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Machine Learning, IoT and Artificial Intelligence for Sustainable Development

Edited by
January 2024
320 pages
  • ISBN978-3-0365-9926-7 (Hardback)
  • ISBN978-3-0365-9925-0 (PDF)
https://doi.org/10.3390/books978-3-0365-9925-0
Free download (PDF)

This book is a reprint of the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development that was published in

Business & Economics
Environmental & Earth Sciences
Social Sciences, Arts & Humanities
Summary

Nowadays, the newest technologies, devices, and techniques related to IoT, machine learning, and artificial intelligence are constantly developing. Therefore, they have a significant impact on our sustainable lifestyle. Accordingly, the application domain of these technologies and tools involves agriculture, water management, healthcare, bioinformatics, smart grid, smart cities, security, and so on. In addition, the IoT has brought an innovative perspective that is totally distinct from the usual approaches: the former consists of a device that can communicate with the network and is capable of implementing intelligent solutions. The goal of this Special Issue is to create a common gateway between researchers, allowing them to exchange and share their results related to the application of IoT, artificial intelligence, and machine learning in various domains.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
optimization techniques; demand-supply system; energy consumption patterns; genetic algorithm; particle swarm optimization; wind driven optimization; home energy management controller; cancer; adenocarcinoma; convolution neural network; CNN; transfer learning; CNN– SVM; medical image processing; deep learning; artificial intelligence; smart cancer diagnosis; AdenoCanNet; AdenoCanSVM; fall detections; fall-detection; cost efficiency; machine learning; multi-criteria analysis; flood modelling; stormwater management system; Western Australia; authentication protocol; wireless sensor network; Internet of Things; privacy-preserving; provably secure; deep learning; transfer learning; medical imaging; CNN; machine learning; Internet of Things; security; design science research; GSM; GPS; elliptic curve integrated encryption scheme; Agriculture 4.0; precision agriculture; privacy; smart farming; security; sustainable energy; solar radiation; times series; machine learning; feature selection; forecasting; politeness prediction; conversation AI; machine learning; transfer learning; agricultural residues; biofuel classification; solid fuel; deep learning; sailfish optimizer; IoT environment; smart cities; imprinted ship characters; automatic recognition; recognition accuracy; dataset augmentation; machine learning classifiers; crowd management; human verification; machine learning; big data analytics; GA classifier; Viola–Jones; IoT failure causes; layered architecture of IoT; quality assurance; testing framework; traffic simulation; smart cities; integrated technologies; machine learning; sustainable AI; AI for environment; green AI; deep learning; complexity; SDGs; ethics