Intelligence of Things: Technologies and Applications -

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Résumé :
This book aims to provide state-of-the-art knowledge in the field of Intelligence of Things to both academic and industrial readers. In particular, undergraduate, graduate, and researchers may find valuable information to drive their future research. This book is considered a reference for numerous courses such as Artificial Intelligence, Internet of Things, Intelligent Systems, and Mobile Networks. In the industrial area, this book provides information on recent studies in applying AI to IoT developments, which help to align and shorten R&D processes to introduce new classes of intelligent IoT products. This book provides a technical reference for interdisciplinary studies which utilize machine learning and IoT as tools in their fields such as constructional management, smart agriculture, Earth sciences and geo-spatial analysis, intelligent business, and digital transformation in education.

Sommaire:
Investigating Ensemble Learning Methods for Predicting Water Quality Index.- Age-Invariant Face Recognition Based on Self-Supervised Learning.- Detection of Kidney Stone Based on Super Resolution Techniques and YOLOv7 Under Limited Training Samples.- Hardware-Based Lane Detection System Architecture for Autonomous Vehicles.- Video Classification Based on the Behaviors of Children in Pre-School Through Surveillance Cameras.- Land Subsidence Susceptibility Mapping Using Machine Learning in the Google Earth Engine Platform.- Building an AI-Powered IoT App for Fall Detection Using Yolov8 Approach.- Seam Puckering Level Classification Using AIoT Technology.- Classification of Pneumonia on Chest X-Ray Image Using Transfer Learning.- Bayesian Approach for Static Object Detection and Localization in Unmanned Ground Vehicles.- Diabetic Retinopathy Diagnosis Leveraging Densely Connected Convolutional Networks and Explanation Technique.- Deep Learning Approach for Inundation Area Detection Using Sentinel Data.- Classification of Raisin Grains Based on Ensemble Learning Techniques in Machine Learning.- An Effective Deep Learning Model for Detecting Plant Diseases Using a Natural Dataset for the Agricultural IoT System.- Real-Time Air Quality Monitoring System Using Fog Computing Technology.- An Intelligent Computing Method for Scheduling Projects with Normally Distributed Activity Times.