This is a popular and well-known machine learning technique used to understand a person’s voice. Artificial intelligence uses natural language processing to help make the computer understand the tone of voice and speech patterns, and infer a person’s attitude.

This type of analysis is useful for detecting situations such as what a person is saying, whether it’s positive or negative, and to judge the content of the conversation.

This type of analysis is usually done by a computer in a microphone to capture speech patterns and understand whether the communication is friendly or aggressive.

Some machine learning companies are working with chatbots and instant messenger bots to make it easy for customers to contact them with messaging software such as Facebook Messenger, Skype, and WhatsApp.

6. Sentiment Analysis

The sentiment is the emotional response to an article, image, or text. At the end of every article, the Internet Archive has a collection of .docx files that contain comments, tags, and user ratings. These are often used for sentiment analysis.

For instance, sentiment analysis might conclude that the tweet above was negative or positive. This can help marketers decide how to react to each piece of content in their email campaigns.

Reaction Detection If you have a website, you may not even know if users are interacting with your site. You can use sentiment analysis to determine if people are happy, disappointed, or confused.

7. Voice Interfaces

The voice user interface (VUI) is a type of conversational interface that responds to human verbal requests and commands.

Voice interfaces also help automate different applications like shopping, controlling home appliances, and getting directions.

VUI systems are becoming more popular in modern applications such as the Amazon Echo and the Apple Siri. Most of the solutions that follow will use an Amazon Echo or the Apple Siri.

Wearables The wearable device market is growing and becoming more important as people seek solutions to various problems. Wearables can provide additional capabilities to existing hardware devices.

8. Virtual Assistants

Computer interfaces can be intimidating to use for people new to computers. Fortunately, virtual assistants like Siri or Alexa are simplifying the process.

Virtual assistants act as intermediaries between users and the computer. The assistant takes over for users in the case of difficulty with the user interface or text input.

To use virtual assistants, it’s important to train the assistant with data. For example, if you wanted your virtual assistant to automatically return a list of nearby restaurants after saying “Where is the closest restaurant”, you’d have to first train the assistant to recognize nearby restaurants.

If you have access to an audio dataset, you can record yourself and record your conversations with an assistant.

These are all the major machine learning topics & research ideas to kick-start your next project.

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iLovePhD is a research education website to know updated research-related information. It helps researchers to find top journals for publishing research articles and get an easy manual for research tools. The main aim of this website is to help Ph.D. scholars who are working in various domains to get more valuable ideas to carry out their research. Learn the current groundbreaking research activities around the world, love the process of getting a Ph.D.

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Machine Learning Research Topics for MS PhD

Machine learning research topic ideas for ms, or ph.d. degree.

I am sharing with you some of the research topics regarding Machine Learning that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.

Applications of machine learning to machine fault diagnosis: A review and roadmap
Significant applications of machine learning for COVID-19 pandemic
Quantum chemistry in the age of machine learning
A survey on machine learning for data fusion
Artificial intelligence and machine learning to fight COVID-19
Machine learning for molecular simulation
A survey on distributed machine learning
Explainable machine learning for scientific insights and discoveries
When Machine Learning Meets Privacy: A Survey and Outlook
Machine learning testing: Survey, landscapes and horizons
Machine learning and psychological research: The unexplored effect of measurement
Universal differential equations for scientific machine learning
Machine learning for active matter
Exploring chemical compound space with quantum-based machine learning
Ten challenges in advancing machine learning technologies toward 6G
Machine learning for materials scientists: An introductory guide toward best practices
Lessons from archives: Strategies for collecting sociocultural data in machine learning
Tslearn, a machine learning toolkit for time series data
A snapshot of the frontiers of fairness in machine learning
How machine learning will transform biomedicine
An introduction to machine learning
Machine learning for protein folding and dynamics
DScribe: Library of descriptors for machine learning in materials science
Advances of four machine learning methods for spatial data handling: A review
New machine learning method for image-based diagnosis of COVID-19
Applications of machine learning methods for engineering risk assessment–A review
A critical review of machine learning of energy materials
State-of-the-art on research and applications of machine learning in the building life cycle
Elastic machine learning algorithms in amazon sagemaker
Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Assessment of supervised machine learning methods for fluid flows
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
First-order and Stochastic Optimization Methods for Machine Learning
Explainable machine learning in deployment
Machine learning for enterprises: Applications, algorithm selection, and challenges
Multiscale modeling meets machine learning: What can we learn?
Machine learning from a continuous viewpoint, I
Machine learning applications in systems metabolic engineering
Single trajectory characterization via machine learning
Adversarial machine learning-industry perspectives
Machine learning approaches for thermoelectric materials research
Machine learning approaches for analyzing and enhancing molecular dynamics simulations
Open graph benchmark: Datasets for machine learning on graphs
Preparing medical imaging data for machine learning
On hyperparameter optimization of machine learning algorithms: Theory and practice
Machine learning techniques for the diagnosis of Alzheimer’s disease: A review
CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
Personality research and assessment in the era of machine learning
Machine learning force fields
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Applications of artificial intelligence and machine learning in smart cities
Machine learning and wearable devices of the future
Integrating physics-based modeling with machine learning: A survey
The non-iid data quagmire of decentralized machine learning
Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
Machine learning and soil sciences: A review aided by machine learning tools
Machine learning and deep learning techniques for cybersecurity: a review
Identifying ethical considerations for machine learning healthcare applications
Introduction to machine learning
Machine learning for quantum matter
Machine learning for glass science and engineering: A review
Machine learning for continuous innovation in battery technologies
Applying machine learning in science assessment: a systematic review
Machine learning for interatomic potential models
Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
FCHL revisited: Faster and more accurate quantum machine learning
Machine-learning-assisted synthesis of polar racemates
Clinical text data in machine learning: Systematic review
Machine learning for genetic prediction of psychiatric disorders: a systematic review
Wake modeling of wind turbines using machine learning
A survey of surveys on the use of visualization for interpreting machine learning models
Big-data science in porous materials: materials genomics and machine learning
Machine learning
The rise of machine learning for detection and classification of malware: Research developments, trends and challenges
Building thermal load prediction through shallow machine learning and deep learning
Machine learning technology in biodiesel research: A review
Machine learning driven smart electric power systems: Current trends and new perspectives
What role does hydrological science play in the age of machine learning?
Early diagnosis of hepatocellular carcinoma using machine learning method
Image-based cardiac diagnosis with machine learning: a review
Unsupervised machine learning and band topology
Cybersecurity data science: an overview from machine learning perspective
A survey of visual analytics techniques for machine learning
Quantum embeddings for machine learning
M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
Coronavirus Disease (COVID-19): A Machine learning bibliometric analysis
Special issue on machine learning and data-driven methods in fluid dynamics
A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic
Metallurgy, mechanistic models and machine learning in metal printing
A perspective on using machine learning in 3D bioprinting
COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach
The relationship between trust in AI and trustworthy machine learning technologies
Improving reproducibility in machine learning research (a report from the neurips 2019 reproducibility program)
COVID-19 future forecasting using supervised machine learning models
Mapping landslides on EO data: Performance of deep learning models vs. traditional machine learning models
A biochemically-interpretable machine learning classifier for microbial GWAS
Identifying scenarios of benefit or harm from kidney transplantation during the COVID‐19 pandemic: a stochastic simulation and machine learning study
Machine learning analysis of whole mouse brain vasculature
Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Machine Learning Calabi–Yau Metrics
Opening the black box: Interpretable machine learning for geneticists
Machine learning in additive manufacturing: State-of-the-art and perspectives
Machine learning approach to identify stroke within 4.5 hours
Machine-learning quantum states in the NISQ era
Machine learning as an early warning system to predict financial crisis
Interpretable machine learning
Landslide identification using machine learning
Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
Recent advances on constraint-based models by integrating machine learning
Machine Learning in oncology: A clinical appraisal
Polymer design using genetic algorithm and machine learning
Performance evaluation of machine learning methods for forest fire modeling and prediction
Machine learning approach for confirmation of covid-19 cases: Positive, negative, death and release
Learning earth system models from observations: machine learning or data assimilation?
Machine Learning Meets Quantum Physics
Clinical applications of continual learning machine learning
Machine learning: accelerating materials development for energy storage and conversion
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
A review on machine learning forecasting growth trends and their real-time applications in different energy systems
A systematic literature review on machine learning applications for sustainable agriculture supply chain performance
Machine learning in geo-and environmental sciences: From small to large scale
Blockchain and machine learning for communications and networking systems
Machine learning and natural language processing in psychotherapy research: Alliance as example use case.
Machine Learning for Solar Array Monitoring, Optimization, and Control
Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
Machine learning in agricultural and applied economics
AutoML-zero: evolving machine learning algorithms from scratch
A comprehensive survey of loss functions in machine learning
COVID-19 epidemic analysis using machine learning and deep learning algorithms
Attention in psychology, neuroscience, and machine learning
Get rich or die trying… finding revenue model fit using machine learning and multiple cases
How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection
Machine learning based solutions for security of Internet of Things (IoT): A survey
Introduction to machine learning, neural networks, and deep learning
Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0
Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies
Determinants of base editing outcomes from target library analysis and machine learning
A primer for understanding radiology articles about machine learning and deep learning
A machine‐learning approach for earthquake magnitude estimation
Applying machine learning in liver disease and transplantation: a comprehensive review
Machine learning approaches for elucidating the biological effects of natural products
Systematic review of machine learning for diagnosis and prognosis in dermatology
Early prediction of circulatory failure in the intensive care unit using machine learning
Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions
Machine learning applications for mass spectrometry-based metabolomics
Improving the accuracy of medical diagnosis with causal machine learning
A machine learning forecasting model for COVID-19 pandemic in India
Machine learning in psychometrics and psychological research
Automatic detection of coronavirus disease (covid-19) in x-ray and ct images: A machine learning-based approach
Machine learning predicts new anti-CRISPR proteins
Machine learning approaches to drug response prediction: challenges and recent progress
Machine learning prediction of mechanical properties of concrete: Critical review
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
Crop yield prediction using machine learning: A systematic literature review
Julia language in machine learning: Algorithms, applications, and open issues
The impact of machine learning on patient care: A systematic review
A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Applications of machine learning predictive models in the chronic disease diagnosis
Your evidence? Machine learning algorithms for medical diagnosis and prediction
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
Towards the systematic reporting of the energy and carbon footprints of machine learning
Machine learning accurate exchange and correlation functionals of the electronic density
Machine learning in additive manufacturing: A review
Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches
Explaining machine learning classifiers through diverse counterfactual explanations
A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models
A review of epileptic seizure detection using machine learning classifiers
Ai explainability 360: An extensible toolkit for understanding data and machine learning models
Using machine learning to predict decisions of the European Court of Human Rights
Intelligent edge computing based on machine learning for smart city
Machine learning and its applications in plant molecular studies
Machine learning for fluid mechanics
A universal machine learning algorithm for large-scale screening of materials
Coronavirus (covid-19) classification using ct images by machine learning methods
Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential
A survey of online data-driven proactive 5g network optimisation using machine learning
Machine learning algorithms for construction projects delay risk prediction
Toward interpretable machine learning: Transparent deep neural networks and beyond
Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE registry
PyFitit: The software for quantitative analysis of XANES spectra using machine-learning algorithms
Machine learning-based classification of vector vortex beams
Machine‐learning scoring functions for structure‐based drug lead optimization
Potential neutralizing antibodies discovered for novel corona virus using machine learning
Machine learning and artificial intelligence in haematology
Machine learning on graphs: A model and comprehensive taxonomy
Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning
MadMiner: Machine learning-based inference for particle physics
Machine learning for asset managers
Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics
Statistical and machine-learning methods for clearance time prediction of road incidents: A methodology review
Hierarchical machine learning of potential energy surfaces
Hybrid decision tree-based machine learning models for short-term water quality prediction
Machine-learning studies on spin models
Machine learning and data analytics for the IoT
Quantum adversarial machine learning
Engaging proactive control: Influences of diverse language experiences using insights from machine learning.
Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
Corporate default forecasting with machine learning
Identification of light sources using machine learning
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making
On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning
Recent developments in machine learning for energy systems reliability management
Machine learning and AI in marketing–Connecting computing power to human insights
Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in …
Machine-learning-accelerated perovskite crystallization
A review on machine learning in 3D printing: Applications, potential, and challenges
Integrated machine learning methods with resampling algorithms for flood susceptibility prediction
Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
An open source machine learning framework for efficient and transparent systematic reviews
Machine learning in breast MRI
Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques
Machine learning models for secure data analytics: A taxonomy and threat model
Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities
Prediction of droughts over Pakistan using machine learning algorithms
From real‐world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges
Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI
Physics-informed machine learning: case studies for weather and climate modelling
How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?
Selecting appropriate machine learning methods for digital soil mapping
Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
A clinician’s guide to artificial intelligence: how to critically appraise machine learning studies
Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability
Machine learning and artificial intelligence
COVID-19 diagnosis prediction in emergency care patients: a machine learning approach
The use of machine learning techniques in trauma-related disorders: a systematic review
A review of machine learning applications in wildfire science and management
Land-use land-cover classification by machine learning classifiers for satellite observations—A review
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
Machine learning for suicidology: A practical review of exploratory and hypothesis-driven approaches
Fairness in machine learning
A machine learning-based model for survival prediction in patients with severe COVID-19 infection
Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning
Modelling of shallow landslides with machine learning algorithms
Predicting Regioselectivity in Radical C− H Functionalization of Heterocycles through Machine Learning
Understanding from machine learning models
Nothing to disconnect from? Being singular plural in an age of machine learning
Supervised classification algorithms in machine learning: A survey and review
Can machine learning find extraordinary materials?
Incorporating biological structure into machine learning models in biomedicine
Bitcoin price prediction using machine learning: An approach to sample dimension engineering
Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0
Secure, privacy-preserving and federated machine learning in medical imaging
Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
Classifying earthquake damage to buildings using machine learning
The state of the art in enhancing trust in machine learning models with the use of visualizations
The digital divide in light of sustainable development: An approach through advanced machine learning techniques
Machine learning-based prediction of COVID-19 diagnosis based on symptoms
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry
Predicting standardized streamflow index for hydrological drought using machine learning models
Machine learning line bundle cohomology
Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study
Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous …
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Secure and robust machine learning for healthcare: A survey
A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions
Dynamic backdoor attacks against machine learning models
Flood susceptibility modelling using advanced ensemble machine learning models
Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
Data-Driven Security Assessment of Power Grids Based on Machine Learning Approach
Machine learning in rheumatology approaches the clinic
A novel randomized machine learning approach: Reservoir computing extreme learning machine
5G vehicular network resource management for improving radio access through machine learning
Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems
Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning
Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm
Data-driven symbol detection via model-based machine learning
Machine learning models for drug–target interactions: current knowledge and future directions
Machine‐learning scoring functions for structure‐based virtual screening
Machine learning and artificial intelligence: Definitions, applications, and future directions
Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review
Closed-loop optimization of fast-charging protocols for batteries with machine learning
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
Improving risk prediction in heart failure using machine learning
A perspective on machine learning methods in turbulence modeling
Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
Machine learning in fetal cardiology: What to expect
Molecular machine learning: the future of synthetic chemistry?
Application and comparison of several machine learning algorithms and their integration models in regression problems
A machine learning application for raising wash awareness in the times of covid-19 pandemic
Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media
Thirty years of machine learning: The road to Pareto-optimal wireless networks
A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
Predicting and explaining corruption across countries: A machine learning approach
Synergy Between Expert and Machine‐Learning Approaches Allows for Improved Retrosynthetic Planning
Machine learning: hands-on for developers and technical professionals
Conservation machine learning: a case study of random forests
Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ …
BLAZE: blazing fast privacy-preserving machine learning
Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches
Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms
Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors
A review on machine learning algorithms to predict daylighting inside buildings
Using machine learning in psychiatry: the need to establish a framework that nurtures trustworthiness
Machine learning threatens 5G security
FUNDAMENTAL APPLICATIONS OF MACHINE LEARNING ACROSS THE GLOBE
Machine learning decomposition onset temperature of lubricant additives
An efficient optimization approach for designing machine learning models based on genetic algorithm
Deep machine learning approach to develop a new asphalt pavement condition index
Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results
Review of machine learning methods in soft robotics
Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning
Utilization of machine-learning models to accurately predict the risk for critical COVID-19
A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition
Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II
Reflections on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning
Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models
Prediction of CO2 solubility in ionic liquids using machine learning methods
Forecasting air quality in Taiwan by using machine learning
Machine learning and network analyses reveal disease subtypes of pancreatic cancer and their molecular characteristics
A trustworthy privacy preserving framework for machine learning in industrial iot systems
Structural Deformation Controls Charge Losses in MAPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics
Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models
A path for translation of machine learning products into healthcare delivery
Data and its (dis) contents: A survey of dataset development and use in machine learning research
Gas turbine performance prediction via machine learning
Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission
Biophysical prediction of protein–peptide interactions and signaling networks using machine learning
Identification of high impact factors of air quality on a national scale using big data and machine learning techniques
Effect of data encoding on the expressive power of variational quantum-machine-learning models
Deep Dive into Machine Learning Models for Protein Engineering
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
Machine learning glass transition temperature of polymers
Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison
Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox
Testing machine learning based systems: a systematic mapping
Flexible piezoelectric acoustic sensors and machine learning for speech processing
Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
Analyzing and predicting students’ performance by means of machine learning: a review
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning.
The role of sensors, big data and machine learning in modern animal farming
Performance comparison of machine learning techniques in identifying dementia from open access clinical datasets
Machine learning for combinatorial optimization: a methodological tour d’horizon
Machine learning: Algorithms, real-world applications and research directions
Machine-learning approach expands the repertoire of anti-CRISPR protein families
ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data
Machine Learning Gauged Supergravity
Machine learning to reveal nanoparticle dynamics from liquid-phase tem videos
Suicidal ideation detection: A review of machine learning methods and applications
Multitask learning over graphs: An approach for distributed, streaming machine learning
6G white paper on machine learning in wireless communication networks
Machine learning prediction of critical transition and system collapse
Machine learning Calabi-Yau four-folds
Machine learning and big data in the impact literature. A bibliometric review with scientific mapping in Web of science
A Machine Learning‐Based Global Atmospheric Forecast Model
MP2ML: a mixed-protocol machine learning framework for private inference
Machine Learning in Quantitative PET Imaging
Visual analysis of discrimination in machine learning
Machine learning for the solution of the Schrödinger equation
Real-world application of machine-learning-based fault detection trained with experimental data
Machine learning strategies applied to the control of a fluidic pinball
Machine-learning-optimized aperiodic superlattice minimizes coherent phonon heat conduction
Combining data assimilation and machine learning to infer unresolved scale parametrization
Introducing Machine Learning: Science and Technology
Machine Learning for Structural Materials
Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach
Estimating entropy production by machine learning of short-time fluctuating currents
Intelligent traffic control for autonomous vehicle systems based on machine learning
Diabetes prediction using ensembling of different machine learning classifiers
Functional input and membership characteristics in the accuracy of machine learning approach for estimation of multiphase flow
Decentralized stochastic optimization and machine learning: A unified variance-reduction framework for robust performance and fast convergence
Machine learning for detecting early infarction in acute stroke with non–contrast-enhanced CT
Machine learning the thermodynamic arrow of time
Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning
Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
Survey on IoT security: challenges and solution using machine learning, artificial intelligence and blockchain technology
A segmented machine learning modeling approach of social media for predicting occupancy
Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city
Double debiased machine learning nonparametric inference with continuous treatments
Long-term prediction of chaotic systems with machine learning
SurvLIME: A method for explaining machine learning survival models
Teaching optics to a machine learning network
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
toward enhanced State of charge estimation of Lithium-ion Batteries Using optimized Machine Learning techniques
When do short-range atomistic machine-learning models fall short?
Machine learning of noise-resilient quantum circuits
Machine learning based code dissemination by selection of reliability mobile vehicles in 5G networks
Predicting product return volume using machine learning methods
A non-review of Quantum Machine Learning: trends and explorations
A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications
Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms
Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System
Machine learning-based design strategy for 3D printable bioink: elastic modulus and yield stress determine printability
The impact of entrepreneurship orientation on project performance: A machine learning approach
Data science in economics: comprehensive review of advanced machine learning and deep learning methods
Prediction of survival for severe Covid-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in …
Machine learning for dummies
Prediction of condition-specific regulatory genes using machine learning
Machine learning the magnetocaloric effect in manganites from lattice parameters
giotto-tda: A topological data analysis toolkit for machine learning and data exploration
Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia
Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes
Analysis on novel coronavirus (COVID-19) using machine learning methods
Towards novel insights in lattice field theory with explainable machine learning
Infrared spectroscopy data-and physics-driven machine learning for characterizing surface microstructure of complex materials
Machine learning models to quantify and map daily global solar radiation and photovoltaic power
Machine learning based intrusion detection systems for IoT applications
Predicting bank insolvencies using machine learning techniques
Precision psychiatry applications with pharmacogenomics: Artificial intelligence and machine learning approaches
Automated classification of significant prostate cancer on MRI: A systematic review on the performance of machine learning applications
Correlation between temperature and COVID-19 (suspected, confirmed and death) cases based on machine learning analysis
Machine learning in IoT security: Current solutions and future challenges
The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review
Machine learning and big scientific data
Machine learning for model error inference and correction
Participation is not a design fix for machine learning
Considerations for selecting a machine learning technique for predicting deforestation
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data
Advancing Biosensors with Machine Learning
The need for a system view to regulate artificial intelligence/machine learning-based software as medical device
Statistical and machine learning models in credit scoring: A systematic literature survey
A review on cooling performance enhancement for phase change materials integrated systems—flexible design and smart control with machine learning applications
Linear Algebra and Optimization for Machine Learning
Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms
Submodularity in action: From machine learning to signal processing applications
Introduction of a time series machine learning methodology for the application in a production system
Machine learning the central magnetic flux density of superconducting solenoids
Flexible machine learning-based cyberattack detection using spatiotemporal patterns for distribution systems
Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science
A machine learning model for emotion recognition from physiological signals
Toward an intelligent edge: Wireless communication meets machine learning
Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction
Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey
Empirical asset pricing via machine learning
Comparison of machine learning models for gully erosion susceptibility mapping
Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings
Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions
Machine learning and knowledge graph based design rule construction for additive manufacturing
Machine learning for long-distance quantum communication
Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed, Iran
Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays
Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease
Machine learning dihydrogen activation in the chemical space surrounding Vaska’s complex
On classifying sepsis heterogeneity in the ICU: insight using machine learning
Phishing web site detection using diverse machine learning algorithms
Automated self-optimisation of multi-step reaction and separation processes using machine learning
Evaluation of short-term freeway speed prediction based on periodic analysis using statistical models and machine learning models
Explainable artificial intelligence and machine learning: A reality rooted perspective
Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process
Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models
Inverse design of topological metaplates for flexural waves with machine learning
Assessing the drivers of machine learning business value
Neural additive models: Interpretable machine learning with neural nets
Conventional and machine learning approaches as countermeasures against hardware trojan attacks
Using machine learning to estimate unobserved COVID-19 infections in North America
Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big …
Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
Improving cyberbullying detection using Twitter users’ psychological features and machine learning
Classifying convective storms using machine learning
Robustness in machine learning explanations: does it matter?
Machine learning modeling of lattice constants for half-Heusler alloys
Asthma-prone areas modeling using a machine learning model
Identification of risk factors associated with obesity and overweight—A machine learning overview
Fairness in Machine Learning for Healthcare
A review of machine learning approaches to power system security and stability
Artificial intelligence and machine learning in design of mechanical materials
Learning to Make chemical predictions: the Interplay of feature representation, data, and machine learning methods
Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data
High-performance diagnosis of sleep disorders: a novel, accurate and fast machine learning approach using electroencephalographic data
Quantum machine learning models are kernel methods
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
Machine learning lattice constants for cubic perovskite A 2 2+ BB′ O 6 compounds
Adversarial Machine Learning for 5G Communications Security
Machine learning lattice parameters of monoclinic double perovskites
A comprehensive survey and analysis of generative models in machine learning
Experimental velocity data estimation for imperfect particle images using machine learning
Artificial intelligence and machine learning in computational nanotoxicology: Unlocking and empowering nanomedicine
Carbon trading volume and price forecasting in China using multiple machine learning models
Machine learning for structural health monitoring: challenges and opportunities
Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems
Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks
Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms
Machine learning on density and elastic property of oxide glasses driven by large dataset
Scientific intuition inspired by machine learning generated hypotheses
A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques
Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
Enhancing gravitational-wave science with machine learning
Machine learning active-nematic hydrodynamics
Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach
Electronic skins and machine learning for intelligent soft robots
Artificial intelligence and machine learning in respiratory medicine
Machine Learning Band Gaps of Doped-TiO2 Photocatalysts from Structural and Morphological Parameters
Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
Machine learning for intelligent optical networks: A comprehensive survey
Designing exceptional gas-separation polymer membranes using machine learning
From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
Mathematics for machine learning
Machine learning methods for microbiome studies
Insights from machine learning of carbon electrodes for electric double layer capacitors
Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques
Machine learning assisted quantum state estimation
Machine Learning in Mass Spectrometric Analysis of DIA Data
Improving pressure drops estimation of fresh cemented paste backfill slurry using a hybrid machine learning method
A Comprehensive Analysis of Triggers and Risk Factors for Asthma Based on Machine Learning and Large Heterogeneous Data Sources.
Machine learning recommends affordable new Ti alloy with bone-like modulus
Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods
Interpreting machine learning of topological quantum phase transitions
Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization
Multi-object detection and tracking (MODT) machine learning model for real-time video surveillance systems
Classification of mechanism of reinforcement in the fiber-matrix interface: Application of Machine Learning on nanoindentation data
Predicting mortgage early delinquency with machine learning methods
An empirical characterization of fair machine learning for clinical risk prediction
Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors
Combining medicinal plant in vitro culture with machine learning technologies for maximizing the production of phenolic compounds
ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services
Urban flow prediction from spatiotemporal data using machine learning: A survey
Machine learning solutions to challenges in finance: An application to the pricing of financial products
Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity
Covid-19 outbreak prediction with machine learning
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Machine learning holography for 3D particle field imaging
Investigating the gene expression profiles of cells in seven embryonic stages with machine learning algorithms
Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine
Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
An empirical study on the perceived fairness of realistic, imperfect machine learning models
Intersections of machine learning and epidemiological methods for health services research
Crystal symmetry determination in electron diffraction using machine learning
A machine learning based approach for predicting blockchain adoption in supply Chain
Machine learning study of the mechanical properties of concretes containing waste foundry sand
Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data
Hierarchical intrusion detection using machine learning and knowledge model
ExplainExplore: visual exploration of machine learning explanations
A machine learning-based prognostic predictor for stage III colon cancer
The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability
Machine learning to detect self-reporting of symptoms, testing access, and recovery associated with COVID-19 on Twitter: retrospective big data infoveillance …
Breast cancer detection by leveraging Machine Learning
Early diagnosis of Parkinson’s disease using machine learning algorithms
Machine learning for accurate force calculations in molecular dynamics simulations
Heart disease identification from patients’ social posts, machine learning solution on Spark
Machine learning models for the prediction of energy, forces, and stresses for platinum
The value of collaboration in convex machine learning with differential privacy
Exploiting machine learning to efficiently predict multidimensional optical spectra in complex environments
Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods
Series dc arc fault detection based on ensemble machine learning
Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation
Epidemic model guided machine learning for COVID-19 forecasts in the United States
Machine learning algorithms for smart data analysis in internet of things environment: taxonomies and research trends
A note on knowledge discovery and machine learning in digital soil mapping
Machine learning for neural decoding
Machine learning, COVID‐19 (2019‐nCoV), and multi‐OMICS
MLPerf: An industry standard benchmark suite for machine learning performance
Machine learning approach for pavement performance prediction
Machine learning: A powerful tool for gene function prediction in plants
Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge
On the relationship of machine learning with causal inference
A Review on Machine Learning for EEG Signal Processing in Bioengineering
Machine learning classifiers for Twitter surveillance of vaping: Comparative machine learning study
A general-purpose machine-learning force field for bulk and nanostructured phosphorus
NMR signal processing, prediction, and structure verification with machine learning techniques
Machine learning time series regressions with an application to nowcasting
Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis
Machine learning on DNA-encoded libraries: A new paradigm for hit finding
Development of an online health care assessment for preventive medicine: A machine learning approach
Finding People with Emotional Distress in Online Social Media: A Design Combining Machine Learning and Rule-Based Classification.
A Machine Learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions
Active flow control using machine learning: A brief review
Machine learning optical band gaps of doped-ZnO films
Using machine learning to model problematic smartphone use severity: The significant role of fear of missing out
Machine learning adds to clinical and CAC assessments in predicting 10-year CHD and CVD deaths
Rapid classification of quantum sources enabled by machine learning
Structure-function coupling in the human connectome: A machine learning approach
Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models
Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models
Modelling bioaccumulation of heavy metals in soil-crop ecosystems and identifying its controlling factors using machine learning
A survey of voice pathology surveillance systems based on internet of things and machine learning algorithms
What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach
Machine learning based video coding optimizations: A survey
FLASH: fast and robust framework for privacy-preserving machine learning
Lattice dynamics simulation using machine learning interatomic potentials
Hybrid machine learning EDFA model
The compatibility of theoretical frameworks with machine learning analyses in psychological research
Petrofacies classification using machine learning algorithms
A machine learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China
Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing
How is machine learning useful for macroeconomic forecasting?
Machine learning for surgical phase recognition: a systematic review
Machine learning maximized Anderson localization of phonons in aperiodic superlattices
Mapping wind erosion hazard with regression-based machine learning algorithms
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method
Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning
Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning
Elucidating Structure–Property Relationships in Aluminum Alloy Corrosion Inhibitors by Machine Learning
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic …
A comparison of machine learning methods for the prediction of traffic speed in urban places
An experimental analysis of machine learning classification algorithms on biomedical data
Maintenance management based on Machine Learning and nonlinear features in wind turbines
Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques
Machine learning accelerated recovery of the cubic structure in mixed-cation perovskite thin films
High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas–Surface Scattering Processes from Machine Learning
Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes
Machine-Learning-Guided Morphology Engineering of Nanoscale Metal-Organic Frameworks
Classifying travelers’ driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
Designing predictive maintenance systems using decision tree-based machine learning techniques
First principles and machine learning virtual flow metering: a literature review
A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques
Improving energy expenditure estimates from wearable devices: A machine learning approach
Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials
A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes
Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting
Extending machine learning classification capabilities with histogram reweighting
CRISPR-based COVID-19 surveillance using a genomically-comprehensive machine learning approach
Interpretability of machine learning‐based prediction models in healthcare
New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning
CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy
Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review
Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India
Machine learning the magnetocaloric effect in manganites from compositions and structural parameters
Machine learning aided air traffic flow analysis based on aviation big data
An accurate and dynamic predictive model for a smart M-Health system using machine learning
Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning
The promise of implementing machine learning in earthquake engineering: A state-of-the-art review
(What) Can Journalism Studies Learn from Supervised Machine Learning?
Predictive modeling of biomass gasification with machine learning-based regression methods
Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework
An Automated Machine Learning architecture for the accelerated prediction of Metal-Organic Frameworks performance in energy and environmental applications
Machine-learning prediction for quasiparton distribution function matrix elements
Classical versus quantum models in machine learning: insights from a finance application
Machine learning predicts large scale declines in native plant phylogenetic diversity
Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects
Feature validity during machine learning paradigms for predicting biodiesel purity
Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches
Realizing an efficient IoMT-assisted patient diet recommendation system through machine learning model
Machine learning and glioma imaging biomarkers
Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning
Machine learning on volatile instances
Machine learning for analysis of microscopy images: A practical guide
Machine learning based very short term load forecasting of machine tools
Machine learning framework for sensing and modeling interference in IoT frequency bands
Machine learning to identify persons at high-risk of human immunodeficiency virus acquisition in rural Kenya and Uganda
A novel machine learning approach combined with optimization models for eco-efficiency evaluation
Tree‐Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level
Applying machine learning optimization methods to the production of a quantum gas
Improving workflow efficiency for mammography using machine learning
Adversarial machine learning: An interpretation perspective
Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward
Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications
Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods
Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods
Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities
Machine learning-based models for the concrete breakout capacity prediction of single anchors in shear
Supervised machine learning techniques and genetic optimization for occupational diseases risk prediction
Accurate prediction of COVID-19 using chest x-ray images through deep feature learning model with smote and machine learning classifiers
Real-time hand gesture recognition using surface electromyography and machine learning: A systematic literature review
Morphological and molecular breast cancer profiling through explainable machine learning
Medical Internet of things using machine learning algorithms for lung cancer detection
When Malware is Packin’Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features
Machine learning lattice constants for cubic perovskite compounds
Medical information retrieval systems for e-Health care records using fuzzy based machine learning model
Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things
Speeding up discovery of auxetic zeolite frameworks by machine learning
Machine learning analysis on stability of perovskite solar cells
Making machine learning a useful tool in the accelerated discovery of transition metal complexes
Generating energy data for machine learning with recurrent generative adversarial networks
Corrauc: a malicious bot-iot traffic detection method in iot network using machine learning techniques
The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers
Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet
Comparative analysis of image classification algorithms based on traditional machine learning and deep learning
Portfolio optimization with return prediction using deep learning and machine learning
NPMML: A framework for non-interactive privacy-preserving multi-party machine learning
Machine-learning nonstationary noise out of gravitational-wave detectors
Machine learning surrogates for molecular dynamics simulations of soft materials
Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach
Three-dimensional vectorial holography based on machine learning inverse design
Applicability of machine learning in spam and phishing email filtering: review and approaches
Nonparametric machine learning and efficient computation with bayesian additive regression trees: the BART R package
MEWS++: enhancing the prediction of clinical deterioration in admitted patients through a machine learning model
Artificial intelligence, machine learning, and deep learning in women’s health nursing
Contextpca: Predicting context-aware smartphone apps usage based on machine learning techniques
A machine learning approach to predict outdoor thermal comfort using local skin temperatures
Applying machine learning in self-adaptive systems: A systematic literature review
Performance of a machine learning algorithm in predicting outcomes of aortic valve replacement
Machine learning for pore-water pressure time-series prediction: application of recurrent neural networks
AGL: a scalable system for industrial-purpose graph machine learning
Scaling tree-based automated machine learning to biomedical big data with a feature set selector
Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand
Reaching the end-game for GWAS: machine learning approaches for the prioritization of complex disease loci
Levels of Analysis for Machine Learning
Predictably unequal? the effects of machine learning on credit markets
Machine learning for recognizing minerals from multispectral data
Real-time forecasting of the COVID-19 outbreak in Chinese provinces: machine learning approach using novel digital data and estimates from mechanistic …
Static and dynamic malware analysis using machine learning
Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing
Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models
Microplastic identification via holographic imaging and machine learning
Congestion prediction for smart sustainable cities using IoT and machine learning approaches
Machine Learning String Standard Models
Recent scalability improvements for semidefinite programming with applications in machine learning, control, and robotics
Machine learning using digitized herbarium specimens to advance phenological research
Yield prediction with machine learning algorithms and satellite images
Machine learning–assisted global optimization of photonic devices
Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
Connecting dualities and machine learning
How we refactor and how we document it? On the use of supervised machine learning algorithms to classify refactoring documentation
Item response theory based ensemble in machine learning
Landslide susceptibility prediction based on remote sensing images and GIS: Comparisons of supervised and unsupervised machine learning models
Teaching yourself about structural racism will improve your machine learning
Performance and cost assessment of machine learning interatomic potentials
A machine learning workflow for raw food spectroscopic classification in a future industry
A machine learning approach predicts future risk to suicidal ideation from social media data
Intelligent compilation of patent summaries using machine learning and natural language processing techniques
Quality classification of Jatropha curcas seeds using radiographic images and machine learning
Ensemble of machine learning algorithms for cryptocurrency investment with different data resampling methods
Challenges to the reproducibility of machine learning models in health care
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Resource allocation with edge computing in iot networks via machine learning
SmartSPR sensor: Machine learning approaches to create intelligent surface plasmon based sensors
Computational system to classify cyber crime offenses using machine learning
Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks
Artificial intelligence and machine learning for HIV prevention: Emerging approaches to ending the epidemic
Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization
Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study
Assessing conformer energies using electronic structure and machine learning methods
A machine learning based intrusion detection system for mobile Internet of Things
Synthesis of control barrier functions using a supervised machine learning approach
Machine Learning-Aided Identification of Single Atom Alloy Catalysts
Obstructive Sleep Apnea: A Prediction Model Using Supervised Machine Learning Method
Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
Data poisoning attacks on federated machine learning
iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning
Radiomics in stratification of pancreatic cystic lesions: Machine learning in action
Selective encryption on ECG data in body sensor network based on supervised machine learning
Machine learning classification of new asteroid families members
Inter-dataset generalization strength of supervised machine learning methods for intrusion detection
First-principles machine learning modelling of COVID-19
Landscape aesthetics: Spatial modelling and mapping using social media images and machine learning
Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid
Machine learning for accurate intraoperative pediatric middle ear effusion diagnosis
Predicting the hydrogen release ability of LiBH4-based mixtures by ensemble machine learning
Predicting crystallization tendency of polymers using multifidelity information fusion and machine learning
An Overview on Predicting Protein Subchloroplast Localization by using Machine Learning Methods.
Machine learning identifies scale-free properties in disordered materials
ORELM: A novel machine learning approach for prediction of flyrock in mine blasting
River water salinity prediction using hybrid machine learning models
[CITATION][C] Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies
Predictive modelling and analytics for diabetes using a machine learning approach
When machine learning meets medical world: Current status and future challenges
An electrocardiographic system with anthropometrics via machine learning to screen left ventricular hypertrophy among young adults
Identifying knot types of polymer conformations by machine learning
The convergence of digital twin, IoT, and machine learning: transforming data into action
Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges
Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method
Interactive three-dimensional visualization of network intrusion detection data for machine learning
Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting
Machine learning and statistical methods for clustering single-cell RNA-sequencing data
Determining Philippine coconut maturity level using machine learning algorithms based on acoustic signal
Machine learning–driven language assessment
Generating high-resolution daily soil moisture by using spatial downscaling techniques: A comparison of six machine learning algorithms
Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging
A hybrid posture detection framework: Integrating machine learning and deep neural networks
MeLIME: Meaningful local explanation for machine learning models
Electric dipole descriptor for machine learning prediction of catalyst surface–molecular adsorbate interactions
A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty
Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
Machine learning in materials genome initiative: A review
Prediction of type 2 diabetes using machine learning classification methods
DLHub: Simplifying publication, discovery, and use of machine learning models in science
Predicting breast cancer in Chinese women using machine learning techniques: algorithm development
Rage Against the Machine: Advancing the study of aggression ethology via machine learning.
Explore the relationship between fish community and environmental factors by machine learning techniques
Towards a theory of machine learning
Stock price prediction using machine learning and LSTM-based deep learning models
Machine learning for the built heritage archaeological study
Mass load prediction for lithium-ion battery electrode clean production: a machine learning approach
Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study
Applying machine learning methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon
Daily retail demand forecasting using machine learning with emphasis on calendric special days
Susceptibility mapping of soil water erosion using machine learning models
Securing Internet of Things (IoT) with machine learning
Using machine learning for measuring democracy: An update
Survey on privacy-preserving machine learning
Uncovering the eutectics design by machine learning in the Al–Co–Cr–Fe–Ni high entropy system
Machine learning for prediction with missing dynamics
Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population
Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions
Distributed gradient methods for convex machine learning problems in networks: Distributed optimization
The Age of Data‐Driven Proteomics: How Machine Learning Enables Novel Workflows
Application of raw accelerometer data and machine-learning techniques to characterize human movement behavior: a systematic scoping review
Machine learning versus economic restrictions: Evidence from stock return predictability
A new machine learning model based on induction of rules for autism detection
H2o automl: Scalable automatic machine learning
Artificial intelligence and machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis
Accelerated first-order optimization algorithms for machine learning
Federated machine learning for intelligent IoT via reconfigurable intelligent surface
A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil
Optimizing high-efficiency quantum memory with quantum machine learning for near-term quantum devices
Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning
Detection of gravitational-wave signals from binary neutron star mergers using machine learning
Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India
Machine learning phase transitions with a quantum processor
Machine learning for predicting properties of porous media from 2d X-ray images
A consensus-based global optimization method for high dimensional machine learning problems
Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles
Machine learning F-doped Bi (Pb)–Sr–Ca–Cu–O superconducting transition temperature
Homomorphic encryption for machine learning in medicine and bioinformatics
Predicting thermal properties of crystals using machine learning
Using favorite data to analyze asymmetric competition: Machine learning models
Machine learning based approaches for detecting COVID-19 using clinical text data
Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model
Forecasting client retention—A machine-learning approach
Ores: Lowering barriers with participatory machine learning in wikipedia
Application of machine learning techniques to predict binding affinity for drug targets. A study of Cyclin-dependent kinase 2
Securing connected & autonomous vehicles: Challenges posed by adversarial machine learning and the way forward
Remote sensing and machine learning for crop water stress determination in various crops: a critical review
Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth
A systematic review on supervised and unsupervised machine learning algorithms for data science
Machine-Learning Assisted Screening of Energetic Materials
Prediction of methane adsorption in shale: Classical models and machine learning based models
Identifying and correcting label bias in machine learning
Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches
A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis
Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions
Automatic Detection and Grading of Multiple Fruits by Machine Learning
Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling
Machine learning from schools about energy efficiency
Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning
Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China
Measuring Social Desirability Using a Novel Machine Learning Approach Based on EEG Data.
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
Validation of a machine learning algorithm to predict 180-day mortality for outpatients with cancer
Machine Learning-based traffic prediction models for Intelligent Transportation Systems
Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan
Targeted prescription of cognitive–behavioral therapy versus person-centered counseling for depression using a machine learning approach.
Machine learning classification of ADHD and HC by multimodal serotonergic data
Classification and prediction of diabetes disease using machine learning paradigm
Machine learning-based models for real-time traffic flow prediction in vehicular networks
Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images—a comparative insight
Machine-learning dessins d’enfants: explorations via modular and Seiberg–Witten curves
Single-and multi-fault diagnosis using machine learning for variable frequency drive-fed induction motors
Classification and clustering algorithms of machine learning with their applications
Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning
Investigating sense of place of the Las Vegas Strip using online reviews and machine learning approaches
Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach
Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys
Depression screening using mobile phone usage metadata: a machine learning approach
Predicting likelihood of psychological disorders in PlayerUnknown’s Battlegrounds (PUBG) players from Asian countries using supervised machine learning
Machine learning and treatment outcome prediction for oral cancer
Detecting DDoS attacks in software-defined networks through feature selection methods and machine learning models
Applications of machine learning and deep learning to thyroid imaging: where do we stand?
Sarcasm detection using machine learning algorithms in Twitter: A systematic review
A large empirical assessment of the role of data balancing in machine-learning-based code smell detection
A Hierarchy of Limitations in Machine Learning
Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms
… assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models
List of Journals on Artificial Intelligence and Machine Learning

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8 Best Topics for Research and Thesis in Artificial Intelligence

Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancement in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, Internet of Things , Robotics , etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996.

Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

Table of Content

1. Machine Learning

2. deep learning, 3. reinforcement learning, 4. robotics, 5. natural language processing (nlp), 6. computer vision, 7. recommender systems, 8. internet of things.

Best-Topics-for-Research-and-Thesis-in-Artificial-Intelligence

So without further ado, let’s see the different Topics for Research and Thesis in Artificial Intelligence!

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

However, generally speaking, Machine Learning Algorithms are generally divided into 3 types: Supervised Machine Learning Algorithms , Unsupervised Machine Learning Algorithms , and Reinforcement Machine Learning Algorithms . If you are interested in gaining practical experience and understanding these algorithms in-depth, check out the Data Science Live Course by us.

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!).

This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.

Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from its own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error.

This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments.

An example of this is Kismet , which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut , which was developed by NASA to work alongside the astronauts in space.

It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation , etc. NLP is currently extremely popular for customer support applications, particularly the chatbot . These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.

Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.

The internet is full of images! This is the selfie age, where taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in.

Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering.

Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things , on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other.

Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by the Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices.

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249+ Innovative Machine Learning Research Topics for Students

Welcome to the exciting world of machine learning research topics! Let’s dive into how computers learn from data to make their own decisions.

Welcome to the captivating world of machine learning research! Join us on a journey to uncover how computers extract insights from data to autonomously navigate decision-making processes.

Explore the forefront of research domains that not only shape industries but also spark innovation. Let’s embark on this exhilarating exploration of advancements together!

Unleashing the Power of Machine Learning

Machine learning (ML) is an AI subset rapidly changing industries. It lets computers learn from data, improving task performance without explicit programming. Here’s how it’s revolutionizing:

Power of ML

Data insights: Analyzes big data, finds patterns, and predicts trends.
Automation: Automates tasks accurately, boosting productivity.
Personalization: Tailors experiences based on user preferences.
Continuous improvement: Learns and adapts with more data exposure.

Real-world impact

Industry transformation: Healthcare, finance, and manufacturing are evolving.
Everyday enhancement: From tailored recommendations to email filtering.
Scientific progress: Accelerates research in genomics, astronomy, and climate science.
Future shaping: Holds potential for self-driving cars, intelligent robots, and personalized education.
Data bias: Requires careful selection and cleaning of training data.
Ethical concerns: Raises questions about privacy and job displacement.
Explainability: Efforts underway for transparent AI models.

Requirements of creating good machine learning Research Topics

Here are essential guidelines for crafting effective machine learning research topics:

Focus and Innovation

Clearly defined problem: Address a specific issue or question within machine learning with well-defined objectives for improvement.
Novelty and Originality: Offer a fresh approach, methodology, or application of ML techniques, avoiding duplicating existing work.

Data and Feasibility

Data Availability: Ensure access to quality and quantity of data suitable for training and testing ML models.
Computational Resources: Choose topics feasible with available computational resources for training ML algorithms.

Impact and Applicability

Potential Impact: Aim for topics with the potential to contribute significantly to ML knowledge, enhancing techniques or addressing real-world problems.
Real-world Applicability: Prioritize topics with practical implications, whether in specific industries or addressing tangible challenges.

Additional Tips

Align with interests: Select topics of genuine interest and passion to enhance engagement throughout the research process.
Stay updated: Keep abreast of latest advancements and challenges in ML to identify emerging areas for research contribution.
Consult with advisor: Seek guidance from advisors or supervisors, leveraging their expertise and experience for topic refinement.
Refine iteratively: Be open to refining topics as research progresses, allowing ideas to evolve based on deeper exploration.

Following these guidelines and tips will help develop compelling ML research topics, driving meaningful contributions to the field’s advancement.

Machine Learning methods

Supervised: Labeled data for prediction.
Unsupervised: Finds patterns in data.
Reinforcement: Learns by trial and error.
Deep Learning: Neural networks for complexity.

Choose based on your problem and data.

How does machine learning work?

Machine learning lets computers learn without explicit instructions:

Get Data: Gather labeled or unlabeled data.
Clean Data: Prepare the data for analysis.
Choose Model: Pick the right method for your task.
Train Model: Teach the model using data.
Test Model: Check how well it works.
Improve: Adjust settings to make it better.
Use Model: Deploy it to make predictions.

Analogy: Like teaching a child to recognize animals by showing pictures.

More data helps.
Choose the right method.
Test and refine.

Benefits of Machine Learning

Machine learning (ML) offers significant benefits:

Enhanced Decision Making

ML uncovers insights from data for informed decisions.
Predictive capabilities help anticipate future trends.

Increased Efficiency and Automation

ML automates tasks, improving productivity.
Streamlines processes, reducing errors.

Improved User Experiences

Personalization enhances engagement.
ML-driven product development meets customer needs.

Innovation and Scientific Advancement

Accelerates discoveries in various fields.
Drives technological innovation.

Considerations

ML reduces costs and enhances safety.
Challenges include data dependency and ethical concerns.

In short, ML empowers smarter decisions, boosts efficiency, and fuels innovation with proper consideration of challenges.

Best Machine Learning Tools

Choosing the right machine learning tool depends on your needs and preferences:

Scikit-learn (Python): Easy-to-use for various tasks.
TensorFlow (Multiple Languages): Flexible for deep learning.
PyTorch (Python): Easy and dynamic for research.
Google Cloud AI, Amazon SageMaker, Microsoft Azure: Cloud platforms for scalable solutions.
Keras (Python): Quick prototyping for deep learning.
XGBoost (Multiple Languages): Fast and accurate for boosting.
OpenCV (Multiple Languages): Ideal for computer vision tasks.

Consider your language, scalability, and ease of use when selecting a tool.

Machine Learning Applications

Check out machine learning applications:-

Diagnosing diseases from medical images and patient data.
Accelerating drug development.
Tailoring treatment plans using patient data.
Identifying fraud in transactions.
Predicting financial risks and assessing loan applicants.
Automating trading strategies based on market analysis.

Manufacturing

Forecasting equipment failures for proactive maintenance.
Identifying defects in products with high accuracy.
Analyzing production data to enhance efficiency.

Retail and E-commerce

Offering personalized product recommendations.
Predicting customer demand for optimized inventory.
Identifying customers at risk of leaving to develop retention strategies.

Media and Entertainment

Personalizing content delivery based on user preferences.
Customizing news feeds on social media platforms.
Creating images, videos, and music using ML algorithms.

Transportation and Logistics

Enabling vehicles to navigate roads and make decisions.
Optimizing delivery routes to reduce travel time.
Forecasting potential vehicle issues to avoid breakdowns.

Customer Service

Providing 24/7 customer support through chatbots.
Understanding customer sentiment from reviews and social media.
Completing tasks using speech recognition and natural language processing.

Machine Learning Research Topics

Here’s a list of machine learning research topics across various categories:

Supervised Learning

Classification improvement methods.
Handling imbalanced datasets.
Ensemble learning for better accuracy.
Transfer learning for different domains.
Deep learning for image classification.
Text classification with NLP techniques.
Incremental learning for evolving data.
Semi-supervised learning for unlabeled data.
Active learning to minimize labeling efforts.
Explainable AI for model interpretation.

Unsupervised Learning

Clustering for data grouping.
Dimensionality reduction for complex data.
Anomaly detection for outlier identification.
Community detection in networks.
Density estimation for probability estimation.
Representation learning for feature extraction.
Graph embedding for graph data.
Generative models for data synthesis.
Unsupervised feature selection methods.
Evaluation metrics for unsupervised learning.

Reinforcement Learning

Deep RL for complex tasks.
Balancing exploration-exploitation.
Transfer learning for knowledge transfer.
Hierarchical RL for structured policies.
Multi-agent RL for collaboration.
Curriculum learning for task complexity.
Safe RL for constraint adherence.
Imitation learning from expert demonstrations.
RL in real-world robotics.
Human feedback integration in RL.

Deep Learning

CNN architectures for image analysis.
RNNs for sequential data modeling.
Transformers for NLP tasks.
Attention mechanisms for focus.
Meta-learning for task adaptation.
GANs for data generation.
VAEs for unsupervised learning.
Neural architecture search methods.
Few-shot learning for limited data.
Federated learning for distributed training.

Natural Language Processing (NLP)

Named entity recognition methods.
Sentiment analysis techniques.
Coreference resolution algorithms.
Question answering systems.
Machine translation models.
Text summarization approaches.
Language modeling techniques.
Dialogue systems for interaction.
Aspect-based sentiment analysis.
Multimodal NLP for diverse data.

Computer Vision

Object detection algorithms.
Semantic segmentation methods.
Instance segmentation techniques.
Image captioning models.
Image synthesis methods.
Video action recognition.
3D object recognition techniques.
Few-shot learning in vision.
Image super-resolution algorithms.
Generative models for image manipulation.

Robotics and Autonomous Systems

Perception algorithms for robots.
Localization and mapping techniques.
Reinforcement learning for control.
Human-robot interaction models.
Robotic grasping strategies.
Multi-robot coordination methods.
Learning-based motion planning.
Transfer learning in robotics.
Safe and robust learning.
Lifelong learning for adaptation.

Healthcare and Medical Imaging

Deep learning in medical imaging.
Predictive modeling from EHRs.
Radiomics for feature extraction.
Explainable AI in healthcare.
Clinical decision support systems.
Medical image synthesis methods.
Transfer learning in medical imaging.
Privacy-preserving ML in healthcare.
Drug repurposing and discovery.
Personalized medicine approaches.

Finance and Fintech

Time series forecasting models.
Sentiment analysis for market prediction.
Credit risk assessment techniques.
Fraud detection algorithms.
Algorithmic trading strategies.
Portfolio optimization methods.
Customer segmentation in finance.
High-frequency trading algorithms.
Explainable AI in finance.
ML applications in blockchain.

Environmental Science and Climate Modeling

ML models for climate forecasting.
Remote sensing data analysis.
Species distribution modeling.
Carbon footprint estimation.
Crop yield prediction techniques.
Oceanographic data analysis.
Satellite image analysis methods.
Climate change impact assessment.
Environmental monitoring systems.
ML in sustainable energy.

Social Media Analysis and Recommender Systems

Social network analysis techniques.
Topic modeling algorithms.
User behavior prediction methods.
Fake news detection models.
Community detection algorithms.
Trust and reputation modeling.
Personalized recommendation systems.
Exploratory data analysis in social media.
Sentiment analysis of user conversations.
Ethical considerations in social media.

Education and E-Learning

Intelligent tutoring systems.
Learning analytics methods.
Recommender systems for courses.
Adaptive assessment techniques.
Automated essay scoring systems.
NLP in educational dialogue.
Educational data mining techniques.
Gamification in learning.
VR and AR applications in education.
Inclusive design principles.

Ethics, Fairness, and Responsible AI

Fairness-aware ML algorithms.
Explainable AI techniques.
Algorithmic accountability frameworks.
Ethical considerations in data usage.
Human-centered AI design principles.
Privacy-preserving ML methods.
Bias detection and mitigation strategies.
Regulatory frameworks for AI.
Socioeconomic implications of AI.
Inclusive AI development practices.

Security and Cybersecurity

Intrusion detection using ML.
Malware detection algorithms.
Anomaly detection in network traffic.
Adversarial ML for cyber defense.
Security risk assessment models.
Insider threat detection methods.
Privacy attacks and defenses.
Secure and privacy-preserving ML.
Cyber threat intelligence analysis.
Automated vulnerability discovery.

Smart Cities and Urban Planning

Traffic prediction models.
Public transportation optimization.
Urban air quality monitoring.
Energy consumption forecasting.
Waste management optimization.
Smart grid management.
Water quality monitoring.
Disaster response systems.
Crime prediction and prevention.
Social equity in smart city development.

Human-Computer Interaction (HCI) and User Experience (UX)

Adaptive UI designs.
Emotion recognition in HCI.
Human activity recognition.
Eye tracking techniques.
Natural user interfaces.
Inclusive design methodologies.
Assistive technologies.
Virtual assistants and chatbots.
Affective computing in HCI.
Ethical UX design considerations.

Cognitive Neuroscience and Brain-Computer Interfaces (BCI)

Decoding brain signals.
Neuroimaging data analysis.
Brain-computer interface development.
Neurofeedback systems.
Brain-inspired computing.
EEG-based emotion recognition.
Brainwave authentication systems.
Neural decoding of perception.
Closed-loop neurostimulation.
Ethical issues in BCI research.

Biomedical Engineering and Biotechnology

ML models for genomic data.
Medical imaging reconstruction.
Wearable biosensors for health monitoring.
Computational drug discovery.
Bioinformatics for sequence analysis.
Patient-specific modeling.
Regenerative medicine approaches.
Precision medicine strategies.
Neural interfaces for prosthetics.
Synthetic biology applications.

Business and Marketing Analytics

Customer churn prediction.
Market basket analysis.
Social media influence tracking.
Customer lifetime value prediction.
Brand sentiment analysis.
Market segmentation techniques.
Price optimization models.
Sales forecasting methods.
Multi-channel marketing attribution.
Product recommendation systems.

Agriculture and Precision Farming

Crop yield prediction models.
Pest and disease detection.
Soil quality assessment techniques.
Precision irrigation systems.
Agricultural robotics.
Climate-resilient agriculture.
Farm management systems.
Livestock monitoring and management.
Agricultural supply chain optimization.
Agro-economic modeling.

These concise topics provide a glimpse into the diverse applications and research areas within machine learning.

What are the best topics for machine learning research paper?

Consider these factors for your ML research topic:

Focus and Originality

Address a specific ML issue with clear goals.
Offer a fresh approach, avoiding rehashing existing work.
Ensure accessible, quality data for training and testing.
Consider available computational resources.
Seek topics with potential to advance ML knowledge or solve real-world problems.
Look for real-world applications in specific industries or practical challenges.

Trending Areas for ML Research

Climate Change Modeling: Predict weather patterns and optimize renewable energy.
Explainable AI (XAI): Develop transparent ML models to address bias concerns.
Generative AI: Explore ethical applications for creating realistic content.

Advanced Techniques

Federated Learning: Research privacy-preserving ML methods for decentralized data.
Continual Learning: Develop adaptable ML models for evolving environments. Quantum Machine Learning: Investigate using quantum computing to enhance ML algorithms.

Remember, explore niche areas within ML like computer vision or NLP to find an engaging topic.

How do you select a research topic in machine learning?

Crafting a research topic in machine learning? Here’s how:

Find your interests

What areas excite you? (e.g., computer vision, healthcare)
Do you prefer theory or real-world stuff?

Check current research

Look at recent conferences (ICLR, NeurIPS ).
Find trends and unanswered questions.

Think feasibility

Is there enough data?
Do you have the tech to handle it?

Focus and originality

Define a specific problem.
Offer a fresh approach, not just a copy.

Impact and applicability

How will your research help machine learning?
Can it solve a real-world problem?

Extra ideas to explore

Climate Change ML: Analyze environmental data.
Explainable AI: Make models transparent.
Federated Learning: Train models while keeping data private.
Continual Learning: Adapt to new data.
Quantum ML: Use quantum computing to speed up algorithms.

Stay updated

Follow ML publications and online groups.
Attend conferences for the latest.

Talk to your advisor

Get advice on topics that match your interests.
Get feedback on your ideas.

Remember, pick a topic you love and can make a real impact on. And be open to refining it as you go!

What are the project topics related to machine learning?

Here’s a breakdown of project topic ideas in machine learning to help you find your match:

By Area of Application

Predict disease risk using medical data.
Automate medical image analysis for tasks like tumor detection.
Create a chatbot for basic medical information.
Detect financial fraud in real-time.
Predict stock market trends.
Assess creditworthiness and personalize loan offerings.
Recommend products based on customer behavior.
Forecast product demand for better inventory management.
Analyze customer sentiment to improve offerings.
Identify objects in images for self-driving cars.
Develop facial recognition or emotion detection systems.
Detect traffic violations using camera footage.

Natural Language Processing

Build engaging chatbots.
Develop sentiment analysis systems.
Create accurate machine translation tools.

By Machine Learning Technique

Compare classification algorithms.
Predict variables like house prices.
Assess data preprocessing impact.

Unsupervised Learning:

Group similar data points with clustering.
Reduce dataset features while retaining information.
Detect anomalies in sensor data.
Optimize game strategies.
Train robots for navigation or manipulation tasks.
Solve resource allocation problems.

By Difficulty Level

Implement basic algorithms from scratch.
Use existing libraries for analysis.
Visualize and analyze model results.

Intermediate

Fine-tune pre-trained deep learning models.
Experiment with hyperparameter optimization.
Compare model performances on complex datasets.
Develop novel ML architectures or algorithms.
Implement explainable AI techniques.
Explore federated or continual learning applications.

Remember, these are starting points.

What is the hottest topic in machine learning?

Here are some hot topics in machine learning:

Explainable AI (XAI): Making models transparent.
Generative AI: Creating new data like images or text.
Federated Learning: Training models on decentralized data.
Continual Learning: Adapting to changing data streams.
Large Language Models (LLMs): Handling tasks like text generation.
Quantum Machine Learning: Using quantum computers to speed up algorithms.
Machine Learning for Climate Change: Analyzing environmental data for sustainability.

Stay updated with research and discussions to keep pace with the field’s evolution.

What is the best topic for a thesis in machine learning?

Choosing a machine learning thesis topic?

Your Interests: Pick what excites you—computer vision, NLP, etc.

Feasibility

Data Access: Ensure quality data for training.
Computational Power: Have enough for complex models.

Originality and Impact

New Ideas: Avoid repetition; aim for novelty.
Real-World Impact: Solve problems or advance the field.

Find Your Topic

Stay Updated: Follow recent research and discuss with your advisor.
Refine: Be open to tweaking your topic as you go.

Explore Areas like

Explainable AI: Making models transparent, addressing biases.
Generative AI: Creating realistic data ethically.
Federated Learning: Improving models while respecting privacy.
ML for Sustainability: Solving environmental challenges.

Remember, choose what aligns with your interests, contributes meaningfully, and is doable with your resources.

What can I research in machine learning?

In the vast world of machine learning, finding your research niche can be simplified:

By Application

Healthcare: Predict diseases or automate medical tasks.
Finance: Detect fraud or optimize investment strategies.
Retail: Improve recommendations or forecast demand.
NLP: Analyze sentiment or develop chatbots.
Computer Vision: Identify objects or enhance image analysis.

By Technique

Supervised Learning: Classify data or predict outcomes.
Unsupervised Learning: Find patterns or reduce data complexity.
Reinforcement Learning: Train agents for tasks or games.

Emerging Trends

Explainable AI: Make models transparent and interpretable.
Generative AI: Create realistic data while addressing ethics.
Federated Learning: Train models with privacy preservation.
Continual Learning: Adapt models to evolving data streams.
Quantum Machine Learning: Explore quantum computing’s potential.

Refine Your Topic

Focus on a specific problem with available data.
Consider computational resources and potential impact.
Stay updated on advancements to shape your research.

What are the topics involved in machine learning?

Here we go:-

Understand supervised vs unsupervised learning, algorithms (e.g., linear regression), and the ML workflow.
Master statistical concepts and optimization techniques.
Explore supervised (classification, regression), unsupervised (clustering, dimensionality reduction), and reinforcement learning.
Dive into deep learning for complex patterns.
Learn ensemble methods and model evaluation.
Understand Explainable AI (XAI) for transparent models. Extra:
Apply ML in domains like healthcare or finance.
Learn scalable techniques and responsible AI. These points cover the essentials, with room for further exploration as you delve deeper into machine learning.

To sum up, machine learning research opens doors to endless possibilities. By grasping the basics, diving into algorithms, and staying curious about emerging trends, researchers can unlock groundbreaking insights and solutions.

With dedication and a thirst for discovery, the journey through machine learning promises excitement and innovation, shaping the future of technology and human progress.

147 Top Machine Learning Topics To Get Your Paper Easy

First of all, let’s determine – what is machine learning ? Generally, it refers to the study of computer algorithms that improve automatically through the use of data and experience. Machine learning is seen as part of AI that makes decisions or predictions without being entirely programmed. The complexity of developing conventional algorithms for performing the much-needed tasks makes this field a choice for the chosen few. Statistics show that the number of college students pursuing this course is few. Are you among the chosen few who would like to improve and excel in your computer science course? Well, our expert help will help springboard you to the next level in writing a research paper .

How To Find Topics in Machine Learning

The task of sourcing for impeccable machine learning topics is the least trodden path. The few resources available in the field and the course’s technicality make it all the more difficult. However, there are places you can find top-tier ideas in machine learning:

Reputable online sources Your well-stocked college library Available computer science papers (articles, journals, theses, etc.) Tech programs such as TED-X

With these readily available sources, you can be confident of a long list of machine learning topics that can impress your professor. Use our online research paper writing servic e and get your winning paper done fast. That said, our professionals have compiled a handpicked list of writing ideas for your inspiration. Have a look at them:

The Best Machine Learning Topics

Discuss supervised learning algorithms approach in machine learning
Evaluate iterative optimization of an object function
How can a function be used to determine the output for inputs correctly?
How to improve the accuracy of predictions or outputs with an algorithm
A case study of the classification, active learning, and regression in college
Analyze the effectiveness of learning from examples using a similarity function
The application of unsupervised learning in density estimation in statistics
Manifold learning algorithms for solving machine learning problems
Discuss the application of machine learning in data mining
How to detect anomalies and deviations in machine learning
The role of developmental robotics in machine learning
Determine the relationship between variables in large databases
How to develop artificial immune systems
Discuss the concept of strict rules in machine learning
Learning classifier systems in machine learning algorithms

Easy Machine Learning Research Topics

Challenges involved in creating intelligent machines that mimic human behavior
Discuss the process of data observations in machine learning
How to enable computers to learn automatically without human intervention
How to analyze training data and produce an inferred function
Drawing inferences from datasets comprising of input data without labeled responses
The role of Artificial Neural Network in learning from observational data
Evaluate the input and output layers of artificial neural networks
How to stack multiple layers of neural networks to create a huge network
Discuss the dependence of machine learning on linear regression
Dealing with the classification problem using logistic regression
The random forest machine learning technique in college

Hot Topics in Machine Learning

New computing technologies that have contributed to machine learning
The essence of machine learning in developing the self-driving Google car
An analysis of online recommendation offers: A case of Netflix
How to know what customers are saying about a product using machine learning
The crucial role of fraud detection in machine learning
Discuss the crucial relationship between AI and machine learning
What has contributed to the resurging interest in machine learning?
The role of machine learning in computational processing
The impact of machine learning in developing faster and more accurate results
The role of data preparation capabilities in machine learning
Discuss the place of machine learning in today’s world

Interesting Machine Learning Thesis Topics

How to apply machine learning to the progressive Internet of Things
Why industries using large amounts of data need machine learning knowledge
The role of machine learning in banks and other financial institutions
How government agencies use machine learning in ensuring public safety
Analyze how sensor data is used in identifying ways to increase efficiency
The role of wearable devices to the healthcare industry
How website recommending systems are transforming the retail sector
The process of finding new energy sources using machine learning
How to identify patterns and trends in transportation using machine learning
Discuss prediction and gradient boosting as machine learning methods
Compare and contrast between machine learning, deep learning, and data mining

College Research Topics in Machine Learning

How to determine the best machine-learning algorithm to use
The role of curiosity in meeting the challenges that lie ahead of machine learning
How scientists have incorporated machine learning in combating the pandemic
The place of innovation, agility, and customer-centricity in machine learning
The underpinnings of resilience in the machine learning process
The role of machine learning in the face of unpredictability
Top-rated analytical skills gained through machine learning
Getting repeatable data using the easy model deployment
Discuss the Graphical User Interfaces for building models and process flaws
Evaluate the sequential covering rule building
Principal component analysis in the machine learning process

Machine Learning Hot Topics

Developing a stock price detector using machine learning
Discuss how to predict wine quality using a wine quality dataset
The process of developing human activity recognition using a smartphone dataset
Evaluate object detection with deep learning
Why do we need to develop machine learning projects?
Why there are a lot of unearthed projects in software development
Machine learning: The efficiency of using textbooks and study materials
Getting hands-on experience through machine learning
Effective software for developing projects in machine learning
Why data scientists are going to be the future of the world
How to leverage various Artificial Intelligence technologies

Current Research Topics in Machine Learning

How to cartoony an image with machine learning
The role of machine learning in aiding coronavirus patients
How easy is it to classify human facial expressions and map them to emojis?
The role of machine learning in the increased cyberbullying claims
Why most developing countries are slow to incorporating machine learning
The effectiveness of the machine learning curriculum in colleges and universities
Are internet sources watering down the essence of machine learning
Discuss the role of machine learning in developing bioweapons
Using machine learning to solve daily problems in life
How effectively can machines recognize handwritten digits?
The role of convolutional neural networks in machine learning

Advanced Topics in AI & Machine Learning

Discuss the latest generative models in machine learning
The role of the Bayesian inference in the mathematics of machine learning
How probabilistic programming is transforming machine learning
Model selection and learning: The challenges herein
Discuss the application of machine learning in natural language processing
The development of neural Turing machines
Evaluate syntactic and semantic parsing in the process of machine learning
Discuss GPU optimization for neural networks
Back-propagation of time through machine learning processes
The role of MIT in advancing research in machine learning
Long-short term memory: A case study of the applications of machine learning

Best Machine Learning Project Topics

Advances made in machine learning in the recent years
A simple way of preventing neural networks from overfitting
How to use deep residual learning for image recognition
The process of accelerating deep network training through batch normalization
Discuss large-scale video classification with convolutional neural networks
Evaluate some of the common objects in Microsoft COCO
Describe how to learn deep machine features for scene recognition
Developing a new framework for generative adversarial nets
The impact of high-speed tracking with kernelled correlation features
A review of the multi-label learning algorithms
Describe how to transfer features in deep neural networks

Top-Rated Machine Learning Research Project Topics

Why we do not have hundreds of classifiers to solve real-world problems of classification
A web-scale approach to dealing with probabilistic knowledge
Supervised machine learning methods for fusing distinct information sources
Suggest new algorithms for evaluating and comparing algorithms
A review of the existing trends in extreme learning machines
A survey of the concept drift adaptation in machine learning
Describe the simultaneous segmentation and detection process
Discuss the most used feature selection methods today
The problem of Face Alignment for a single image
Evaluate the various multiple classifier systems in the world
How to achieve a super-real-time performance with high-quality predictions

Credible Machine Learning Dissertation Topics

Describe a semi-supervised setting in machine learning
Concepts of hypothesis sets in machine learning
Preprocessing of data: A case study of data normalization
Some of the most common problems in machine learning
Terminology and basic concepts: A case study of convex optimization
Discuss batch gradient descent and stochastic gradient descent
Assess the notion of support vectors in support machines
Online tools used for getting some intuition of an algorithm
Describe the generative model and basic ideas of parameter estimation
Discuss the memory-based neural networks
What is the Markov decision process

Research Topics in Human Visual System and Machine Learning

The role of video processing experts
Understanding the psychology of vision
Using the HVS model
Discuss the process of Chroma subsampling
Image compression techniques
The low-pass filter characteristic of the HVS model
Describe the human eye
The impact of 3D resolution
How does a depth-inverted face look like?
Brightness resolution
Complex visual systems

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64 Machine Learning Essay Topic Ideas & Examples

🏆 best machine learning topic ideas & essay examples, ✅ good essay topics on machine learning, 📑 interesting topics to write about machine learning.

Artificial Intelligence and Machine Learning There are both benefits and challenges to the use of AI and ML in the customer complaint resolution process. The ability of a company to provide a customer experience depends on that business’s power to […]
Data Analysis Package for Machine Learning All sides of the tale investigate and evaluate a variety of topics and concerns. The benefits of open-source software include the fact that it is free to experiment, use, alter, and redistribute.
How AI and Machine Learning Influence Marketing in the Fashion Industry As governments shut down factories, stores, and events to stop the transmission of the virus, the COVID-19 pandemic has had a tremendous impact on the worldwide fashion industry.
Diagnosis of Dementia by Machine Learning Methods in Epidemiological Studies Therefore, epidemiological studies directly impact the diagnosis, prognosis, and clinical treatment by presenting medical practitioners with relevant data on the course, presentation, and treatment of an illness.
Regularization Techniques in Machine Learning In another example, when predicting the payback of a business product, the system can use indicators of the area’s population and the presence of competitors in the district, ignoring the age or gender aspects of […]
Machine Learning: Bias and Variance High variance can be similarly detrimental for a prediction, as a model trained on a highly specific data cluster will be able to predict outcomes that are too complex for utilizing outside of the example […]
Machine Learning and Regularization Techniques The last regularization technique is adversarial regularization; the reason for attention is the privacy protection. In the need for additional regularization outside the learning process, dropout will be of use.
Aspects of Machine Learning in Clinical Research As computers and machines have a place in every sphere of life, it is obvious that it is the safest route for proposing further changes in clinical research and practice.
Machine Learning for Internet of Things Devices Hussain et al.justify the use of ML for IoT by pointing out the vast amount of data that IoT gathers. Other recent papers, such as the one by Diedrichs et al, focus on the more […]
Machine Learning Algorithms in Cancer Detection One of the most fundamental tools for machine learning in cancer detection is the use of imaging, with the premise that prognostic data is embedded in pathology images and digital pathology can provide big data […]
The Concept of Machine Learning in Business This research is very important since it will explain how modern-day managers can increase their reliance on information technology to enhance their managerial functions.
Approach For Understanding Machine Learning Methods It can be used to set the degree of influence of independent variables on the dependent ones. Before proceeding to the analysis of data, it is vital to identify the variables.
Epilepsy Prediction Using Machine Learning Method The findings clearly match those of Wundari et al.and Deriche et al.that innovative seizure detection techniques are more accurate in detecting epilepsy.
Data Mining and Machine Learning Algorithms The shortest distance of string between two instances defines the distance of measure. However, this is also not very clear as to which transformations are summed, and thus it aims to a probability with the […]
Developments in the Field of Machine Learning The environment of learning consists of a machine input, or a piece of information that a machine can respond to. One of the best ways that we can think of in solving the problem of […]
Machine Learning and Bagging Predictors The aggregate uses the average of the single predictors, to improve the accuracy of prediction especially for unstable procedures such as neural sets, regression trees and classification trees.
Concept Drifts and Machine Learning The main theory that is used in explaining machine learning is referred to as the computational learning theory where the learning theory is focused on the probabilistic performance bounds of the learning algorithm because the […]
Geographical Information System and Machine Learning Without the need for a more detailed discussion of the advantages and disadvantages of each method, it is essential to postulate that both DT and SVM have sufficient potential to improve flood modeling in hydrological […]
Machine Learning for Improved Management The theoretical perspectives that will be used in the proposed study will discuss the question of information technologies’ impact on the management of professional activities and the world in general.
Technology and Healthcare Ethics: Machine Learning Programmers and pioneers of machine learning must, therefore, be on the frontline to consider emerging ethical issues that can affect a patient’s autonomy throughout the medical care delivery process.
Advertising Technology: Machine Learning Advancements This paper focus on the description of advertising technology, the insights gained in its development, and the interpretation of machine learning coupled with how tech ads contributed to the development of machine learning and other […]
Ethical Questions of Machine Learning: Racist Hiring Policies And Increasing Profits
Machine Learning Approaches: Supervised, Unsupervised, and Reinforcement Learning
How Does Artificial Intelligence Use Machine Learning
The Limitations of Machine Learning in an Enterprise Setting
Analysis of Machine Learning Algorithm for Facial Expression Recognition
Large Data Sets and Machine Learning: Applications to Statistical Arbitrage
Can Machine Learning Approaches Lead Toward Personalized Cognitive Training
Machine Learning for Predicting Vaccine Immunogenicity
Urban Data Streams and Machine Learning: A Case of Swiss Real Estate Market
The Most Common Risk in Machine Learning: Protect Sensitive or Confidential Data
Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability
Understanding the Security Implications of the Machine-Learning Supply Chain
Machine Learning for Detection of Safety Signals: Example of Nivolumab and Docetaxel
Optimal Taxation and Insurance Using Machine Learning: Learning Sufficient Statistics and Beyond
Machine Learning for Quantitative Finance: Fast Derivative Pricing, Hedging, and Fitting
Accelerating the Branch-And-Price Algorithm Using Machine Learning
Machine Learning Versus Econometrics: Prediction of Box Office
How Netflix Uses Machine Learning
Machine Learning-Based Algorithm for Circularity Analysis
The Uses of Social Theory in Machine Learning for Social Science
Investigating Genetic Interactions Through Machine Learning
Problems of Human-Like Biases in Machine Learning
Malware Classification Using Machine Learning: Knime and Orange
Machine Learning for Dynamic Discrete Choice
Credit Scoring Application of Machine Learning
Two Main Sub-Fields of Music Machine Learning: Music Information Retrieval and Generative Music
Software Reliability Prediction Using Machine Learning Techniques
The Role of Machine Learning in Clinical Research
Computational Learning Theory and Statistical Learning Theory in Machine Learning
Fundamentals and Exchange Rate Forecastability With Simple Machine Learning Methods
Nowcasting New Zealand GDP Using Machine Learning Algorithms
Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management
Exploiting the Sports-Betting Market Using Machine Learning
Machine Learning: History and Relationships to Other Fields
Supervised Machine Learning: Regression and Classification
Machine Learning Models for the Classification of Sleep Deprivation Induced Performance Impairment
Orthogonal Machine Learning: Power and Limitations
Financial Time Series Data Processing for Machine Learning
Machine Learning Approaches for Myocardial Motion and Deformation Analysis
Machine Learning for Set-Identified Linear Models
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Latest thesis topics in Machine Learning for research scholars:

Choosing a research and thesis topics in Machine Learning is the first choice of masters and Doctorate scholars now a days. Though, choosing and working on a thesis topic in machine learning is not an easy task as Machine learning uses certain statistical algorithms to make computers work in a certain way without being explicitly programmed. The algorithms receive an input value and predict an output for this by the use of certain statistical methods. The main aim of machine learning is to create intelligent machines which can think and work like human beings. Achieving the above mentioned goals is surely not very easy because of which students who choose research topic in machine learning face difficult challenges and require professional thesis help in their thesis work.

Below is the list of the latest thesis topics in Machine learning for research scholars:

The classification technique for the face spoof detection in artificial neural networks using concepts of machine learning .
The iris detection and reorganization system using classification and glcm algorithm in machine learning.
Using machine learning algorithms in the detection of pattern system using algorithm of textual feature analysis and classification
The plant disease detection using glcm and KNN classification in neural networks merged with the concepts of machine learning
Using the algorithms of machine learning to propose technique for the prediction analysis in data mining
The sentiment analysis technique using SVM classifier in data mining using machine learning approach
The heart disease prediction using technique of classification in machine learning using the concepts of data mining.

So let’s start with machine learning.

First of all…

What exactly is machine learning?

Find the link at the end to download the latest topics for thesis and research in Machine Learning

What is Machine Learning?

Machine Learning is a branch of artificial intelligence that gives systems the ability to learn automatically and improve themselves from the experience without being explicitly programmed or without the intervention of human. Its main aim is to make computers learn automatically from the experience.

Requirements of creating good machine learning systems

So what is required for creating such machine learning systems? Following are the things required in creating such machine learning systems:

Data – Input data is required for predicting the output.

Algorithms – Machine Learning is dependent on certain statistical algorithms to determine data patterns.

Automation – It is the ability to make systems operate automatically.

Iteration – The complete process is iterative i.e. repetition of process.

Scalability – The capacity of the machine can be increased or decreased in size and scale.

Modeling – The models are created according to the demand by the process of modeling.

Methods of Machine Learning

Machine Learning methods are classified into certain categories These are:

Supervised Learning
Unsupervised Learning

Reinforcement Learning

Supervised Learning – In this method, input and output is provided to the computer along with feedback during the training. The accuracy of predictions by the computer during training is also analyzed. The main goal of this training is to make computers learn how to map input to the output.

Unsupervised Learning – In this case, no such training is provided leaving computers to find the output on its own. Unsupervised learning is mostly applied on transactional data. It is used in more complex tasks. It uses another approach of iteration known as deep learning to arrive at some conclusions.

Reinforcement Learning – This type of learning uses three components namely – agent, environment, action. An agent is the one that perceives its surroundings, an environment is the one with which an agent interacts and acts in that environment. The main goal in reinforcement learning is to find the best possible policy.

How does machine learning work?

Machine learning makes use of processes similar to that of data mining. Machine learning algorithms are described in terms of target function(f) that maps input variable (x) to an output variable (y). This can be represented as:

There is also an error e which is the independent of the input variable x. Thus the more generalized form of the equation is:

In machine the mapping from x to y is done for predictions. This method is known as predictive modeling to make most accurate predictions. There are various assumptions for this function.

Benefits of Machine Learning

Everything is dependent on machine learning. Find out what are the benefits of machine learning.

Decision making is faster – Machine learning provides the best possible outcomes by prioritizing the routine decision-making processes.

Adaptability – Machine Learning provides the ability to adapt to new changing environment rapidly. The environment changes rapidly due to the fact that data is being constantly updated.

Innovation – Machine learning uses advanced algorithms that improve the overall decision-making capacity. This helps in developing innovative business services and models.

Insight – Machine learning helps in understanding unique data patterns and based on which specific actions can be taken.

Business growth – With machine learning overall business process and workflow will be faster and hence this would contribute to the overall business growth and acceleration.

Outcome will be good – With machine learning the quality of the outcome will be improved with lesser chances of error.

Branches of Machine Learning

Computational Learning Theory
Adversarial Machine Learning
Quantum Machine Learning
Robot Learning
Meta-Learning

Computational Learning Theory – Computational learning theory is a subfield of machine learning for studying and analyzing the algorithms of machine learning. It is more or less similar to supervised learning.

Adversarial Machine Learning – Adversarial machine learning deals with the interaction of machine learning and computer security. The main aim of this technique is to look for safer methods in machine learning to prevent any form of spam and malware. It works on the following three principles:

Finding vulnerabilities in machine learning algorithms.

Devising strategies to check these potential vulnerabilities.

Implementing these preventive measures to improve the security of the algorithms.

Quantum Machine Learning – This area of machine learning deals with quantum physics. In this algorithm, the classical data set is translated into quantum computer for quantum information processing. It uses Grover’s search algorithm to solve unstructured search problems.

Predictive Analysis – Predictive Analysis uses statistical techniques from data modeling, machine learning and data mining to analyze current and historical data to predict the future. It extracts information from the given data. Customer relationship management(CRM) is the common application of predictive analysis.

Robot Learning – This area deals with the interaction of machine learning and robotics. It employs certain techniques to make robots to adapt to the surrounding environment through learning algorithms.

Grammar Induction – It is a process in machine learning to learn formal grammar from a given set of observations to identify characteristics of the observed model. Grammar induction can be done through genetic algorithms and greedy algorithms.

Meta-Learning – In this process learning algorithms are applied on meta-data and mainly deals with automatic learning algorithms.

Best Machine Learning Tools

Here is a list of artificial intelligence and machine learning tools for developers:

ai-one – It is a very good tool that provides software development kit for developers to implement artificial intelligence in an application.

Protege – It is a free and open-source framework and editor to build intelligent systems with the concept of ontology. It enables developers to create, upload and share applications.

IBM Watson – It is an open-API question answering system that answers questions asked in natural language. It has a collection of tools which can be used by developers and in business.

DiffBlue – It is another tool in artificial intelligence whose main objective is to locate bugs, errors and fix weaknesses in the code. All such things are done through automation.

TensorFlow – It is an open-source software library for machine learning. TensorFlow provides a library of numerical computations along with documentation, tutorials and other resources for support.

Amazon Web Services – Amazon has launched toolkits for developers along with applications which range from image interpretation to facial recognition.

OpenNN – It is an open-source, high-performance library for advanced analytics and is written in C++ programming language. It implements neural networks. It has a lot of tutorials and documentation along with an advanced tool known as Neural Designer.

Apache Spark – It is a framework for large-scale processing of data. It also provides a programming tool for deep learning on various machines.

Caffe – It is a framework for deep learning and is used in various industrial applications in the area of speech, vision and expression.

Veles – It is another deep learning platform written in C++ language and make use of python language for interaction between the nodes.

Machine Learning Applications

Following are some of the applications of machine learning:

Cognitive Services

Medical Services

Language Processing

Business Management

Image Recognition

Face Detection

Video Games

Computer Vision

Pattern Recognition

Machine Learning in Bioinformatics

Bioinformatics term is a combination of two terms bio, informatics. Bio means related to biology and informatics means information. Thus bioinformatics is a field that deals with processing and understanding of biological data using computational and statistical approach. Machine Learning has a number of applications in the area of bioinformatics. Machine Learning find its application in the following subfields of bioinformatics:

Genomics – Genomics is the study of DNA of organisms. Machine Learning systems can help in finding the location of protein-encoding genes in a DNA structure. Gene prediction is performed by using two types of searches named as extrinsic and intrinsic. Machine Learning is used in problems related to DNA alignment.

Proteomics – Proteomics is the study of proteins and amino acids. Proteomics is applied to problems related to proteins like protein side-chain prediction, protein modeling, and protein map prediction.

Microarrays – Microarrays are used to collect data about large biological materials. Machine learning can help in the data analysis, pattern prediction and genetic induction. It can also help in finding different types of cancer in genes.

System Biology – It deals with the interaction of biological components in the system. These components can be DNA, RNA, proteins and metabolites. Machine Learning help in modeling these interactions.

Text mining – Machine learning help in extraction of knowledge through natural language processing techniques.

Deep Learning

Deep Learning is a part of the broader field machine learning and is based on data representation learning. It is based on the interpretation of artificial neural network. Deep Learning algorithm uses many layers of processing. Each layer uses the output of previous layer as an input to itself. The algorithm used can be supervised algorithm or unsupervised algorithm. Deep Learning is mainly developed to handle complex mappings of input and output. It is another hot topic for M.Tech thesis and project along with machine learning.

Deep Neural Network

Deep Neural Network is a type of Artificial Neural Network with multiple layers which are hidden between the input layer and the output layer. This concept is known as feature hierarchy and it tends to increase the complexity and abstraction of data. This gives network the ability to handle very large, high-dimensional data sets having millions of parameters. The procedure of deep neural networks is as follows:

Consider some examples from a sample dataset.

Calculate error for this network.

Improve weight of the network to reduce the error.

Repeat the procedure.

Applications of Deep Learning

Here are some of the applications of Deep Learning:

Automatic Speech Recognition

Natural Language Processing

Customer Relationship Management

Bioinformatics

Mobile Advertising

Advantages of Deep Learning

Deep Learning helps in solving certain complex problems with high speed which were earlier left unsolved. Deep Learning is very useful in real world applications. Following are some of the main advantages of deep learning:

Eliminates unnecessary costs – Deep Learning helps to eliminate unnecessary costs by detecting defects and errors in the system.

Identifies defects which otherwise are difficult to detect – Deep Learning helps in identifying defects which left untraceable in the system.

Can inspect irregular shapes and patterns – Deep Learning can inspect irregular shapes and patterns which is difficult for machine learning to detect.

From this introduction, you must have known that why this topic is called as hot for your M.Tech thesis and projects. This was just the basic introduction to machine learning and deep learning. There is more to explore in these fields. You will get to know more once you start doing research on this topic for your M.Tech thesis. You can get thesis assistance and guidance on this topic from experts specialized in this field.

Research and Thesis Topics in Machine Learning

Here is the list of current research and thesis topics in Machine Learning :

Machine Learning Algorithms

Supervised Machine Learning

Unsupervised Machine Learning

Neural Networks

Predictive Learning

Bayesian Network

Data Mining

For starting with Machine Learning, you need to know some algorithms. Machine Learning algorithms are classified into three categories which provide the base for machine learning. These categories of algorithms are supervised learning, unsupervised learning, and reinforcement learning. The choice of algorithms depends upon the type of tasks you want to be done along with the type, quality, and nature of data present. The role of input data is crucial in machine learning algorithms.

Computer Vision is a field that deals with making systems that can read and interpret images. In simple terms, computer vision is a method of transmitting human intelligence and vision in machines. In computer vision, data is collected from images which are imparted to systems. The system will take action according to the information it interprets from what it sees.

It is a good topic for machine learning masters thesis. It is a type of machine learning algorithm in which makes predictions based on known data-sets. Input and output is provided to the system along with feedback. Supervised Learning is further classified into classification and regression problems. In the classification problem, the output is a category while in regression problem the output is a real value.

It is another category of machine learning algorithm in which input is known but the output is not known. Prior training is not provided to the system as in case of supervised learning. The main purpose of unsupervised learning is to model the underlying structure of data. Clustering and Association are the two types of unsupervised learning problems. k-means and Apriori algorithm are the examples of unsupervised learning algorithms.

Deep Learning is a hot topic in Machine Learning. It is already explained above. It is a part of the family of machine learning and deals with the functioning of the artificial neural network. Neural Networks are used to study the functioning of the human brain. It is one of the growing and exciting field. Deep learning has made it possible for the practical implementation of various machine learning applications.

Neural Networks are the systems to study the biological neural networks. It is an important application of machine learning and a good topic for masters thesis and research. The main purpose of Artificial Neural Network is to study how the human brain works. It finds its application in computer vision, speech recognition, machine translation etc. Artificial Neural Network is a collection of nodes which represent neurons.

Reinforcement Learning is a category of machine learning algorithms. Reinforcement Learning deals with software agents to study how these agents take actions in an environment in order to maximize their performance. Reinforcement Learning is different from supervised learning in the sense that correct input and output parameters are not provided.

Predictive Learning is another good topic for thesis in machine learning. In this technique, a model is built by an agent of its environment in which it performs actions. There is another field known as predictive analytics which is used to make predictions about future events which are unknown. For this, techniques like data mining, statistics, modeling, machine learning, and artificial intelligence are used.

It is a network that represents probabilistic relationships via Directed Acyclic Graph(DAG). There are algorithms in Bayesian Network for inference and learning. In the network, a probability function is there for each node which takes an input to give probability to the value associated with the node. Bayesian Network finds its application in bioinformatics, image processing, and computational biology.

Data Mining is the process of finding patterns from large data-sets to extract valuable information to make better decisions. It is a hot area of research. This technology use method from machine learning, statistics, and database systems for processing. There exist data mining techniques like clustering, association, decision trees, classification for the data mining process.

Click on the following link to download the latest thesis and research topics in Machine Learning

Latest Thesis and Research Topics on Machine Learning(pdf)

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Design and prototyping of a collaborative station for machine parts assembly.

1. Introduction

Using a collaborative robot to handle tolerated assembly of multiple machine parts without the need for a tool changing system.
Developing a flexible and reconfigurable station which can be modified or expanded to process different parts.
Establishing a method to validate safety measures by identifying a proper set of devices.
Cobot choice : The first step of the procedure is verification of the payload, the reachability of all areas of interest, and the cycle times. This analysis can be conducted by consulting the user manuals and through simulations, providing the most suitable cobot manufacturer and model for the application under study as an output.
Application analysis and implementation : This intermediate step aims to verify that the collaborative robot can efficiently perform all of the operations required in the application. If the result of this investigation is negative, the integration of auxiliary support systems for the cobot must be considered.
Risk analysis and safety measures validation : Finally, in the last step, the risks arising from the application, in particular from the auxiliary systems, are analyzed and evaluated. If the presence of the operator in the shared workspace of the robot is one of the project requirements, then appropriate strategies must be adopted in order to mitigate any risks highlighted by the analysis in accordance with current regulations. The chosen safety measures must then be experimentally validated with appropriate and certified tools.

2. Scenarios and Preliminary Tests

Flexibility and customisation, enabling customised mass production to better respond to customer needs.
Automation and efficiency, as advanced automation reduces human errors while increasing productivity and operational efficiency.
Competitiveness, as the adoption of advanced technologies enables companies to remain competitive in the ever-changing global marketplace.
Sustainability, as more advanced technologies can contribute to more sustainable practices and reduce environmental impact.
Resilience, ensuring business continuity via increased ability to adapt and respond to changes and crises.

2.1. Overview of Components and Manual Assembly Processes

Pick up and position the eyelet.
Pick up and position the cap.
Pick up and position the conical interface.
Pick up and position the ring on the conical interface.
Insert the ring into its seat by sliding it along the conical interface by means of a manual press operated by the human operator.
Remove the conical interface from the workpiece and reposition it in its starting position.
Physical and mental fatigue, which can reduce the efficiency and speed of the operator, especially in repetitive and monotonous operations such as those of the component 1 assembly cycle.
Unplanned interruptions and distractions in the workplace.
Mounting of Seeger ring JV28 inside the steel case; the Seeger ring is inserted into its seat by means of a ring gripper.
Insertion of the needle bearing into the housing until it touches Seeger ring JV28; as the case–needle bearing coupling requires interference fitting, the manual press is used to push it into position.
Again using the manual press, Seeger ring JV30 and the ball bearing are inserted in a single operation.
Insertion of the steel ring; this element is pushed into the case using the press until its flat surface aligns with the lower edge of the case.
Installation of the rubber gasket, for which a seat on the gasket is provided for the steel ring previously inserted into the case.

2.2. Preliminary Tests

Because Seeger ring JV28 acts as a reference for the position of the needle bearing, this element had already been inserted into its seat.
The ball bearing was inserted together with Seeger ring JV30, as these two parts are pushed into the case with a single piston stroke during the assembly cycle.
Insertion of the gripper pins into the holes of the Seeger ring is particularly difficult, as this operation requires high precision in positioning both the robot and the Seeger ring in the gripping position.
When the JV28 Seeger ring is compressed by the gripper, it tends to rotate out of the plane in a way that is difficult to predict and cannot be repeated.

3. Robotic Station Auxiliary Systems

4. automated assembly station.

The first area, intended for component 1, contains the automatic Seeger ring feeder and the assembly base.
The second area, dedicated to component 2, contains the auxiliary systems necessary for assembly, i.e., the pneumatic presses.

4.1. Station Logic

For component 1: 14 s, compared to 15 s for the manual process.
For component 2: 93 s, compared to 2 min for the manual process.
Area 1 (green): Includes the shared and collaborative workspace.
Area 2 (red): Behind the pneumatic presses designed for assembly of component 2

4.2. Experimental Collaborative Tests

A simulation of a free collision with the human operator’s shoulder and back; in this test, the UR10e collaborative robot was rotated around its axis 1 until it hit the CBSF-35 sensor.
A free collision with the operator’s hand, simulating the unloading of a finished component in the inspection area; in this case, the UR10e robot descended along the z-axis until the end effector collided with the CBSF-75 sensor.

5. Conclusions

The developed solution improves the efficiency of the specified manual assembly processes by significantly reducing the cycle time of component 2 while meeting the requirements imposed by the company, including for manual tests and inspections performed by the operator.
The design methodology introduced in this work and the tools used to verify the compliance of the adopted collaborative strategy could be applied to other projects to certify the collaborative nature of robotic applications, even when non-collaborative robots are involved. This is anticipated to be supported by a forthcoming new version of the regulations which is currently in development.
The auxiliary device developed to insert the Seeger rings can be industrialized to automate the assembly of machine components.
The development of this application demonstrates the growing interest and research in implementing collaborative solutions in the manufacturing industry.
The parts could be placed in the picking area according to an orderly and repeatable pattern, allowing the robot to always pick up the parts from the same places and in the same way.
The parts could be arranged in the picking station in a random arrangement; in this case, a vision system and component reorientation and separation system would need to be implemented in order to provide the robot with the position and orientation of the components to be picked.

Author Contributions

Data availability statement, acknowledgments, conflicts of interest.

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Click here to enlarge figure

System	Features	Functionality	Special Features	Component
Automatic Seeger ring Feeder	pneumatic piston, ‘sandwich’ structure of Plexiglas and aluminium plates.	Automates the repeatable feeding and placement of Seeger rings into the conical interface.	Utilizes a ‘sandwich’ structure with mechanically synchronized plates to select and align Seeger rings precisely for insertion.	1
Assembly Base	3D-printed base	Ensures correct assembly of component parts	Designed for high repeatability, free of support structures on functional surfaces.	1
Pneumatic Presses	3D-printed cap, pneumatic piston, spring-supported disc	Used for inserting bearings and Seeger rings in component 2	The first press inserts the JV28 Seeger ring into its seat inside the metal case. This involves a conical guide and a pneumatic piston with a 3D-printed cap that compresses the Seeger ring. A spring-supported disc prevents jamming. The second press pushes bearings and other elements into their positions within the metal case.	2
Gripper fingers	3D-printed fingers, SCHUNK Co-Act gripper	Used for manipulate parts of the two components	Designed to grip all the parts of the two components using only the ‘fully open’ and ‘fully closed’ gripper positions.	1–2

Collision Test	Sensor	Force [N]	Threshold	Pressure [N/cm ]	Threshold	Result
Back 1	CBSF-35	171	420	53	420	PASS
Back 2	CBSF-35	171	420	37	420	PASS
Shoulder 1	CBSF-35	152	420	266	320	PASS
Hand 1	CBSF-75	27	280	54	380	PASS
Hand 2	CBSF-75	27	280	50	380	PASS

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Share and Cite

Emiliani, F.; Bajrami, A.; Costa, D.; Palmieri, G.; Polucci, D.; Leoni, C.; Callegari, M. Design and Prototyping of a Collaborative Station for Machine Parts Assembly. Machines 2024 , 12 , 572. https://doi.org/10.3390/machines12080572

Emiliani F, Bajrami A, Costa D, Palmieri G, Polucci D, Leoni C, Callegari M. Design and Prototyping of a Collaborative Station for Machine Parts Assembly. Machines . 2024; 12(8):572. https://doi.org/10.3390/machines12080572

Emiliani, Federico, Albin Bajrami, Daniele Costa, Giacomo Palmieri, Daniele Polucci, Chiara Leoni, and Massimo Callegari. 2024. "Design and Prototyping of a Collaborative Station for Machine Parts Assembly" Machines 12, no. 8: 572. https://doi.org/10.3390/machines12080572

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Machine Learning Research Topics 2023

By : Aksaht Gaurav , Ronin Institute, U.S

Machine learning (ML) is a rapidly evolving field, with new technologies and approaches being developed at a breakneck pace [1-6]. As we approach the year 2023, the field is poised to make significant advancements in a number of areas. This blog post will explore some of the top machine-learning research topics for 2023.

Explainable Artificial Intelligence (XAI)

XAI is an area of research that focuses on developing machine learning algorithms that can provide clear explanations for their decisions [7-11]. As machine learning becomes more widespread, there is a growing need for algorithms that can be easily understood and interpreted by humans. XAI research is expected to make significant strides in 2023 and beyond, with new models and algorithms that offer more transparency and accountability.

Federated learning is a technique that allows multiple devices to contribute to a shared machine learning model without sending their data to a centralized server [12-16]. This technique has many potential applications, from improving personalized recommendations to developing better predictive models for medical research. In 2023, we can expect to see significant advancements in federated learning, with new algorithms and techniques that improve its efficiency and accuracy.

Continual Learning

Continual learning is an area of research that focuses on developing machine learning algorithms that can learn new tasks without forgetting their previous knowledge [4]. This is an important area of research, as current machine learning models are often unable to learn new tasks without significant retraining. In 2023, we can expect to see significant advancements in continual learning, with new models and algorithms that can learn new tasks more efficiently.

Reinforcement Learning

Reinforcement learning is a type of machine learning that focuses on training agents to make decisions in complex environments [16-20]. In 2023, we can expect to see significant advancements in reinforcement learning, with new techniques that improve the stability and efficiency of training algorithms.

Machine Learning for Healthcare

Machine learning has the potential to revolutionize the healthcare industry, from improving diagnosis and treatment to developing better predictive models for disease outbreaks [6]. In 2023, we can expect to see significant advancements in machine learning for healthcare, with new models and algorithms that offer better accuracy and efficiency.

Machine Learning for Metaverse

The metaverse is an emerging concept that refers to a virtual world that combines elements of gaming, social media, and other online experiences [7]. As the metaverse becomes more widespread, there will be a growing need for machine learning algorithms that can analyze and interpret the vast amounts of data generated by these virtual worlds. In 2023, we can expect to see significant advancements in machine learning for the metaverse, with new models and algorithms that offer better insights and predictions.

Natural Language Processing

Natural language processing (NLP) is an area of machine learning that focuses on developing algorithms that can analyze and understand human language [8]. In 2023, we can expect significant NLP advancements, with new models and algorithms that offer better accuracy and efficiency. This could have many potential applications, from improving voice assistants and chatbots to developing better text-to-speech and speech-to-text systems.

Autonomous Systems

Autonomous systems, such as self-driving cars and drones, rely on machine learning algorithms to make decisions and navigate complex environments [9]. In 2023, we can expect significant advancements in autonomous systems, with new models and algorithms offering better accuracy and safety. This could have many potential applications, from improving transportation and logistics to developing better surveillance and security systems.

Quantum Machine Learning

Quantum computing is an emerging technology that has the potential to revolutionize machine learning. In 2023, we can expect to see significant advancements in quantum machine learning, with new algorithms and techniques that offer better efficiency and scalability[10]. This could have many potential applications, from improving drug discovery and materials science to developing better machine learning models for financial and insurance industries.

Overall, the field of machine learning is rapidly evolving, with many exciting research topics on the horizon. From machine learning for metaverse and autonomous systems to natural language processing and quantum machine learning, there is a lot to look forward to in 2023 and beyond. By staying up to date with the latest research, we can help shape the future of machine learning and its impact on our world.

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Pai, M. L., et al., (2020). Application of Artificial Neural Networks and Genetic Algorithm for the Prediction of Forest Fire Danger in Kerala . In Intelligent Systems Design and Applications: 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) held in Vellore, India, December 6-8, 2018, Volume 2 (pp. 935-942). Springer International Publishing.
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Gupta, B. B., et al., (2011). On estimating strength of a DDoS attack using polynomial regression model. In Advances in Computing and Communications: First International Conference, ACC 2011, Kochi, India, July 22-24, 2011, Proceedings, Part IV 1 (pp. 244-249). Springer Berlin Heidelberg.
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A. Gaurav (2023) Machine Learning Research Topics 2023 , Insights2Techinfo, pp.1

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Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro

In the long run, we are all dead. Nonetheless, when studying the short-run dynamics of economic models, it is crucial to consider boundary conditions that govern long-run, forward-looking behavior, such as transversality conditions. We demonstrate that machine learning (ML) can automatically satisfy these conditions due to its inherent inductive bias toward finding flat solutions to functional equations. This characteristic enables ML algorithms to solve for transition dynamics, ensuring that long-run boundary conditions are approximately met. ML can even select the correct equilibria in cases of steady-state multiplicity. Additionally, the inductive bias provides a foundation for modeling forward-looking behavioral agents with self-consistent expectations.

n/a The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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What ails Indian research papers? Poor quality or just bad grammar? Premium

India comes third after china and the u.s. in the number of research papers published, but the rejection rate of indian papers is also high, not so much due to poor research but more so due to weak language and grammar.

Published - August 20, 2024 11:34 am IST

Image for representation

K.M. Ajith’s first research paper, co-authored with his supervisor in 2005, was about mathematical physics they had worked out in quantum field theory. The U.K. journal to which the paper was submitted had no hesitation in accepting the quality of the research work, yet the review was quite scathing.

“The reviewer pointed out grammatical errors, including for punctuation marks. And asked us to re-write from scratch,” says Mr. Ajith, who is now a professor at the National Institute of Technology, Karnataka.

The authors may have known quantum mechanics but not how to write succinctly. They asked for help from friends who were also pursuing research but whose English was better. Part of the difficulty was in rewriting the technical terms. Yet they managed to avoid jargon as much as possible to make it to the journal.

Mr. Ajith studied in a Malayalam medium school, and his exposure to English was minimal at that time. Twenty years into research and publishing, Mr. Ajith now speaks about why budding researchers should be good writers too.

India comes third after China and the U.S. in the number of research papers published, says a paper titled, Academic Writing in India: A Research Scholar’s View . But in the same paper, the authors also say the rejection rate of Indian papers is high, not so much due to poor research but more so due to weak language and grammar.

In a 2019 public notice, UGC said that writing programmes should be organised in research institutions to overcome this skill deficiency.

Somadatta Karak, head of science communication and public outreach at the Centre for Cellular and Molecular Biology, says, despite the courses, Indian students struggle with writing. She is concerned about the intensity and reach of the writing workshops and frameworks.

“When I go to tier 2 cities and take workshops on science communication, students there have not even heard or thought about all of these,” says Ms. Karak.

According to Kanika Singh, who directs the writing program at Ashoka University, the higher education system in India has no separate emphasis on writing. “If writing is institutionalised as part of your curriculum and you write in different ways daily, then your science research thesis will become better,” says Ms. Singh.

Eldho Mathews, programme officer (Internationalisation of Higher Education), The Kerala State Higher Education Council, says even students who join top-tier research institutions are trained in a way that gives little importance to writing.

“At the level of screening [for admissions to research institutions], it is important to evaluate the level of language skills. By incorporating this factor into testing systems, the government and institutions can effectively motivate students to develop their writing skills early on,” he said.

Why writing should be taught as core skill

Asha Channakar, a researcher at the Institute for Stem Cell Science and Regenerative Medicine (InStem), Bengaluru, had a similar experience like Mr. Ajith with her first paper. “The first time I wrote, it took a lot of time to understand how to write.”

Ms. Channakar says that when she started to write, she read a lot of papers, and tried to connect the writing and presentation with what she wanted to convey. This was while she was a project assistant at the National Brain Research Centre in 2019. Later, she took research writing classes at the National Centre for Biological Sciences as part of her PhD at InStem.

“They taught how to write a scientific manuscript, and there was also an assignment to write for the non-scientific community,” says Ms. Channakar. She has now grown to become the first author of a paper published recently at InStem.

Ranjana Sarma, who has a PhD in Biochemistry from Montana State University, says, “Our researchers struggle with the flow of ideas more than the language.”

Unlike Mr. Ajith and Ms. Channakar, Ms. Sarma got the benefit of the U.S. research ecosystem. When she first wrote a review paper, the feedback was, “Ranjana doesn’t know English.”

“Coming from India, this was a huge ego-crusher,” says Ms. Sarma, who consistently scored high in English back home.

In 2004, she was put into a course offered by Penn State University to learn not only writing but also how to present and peer review. In the U.S., she learned that writing should be simple and easy to read with short sentences. The writing classes Ms. Sarma took influenced her not only to write but also to think and how to pay attention to what she reads.

“Language does look like a challenge for most researchers, as they write in a heavy, academic style. Despite English being the language of science in India, most researchers find it difficult to express themselves in plain, simple English,” says Subhra Priyadarshini, Chief Editor of Global Supported Projects, Nature Portfolio.

Can AI help?

Of late, students use software like Grammarly to correct language and grammar. Although Mr. Ajith appreciates such software, he also says that the tools will not help students to do the critical thinking while writing. “Grammarly is not writing a paper for you; all it does is to check the grammar of what you have already written,” says Anannya Dasgupta, Associate Professor of literature and arts at Krea University, Andhra Pradesh.

Ms. Dasgupta, who is now the director of the Centre for Writing and Pedagogy at Krea University, started her writing stint as a course coordinator while pursuing her PhD at Rutgers University, the U.S. According to Ms. Dasgupta, to improve the quality of writing, more people should be trained to teach writing.

Teaching writing also involves teaching how to think through the questions and how to build an argument, says Pooja Sagar, who teaches the Writing for Research and Analysis at the Indian Institute for Human Settlements (I.I.H.S.), Bengaluru.

Can AI help? Almost all the established researchers said it could help to an extent. But at the level of research papers, a lot of critical thinking is required that AI can’t deliver. They also cautioned about AI providing false information.

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Reforming the US Long-Term Care Insurance Market

R. Anton Braun
Karen Kopecky

Nursing home risk is significant and costly. Yet, most Americans pay for long-term care (LTC) expenses out-of-pocket. This chapter examines reforms to both public and private LTCI provision using a structural model of the US LTCI market. Three policies are considered: universal public LTCI, no public LTCI coverage, and a policy that exempts asset holdings from the public insurance asset test on a dollar-for-dollar basis with private LTCI coverage. We find that this third reform enhances social welfare and creates a vibrant private LTCI market while preserving the safety net provided by public insurance to low-income individuals.

Working Papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views expressed in this paper are those of the authors and do not represent the views of the Federal Reserve Bank of Cleveland or the Federal Reserve System.

Suggested Citation

Braun, R. Anton, and Karen Kopecky. 2024. “Reforming the US Long-Term Care Insurance Market.” Federal Reserve Bank of Cleveland, Working Paper No. 24-17. https://doi.org/10.26509/frbc-wp-202417

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Machine Learning Research Topics for MS PhD
I am sharing with you some of the research topics regarding Machine Learning that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree. Applications of machine learning to machine fault diagnosis: A review and roadmap. Significant applications of machine learning for COVID-19 pandemic.
8 Best Topics for Research and Thesis in Artificial Intelligence
So without further ado, let's see the different Topics for Research and Thesis in Artificial Intelligence! 1. Machine Learning. Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without ...
249+ Innovative Machine Learning Research Topics for Students
What are the best topics for machine learning research paper? Consider these factors for your ML research topic: Focus and Originality. Address a specific ML issue with clear goals. Offer a fresh approach, avoiding rehashing existing work. Data and Feasibility. Ensure accessible, quality data for training and testing.
147 Outstanding Machine Learning Topics For Students
Easy Machine Learning Research Topics. Challenges involved in creating intelligent machines that mimic human behavior. Discuss the process of data observations in machine learning. How to enable computers to learn automatically without human intervention. How to analyze training data and produce an inferred function.
2021 Hot Topics in Machine Learning Research
2021 Hot Topics in Machine Learning Research. By: K. Yadav, M. Quamara, B. Gupta. What is it about? Federated machine learning is about training a model or an algorithm over dataset across decentralized edge devices in distributed networks via several training rounds until the model or the algorithm converge [1].
64 Machine Learning Essay Topic Ideas & Examples
Financial Time Series Data Processing for Machine Learning. Machine Learning Approaches for Myocardial Motion and Deformation Analysis. Machine Learning for Set-Identified Linear Models. 83 Listening Skills Essay Topic Ideas & Examples 78 MIS Topics for Presentations and Essays.
Latest Thesis Topics in Machine Learning for Research Scholars
Latest thesis topics in Machine Learning for research scholars: Choosing a research and thesis topics in Machine Learning is the first choice of masters and Doctorate scholars now a days. Though, choosing and working on a thesis topic in machine learning is not an easy task as Machine learning uses certain statistical algorithms to make computers work in a certain way without being explicitly ...
Machines
Collaboration between humans and machines is the core of the Industry 5.0 paradigm, and collaborative robotics is one the most impactful enabling technologies for small and medium Enterprises (SMEs). In fact, small batch production and high levels of product customization make parts assembly one of the most challenging operations to be automated, and it often still depends on the versatility ...
Machine Learning Research Topics 2023
Machine learning (ML) is a rapidly evolving field, with new technologies and approaches being developed at a breakneck pace [1-6]. As we approach the year 2023, the field is poised to make significant advancements in a number of areas. This blog post will explore some of the top machine-learning research topics for 2023.
A robust, agnostic molecular biosignature based on machine learning
The search for definitive biosignatures—unambiguous markers of past or present life—is a central goal of paleobiology and astrobiology. We used pyrolysis-gas chromatography coupled to mass spectrometry to analyze chemically disparate samples, including living cells, geologically processed fossil organic material, carbon-rich meteorites, and laboratory-synthesized organic compounds and ...
Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and
Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics ... Working Paper 32850 DOI 10. ... conditions that govern long-run, forward-looking behavior, such as transversality conditions. We demonstrate that machine learning (ML ...
How to Write a Research Paper: A Step by Step Writing Guide
2. Choose your topic. 3. Research your topic. 4. Build an outline and thesis statement. 5. Write your first draft. 6. Cite your sources. 7. Edit and proofread. 1. Understand your goal. Trying to write a research paper without understanding the guidelines is like trying to bake a cake without following a recipe.
What ails Indian research papers? Poor quality or just bad grammar
K.M. Ajith's first research paper, co-authored with his supervisor in 2005, was about mathematical physics they had worked out in quantum field theory. The U.K. journal to which the paper was ...
Reforming the US Long-Term Care Insurance Market
Working Papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views expressed in this paper are those of the authors and do not represent the views of the Federal ...

Exploring 250+ Machine Learning Research Topics

Why Does Machine Learning Research Matter?

How to Choose the Machine Learning Research Topics?

250+ Machine Learning Research Topics: Category-wise

Unsupervised Learning

Reinforcement Learning

Natural Language Processing (NLP)

Computer Vision

Deep Learning

Explainable AI

Transfer Learning

Federated Learning

Quantum Machine Learning

Ethical AI and Bias Mitigation

Meta-Learning and AutoML

AI for Healthcare and Medicine

AI in Finance and Investment

AI in Climate Change and Sustainability

Data Privacy and Security

Global Collaboration in Research

Emerging Trends and Hot Topics in Machine Learning Research

Interpretable and Explainable Models

Challenges and Future Directions

Resources for Machine Learning Researchers

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Text Mining and Text Classification

Image-Based Applications

Machine Vision

Optimization

Voice Classification

Sentiment Analysis

Recommendation Framework Project

Mall Customers' Project

Object Detection with Deep Learning

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37 Research Topics In Data Science To Stay On Top Of

37 Research Topics in Data Science

2.) Big Data Analytics

3.) Auto Machine Learning

4.) Text Mining

5.) Natural Language Processing

6.) Recommender Systems

7.) Deep Learning

8.) Reinforcement Learning

9.) Data Visualization

10.) Predictive Maintenance

11.) Financial Analysis