Reprint

AI for Smart Home Automation

Edited by
September 2023
212 pages
  • ISBN978-3-0365-8172-9 (Hardback)
  • ISBN978-3-0365-8173-6 (PDF)

This is a Reprint of the Special Issue AI for Smart Home Automation that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary

In recent years, the growth of the IoT has made an increasing amount of data available to be analyzed and exploited for the creation of intelligent models and solutions. In the Smart Home domain, new application scenarios have opened up, facilitated by the emergence of new techniques and paradigms that enable the development of smart devices. The application of Artificial Intelligence in this context aims to simplify and automate activities, increase security, reduce energy consumption, and generally optimize home-related processes.

This Special Issue discusses the application of Artificial Intelligence to smart home automation through the use of various techniques, ranging from speech recognition, human behavior recognition, automatic home temperature control, electricity consumption monitoring, and the development of intelligent agents for building knowledge representations of unknown environments.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
timed up-and-go test; TUG subtask segmentation; deep learning; temporal convolutional network; artificial intelligence; autonomous agent; unknown built environment; hierarchical framework; path finding; robotic system design; affective recommendation; pet social network; emotion recognition model; dog barking recognition; deep learning; myocardial infraction; heart rate variability; 10-second heart rate variability; diagnostics; machine learning; k-nearest neighbor classifier; radial basis function; decision tree; random forest; brain–computer interface (BCI); event-related potential (ERP); row-column paradigm (RCP); stimulus; face; picture; machine learning; deep learning; recommendation system; energy consumption; smart home; neural network; performance evaluation; smart environment; real-time applications; QoS performance analysis; IEEE technologies; human behavior recognition; energy image species; hierarchical patches descriptor; approximate locality-constrained linear coding algorithm; machine learning (ML); nonintrusive load monitoring (NILM); smart home; support vector machine (SVM); sweep frequency response analysis (SFRA); home automation systems; speech recognition; natural language understanding; smart plug socket