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Showing 20 of 1,870 academic articles

Attention-based functional-group coarse-graining: a deep learning framework for molecular prediction and design

Ming Han, Ge Sun, Paul F. Nealey, Juan J. de Pablo • npj Computational Materials (2025)

Abstract Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41524-025-01836-7

A First Course in Machine Learning

Simon Rogers, Mark Girolami • Chapman & Hall/CRC eBooks (2016)

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations ...

OPENALEX DOI: https://doi.org/10.1201/9781315382159

Machine Learning Approaches for Clinical Psychology and Psychiatry

Dominic Dwyer, Peter Falkai, Nikolaos Koutsouleris • Annual Review of Clinical Psychology (2018)

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide...

OPENALEX DOI: https://doi.org/10.1146/annurev-clinpsy-032816-045037

Machine Learning for Stock Selection

Keywan Christian Rasekhschaffe, Robert C. Jones • Financial Analysts Journal (2019)

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can un...

OPENALEX DOI: https://doi.org/10.1080/0015198x.2019.1596678

Fatigue Monitoring Through Wearables: A State-of-the-Art Review

Neusa R. Adão Martins, Neusa R. Adão Martins, Simon Annaheim, Christina M. Spengler et al. • Frontiers in Physiology (2021)

The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems...

DOAJ Open Access (DOAJ) DOI: 10.3389/fphys.2021.790292

Mathematics for Machine Learning

Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong • Cambridge University Press eBooks (2020)

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in d...

OPENALEX DOI: https://doi.org/10.1017/9781108679930

Machine Learning

Stephen Marsland • Chapman & Hall/CRC eBooks (2011)

Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is re...

OPENALEX DOI: https://doi.org/10.1201/9781420067194

Physics-Informed Machine Learning

Steven L. Brunton, J. Nathan Kutz • Cambridge University Press eBooks (2022)

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering...

OPENALEX DOI: https://doi.org/10.1017/9781009089517.019

TRIST: TREE RECOGNITION INTELLIGENT SYSTEM

Laura ONAC • Studia Universitatis Babes-Bolyai: Series Informatica (2019)

Plant recognition represents a challenging computer vision problem due to the great variations of shape and texture among plant organs, within the same species. This paper proposes a light-weight, but reasonably deep Convolutional Neural Ne...

DOAJ Open Access (DOAJ) DOI: 10.24193/subbi.2019.1.01

Acceptance of Machine Learning for Medication Selection in Epilepsy to Inform Clinical Trial Design: Co-Design Survey Study

Madeleine J Smith, Sandra Reeder, Fiona Waugh, Shobi Sivathamboo et al. • JMIR AI (2026)

Abstract BackgroundAntiseizure medications (ASMs) are the mainstay of epilepsy treatment; however, there is currently no reliable way to predict which medication will be most effective for an individual patient. Machine learning...

DOAJ Open Access (DOAJ) DOI: 10.2196/85047

Foundations of Machine Learning

• The MIT Press eBooks (2012)

Foundations of Machine LearningSoon we will embark on a theoretical study of AdaBoost in order to understand its properties, particularly its ability as a learning algorithm to generalize, that is, to make accurate predictions on data not s...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.7551/mitpress/8291.003.0006

Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia

Eleftheria Polychronidou, Ilias Kalamaras, Andreas Agathangelidis, Lesley-Ann Sutton et al. • BMC Bioinformatics (2018)

Abstract Background Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D stru...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12859-018-2381-1

A metaheuristic with a neural surrogate function for Word Sense Disambiguation

Azim Keshavarzian Nodehi, Nasrollah Moghadam Charkari • Machine Learning with Applications (2022)

Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context. The semantic information provided by WSD systems is highly beneficial to many task...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.mlwa.2022.100369

AN IMPROVED GRU BASED ON RECURRENT ATTENTION UNIT AND SELF- ATTENTION TECHNIQUE FOR TEXT SENTIMENT ANALYSIS

Dhurgham Ali Mohammed, Kalyani A. Patel • ICTACT Journal on Soft Computing (2025)

In text sentiment analysis, a crucial challenge is that conventional word vectors fail to capture lexical ambiguity. The Gated Recurrent Unit (GRU), an advanced variant of RNN, is extensively utilized in natural language processing tasks su...

DOAJ Open Access (DOAJ) DOI: 10.21917/ijsc.2025.0518

Non-linear and probabilistic state discretization functions for enhanced discrete reinforcement learning: Application on wind turbine pitch control

Alberto Gil-Maciá, J. Enrique Sierra-García, Matilde Santos • Machine Learning with Applications (2026)

Discrete reinforcement learning (RL) offers interpretability advantages compared to continuous reinforcement learning. Neural networks used in continuous RL behave like black boxes that do not show an obvious relationship between the system...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.mlwa.2026.100919

Foundations of Machine Learning

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar • The MIT Press eBooks (2018)

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference fo...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.7551/mitpress/10290.001.0001

Understanding Machine Learning

Shai Shalev‐Shwartz, Shai Ben-David • Cambridge University Press eBooks (2014)

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book prov...

OPENALEX DOI: https://doi.org/10.1017/cbo9781107298019