Web Information Systems Engineering - WISE 2024

This five-volume set LNCS 15436 -15440 constitutes the proceedings of the 25th International Conference on Web Information Systems Engineering, WISE 2024, held in Doha, Qatar, in December 2024.

The 110 full papers and 55 short papers were presented in these proceedings were carefully reviewed and selected from 368 submissions. The papers have been organized in the following topical sections as follows:

Part I : Information Retrieval and Text Processing; Text and Sentiment Analysis; Data Analysis and Optimisation; Query Processing and Information Extraction; Knowledge and Data Management.

Part II: Social Media and News Analysis; Graph Machine Learning on Web and Social; Trustworthy Machine Learning; and Graph Data Management.

Part III: Recommendation Systems; Web Systems and Architectures; and Humans and Web Security.

Part IV: Learning and Optimization; Large Language Models and their Applications; and AI Applications.

Part V: Security, Privacy and Trust; Online Safety and Wellbeing through AI; and Web Technologies.a

Contenu

.- Learning and Optimization.

.- PR-Rank: A Parameter Regression Approach for Learning-to-Rank Model Adaptation without Target Domain Data.

.- Weighted Linear Regression with Optimized Gap for Learned Index.

.- TAKE: Tracing Associative Empathy Keywords for Generating Empathetic Responses Based on Graph Attention.

.- Intent identification using few-shot and active learning with user feedback.

.- CLIMB: Imbalanced Data Modelling using Contrastive Learning with Limited Labels.

.- Equivariant Diffusion-based Sequential Hypergraph Neural Networks with Co-Attention Fusion for Information Diffusion Prediction.

.- CL3: A Collaborative Learning Framework for the Medical Data Ensuring Data Privacy in the Hyperconnected Environment.

.- Selectivity Estimation for Spatial Filters using Optimizer Feedback: A Machine Learning Perspective.

.- On Adversarial Training with Incorrect Labels.

.- Model Lake: a New Alternative for Machine Learning Models Management and Governance.

.- A Benchmark Test Suite for Multiple Traveling Salesmen Problem with Pivot Cities.

.- Large Language Models and their Applications.

.- Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs.

.- Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery.

.- Combining Uncensored and Censored LLMs for Ransomware Generation.

.- Therapying Outside the Box: Innovating the Implementation and Evaulation of CBT in Therapeutic Artificial Agents.

.- iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models.

.- Is this site legit?": LLMs for Scam Website Detection.

.- Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models.

.- BioLinkerAI: Capturing Knowledge using LLMs to Enhance Biomedical Entity Linking.

.- Enhancing LLMs Contextual Knowledge with Ontologies for Personalised Food Recommendation.

.- ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources.

.- AI Applications.

.- Web-based AI Assistant for Medical Imaging: A Case Study on Predicting Spontaneous Preterm Birth via Ultrasound Images.

.- Satellite-Driven Deep Learning Algorithm for Bathymetry Extraction.

.- Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty.

.- Empowering Visual Navigation: A Deep-Learning Solution for Enhanced Accessibility and Safety among the Visually Impaired.

.- A Transformer and LSTM Model for Electricity Consumption Forecasting and User's Behavior Influence.

.- Enhancing Customer Service Efficiency in the Holy Makkah Municipality Using Machine Learning.

.- Motivation, Concerns, and Attitudes Towards AI: Differences by Gender, Age, and Culture.

.- DefectClassifierX: A Cross-Platform Automated Pattern Classification System for Wafer Defects.

.- Safe-Path: A Perspective on Next-Generation Road Safety Recommendations.

.- Unsupervised and Dynamic Dendrogram-based Visualization of Medical Data.

.- Federated Deep Learning Models for Stroke Prediction.