Proceedings of International Conference on Computational Technologies for Research in Data Analytics
Description
This book discusses the outcomes of International Conference on Computational Technologies for Research in Data Analytics (ICCTRDA-2025) was held on November 21-22, 2025, at DuyTan University, Da Nang, Vietnam. The conference has attracted many high-quality submissions and stimulated the cutting-edge research discussions among many academic pioneering researchers, scientists, industrial engineers, students with the reception of more than 1200 papers from different parts of the world. Only 180 papers have been accepted and registered with an acceptance ratio of 15% to be published in four volumes of prestigious springer Lecture Notes on Networks and Systems (LNNS) series.
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Persons
Dr. Nhu Gia Nguyen received a Ph.D. degree in computer science from Ha Noi University of Science at Vietnam National University, Vietnam. Currently, he is Dean of the Graduate School at Duy Tan University, Vietnam. He has a total academic teaching experience of 18 years with more than 50 publications in reputed international conferences, journals, and online book chapter contributions (indexed by: SCI, SCIE, SSCI, Scopus, DBLP). His areas of research include: network communication, security and vulnerability, network performance analysis and simulation, cloud computing, image processing in biomedical. Presently he is Associate Editor of the International Journal of Synthetic Emotions (IJSE).
Prof. Ajay Jaiswal has an academic background from prestigious institutions like IIT Roorkee and JNU. With over 25 years of experience in academics, research, and administration, he currently serves as Principal of the School of Open Learning, University of Delhi. Additionally, he holds the position of OSD at the Institute of Cyber Security Law, University of Delhi. A prolific author and researcher, Prof. Jaiswal has contributed to numerous research articles and books and has been actively involved in editorial and organizing committees for journals and international conferences. His research interests include pattern recognition, image processing, and machine learning. He was honored as the "Best Teacher" by the Government of NCT Delhi for 2013-14.
Dr. Ta Minh Thanh received the Bachelor of Engineering degree and the Master degree of Computer Science from National Defence Academy, Japan, in 2005 and 2008, respectively. He was Lecturer of Le Quy Don University, Ha Noi, Viet Nam, from 2005. In 2015, he received the Ph.D. degree in computer science from Tokyo Institute of Technology, Tokyo, Japan. He received the standards of associate professor title in 2019. His research interests lie in the area of watermarking, network security, and computer.
Content
Bridging Manual Coding and Automation: A Comparative Study of Data-Driven Analysis Systems.- Comparative Performance Analysis of Cancellable Biometric Techniques and Recommendations on their Real-World Applications.- Research on Performance Improvement of Personalized Recommendation System for E-commerce Platform Based on Big Data and Collaborative Filtering Algorithm.- News-Infused Stock Price Prediction: A GAN-GRU Generator Coupled with CNN Discriminator.- Predicting Alzheimer's Disease based on Interpretive Ensemble Learning.- Explainable Brain Tumor Classification using CBAM-Augmented ResNet and Counterfactual Visual Explanation.- Generative Model to Enhance Image Quality of Microscopy Data.- Leaf Disease Detection Using Convolutional Neural Networks for Smart Agriculture.- Schedule Risk Control of Power Transmission and Transformation Projects Based on Cost Factors.- Health and Personal Care Recommendation Systems with Multimodal Approach.- Dynamic Visualization and Analysis of Air Quality Patterns in Indian City Using Power BI.- PerceptiPath: Real-Time Obstacle Awareness for the Visually Impaired Using Intelligent Vision.- Adaptive Fitness Prediction Using Wearable Technologies and Machine Learning.- An Empirical Study on Building a High-precision Forecasting System for Financial Market Trends Based on the PyTorch Deep Learning Framework.- Prediction of Earthquake Magnitude Scales Based on A Supervised Learning Neural Network Model.