Statistical Data Modeling and Machine Learning with Applications II

doi:10.3390/books978-3-0365-8201-6

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Statistical Data Modeling and Machine Learning with Applications II

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July 2023

344 pages

ISBN978-3-0365-8200-9 (Hardback)
ISBN978-3-0365-8201-6 (PDF)

https://doi.org/10.3390/books978-3-0365-8201-6

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This book is a reprint of the Special Issue Statistical Data Modeling and Machine Learning with Applications II that was published in

Computer Science & Mathematics

Engineering

Physical Sciences

Public Health & Healthcare

Summary

The present reprint contains all of the articles in the second edition of the Special Issue titled “Statistical Data Modeling and Machine Learning with Applications II”. This Special Issue belongs to the “Mathematics and Computer Science” Section and aims to publish research on the theory and application of statistical data modeling and machine learning. New mathematical methods and approaches, new algorithms and research frameworks, and their applications aimed at solving diverse and nontrivial practical problems are proposed and developed in this SI. We believe that the chosen papers are attractive and useful to the international scientific community and will contribute to further research in the field of statistical data modeling and machine learning.

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Keywords

forecasting model; electricity energy consumption; grey model; artificial neural network; machine learning; rotation CART ensemble; bagging; boosting; arcing; simplified selective ensemble; linear stacked model; IoV; xNN; K-MEANS; anomaly detection; single-index models; composite quantile regression; SCAD; Laplace error penalty (LEP); causality; Bayesian networks; scalability; group lasso penalty; data integration; network estimation; stability selection; time series model; wavelet transform; neural network NARX; ionospheric parameters; gambling; jackpot; multidimensional integrals; Monte Carlo methods; lattice sequences; digital sequences; surface approximation; surface segmentation; surface denoising; gaussian process latent variable model; line geometry; line elements; machine learning; regression; classification; prediction; meteorological parameters; traffic incidents; multi-agent architecture; air pollution; machine learning; random forest; arcing; ARIMA errors; MIMO averaging strategy; multi-step ahead prediction; unmeasured forecast; Explainableartificial intelligence; credit card frauds; deep learning; long short-term memory; fraud classification; lung cancer; tumor; CT image; one-stage detector; YOLO; multi-scale; receptive field; data analysis; classification; decision trees; LightGBM; SHAP; leisure time; influencing factors; time allocation; data analysis; anomaly detection; neural networks; wavelet transform; cosmic rays; space weather; n/a