Data Mining and Machine Learning with Applications

doi:10.3390/books978-3-0365-9818-5

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Data Mining and Machine Learning with Applications

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January 2024

272 pages

ISBN978-3-0365-9807-9 (Hardback)
ISBN978-3-0365-9818-5 (PDF)

https://doi.org/10.3390/books978-3-0365-9818-5

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

Computer Science & Mathematics

Engineering

Physical Sciences

Public Health & Healthcare

Summary

The Special Issue entitled "Data Mining and Machine Learning with Applications” aimed to connect researchers in the fields of deep learning, artificial intelligence, data mining, and machine learning. Included within this reprint are all the accepted articles that were published in the Special Issue. It is our hope that readers can benefit from the valuable insights presented in these papers, ultimately contributing to the advancement of these rapidly expanding areas. Furthermore, we believe that this Special Issue will shed light on major developments in machine learning and data mining, drawing the attention of the scientific community towards further investigations that will expedite the implementation of these techniques.

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Keywords

Flight Maneuver Recognition (FMR); unsupervised clustering; phase space reconstruction; climate disasters; ENSO forecasting; artificial intelligence; machine learning; deep learning; intrusion detection; genetic algorithm; greedy search; backward elimination learning; NSL-KDD; CIC-IDS-2017; CIC-IDS2018; Markov decision process; maintenance decision-making; rolling bearing; LSTM; IoT; ECDH; symmetric cryptographic; authentication; assisting glaucoma screening; convolutional neural network; deep learning; fundus image analysis; information aggregation; logic mining; data mining; log-linear analysis; reverse analysis; statistical classification; evolutionary computation; discrete Hopfield neural network; ransomware detection; machine learning; malware analysis; feature extraction; Internet of Things (IoT); GDP; deep learning; time fusion transformers; multi-horizon forecasting; interpretability; image denoising; wavelet transform; Unet; image pyramid; multi-scale features; clustering; machine learning; greenhouse gas; finite-time thermodynamics; climate change; class association rules; clustering; representative rule; model coverage; classification