Statistical Data Modeling and Machine Learning with Applications II

• Other Authors: Gocheva-Ilieva, Snezhana (Editor), Ivanov, Atanas (Editor), Kulina, Hristina (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Information technology industries; Computer science; 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; regression; classification; prediction; meteorological parameters; traffic incidents; multi-agent architecture; air pollution; random forest; 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; decision trees; LightGBM; SHAP; leisure time; influencing factors; time allocation; neural networks; cosmic rays; space weather; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.