AI Techniques for Association Rule Mining in Medical Data: Trends and Practical Applications - Softcover

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Dr. Anandhavalli Muniasamy is an Associate Professor in the Department of Informatics and Computer Systems, College of Computer Science at King Khalid University (KKU), Saudi Arabia. She holds a Ph.D. in Computer Science & Engineering from SMIT, Sikkim Manipal University, India. Her research expertise spans data mining, data analytics, artificial intelligence (AI), soft computing, and data science, with a particular focus on machine learning and deep learning applications in healthcare data analysis. She has an impressive academic and research portfolio, having published over 40 research articles in prestigious international journals, conferences, and book chapters. She has actively participated in more than 35 national and international conferences, presenting her work on cutting-edge computational techniques and contributing to edited book publications. A recognized innovator, she holds four patents, demonstrating her expertise in advanced computational methodologies. As a Principal Investigator (PI) and Co-Principal Investigator (Co-PI), she has led five university-funded research projects and served as PI for a funded project supported by the All India Council for Technical Education (AICTE). Beyond her research, she has been a dedicated manuscript reviewer for leading international journals for over a decade and a Ph.D. thesis examiner for multiple universities. She is frequently invited to deliver guest lectures and serves as an editorial board member for various renowned journals and conferences. She is an active member of professional organizations such as IEEE, the International Association of Engineers (IAENG), the Computer Society of India (CSI), and the International Association of Computer Science and Information Technology (IACSIT). Her current research focuses on leveraging AI-driven techniques in healthcare analytics, particularly machine learning and deep learning, to address complex challenges in medical data analysis and drive innovative solutions in her field.