Full 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.
Contents
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.
