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The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications.

Research & information: general --- Mathematics & science --- large margin nearest neighbor regression --- distance metrics --- prototypes --- evolutionary algorithm --- approximate differential optimization --- multiple point hill climbing --- adaptive sampling --- free radical polymerization --- autonomous driving --- object tracking --- trajectory prediction --- deep neural networks --- stochastic methods --- applied machine learning --- classification and regression --- data mining --- ensemble model --- engineering informatics --- gender-based violence in Mexico --- twitter messages --- class imbalance --- k-nearest neighbor --- instance-based learning --- graph neural network --- deep learning --- hyperparameters --- machine learning --- optimization --- inference --- metaheuristics --- animal-inspired --- exploration --- exploitation --- hot rolled strip steel --- surface defects --- defect classification --- knockout tournament --- dynamic programming algorithm --- computational complexity --- combinatorics --- intelligent transport systems --- traffic control --- spatial-temporal variable speed limit --- multi-agent systems --- reinforcement learning --- distributed W-learning --- urban motorways --- multi-agent framework --- .NET framework --- simulations --- agent-based systems --- agent algorithms --- software design --- multisensory fingerprint --- interoperability --- DeepFKTNet --- classification --- generative adversarial networks --- image classification --- transfer learning --- plastic bottle --- n/a

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The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.

mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower’s interpolation formula --- Gower’s metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering --- n/a --- Gower's interpolation formula --- Gower's metric

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The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.

Information technology industries --- mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower's interpolation formula --- Gower's metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering

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Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture, sediment transport, surface water and groundwater quality modeling, ecosystems modeling, water distribution networks, environmental and water resources management, and parameter estimation. Such applications have used several different entropy formulations, such as Shannon, Tsallis, Reacutenyi Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in time, space, or frequency domains. More recently, entropy-based concepts have been coupled with other theories, including copula and wavelets, to study various issues associated with environmental and water resources systems. Recent studies indicate the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering, including establishing and explaining physical connections between theory and reality. The objective of this Special Issue is to provide a platform for compiling important recent and current research on the applications of entropy theory in environmental and water engineering. The contributions to this Special Issue have addressed many aspects associated with entropy theory applications and have shown the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering.

hydrological risk analysis --- modeling --- water level --- Poyang Lake basin --- trend --- composite multiscale sample entropy --- flood frequency analysis --- canopy flow --- precipitation --- water resources --- complex systems --- frequency analysis --- optimization --- combined forecast --- neural network forecast --- entropy spectral analysis time series analysis --- environmental engineering --- hydrometric network --- sea surface temperature --- kernel density estimation --- robustness --- turbulent flow --- entropy production --- connection entropy --- flux concentration relation --- turbulence --- tropical rainfall --- generalized gamma (GG) distribution --- multi-events --- El Niño --- joint entropy --- entropy weighting method --- Anhui Province --- changing environment --- complexity --- multiplicative cascades --- Tsallis entropy --- Hexi corridor --- coherent structures --- water resources vulnerability --- uncertainty --- variability --- flow entropy --- Hei River basin --- fuzzy analytic hierarchy process --- substitute --- crop yield --- conditional entropy production --- entropy --- flow duration curve --- mean annual runoff --- temperature --- hydrometeorological extremes --- resilience --- Loess Plateau --- information entropy --- scaling --- water distribution networks --- cross entropy --- randomness --- forewarning model --- entropy applications --- quaternary catchment --- spatio-temporal variability --- probability distribution function --- ant colony fuzzy clustering --- radar --- continuous probability distribution functions --- Shannon entropy --- informational entropy --- information --- confidence intervals --- marginal entropy --- rainfall forecast --- entropy of information --- streamflow --- power laws --- bootstrap aggregating --- maximum entropy-copula method --- spatial and dynamics characteristic --- projection pursuit --- set pair analysis --- entropy theory --- water resource carrying capacity --- entropy parameter --- precipitation frequency analysis --- principle of maximum entropy --- information theory --- stochastic processes --- network design --- complement --- cross elasticity --- climacogram --- methods of moments --- hydrology --- bagging --- principle of maximum entropy (POME) --- rainfall network --- entropy ensemble filter --- ensemble model simulation criterion --- Lagrangian function --- Beta-Lognormal model --- cross-entropy minimization --- ANN --- configurational entropy --- variation of information --- statistical scaling --- EEF method --- water monitoring --- maximum likelihood estimation --- GB2 distribution --- NDVI --- four-parameter exponential gamma distribution --- hydraulics --- spatial optimization --- Kolmogorov complexity --- bootstrap neural networks --- mutual information --- accelerating genetic algorithm --- groundwater depth --- rainfall --- tropical Pacific --- water engineering --- monthly streamflow forecasting --- ENSO --- nonlinear relation --- Bayesian technique --- non-point source pollution --- Burg entropy --- data-scarce --- scaling laws --- soil water content --- arid region --- land suitability evaluation --- information transfer

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• Reference Manager
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Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture, sediment transport, surface water and groundwater quality modeling, ecosystems modeling, water distribution networks, environmental and water resources management, and parameter estimation. Such applications have used several different entropy formulations, such as Shannon, Tsallis, Reacutenyi Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in time, space, or frequency domains. More recently, entropy-based concepts have been coupled with other theories, including copula and wavelets, to study various issues associated with environmental and water resources systems. Recent studies indicate the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering, including establishing and explaining physical connections between theory and reality. The objective of this Special Issue is to provide a platform for compiling important recent and current research on the applications of entropy theory in environmental and water engineering. The contributions to this Special Issue have addressed many aspects associated with entropy theory applications and have shown the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering.

hydrological risk analysis --- modeling --- water level --- Poyang Lake basin --- trend --- composite multiscale sample entropy --- flood frequency analysis --- canopy flow --- precipitation --- water resources --- complex systems --- frequency analysis --- optimization --- combined forecast --- neural network forecast --- entropy spectral analysis time series analysis --- environmental engineering --- hydrometric network --- sea surface temperature --- kernel density estimation --- robustness --- turbulent flow --- entropy production --- connection entropy --- flux concentration relation --- turbulence --- tropical rainfall --- generalized gamma (GG) distribution --- multi-events --- El Niño --- joint entropy --- entropy weighting method --- Anhui Province --- changing environment --- complexity --- multiplicative cascades --- Tsallis entropy --- Hexi corridor --- coherent structures --- water resources vulnerability --- uncertainty --- variability --- flow entropy --- Hei River basin --- fuzzy analytic hierarchy process --- substitute --- crop yield --- conditional entropy production --- entropy --- flow duration curve --- mean annual runoff --- temperature --- hydrometeorological extremes --- resilience --- Loess Plateau --- information entropy --- scaling --- water distribution networks --- cross entropy --- randomness --- forewarning model --- entropy applications --- quaternary catchment --- spatio-temporal variability --- probability distribution function --- ant colony fuzzy clustering --- radar --- continuous probability distribution functions --- Shannon entropy --- informational entropy --- information --- confidence intervals --- marginal entropy --- rainfall forecast --- entropy of information --- streamflow --- power laws --- bootstrap aggregating --- maximum entropy-copula method --- spatial and dynamics characteristic --- projection pursuit --- set pair analysis --- entropy theory --- water resource carrying capacity --- entropy parameter --- precipitation frequency analysis --- principle of maximum entropy --- information theory --- stochastic processes --- network design --- complement --- cross elasticity --- climacogram --- methods of moments --- hydrology --- bagging --- principle of maximum entropy (POME) --- rainfall network --- entropy ensemble filter --- ensemble model simulation criterion --- Lagrangian function --- Beta-Lognormal model --- cross-entropy minimization --- ANN --- configurational entropy --- variation of information --- statistical scaling --- EEF method --- water monitoring --- maximum likelihood estimation --- GB2 distribution --- NDVI --- four-parameter exponential gamma distribution --- hydraulics --- spatial optimization --- Kolmogorov complexity --- bootstrap neural networks --- mutual information --- accelerating genetic algorithm --- groundwater depth --- rainfall --- tropical Pacific --- water engineering --- monthly streamflow forecasting --- ENSO --- nonlinear relation --- Bayesian technique --- non-point source pollution --- Burg entropy --- data-scarce --- scaling laws --- soil water content --- arid region --- land suitability evaluation --- information transfer