Guest Editors
Prof. Dr. Jihoon Moon
Email: jmoon25@duksung.ac.kr
Affiliation: Department of Data Science, Duksung Women's University, 33 Samyang-ro 144-gil, Dobong-gu, Seoul 01369, Republic of Korea
Homepage:
Research Interests: autoML, explainable AI (XAI), EDA & XAI integration, deep learning, AI-driven industrial analytics
Prof. Dr. Jehyeok Rew
Email: jhrew@duksung.ac.kr
Affiliation: Department of Data Science, Duksung Women's University, 33 Samyang-ro 144-gil, Dobong-gu, Seoul 01369, Republic of Korea
Homepage:
Research Interests: explainable AI, AI for industrial automation, AI-driven decision systems, computer vision, AI in bioinformatics
Prof. Dr. Hyeonwoo Kim
Email: hwkim24@sch.ac.kr
Affiliation: Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
Homepage:
Research Interests: autoML, AI-driven data science, Python & R in industry, AI-driven cybersecurity, data engineering
Summary
The rapid growth of artificial intelligence (AI) has led to increased interest in automated machine learning (AutoML) and explainable AI (XAI). As AI systems become more complex, the ability to automate machine learning processes while ensuring transparency and interpretability is crucial for industrial and healthcare applications. This Special Issue explores cutting-edge research in AutoML frameworks, the role of XAI in making AI models more interpretable, and the integration of exploratory data analysis (EDA) with explainability techniques.
This Special Issue invites high-quality research contributions that address the challenges of automated AI model selection, feature engineering, and hyperparameter tuning in real-world applications. Additionally, we encourage research on best practices for Python and R in industrial and healthcare settings, enabling AI practitioners to streamline workflows and enhance reproducibility. Studies on AI-driven decision-making, industry-specific applications of XAI, and methodologies for transforming beginners into expert data scientists are also welcome.
Suggested Themes:
· Advancements in AutoML & Automated Feature Engineering for Scalable AI
· Explainable AI (XAI) for Transparent and Trustworthy AI Systems
· EDA vs. XAI: A Comparative Study on Model Interpretability and Data Exploration
· AI-driven Decision-Making in Industry & Healthcare with Enhanced Explainability
· Python & R for AI-driven Industrial and Medical Applications
· AI-driven Cybersecurity & Risk Mitigation with Explainable Approaches
· Human-in-the-Loop AI Systems for Adaptive and Interpretable Learning
· AI for Personalized Healthcare & Precision Medicine through Transparent AI
· Automated & Trustworthy AI: Bridging AutoML and Explainability
· AI for Industry & Society: Ensuring Automation with Interpretability
· AI for Smart & Sustainable Systems with Adaptive and Explainable Models
· AI for Science & Multimodal Data: Advancing Discovery through AutoML and XAI
Keywords
AutoML, Explainable AI, XAI, Exploratory Data Analysis (EDA), Python for AI, R for AI, AI-driven Industrial Analytics, Trustworthy AI, AI-driven Decision-Making, Healthcare AI
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