Model and Data Engineering

14th International Conference, MEDI 2025, Cairo, Egypt, November 2-4, 2025, Proceedings

AvAhmed Awad,Carlos Ordonez

Häftad, Engelska, 2026

Del 16427 i serien Lecture Notes in Computer Science

673 kr

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Beskrivning

This book constitutes the refereed proceedings of the 14th International Conference on Model and Data Engineering, MEDI 2025, held in Cairo, Egypt, during November 2–4, 2025.The 16 full papers and 17 short papers were carefully reviewed and selected from 72 submissions. The conference program for MEDI 2025 covered an extensive array of topics, such as data modeling, data management, ontologies, machine learning, model driven engineering, large language models engineering, federated learning, image processing, natural language processing, optimization, and advanced AI applications in healthcare and security.

Produktinformation

Utgivningsdatum:2026-05-19
Mått:155 x 235 x 25 mm
Vikt:626 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:404
Förlag:Springer Nature Switzerland AG
ISBN:9783032198648

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Innehållsförteckning

.- Assessing the Impact of Meteorological and Infrastructure Factors on Internet Performance Using Machine Learning..- Non-Intrusive Fall Detection Using Pose Estimation and Polynomial Temporal Modeling..- Towards an Integrated Metaheuristic Approach for Workplace Performance Op timization..- Efficient Algorithms for Spatio-Textual Similarity Join using In-Memory IR-trees..- Managing Model Evolution from NoSQL Data Lakes to Decision Support Sys tem..- Out-Of-Distribution Generalization for Knee Calcium Deposition under X-Ray Manufacturer’s Domain Shifts..- Face Generation from Arabic Text using GAN-CLS and AraBERT..- Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets..- Exploring the Landscape of Generative Adversarial Networks: A Comprehensive Survey of Variants and Applications..- Comparative Analysis of Zero-Shot Testing on Different LLMs for Automated Grading Systems for Business Education..- From Sound to Success: An AI Framework for Predicting Music Popularity and Sentiment Analysis..- Scattering Transformer: A Training-Free Transformer Architecture for Heart Murmur Detection..- Speeding up SQL Subqueries via decoupling of non-correlated predicate..- Advanced Crime-Mobility Relationship Modeling: A Multi-Task Learning Ap proach for Urban Safety Prediction. .- Semantic-Driven Multimodal Learning Framework for Lymphoma Diagnosis..- MARCADE: A Framework for Architecting Resilient and Intelligent Cloud Native Application..- Multi Objective Analysis of Urban Food Insecurity and Hunger..- Enhancing Long-Text Summarization through Hybrid Extractive-Abstractive Methods with LLaMA and Qwen..- A Mixed-Methods Framework for Analyzing Travel Reviews with LLMs and MAXQDA: A Case Study in Osaka..- A Stacked Hybrid Ensemble of Swin-V2 and MViT-V2 for High-Performance Skin Disease Classification..- Codecard: Leveraging LLMs to Evaluate AI Model Code Development with the System Card Framework..- A Hybrid Deep Learning and Ontology-Based Framework for Contextual Hy peractivity Detection in Children with ADHD..- Adapting Deep Learning Models for Image-Level Multi-Class Dental Pathology Identification in OPG X-Rays..- A Semantic Web Approach to Boost Engagement and Collaboration Among Online Health Communities..- Specification-Driven Application Skeleton Generation Using a Multi-Agent Sys tem..- Leveraging Counting Data from Camera Videos to Improve Transport Demand Modeling in African Cities..- Next Generation Cloud-Native In-Memory Stores: From Redis to Valkey and Beyond..- Job Recommendation using Heterogeneous Graph Convolutional Networks and LDA Model..- Real-Time Lesion-Level Acne Detection Using YOLOv8n: A Lightweight Deep Learning Approach..- Empirical Performance Comparison of Traditional Machine Learning and Large Language Models on Classification Tasks: A Study of Structured Sensor Data and Text Classification..- Federated Deep Learning for Intrusion Detection Achieving Centralized-Level Accuracy with Privacy..- Machine Learning and Deep Learning Approaches for Mobile Malware Detection: A Survey.