Intelligent Information and Database Systems by Nguyen, Ngoc Thanh; Matsuo, Tokuro; Gaol, Ford Lumban;

Hosszú leírás:

This two-volume proceedings, LNAI 15683 and LNAI 15684, constitutes the proceedings of the 17th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2025, held in Kitakyushu, Japan, during April 23-25, 2025.

The 56 full papers and presented in these two volumes were carefully reviewed and selected from 301 submissions.

The papers are organized in the following topical sections:

Part I: Data Mining, Processing and Integration; Deep Learning Methods and Applications; Generative Models Applications; Intelligent Information Systems and Problem-Solving Algorithms.

Part II: Games and Decision Theories; Image Processing and Computer Vision; Intelligent Techniques in Optimization; Machine Learning Techniques and Applications.

Tartalomjegyzék:

.- Data Mining, Processing and Integration.

.- Physics-informed Discovery of State Variables in Second-Order and
Hamiltonian Systems.

.- A Hybrid Discovery Framework for USV Voyage Route Generation
Using AIS Data.

.- Proactive approach to personnel assignment in University Course
Timetabling Problem.

.- Improving Traffic Sign Detection Using Synthetic Data and Automatic
Labeling.

.- Unraveling Research Patterns in Quantum Computing Through Topic
Modeling and Network Analysis.

.- Mining Erasable Patterns Using the Bitmap Method in Quantitative
Component Databases.

.- Privacy Preserving Based on SHA Encryption and Cluster Analysis in
Federated Frequent Itemset Mining.

.- An Optimization Algorithm for Finding Extractive Summary from
Multiple Source Documents Based on Association and Clustering.

.- Improving Stress Detection with Synthetic Datasets: GPT-4o-mini and
Transformer Model Evaluation.

.- On Translating Association-Oriented Structures of Unified Process
Metamodel into OWL.

.- A Method for Evaluating Expressive Power and Semantic Capacity of
Metamodel in Conceptual Data Modeling.

.- Optimizing Task Estimation Accuracy in Agile Projects: A Data-Driven
Study.

.- Low Computational Aggregation Method for Time Series Forecasting.

.- Information graphs in process analysis.

.- Deep Learning Methods and Applications.

.- ESM-VAE: Bias Reduction in EEG Models via Synthetic Data
Generation with Variational Autoencoders.

.- Chromatic Aberration Detection Using Fully Convolutional Networks.

.- Contextual Statistical Evaluation of Selected CRISPR-Cas9 Recurrent
Deep Learning Models Predicting Off-target Activities for K562 and
Hek293 Cell Lines.

.- CerMixer: An Efficient Model for Cervical Cancer Classification Based
on Patching and Multi-Scale Depthwise Convolutional Fusion.

.- Generative Models Applications.

.- Prompts De-Biasing Augmentation to Mitigate Gender Stereotypes in
Large Language Models.

.- Automatic Creation of Multilingual Knowledge Graph with Large
Language Models.

.- Simulating Oxford-Style Debates with LLM-based Multi-Agent Systems.

.- An empirical review of the effectiveness of different language processing
approaches in Software Code Vulnerability Detection.

.- Intelligent Information Systems and Problem-Solving Algorithms.

.- Analysis of Artificial Intelligence Solutions in Offensive Cybersecurity
Domains.

.- Research on the Application of Large Language Models (LLM) for
Improving Recruitment Efficiency and Accuracy.

.- A Trading Strategy Portfolio Optimization Approach Based on Fuzzy
Mean-semivariance Model Using Memetic Algorithm.

.- Brain-Computer Interface Framework for Real-Time Application
Adaptation.

.- Jaccard Distance under Incompleteness.