Nyhet

Asia Pacific Advanced Network

Third International Conference, APANConf 2025, Hong Kong, China, July 31, 2025, Revised Selected Papers

AvDamayanthi Herath,Qifeng Chen

Häftad, Engelska, 2026

Del i serien Communications in Computer and Information Science

777 kr

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Beskrivning

This book constitutes the refereed proceedings of the Third International Conference, APANConf 2025, held in Hong Kong, China, on July 31, 2025.The 12 full papers presented in this volume were carefully reviewed and selected from 52 submissions. These papers focus on Artificial Intelligence and Machine Learning, Computer Vision and Wireless Communications and Networks.

Produktinformation

Utgivningsdatum:2026-05-12
Mått:155 x 235 x undefined mm
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:213
Förlag:Springer Nature Switzerland AG
ISBN:9783032183187

Utforska kategorier

Artificiell intelligens inom Data och IT

Innehållsförteckning

.- Design and Development a Low – Cost Modular Swarm Robotic Platform..- Reinforcement Learning Based Activity Recommendation for Providing Emotional Support..- Investigation of Quantum Key Distribution Protocols Under Trojan Horse Side-Channel Attack..- Dynamic Workflow Scheduling for Cloud Computing: Strategies for Optimizing Response Time and Energy Consumption..- The Rise of AI in Asia: Examining Relationships Between Artificial Intelligence Adoption and Social Capacity Building..- PoliticaNet: a Transformer-Driven Framework for Fine-grained Classification of Bangla Political Hate Speech in Social Media..- Oceanographic Acoustics for Minimal Invasive Whale Identification..- Personalized Multi-modal Emotion Prediction..- Enhancing Lung Cancer Prediction Using Machine Learning: A Comparative Analysis Of Hyperparameter Optimization Techniques..- LIME-Enhanced Sentiment Classification for UI/UX Improvement in Sri Lankan Mobile Banking..- Oil Spill Identification in Satellite Imagery using Transformer-Based Deep Learning Models..- Subject-Wise Impact of VAK Learning Styles on Student Personalization Using Ensemble Learning.