phd research topics in machine learning 2020

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Latest thesis topics in Machine Learning for research scholars:

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

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

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

So let’s start with machine learning.

First of all…

What exactly is machine learning?

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

What is Machine Learning?

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

Requirements of creating good machine learning systems

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

Data – Input data is required for predicting the output.

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

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

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

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

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

Methods of Machine Learning

Machine Learning methods are classified into certain categories These are:

Supervised Learning
Unsupervised Learning

Reinforcement Learning

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

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

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

How does machine learning work?

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

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

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

Benefits of Machine Learning

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

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

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

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

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

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

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

Branches of Machine Learning

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

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

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

Finding vulnerabilities in machine learning algorithms.

Devising strategies to check these potential vulnerabilities.

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

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

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

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

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

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

Best Machine Learning Tools

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

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

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

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

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

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

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

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

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

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

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

Machine Learning Applications

Following are some of the applications of machine learning:

Cognitive Services

Medical Services

Language Processing

Business Management

Image Recognition

Face Detection

Video Games

Computer Vision

Pattern Recognition

Machine Learning in Bioinformatics

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

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

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

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

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

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

Deep Learning

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

Deep Neural Network

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

Consider some examples from a sample dataset.

Calculate error for this network.

Improve weight of the network to reduce the error.

Repeat the procedure.

Applications of Deep Learning

Here are some of the applications of Deep Learning:

Automatic Speech Recognition

Natural Language Processing

Customer Relationship Management

Bioinformatics

Mobile Advertising

Advantages of Deep Learning

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

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

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

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

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

Research and Thesis Topics in Machine Learning

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

Machine Learning Algorithms

Supervised Machine Learning

Unsupervised Machine Learning

Neural Networks

Predictive Learning

Bayesian Network

Data Mining

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

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

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

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

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

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

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

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

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

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

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

Latest Thesis and Research Topics on Machine Learning(pdf)

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Machine Learning -Topics Coverage

Machine Learning Models: Supervised - Unsupervised - Semi-Supervised - Regression - Ensemble - Reinforcement Deep Learning Models: Deep Neural Networks - Deep Recurrent Neural Networks - Deep Belief Networks - Deep Boltzmann Machine - Deep Autoencoder -Generative Neural Networks - Deep Ensemble Learning - Deep Reinforcement Learning - Convolutional Neural Networks- Transfer Learning - Extreme Learning Machines - Deep Generative Models - Dynamic Neural Networks - Radial Basis Function Networks - Long Short-Term Memory Networks - Restricted Boltzmann Machines - Self Organizing Maps - Transfer Reinforcement Learning - Multi-Goal Reinforcement Learning - Unsupervised Representation Learning - Distributional Reinforcement Learning -Extreme Multi-Label Classification - Generalized Few-Shot Classification - Multimodal Deep Learning - Quantum Machine Learning - One-Shot Learning - Hierarchical Reinforcement Learning - Multiple Instance Learning - Interpretable Machine Learning - Imitation Learning - Federated Learning - Active Learning - Few-Shot Learning - Meta-Learning - Representation Learning - Deep Cascade Learning- Explainable Deep Neural Networks - Evidential Deep Learning -Graph Representation Learning - Meta Reinforcement Learning - Graph Convolutional Networks - Hopfield Neural Networks - Quaternion Factorization Machines - Adversarial Machine Learning - Hyperbolic Deep Neural Networks - Few-Shot Class-Incremental Learning - Non-Local Graph Neural Networks -Distributed Active Learning - Triple Generative Adversarial Network - Shallow Broad Neural Network - Spiking Neural Networks - Bayesian Neural Networks - Word Embedding Models-Neural Machine Translation - Attention Mechanisms - Domain Adaptation - Data Augmentation - Image Augmentation - Text Augmentation -Neural Architecture Search - Hyperparameter Optimization - Neural Architecture Search - Feature Engineering Applications: Natural Language Processing - Stream Processing - Recommendation Systems - Sentiment Analysis - Opinion Mining - Time Series Data Analysis - Medical Machine Learning - Disease Prediction - Multimedia - Stock Market Prediction - Cyber security - Pattern Recognition - Medical Imaging - Healthcare - Speech Recognition - Computer Vision - Malware Detection System - Intrusion Detection System - Intelligent Wireless Networks - Big Data Analytics - Intelligent Vehicular Networks -Autonomous Vehicles - Time Series Forecasting - Edge Intelligence - Cloud Computing - Internet of Vehicles - Semantic Similarity

Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness (unavailable) Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology (Unavailable) Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation (Unavailable) Akash Umakantha, 2021

Structure and time course of neural population activity during learning (Unavailable) Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference (unavailable) Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

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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Automatic software vulnerability detection

Cyber ranges, translation validation for security, provably secure hardware-based countermeasures against software-exploitable side channels, security in critical information infrastructure, physical-layer security in 6g networks, machine learning for cultural heritage, deep learning for trajectory data, understanding business firms performance through the lens of machine learning, cybersecurity.

Formal modeling and verification techniques have the potential to provide the strongest security guarantee and to support full automation. However, implementing effective vulnerability detection tools based on these techniques is still an open issue. The main reasons are the poor scalability and the lack of formal semantics of real programming languages. The goal of this project is to investigate novel methodologies that (i) provide formal security guarantees and (ii) can be applied to real world software. Many types of analysis may be considered, including, for instance, symbolic exploration, model checking and security testing.

Formal methods; vulnerability analysis; security testing; evolutionary testing; white-box testing; code analysis.

Contact person

Gabriele Costa

G. Costa, A. Valenza: “Why Charles Can Pen-test: an Evolutionary Approach to Vulnerability Testing” ,

A. Valenza, G. Costa, A. Armando: “Never Trust Your Victim: Weaponizing Vulnerabilities in Security Scanners” , 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020),

The continuous evolution of threats as well as the growing complexity of modern infrastructures makes the security assessment of critical systems harder. Cyber ranges are virtual infrastructures that mimic real ones to support security-related activities such as testing, training and incident simulation. The development of a cyber range poses several theoretical and technical issues. The goal of this project is to study new approaches to improve the simulation quality and the training experience of cyber ranges. The activity also includes practical testing and integration with the already existing cyber range infrastructure owned by IMT Lucca.

Cyber range; security assessment; Cybersecurity training; simulation.

E. Russo, G. Costa, A. Armando: “Building next generation Cyber Ranges with CRACK” . Computers and Security, 2020

High-level languages provide a variety of abstractions and mechanisms (e.g. types, modules, automatic memory management) that enforce good programming practices and ease programmers in writing correct and secure code. However, those high-level abstractions do not always have counterparts when a program is compiled into a low-level language. This discrepancy can be dangerous when the source level abstractions are used to enforce security: if the target language provides no mechanism to preserve such properties, the resulting code is vulnerable to attacks. The emerging field of formally secure compilation aims at granting that the security properties at the source level are preserved as they are at the object level. Currently, many papers propose to manually prove (with the help of a proof-assistant) once and for all that the compiler is security preserving. Although these manual proofs are very effective, they require huge efforts in terms of time and resources, even when one considers simple languages. Instead of proving that a compiler is security preserving once and for all, a possible alternative is to use translation validation to prove that the compilation of a specific program preserves all security properties of interest. The goal of this project is to design and implement a translation validation technique to automatically check that a given compilation run does not break security.

Secure compilation; translation validation; static analysis

Letterio Galletta

M. Busi, and L. Galletta, “A Brief Tour of Formally Secure Compilation” in 3th Italian Conference on Cyber Security (ITASEC), 2019

M. Busi, P. Degano, L. Galletta, “Translation Validation for Security Properties” , PriSC 2019

Computer systems often provide hardware support for isolation mechanisms that are intended to confine the interactions between two isolated programs to a well-defined communication interface. A well known example of these isolation mechanisms is enclaved execution, which supports the software modules that runs isolated from all other software on the same platform, including system software such as the operating system. The isolation guarantees offered by enclaved execution are simple: data of a module can only be manipulated by code of the same module, external (untrusted) code cannot access the internal state of a module. Untrusted code can only interact with the enclave by calling a function in its public interface. Recently, security researchers have shown that enclaved execution can be attacked by means of software-exploitable side channels. Such side channels have been shown to violate integrity of victim programs, as well as their confidentiality. These attacks often exploit, or at least rely on, specific hardware features that were designed without security in mind. Thus, any architectural or micro-architectural feature of a processor brings a risk of introducing new software-based side-channel attacks. A recent class of attacks exploited the ability of an attacker to control the power supply of a computer system, or the API provided by modern microprocessors to control and schedule frequency and voltage. The goal of this project is to use programming language techniques developed in the field of secure compilation to design and prove secure countermeasures against this kind of attacks.

Secure compilation; enclaved execution; formal methods; formal verification

M. Busi, J. Noorman, J. Van Bulck, L. Galletta, P. Degano, J. T. Mühlberg, F. Piessens, “Provably secure isolation for interruptible enclaved execution on small microprocessors” , 33rd IEEE Computer Security Foundations Symposium, CSF 2020

Information and communications technologies (ICTs) are increasingly common in our daily activities. Some of the ICT systems, services, networks, and infrastructures form a vital part of our society, providing essential goods and services or constituting the underpinning platform of other critical infrastructures. They are typically regarded as critical information infrastructures (CIIs) as their disruption would seriously impact vital societal functions. Since cyber threats to CIIs could potentially affect the safety of citizens, many of these systems require a high level of security.

Security engineering for CIIs is a multidisciplinary field involving various topics ranging from secure software development and cryptography to embedded systems design and network security. Cyber-ranges represent an invaluable tool for testing the capabilities and effectiveness of the proposed solutions in a multipurpose virtual environment, which is also suitable as a platform for security training.

This project aims to study new methodologies for the security assessment of CIIs with the support of cyber-ranges. The research topics include multilevel security investigations involving, for instance, network security, protocol security, and application security.

Network security; protocol security; vulnerability analysis; cyber-range; security assessment.

Contact persons

Simone Soderi , Yuriy Zacchia Lun

S. Soderi. “Evaluation of industrial wireless communications systems’ security” . Ph.D. Thesis, University of Oulu, Faculty of Information Technology and Electrical Engineering; Centre for Wireless Communications, June 2016.

The sixth-generation (6G) mobile communication technology is one of the most prominent emerging research areas which will change our society and business. Its launch is expected to occur around 2030 when our society becomes data-driven and unlimited wireless connectivity.

We live in a hyper-connected society where sensors can exchange data even without any need for human interactions. Internet of Things (IoT) systems generate a massive amount of data transmitted via a networking infrastructure in which plenty of computing devices communicate among them. The physical-layer security techniques could represent an efficient solution to secure IoT. Indeed, physical-layer security aims at securing communications exploiting the physical properties of the communication channel. The next generation of low-power sensor networks is an area where physical-layer security can provide better computations than cryptography and low energy consumption, extending the battery life.

This project aims to develop and prove new physical-layer algorithms that enhance IoT security by exploiting signal processing techniques (e.g., watermarking) or even through an alternative medium (e.g., visible light communications, acoustic communications).

Physical-layer security; 6G; IoT; visible light communications.

Simone Soderi

S. Soderi. “Enhancing Security in 6G Visible Light Communications” . 2nd 6G Wireless Summit (6G SUMMIT). 2020.

Machine Learning

The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning (DL) models. Typically, in this context the data are coming from different sources such as text, scanned images, photos, 3D models, and so on. The main task required to Machine Learning researchers is to make available to CL experts the possibility to access, query, and explore all these sources of information together. However, despite the evolution of ML/DL image and text processing systems, multimodal matching remains a challenging problem. This requires the development of new methods that are able to combine the information gathered from several sources in order to retrieve them in an efficient way. Thus, the goal of this process is to explore and implement new multimodal machine learning approaches and applications on top of them.

Deep learning, Machine learning, cultural heritage, pattern recognition, Natural language processing

Fabio Pinelli

M. Fiorucci, M. Khoroshiltseva, M. Pontil, A. Traviglia, A. Del Bue, S. James Machine Learning for Cultural Heritage: A Survey Pattern Recognition Letter, 2020

The study of human mobility, and mobility in general, is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, immigrations and so on. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the outstanding predictive power of artificial intelligence, triggered the application of deep learning to human mobility. In particular, the literature is focusing on several tasks, just to name fews: next-location prediction, crowd trajectory prediction, trajectory reconstruction, mobility pattern detection, and so on. The goal of this project is to develop new and advanced deep learning methods applied to trajectory data in order to solve one of the above mentioned problems to be applied in different contexts from urban mobility to vessel mobility.

Machine learning, Urban mobility, deep learning

M. Luca, G. Barlacchi, B. Lepri, L. Pappalardo. “Deep Learning for Human Mobility: a Survey on Data and Models”

Thanks to the increasing availability of granular, yet high-dimensional, firm level data, machine learning (ML) algorithms have been successfully applied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms’ performance. In this contribution, we will illustrate a series of SL approaches to be used for prediction tasks, relevant at different stages of the company life cycle. The goal of this project is the application and development of new ML methods to firm level data in order to investigate firm performance over the years.

Machine learning, Econometrics, Business performance, Statistics

F. J. Bargagli-Stoffi, J. Niederreiter, M. Riccaboni. “Supervised learning for the prediction of firm dynamics”

SySMA - Systems Security Modelling and Analysis

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Our experienced advisors offer the following doctorate topics for applicants interested in a Ph.D. study at selected study programmes.

• Control and optimization algorithms for autonomous cars The goal is to build an autonomous race model car and participate in F1/10 competition. The model car construction (mechanical design, electronics design, and basic software) being available, the thesis will focus on advanced aspects of the SW architecture, namely on control and optimization algorithms. Study program: Control Engineering and Robotics Czech Technical University – Faculty of Electrical Engineering Advisor: Prof. Dr. Ing. Zdeněk Hanzálek

• Optimization algorithms for electric cars The task is to design and test algorithms for energetically optimal control of an electric vehicle. Using an appropriate model of the vehicle, analyse existing dynamic programming based optimization algorithm, create your own, make simulations, and choose the most appropriate algorithm. Study program: Control Engineering and Robotics Czech Technical University – Faculty of Electrical Engineering Advisor: Prof. Dr. Ing. Zdeněk Hanzálek

• Data-driven design of robust scheduling algorithms for flexible production Production companies are embarking on an ambitious Factory of Future (or Industry 4.0) program to take advantage of digital technologies. This upgrade will put scheduling and data analytics at the centre of the production system. The thesis aims at designing data-driven algorithms to generate schedules for production, both static and dynamic. Study program: Control Engineering and Robotics Czech Technical University – Faculty of Electrical Engineering Advisor: Prof. Dr. Ing. Zdeněk Hanzálek

• 3D scene reconstruction from images 3D scene reconstruction from images is a fundamental problem of computer vision. It finds many applications in industry ranging from autonomous driving to movie special effects. The topic is best for students with interest in algorithms, experimental work, and engineering of really working systems. Study program: Artificial Intelligence and Biocybernetics Czech Technical University – Faculty of Electrical Engineering Advisor: Doc. Ing. Tomáš Pajdla, Ph.D.

• Algebraic methods in computer vision and robotics Algebraic techniques have proved very useful in solving difficult problems in geometry of computer vision. We will aim at studying more advanced elements of algebraic geometry and applying them to real engineering problems. The topic is best for students with interest in applied mathematics. Study program: Artificial Intelligence and Biocybernetics Czech Technical University – Faculty of Electrical Engineering Advisor: Doc. Ing. Tomáš Pajdla, Ph.D.

• Image-based scene recognition and visual localization Visual scene recognition and image based localization is an important problem in computer vision and machine learning. We will aim developing new approaches to place representation and its search. The topic is suitable for students with interest computer vision and machine learning applied to real engineering problems. Study program: Artificial Intelligence and Biocybernetics Czech Technical University – Faculty of Electrical Engineering Advisor: Doc. Ing. Tomáš Pajdla, Ph.D.

• Polynomial optimization in computer vision and robotics Polynomial optimization techniques proved very useful in solving interesting problems in geometry of computer vision and robotics. We will aim at studying polynomial optimization techniques and applying them in computer vision and robotics. The topic is suitable for students with interest in mathematics applied to real engineering problems. Study program: Artificial Intelligence and Biocybernetics Czech Technical University – Faculty of Electrical Engineering Advisor: Doc. Ing. Tomáš Pajdla, Ph.D.

• Navigation and planning for complex robots The focus is on the development of new algorithms for processing, analysis, and fusion of data produced by chosen sensors (2D and 3D range sensors or cameras), fast and robust navigation based on these data, and efficient planning in tasks like multi-robot surveillance and reconnaissance, formation keeping, and dexterous cooperative manipulation. Study program: Artificial Intelligence and Biocybernetics Czech Technical University – Faculty of Electrical Engineering Advisor: RNDr. Miroslav Kulich, Ph.D.

• Routing problems in mobile robotics Typical tasks for mobile robots include inspection of a priori known environment, exploration of an unknown environment, or search for an object of interest. These tasks lead to a solution of NP-hard optimization problems. The thesis will focus on research and development of novel approximation methods to solve such an optimization problems. Study program: Artificial Intelligence and Biocybernetics Czech Technical University – Faculty of Electrical Engineering Advisor: RNDr. Miroslav Kulich, Ph.D.

• The Spatial Human Locomotion Analysis The project includes analysis of multichannel data resulting from recording by motion sensors and wireless EEG systems. Research part of the project includes the study of image registration methods, time evolution of their changes and the proposal of Bayesian classification of selected features using computational intelligence methods. Resulting algorithms will be verified for the group of diseased and healthy individuals related to the illness progression and they will be used for early diagnostics of movement disorders in the clinical environment. The thesis will be co-supervised at the Dept of Neurology of the Faculty of Medicine in Hradec Kralove (MUDr. Oldřich Vyšata, Ph.D.). Study program: Engineering Cybernetics University of Chemistry and Technology – Faculty of Chemical Engineering Advisor: Prof. Ing. Aleš Procházka, CSc.

• Multi-Channel Data and Image Analysis for Monitoring and Diagnostics in Physiological Signals The project is devoted to adaptive methods of multi-channel data analysis using computational intelligence and digital multidimensional signal processing tools both in the time and frequency domains. The methodology includes processing of videosequences, 3D geometric modelling, machine learning and pattern recognitiion for data classification. The application will be devoted to rehabilitation analysis and correlations of physiological and GPS signals to physical activities. The thesis will be co-supervised at the Dept of Neurology of the Faculty of Medicine in Hradec Kralove (MUDr. Oldřich Vyšata, Ph.D.). Study program: Engineering Cybernetics University of Chemistry and Technology – Faculty of Chemical Engineering Advisor: Prof. Ing. Aleš Procházka, CSc.

• Information Entropy in Cell Motion Detection Using Bright-field Light Microscopy Bright-field light microscopy is the microscopic technique which can bring undistorted information on a physiological and morphological state of a live cell. The aim of the project will be in the proposal of a method for automatic detection and statistic evaluation of mammalian organelles´ trajectories in micrographs obtained using time-lapse bright-field light microscopy. Digital image processing tools will include methods of information entropy and multivariate statistical analysis. The thesis will be co-supervised at the Inst. of Complex Systems of University of South Bohemia (Ing. Renata Štýsová-Rychtáriková, Ph.D.). Study program: Engineering Cybernetics University of Chemistry and Technology – Faculty of Chemical Engineering Advisor: Prof. Ing. Aleš Procházka, CSc.

• Multivariate Statistics in Spatial Reconstruction of Cell Organelles Based on Fluorescence Microscopy Fluorescence microscopy is a tool of cell biologists in the study of intracellular relations using markers specifically bounded to organelles or using autofluorescence. During observation of autofluorescence and multiple labelling, there exists a common problem of colour aberration that projects each emitted wavelength into a different spatial point. The dissertation will use image processing methods, information entropy and new mathematical procedure for identification of the focal level of each individual fluorophore´s response from the z-stack of microscopic images. The goal is to propose a methodology for automatic fluorophores 3D mapping to enable the full utilization of the information content of microscopy datasets. The thesis will be co-supervised at the Inst. of Complex Systems of the University of South Bohemia (Ing. Renata Štýsová-Rychtáriková, Ph.D.). Study program Engineering Cybernetics University of Chemistry and Technology – Faculty of Chemical Engineering Advisor: Prof. Ing. Aleš Procházka, CSc.

• Advanced methods of long-term multi-channel signal processing in neurosciences

Topic of the thesis is motivated by clinical research performed at the National Institute of Mental Health and neurological departments, focused on sleep medicine. In neurosciences and neurology we meet more and more frequently long-term recordings (mostly so-called polygraphic), where in various channels different physiological signals and additional data are recorded, usually with different sampling frequency. The work will be performed using neurological and neurophysiological data provided by cooperating university hospitals. New approaches will be studied, as for example application of advanced data mining algorithms, including metaheuristics and integration of medical background knowledge in the decision support process. Main aim is design and implementation of advanced methods of biomedical signal preprocessing and processing that will be able to find mutual relations in parallel time series (individual channels in the recordings) and detect significant segments. Study program: Artificial Intelligence and Biocybernetics Czech Technical University – Faculty of Biomedical Engineering Advisor: doc. Ing. Lenka Lhotská, CSc.

• Advanced methods of heterogeneous multidimensional data processing in electrophysiology

This topic is proposed based on close cooperation with clinical practice where we acquire data about one patient from various modalities. The basic requirement is to find mutual relations in such data. Thus we speak about large volumes of heterogeneous data and signals that must be evaluated in interrelated context. Representation methods are based on requirements on semantic interoperability. Processing methods are inspired by advanced mathematical transforms, methods of digital signal processing and data mining methods. Aim of the research is design of suitable methods of data and knowledge representation that serve for efficient storing and communication on one side and design and implementation of advanced methods of processing that allow searching for mutual relations in data and reveal hidden information on the other side. Study program: Artificial Intelligence and Biocybernetics Czech Technical University – Faculty of Biomedical Engineering Advisor: doc. Ing. Lenka Lhotská, CSc.

• Application of virtual reality to rehabilitation The aim of the project is the research and development of a customized virtual reality system based on a serious game which allows the user to carry out physical and cognitive rehabilitation therapies using a natural user interface based on virtual reality. Within these serious games we can find the exergames. It is a type of serious game which aims to stimulate body mobility through an immersive experience that situates the user inside virtual interactive landscapes. Study program: Artificial Intelligence and Biocybernetics Czech Technical University – Faculty of Biomedical Engineering Advisor: doc. Ing. Lenka Lhotská, CSc.

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