phd research topics in machine learning 2021

Artificial Intelligence Research Topics for PhD Manuscripts 2021

Introduction.

Imagine a world where knowledge isn’t limited to humans!!! A world in which computers will think and collaborate with humans to create a more exciting universe. Although this future is still a long way off, Artificial Intelligence has made significant progress in recent years. In almost every area of AI, such as quantum computing, healthcare, autonomous vehicles, the internet of things, robotics, and so on, there is a lot of research going on. So much so that the number of annual Published Research Papers on Artificial Intelligence has increased by 90% since 1996.

phd research topics in machine learning 2021

Keeping this in mind, there are several sub-topics on which you can concentrate if you want to study and write a thesis on Artificial Intelligence. This article covers a few of these subjects and provides a short overview. Here some of the recent Research Topics ,

Artificial Intelligence and Machine learning – Recent Trands
How AI and ML can aid healthcare systems in their response to COVID-19
Machine learning and artificial intelligence in haematology
Tackling the risk of stranded electricity assets with machine learning and artificial intelligence

Deep Learning

Deep Learning is a type of machine learning that learns by simulating the internal workings of the human brain in order to process data and make decisions.Deep Learning is a form of machine learning that employs artificial neural networks. These neural networks are linked in a web-like structure, similar to the human brain’s networks (basically a condensed version of our brain!).

Artificial neural networks have a web-like structure that allows them to process data in a nonlinear manner, which is a major advantage over conventional algorithms that can only process data in a linear manner. Rank Brain, one of the variables in the Google Search algorithm, is an example of a deep neural network.

Recent research topics

Artificial intelligence & deep learning : PET and SPECT imaging
Hierarchical Deep Learning Neural Network (HiDeNN): A computational science and engineering in AI architecture.
AI for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using Deep Learning
Deep learning-enabled medical computer vision

Reinforcement Learning

Reinforcing Learning is an aspect of Artificial Intelligence in which a computer learns something in the same way as humans do. Assume the computer is a student, for example. Over time, the hypothetical student learns from its errors. As a outcome of trial and error, Reinforcement Machine Learning Algorithms learn optimal behaviour.

This means that the algorithm determines the next way to proceed by learning behaviours based on its current state that will increase the reward in the future. This also works for robots, just as it does for humans!

Google’s AlphaGo Computer Programme , for example, used Reinforcement Learning to defeat the world champion in the game of Go (a human!) in 2017.

Experimental quantum speed-up in reinforcement learning agents
Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety

Robotics is an area concerned with the creation of humanoid robots that can assist humans and perform several acts. In certain cases, robots can behave like humans, but can they think like humans as well?

Kismet, a social interaction robot developed at M.I.T.’s Artificial Intelligence Lab, is an example of this. It understands human body language as well as our voice and responds to them appropriately. Another example is NASA’s Robonaut, which was designed to assist astronauts in space.

Regulating artificial intelligence and robotics: ethics by design in a digital society
Regional anaesthesia :usages of artificial intelligence and robotics in
Third Millennium Life Saving Smart Cyberspace Driven by AI and Robotics

Natural Language Processing

Humans can obviously communicate with each other by speech, but now machines can as well! This is known as Natural Language Processing, and it involves machines analysing and understanding language and expression as it is spoken (which means that if you speak to a computer, it might only respond!). Speech recognition, natural language production, natural language translation, and other aspects of NLP are all concerned with language. NLP is recently very important in customer service applications, particularly chatbots. These chatbots use machine learning and natural language processing to communicate with users in textual form and respond to their questions. As a result, you get a personal touch in your customer service experiences without actually speaking with a human.

Here are several research papers in the field of Natural Language Processing that have been published. You can look at them to get more ideas for research and thesis topics on this subject.

Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study
Sympathetic the temporal evolution of COVID-19 Research Through machine learning and natural language processing

Computer Vision

The internet is full of images! This is the selfie age, and taking and posting a photo has never been easier. Each day, millions of images are uploaded to the internet and viewed. It’s important for computers to be able to see and understand images in order to make the most of the vast amount of images available online. And, while humans can do this without thinking about it, computers find it more difficult! This is where Computer Vision enters the image.

To extract information from images, Computer Vision utilizes Artificial Intelligence. This knowledge may include object detection in the image, image content recognition to group images together, and so on. Navigation for autonomous vehicles using images of the surroundings is one use of computer vision, such as AutoNav, which was used in the Spirit and Opportunity rovers that landed on Mars.

Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning
An Open‐Source Computer Vision Tool for Automated Vocal Fold Tracking From Video endoscopy

Recommender Systems

Do you get movie and series recommendations from Netflix based on your previous choices or favourite genres? This is achieved by Recommender Systems, which offer you advice about what to do next from the vast array of options available online. Content-based Recommendation or even Collaborative Filtering may be used in a Recommender System.

The content of all the products is analysed in Content-Based Recommendation. For example, based on Natural Language Processing performed on the books, you might be recommended books that you may enjoy. Collaborative Filtering, on the other hand, analyses your past reading behaviour and then recommends books based on it.

Artificial intelligence in recommender systems
Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems.
Recommender systems for configuration knowledge engineering

Internet Of Things

Artificial intelligence is concerned with the creation of systems that can learn to perform human-like tasks based on prior experience and without the need for human interaction. The Internet of Things, on the other hand, is a network of different devices linked to the internet and capable of collecting and exchanging data.

All of these IoT devices now generate a large amount of data, which must be collected and mined in order to produce actionable results. Artificial Intelligence enters the picture at this stage. The Internet of Things is used to collect and manage the massive amounts of data that Artificial Intelligence algorithms need. As a consequence, these algorithms transform the data into useful actionable results that IoT devices can use.

Enhanced Medical Systems by using Artificial Intelligence and Internet of Things
Artificial Intelligence and Internet of Things in Instrumentation and Control in Waste Biodegradation Plants: Recent Developments
AIoT-Artificial Intelligence of Things

In this blog discussed the recent enhancement for artificial intelligences and their sub field. This will help to the PhD scholar who are interested to research in artificial intelligences domain.

Shouval, R., Fein, J. A., Savani, B., Mohty, M., & Nagler, A. (2021). Machine learning and artificial intelligence in haematology. British journal of haematology, 192(2), 239-250.
van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., … & Ercole, A. (2021). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110(1), 1-14.
Nyangon, J. (2021). Tackling the risk of stranded electricity assets with machine learning and artificial intelligence. In Sustainable Energy Investment-Technical, Market and Policy Innovations to Address Risk. IntechOpen.
Saha, S., Gan, Z., Cheng, L., Gao, J., Kafka, O. L., Xie, X., … & Liu, W. K. (2021). Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering. Computer Methods in Applied Mechanics and Engineering, 373, 113452.
Mascagni, P., Vardazaryan, A., Alapatt, D., Urade, T., Emre, T., Fiorillo, C., … & Padoy, N. (2021). Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Annals of Surgery.
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., … & Socher, R. (2021). Deep learning-enabled medical computer vision. npj Digital Medicine, 4(1), 1-9.
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Latest phd thesis topics in machine learning.

With progressive technological development, the exploration of machine learning has increased a huge number of applications. Consequentially. Machine learning instigates an essential part of implementing smart and automated applications by intelligent data analysis.
The applicability of machine learning is abundant in many real-world application fields, such as predictive analytics and intelligent decision-making, cyber-security systems, smart cities, healthcare, e-commerce, agriculture, finance, retail, social media, traffic prediction and transportation, computer vision applications, user behavior analytics and context-aware smartphone applications, bioinformatics, cheminformatics, computer networks, DNA sequence classification, economics and banking, robotics, advanced engineering, and many more.
Recently described branches of machine learning are computational learning theory, adversarial machine learning, quantum machine learning, robot learning, and meta-learning. Efficient data processing and handling the diverse learning algorithms are the constraints that are needed to be the focus in machine learning. PHD thesis on machine learning contributes to the challenges, promising research opportunities, and effective solutions in various application areas. Below is the list of PHD thesis topics on machine learning to effectively explore, scrutinize and discover the new findings of machine learning systems.

List of Sample PHD Thesis in Machine Learning

Incremental Learning for Large-Scale Data Stream Analytics in a Complex Environment
Neural Sequential Transfer Learning for Relation Extraction
Predicting Depression and Suicide Ideation in the Canadian Population Using Social Media Data
Neural Aspect-based Text Generation
Leveraging Social Media and Machine Learning for enhanced Communication and Understanding between Organizations and Stakeholders
Deep Learning Methods for Short,Informal, and Multilingual Text Analytics
Deep Learning Based Cursive Text Detection and Recognition in Natural Scene Images
Deep Learning-Based Text Detection and Recognition
Explaining Deep Neural Networks
Machine Learning Techniques in Spam Filtering
Anomaly-Based Network Intrusion Detection Using Machine Learning
Machine Learning for Financial Products Recommendation
Sentiment Analysis of Textual Content in Social Networks
Deep Learning For Time Series Classification
Deep Learning for Traffic Time Series Data Analysis
Novel applications of Machine Learning to Network Traffic Analysis and Prediction
Deep Learning for Animal Recognition
Neural Transfer Learning for Natural Language Processing
Scalable and Ensemble Learning for Big Data
Ensembles for Time Series Forecasting
Sample-Efficient Deep Reinforcement Learning for Continuous Control
Towards Generalization and Efficiency in Reinforcement Learning
Transfer Learning with Deep Neural Networks for Computer Vision
Deep Learning for Recommender Systems
CHAMELEON: A Deep Learning Meta-Architecture For News Recommender Systems
Learning in Dynamic Data-Streams with a Scarcity of Labels
Learning Meaning Representations For Text Generation With Deep Generative Models
Social Media Sentiment Analysis with a Deep Neural Network: An Enhanced Approach Using User Behavioral Information
Global-Local Word Embedding for Text Classification
Measuring Generalization and Overfitting in Machine Learning
Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions
Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn
Deep Learning Based Imbalanced Data Classification and Information Retrieval for Multimedia Big Data
Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data
An Investigation Into Machine Learning Solutions Involving Time Series Across Different Problem Domains
Deep Learning Applications for Biomedical Data and Natural Language Processing
Deep Neural Network Models for Image Classification and Regression
Deep learning for medical report texts
Deep multi-agent reinforcement learning
Artificial intelligence methods to support people management in organisations
An Intelligent Recommender System Based on Short-term Disease Risk Prediction for Patients with Chronic Diseases in a Telehealth Environment
Bringing Interpretability and Visualization with Artificial Neural Networks
Investigating machine learning methods in Recommender systems
Adaptive Machine Learning Algorithms For Data Streams Subject To Concept Drifts
Active Learning for Data Streams
Heart Diseases Diagnosis Using Artificial Neural Networks
Advanced Natural Language Processing and Temporal Mining for Clinical Discovery
Uncertainty in Deep Learning
Parallel Transfer Learning: Accelerating Reinforcement Learning in Multi-Agent Systems
Sentiment analysis on students Real-time Feedback
Aspect-Based Opinion Mining From Customer Reviews
Word Embeddings for Natural Language Processing
On Effectively Creating Ensembles of Classifiers
Design of Intelligent Ensembled Classifiers Combination Methods
ELSE: Ensemble Learning System with Evolution for Content Based Image Retrieval
Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks
Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks
Using Assessments of Contextual Learning to Identify Characteristics of Adaptive Transfer in Medical Students
Recursive Deep Learning for Natural Language Processing and Computer Vision
Machine learning strategies for multi-step-ahead time series forecasting
General Attention Mechanism for Artificial Intelligence Systems
Defense Acquisition University: A Study of Employee Perceptions on Web Based Learning Transfer
Incremental Learning with Large Datasets
Machine Learning and Data Mining Methods for Recommender Systems and Chemical Informatics
Transfer of Learning in Leadership Development: Lived Experiences of HPI Practitioners
Online Ensemble Learning in the Presence of Concept Drift
Learning From Data Streams With Concept Drift
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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
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Research Topics & Ideas: AI & ML

50+ Research ideas in Artifical Intelligence and Machine Learning

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.

Research topics and ideas about AI and machine learning

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.

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Machine Learning Research Topics for PhD

Achieving a Ph.D. in machine learning provides a chance to gain skills in both basic ideas and new improvements. While choosing a project concept, it is very essential to think about the upcoming approach and possible societal effects in addition to the latest research techniques. More than 200+ Machine Learning experts are there in phddirection.com to guide in what encounters that you may face in your research process. If you are reading this it means that you are in need for topics help. Have one to one discussion with our experts to get innovative topic ideas as per your specifications.

Here we describe various Ph.D.-based machine learning project ideas:

Foundational Theories in ML:
Generalization & Overfitting: In this, we interpret the subject-based knowledge of why deep neural networks generalize efficiently even with a large number of parameters.
Optimization in Deep Learning: Our work explores the factors of optimization methods such as SGD and aspects of loss functions in non-convex platforms.
Explainability & Understandability:
Transparent Techniques: We build efficiently understandable ML techniques, specifically deep learning.
Post-hoc Explanation Methods: After the black-box frameworks carried out the forecasting process, we develop techniques to produce explanations for the framework.
Fairness, Accountability & Transparency:
Bias Reduction: To identify and rectify biases in ML frameworks to check the fairness among various groups, we create methods.
Moral Considerations: Our work explores the wider moral considerations of automatic decision-making.
Reinforcement Learning :
Deep Meta-learning: In this, we utilize methods where the models learn the learning procedures and altering rapidly to new tasks.
Multi-agent Systems: When there are various models communicating in a platform, our research intends to explore the aspects and learning techniques.
Efficient ML:
Model Compression & Distillation: To minimize the dimension of our framework while ensuring its efficiency, we utilize various approaches.
On-device ML: For limited-resource devices such as IoT devices or smartphones, our research optimizes methods.
Neurosymbolic Computing :
Integrated Frameworks: To overcome the issue among connectionist and symbolic AI approaches, we combine deep learning with symbolic reasoning.
Out-of-distribution Generalization:
We generalize the data that are not efficiently defined in the training set by exploring the capacity of our framework.
Graph Neural Networks :
Scalability: To effectively manage huge graphs, we aim to create GNNs.
Dynamic Graph Learning: In this, our framework learns and alters to graphs that modify periodically.
Transfer Learning & Domain Adaptation:
To enhance the efficiency of one field or task, our project utilizes methods to alter the skills from another related field or task.
Quantum Machine Learning :
We investigate how quantum computing can improve machine learning methods and others.
Multimodal Learning :
Continuously, our techniques combine and reason beyond data from various sources such as images, text and audio.
Neural Architecture Search:
Efficient Search Plans: In this, we minimize the computational expense of examining for best network architectures.
Privacy-preserving:
Homomorphic Encryption: By using this, our work carries out the training and interpretation on encrypted data.
Differential Privacy: By concatenating noise to computations, we make sure about data confidentiality.
Self-supervised & Unsupervised Learning:
Representation Learning: Without utilizing labeled data, we find out the potential data presentations.
Contrastive Learning: By distinguishing positive and negative instances, our approaches learn embeddings.
Bio-inspired:
Neuromorphic Computing: We develop methods and hardware motivated by the design and task of the brain.
Evolutionary Techniques: To optimize ML frameworks, our work utilizes algorithms motivated by natural development.
Causal Inference:
Causal Representation Learning : It is about learning of representations that not only capture correlations but also capture causal connections.

It is very significant to consider the following factors while choosing a concept for Ph.D. research:

Find out domain experts or professionals with our selecting field-based knowledge.
Check whether we have accessibility to required datasets and resources.
Make sure that the selected concept relates with our passion and long-term professional objective.

We conclude that the Ph.D. research must intend to enhance the domain by solving important issues or problems in skills.

By following the above factors, we attain a 100% success in your work. Professional thesis support and help will be assisted in all your research needs. Thesis editing is also done by our editing department along with description. More than 120+ countries scholars we have provided best Machine Learning Research Topics for PhD and all have gained entire success. We are updated on trending technologies constantly and have huge resources to finish of the work.

Machine Learning Research Projects for PhD

PhD Projects In Machine Learning

Wide varieties of PhD Projects in Machine Learning topics are covered by phddirection.com under machine learning. For both masters and doctorate degree students our team provide the best research service as per your needs. No matter where you are struck up with our lead technicians will follow the latest methodologies, correct algorithms and pattern and finish of your PhD Projects in Machine Learning with prospective research work.

Machine Learning Algorithm in Network Traffic Classification
On the Integration of Machine Learning and Array Databases
Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning
Sentiment Analysis on IMDB Movie Reviews using Machine Learning and Deep Learning Algorithms
Horizon Detection Using Machine Learning Techniques
Machine learning contributions on the field of security and privacy of android
Performance Evaluation of Machine Learning Algorithms for Credit Card Fraud Detection
Comparison of text sentiment analysis based on traditional machine learning and deep learning methods
Advanced Machine Learning Scenarios for Real World Applications using Weka Platform
A 4-way Matrix Multiply Unit for High Throughput Machine Learning Accelerator\
A Intrusion Detection Algorithm Based on Improved Slime Mould Algorithm and Weighted Extreme Learning Machine
IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning
Understanding the learning of disabled students: An exploration of machine learning approaches
Probabilistically shaped signaling and machine learning detection for optical interconnection
Evaluation of Principal Component Analysis Algorithm for Locomotion Activities Detection in a Tiny Machine Learning Device
Applications of Hybrid Machine Learning for Improved Content Based Image Classification
Credit Card Fraud Detection Using Machine Learning Techniques
Knowledge acquisition through machine learning: minimising expert’s effort
Interpretable Machine Learning Techniques for Causal Inference Using Balancing Scores as Meta-features
Research on Constant Power Loads Stability of DC Microgrid Based on Machine Learning

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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.

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Latest Thesis and Research Topics on Machine Learning(pdf)

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7 of The Coolest Machine Learning Topics of 2021

Machine Learning Modeling West 2021 posted by Sheamus McGovern November 1, 2021 Sheamus McGovern

ODSC West 2021 is only a few short weeks away and the agenda is live so now is a good time for a quick pass on what’s cool and trending at this upcoming event. Here are some of our top picks to keep machine learning and data science practitioners abreast of trending topics in the field with these popular machine learning topics.

Machine Learning Safety

Pause for a moment to realize the number of machine learning models trained on crowdsourced data from social media and the web, and realize how easy it is to poison training data. In fact, this Microsoft paper from last year identifies it as a top concern (p.2). Driven by foundational models, large-scale models, and autonomous systems, ML safety is quickly becoming a broad topic encompassing many areas of AI and ML. Adversarial attacks, backdoor model vulnerabilities, real-world deployment tail risks, risk monitoring, and boosting defense are a few of the topics to fall under the ML safety umbrella. Expect to hear a lot more on this fast-trending topic.

Foundational Models

Massive trained models such as GPT-3 and BERT have been all the rage over the last few years, deserving acclaim for their breakthrough accomplishments. Termed foundational models by the Stanford HAI center, these models have come under new scrutiny. A single model can be employed across many applications, amplifying the challenges and risks of machine learning system design. Understanding the power, opportunity, and risk associated with these models will be fundamental to building responsible AI. ODSC West will feature many of these models, dissecting their capabilities and vulnerabilities.

Machine Learning Observability

MLOPs, AIOPs, DataOps. Any acronym can be the flavor of the moment thanks to heavy industry investment and a wall of VC funding. Dig a little deeper and you’ll notice a lot of unsolved problems in what, acronyms aside, is the ML systems engineering space. Once deployed to production, ML engineers need to monitor for model drift, data drift, data degradation, model improvement, and of course error detection. Observability is not just for real-time systems or even production environments. Applying the discipline of ML observability can identify problems early and display the belief that some ML lifecycles are static. ODSC West will have one of its strongest lineups of data engineering and MLOps sessions to date.

Deep Generative Learning

Deep Generative models (DGMs) have been around for a while now and received a lot of attention for generating deep fakes, but they have also been successfully used in hidden Markov models, GANs, bayesian networks, autoregressive models, and more. DGMs are neural nets with many hidden layers trained to high-dimensional probability distributions using a large number of samples. Despite these early successes, the broader use of DGMs is still in the early stages. It’s one of the hottest research topics in many of the top universities as researchers seek better ways to design and train these models. With this continued focus from some of the industry’s top minds, we can look forward to more breakthroughs and wider adoption for practical applications.

Privacy-Preserving Machine Learning and Differential Privacy

Permitting multiple organizations to collaboratively build, train, and deploy machine learning models without jeopardizing data privacy continues to gain importance. Responsible AI is a broad term employed by industry while practitioners prefer to focus on the many challenging issues of ensuring true end-to-end, privacy-preserving machine learning models. The focus is now on all states of the machine learning life cycle, including understanding privacy as it relates to training data, model inputs, model weights, model outputs, and model monitoring. Additionally, the field is evolving beyond basic differential privacy techniques, such as purposely introducing statistical or other types of noise to model inputs and outputs. Machine learning practitioners will find the latest on this topic at ODSC West.

Deep Learning-Based Natural Language Processing

NLP continues to enjoy a resurgence of interest in the industry thanks to developments in the last few years, including transfer learning and transformer models. New techniques combining supervised learning and unsupervised learning are gaining traction and advances continue to be made employing various deep learning techniques. Recursive Neural Networks and Recurrent Neural Networks’ (RNNs) specialty for processing sequential information, such as text, make them especially useful for NLP models. Deep Generative Models (DGMs), as previously mentioned, have led to massive breakthroughs in NLP. ODSC West will have many exciting sessions on NLP .

Machine Learning for Cybersecurity

Given the increasing importance of machine learning safety, it is essential that engineers and experts in AI broaden their knowledge of cybersecurity. In addition, cybersecurity is a field that is rapidly growing thanks to the deployment of machine learning tools and methods. Experts are employing machine learning to help predict and craft better threat incident response, monitor and counter evolving threats, and vastly speed up digital forensics techniques. Add to this the increased risk of adversarial attacks on machine learning, deep learning, and autonomous systems, and you have a field that’s poised to grow massively over the next decade. This is a new focus area for ODSC . West features some of the leading experts in the CyberML field .

Register Now for ODSC West 2021

At our upcoming event this November 16th-18th in San Francisco, ODSC West 2021 will feature a plethora of talks, workshops, and training sessions on machine learning topics, deep learning, NLP, MLOps, and so on. You can register now for 20% off all ticket types , or register for a free AI Expo Pass to see what some big names in AI are doing now. Some highlighted sessions on machine learning topics include:

Towards More Energy-Efficient Neural Networks? Use Your Brain!: Olaf de Leeuw | Data Scientist | Dataworkz
Practical MLOps: Automation Journey: Evgenii Vinogradov, PhD | Head of DHW Development | YooMoney
Applications of Modern Survival Modeling with Python: Brian Kent, PhD | Data Scientist | Founder The Crosstab Kite
Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems: Veena Mendiratta, PhD | Adjunct Faculty, Network Reliability and Analytics Researcher | Northwestern University

Sessions on MLOps:

Tuning Hyperparameters with Reproducible Experiments: Milecia McGregor | Senior Software Engineer | Iterative
MLOps… From Model to Production: Filipa Peleja, PhD | Lead Data Scientist | Levi Strauss & Co
Operationalization of Models Developed and Deployed in Heterogeneous Platforms: Sourav Mazumder | Data Scientist, Thought Leader, AI & ML Operationalization Leader | IBM
Develop and Deploy a Machine Learning Pipeline in 45 Minutes with Ploomber: Eduardo Blancas | Data Scientist | Fidelity Investments

Sessions on Deep Learning:

GANs: Theory and Practice, Image Synthesis With GANs Using TensorFlow: Ajay Baranwal | Center Director | Center for Deep Learning in Electronic Manufacturing, Inc
Machine Learning With Graphs: Going Beyond Tabular Data: Dr. Clair J. Sullivan | Data Science Advocate | Neo4j
Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0: Oliver Zeigermann | Software Developer | embarc Software Consulting GmbH
Get Started with Time-Series Forecasting using the Google Cloud AI Platform: Karl Weinmeister | Developer Relations Engineering Manager | Google

Sheamus McGovern

Founder of ODSC and Software Architect specializing in, complex multi-platform systems across multiple industries including finance, healthcare, and education.

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East 2025 Conferences posted by ODSC Team Dec 19, 2024 For years, ODSC East in Boston has stood apart as more than just another data science...

Enhancing Evaluation Practices for Large Language Models

West 2024 Modeling posted by ODSC Team Dec 17, 2024 In the rapidly evolving fields of natural language processing (NLP) and artificial intelligence (AI), evaluating the...

AI is Changing the Future of Remote Work

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AI Research Topics for PhD Manuscripts 2021 - PhD Assistance
Jun 18, 2021 · Recent research topics . Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study; Sympathetic the temporal evolution of COVID-19 Research Through machine learning and natural language processing Computer Vision. The internet is full of images!
Cutting-Edge Research Topics in Machine Learning - ScholarsEdge
Jul 8, 2024 · Some promising machine learning research topics for a thesis include exploring novel deep learning architectures for tasks like text classification, image recognition, or time series forecasting; developing incremental or meta-learning approaches to improve model generalization; applying machine learning to real-world challenges like depression detection from social media data, network ...
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Machine Learning Research Topics for MS PhD - T4Tutorials.com
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.
2021 Hot Topics in Machine Learning Research - Insights2Techinfo
By: K. Yadav, M. Quamara, B. Gupta Federated Machine Learning. 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].
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Machine Learning Research Topics for PhD - PhD Direction
Machine Learning Research Topics for PhD Achieving a Ph.D. in machine learning provides a chance to gain skills in both basic ideas and new improvements. While choosing a project concept, it is very essential to think about the upcoming approach and possible societal effects in addition to the latest research techniques.
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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.
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Jun 23, 2021 · Here some of the recent Research Topics, 1. Artificial Intelligence and Machine learning – Recent Trands 2. How AI and ML can aid healthcare systems in their response to COVID-19 3. Machine learning and artificial intelligence in haematology 4. Tackling the risk of stranded electricity assets with machine learning and artificial intelligence II.
7 of The Coolest Machine Learning Topics of 2021
Nov 1, 2021 · Here are some of our top picks to keep machine learning and data science practitioners abreast of trending topics in the field with these popular machine learning topics. Machine Learning Safety. Pause for a moment to realize the number of machine learning models trained on crowdsourced data from social media and the web, and realize how easy ...