Machine Learning and the Internet of Things in Education: Studies in Computational Intelligence

Introduction to Machine Learning and IoT.- Deep Convolutional Network for Food Image Identification.- Face Mask Recognition System-Based Convolutional Neural Network.- Fuzzy Inference System Based-AI for Diagnosis of Esophageal Cancer.- Skin Detection System Based Fuzzy Neural Networks for Skin Identification.- Machine Learning Based Cardless ATM Using Voice Recognition Techniques.- Intelligent Systems for Automated Classification of Cardiac Arrhythmias.- A Fuzzy Logic Implemented Classification Indicator for the Diagnosis of Diabetes Mellitus in TRNC.- Implementation and Evaluation of a Mobile Smart School Management System NEUKinderApp.- The Emerging Benefits of Gamification Techniques.- A Comprehensive Review of Virtual E-Learning System Challenges.- A Semantic Portal to Improve Search on Rivers State's Independent National Electoral Commission.- Implementation of Semantic Web Service and Integration of e-Government Based Linked Data.- Application of Zero-Trust Networks in e-Health Internet of Things (IoT) Deployments.- IoT Security Based Vulnerability Assessment of e-Learning Systems.- Blockchain technology, artificial intelligence, and big data in education.- Sustainable Education Systems with IoT Paradigms.- Post Covid Era - Smart Class Environment.

This book is designed to provide rich research hub for researchers, teachers, and students to ease research hassle/challenges. The book is rich and comprehensive enough to provide answers to frequently asked research questions because the content of the book touches several disciplines cutting across computing, engineering, medicine, education, and sciences in general. The rich multidisciplinary contents of the book promise to leave all users satisfied. The valuable features in the book include but not limited to: demonstration of mathematical expressions for implementation of machine learning models, integration of learning techniques, and projection of future AI and IoT technologies. These technologies will enable systems to be simulative, predictive, and self-operating smart systems. The primary audience of the book include but not limited to researchers, teachers, and postgraduate and undergraduate students in computing, engineering, medicine, education, and science fields.

Depicts the challenges and understanding of role of technology in the fields of machine learning and IoT for teaching Focuses on the design and implementation of algorithmic applications in AI and IoT with pertinent applications Showcases recent machine learning techniques and Internet of Things technologies, particularly for learning purposes