Dr. Haewon Byeon

Dr. Haewon Byeon received the Dr. degree in Biomedical Science from Ajou University School of Medicine. Haewon Byeon currently works at the Department of Medical Big Data, Inje University. His recent interests focus on health promotion, AImedicine, and biostatistics. He is currently a member of international committee for a Frontiers in Psychiatry, and an editorial board for World Journal of Psychiatry. Also, He were worked on 4 projects (Principal Investigator) from the Ministry of Education, the Korea Research Foundation, and the Ministry of Health and Welfare. Byeon has published more than 343 articles and 19 books.

Ebooks

5G TECHNOLOGY AND IT'S APPLICATION

DATA STRUCTURES FOR MODERN APPLICATIONS

Internet of Things (IoT): Principles and Applications in Engineering

BLOCKCHAIN AND THE INTERNET OF THINGS (IOT): A CONVERGENCE OF TECHNOLOGIES

PRACTICAL MACHINE LEARNING APPLICATIONS

MACHINE LEARNING EXPLAINED: A PRACTICAL GUIDE TO DATA-DRIVEN DECISION MAKING

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

APPLICATIONS BEYOND CRYPTOCURRENCIES - NON-FINANCIAL USE CASES FOR BLOCKCHAIN TECHNOLOGY

MACHINE LEARNING APPLICATIONS

AI, IOT AND MACHINE LEARNING BASICS

MACHINE LEARNING MASTERY: ALGORITHMS, APPLICATIONS AND INSIGHTS

NETWORK SECURITY FUNDAMENTALS: CONCEPTS, TECHNOLOGIES, AND BEST PRACTICES

MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

ARTIFICIAL INTELLIGENCE IN BIOINFORMATICS: ENHANCING ANALYSIS

MACHINE LEARNING FOR HEALTHCARE PREDICTIVE ANALYTICS AND PERSONALIZED MEDICINE

Explainable AI (XAI): Making Machine Learning Models Interpretable and Trustworthy Cloud Computing

INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

REINFORCEMENT LEARNING: FOUNDATIONS, ALGORITHMS AND APPLICATIONS

DATA SCIENCE ETHICS AND RESPONSIBLE AIEITHICAL CONSIDERATIONS IN DATA SCIENCE AND AI

AI AND ROBOTICS: A SYMBIOTIC RELATIONSHIP

MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ADVANTAGES AND DISADVANTAGES

FOUNDATION OF DATA SCIENCE

CLIMATE CHANGE IMPACT ASSESSMENT ON AGRICULTURE WITH ML

MACHINE LEARNING FOR IMAGE GENERATION AND STYLE TRANSFER

KEY PERSONALITY TRAITS FOR SUCCESSFUL LEADERSHIP

TRANSFORMING INDUSTRIES WITH AI: EXPLORING THE UPSIDES AND DOWNSIDES OF EMERGING TECHNOLOGY

MACHINE LEARNING: APPLICATION AND CHALLENGES

NLP (NATURAL LANGUAGE PROCESSING) IN BIOINFORMATICS HEALTHCARE APPLICATIONS

REINFORCEMENT LEARNING FUNDAMENTALS - LEARNING THROUGH REWARDS AND PUNISHMENTS

DEEP LEARNING FOR DATA MINING UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

MACHINE LEARNING FOR HEALTHCARE: PREDICTIVE ANALYTICS AND PERSONALIZED MEDICINE

DEEP LEARNING: NEURAL NETWORK AND BEYOND

BLOCKCHAIN FUNDAMENTALS CORE AND CONCEPTS AND DECENTRALIZED LEDGER TECHNOLOGY

THE ART OF INTELLIGENT MACHINES UNLEASHING THE POWER OF MACHINE LEARNING

DATA SCIENCE: FOUNDATION & FUNDAMENTALS