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 Io T developments, which help to align and shorten R&D processes to introduce new classes of intelligent Io T products. This book provides a technical reference for interdisciplinary studies which utilize machine learning and Io T as tools in their fields such as constructional management, smart agriculture, Earth sciences and geo-spatial analysis, intelligent business, and digital transformation in education.
表中的内容
FPGA/AI-Powered Data Security for Io T Edge Computing Platforms: A Survey and Open Issues.- A Review in Deep Learning-Based Thyroid Cancer Detection Techniques Using Ultrasound Images.- Bio-Inspired Clustering: An Ensemble Method for User-Based Collaborative Filtering.- Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-User SIMO-RSMA Systems.- Multiobjective Logistics Optimization for Automated ATM Cash Replenishment Process.- Adaptive Conflict-Averse Multi-Gradient Descent for Multi-Objective Learning.- Multicriteria Portfolio Selection with Intuitionistic Fuzzy Goals as a Pseudoconvex Vector Optimization.- Research and Develop Solutions to Traffic Data Collection Based on Voice Techniques.- Using Machine Learning Algorithms to Diagnosis Melasma from Face Images.- Reinforcement Learning for Solving Portfolio Selection Problems in the Vietnamese Market.- Towards a Smart Parking System with the Jetson Xavier Edge Computing Platform.- Al Pico So C: A Low-Power RISC-V Based System on Chip for Edge Devices with a Deep Learning Accelerator.- A Transparent Scalable E-Voting Protocol Based on Open Vote Network Protocol and Zk-STARKs.- Dark MDE: Excavating Synthetic Images for Nighttime Depth Estimation Using Cross-Domain Supervision.
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