Applied Machine Learning Ⅱ

Edited by
March 2024
248 pages
  • ISBN978-3-7258-0073-5 (Hardback)
  • ISBN978-3-7258-0074-2 (PDF)

This book is a reprint of the Special Issue Applied Machine Learning Ⅱ that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences

This reprint focuses on applications of machine learning models in a diverse range of fields and problems. It reports substantive results on a wide range of learning methods; discusses the conceptualization of problems, data representation, feature engineering, and machine learning models; undertakes critical comparisons with existing techniques; and presents an interpretation of the results. This reprint strives not only to showcase the prolific applications of machine learning, but also to provide insights into the methodologies and interpretations that underpin these advancements.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
activity recognition; machine learning; wearable sensors; spinal cord injury; telerehabilitation; consumer analysis; cost-sensitive learning; imbalanced dataset; machine learning; over-the-top; training data update; demand forecasting; smart manufacturing; artificial intelligence; supply chain agility; digital twin; path planning; unmanned aerial vehicles; neural networks; evolutionary algorithms; visible light communications (VLC); gesture recognition (GR); human-computer interaction (HCI); human activity recognition (HAR); machine learning (ML); neural network; long short-term memory (LSTM); photo-diode (PD); machine learning; road traffic accidents; data analysis; missing data; dimensionality reduction; textual analysis; emotional tone; machine learning; financial crisis early warning; machine learning; DNA computer; biochips; queue automata; type IIB endonucleases; machine learning; COVID-19; prediction; machine learning; medical diagnosis optimisation; Generative Adversarial Networks; Wasserstein GAN; regression; multi-output regression; multi-modal distributions; computer vision; object detection; human detection; convolutional neural networks; natural language; controlled natural language; natural language processing; privacy policies; social networks; machine learning; n/a