Advanced Machine Learning Applications in Big Data Analytics

doi:10.3390/books978-3-0365-8487-4

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Advanced Machine Learning Applications in Big Data Analytics

Edited by

August 2023

654 pages

ISBN978-3-0365-8486-7 (Hardback)
ISBN978-3-0365-8487-4 (PDF)

https://doi.org/10.3390/books978-3-0365-8487-4

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This book is a reprint of the Special Issue Advanced Machine Learning Applications in Big Data Analytics that was published in

Computer Science & Mathematics

Engineering

Physical Sciences

Summary

With the development of computer technology and communication technology, various industries have collected a large amount of data in different forms, so-called big data. How to obtain valuable knowledge from these data is a very challenging task. Machine learning is such a direct and effective method for big data analytics. In recent years, a variety of advanced machine learning technologies have emerged, and they continue to play important roles in the era of big data.

Considering advanced machine learning and big data together, we have selected a series of relevant works in this Special Issue to showcase the latest research advancements in this field. Specifically, a total of thirty-three articles are included in this Special Issue, which can be roughly categorized into six groups: time series analysis, evolutionary computation, pattern recognition, computer vision, image encryption, and others.

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Keywords

energy storage; model predictive control; peak shaving and frequency regulation; output optimization; global optimization; meta-heuristic; support vector machine swarm intelligence; hyperspectral image classification; CNN; ELM; PSO; deep feature; butterfly optimization algorithm; random replacement; crisscross search; overseas Chinese associations; support vector machine; short-term traffic-flow forecasting; bagging model; stacking model; ridge regression; error coefficient; least squares method; support vector machines; principal component analysis; quick access recorder; mean absolute error; high-plateau flight; event extraction; event type; event trigger words; stock announcement news; stock return; traffic flow forecasting; long short-term memory network; graph convolutional network; target detection; infrared; deep learning; YOLOv5 algorithm; design science research; performance analysis; machine learning; classification algorithms; clustering algorithms; pilot abnormal behavior; behavior detection; YOLOv4 algorithm; CBAM; flight safety; fault diagnosis; variational mode decomposition; composite multi-scale dispersion entropy; particle swarm optimization; deep belief network; CBCFI; combined prediction model; ARMA; GM; GA; BP; hierarchical clustering; Jaccard distance; membership grade; community clustering; deep learning; lightweight neural networks; attentional mechanisms; Hemerocallis citrina Baroni; maturity detection; cloud; digital archives; confidentiality management; information system; emotion-cause pair extraction; heterogeneous graph; graph attention network; hierarchical model; spatial-temporal systems; neural networks; machine learning; information systems; forecasting; time series; coupled map lattice; polymorphic mapping; color image; hash function; pixel level; differential evolution; capacitated vehicle routing planning; saving mileage; gravity search; object detection; deep learning; computer vision; border patrol; time series; forecasting; COVID-19; machine learning; warning system; PROPHET; health; quantum dynamics; global optimization; neural architecture search; image classification; swarm intelligence; whale optimization algorithm; extreme learning machine; talent stability prediction; machine learning; adversarial attacks; document classification; CNN; NLP; convolutional neural networks; deep learning; disease classification; generative adversarial network; tomato leaf; PSO; multi-strategy; dual-update strategy; mean-semivariance model; portfolio optimization; DNA computing; DNA sequences design; improved matrix particle swarm optimization algorithm (IMPSO); opposition-based learning; signal-to-noise ratio distance; time series classification; complementary ensemble empirical mode decomposition (CEEMD); MultiRocket; feature selection; hybrid model; multi-behavior recommendation; sequential recommendation; graph neural network; embedding propagation; 1D quadratic chaotic system; image encryption; splicing model; DNA coding; BaaS system; blockchain consensus algorithm; KNN; service level agreement; transaction priority; data stream mining; forex; online learning; adaptive learning; incremental learning; sliding window; concept drift; financial time series forecasting; n/a