Intelligent Vision and Computing : Proceedings of ICIVC 2024 (Studies in Smart Technologies)

Saha, Apu Kumar (EDT)/ Sharma, Harish (EDT)/ Prasad, Mukesh (EDT)

Springer Nature Switzerland AG（2025/06発売）

• 製本 Hardcover:ハードカバー版／ページ数 308 p.
• 言語 ENG
• 商品コード 9789819647217

Full Description

This book includes selected papers presented at 4th International Conference on Intelligent Vision and Computing (ICIVC 2024), held at National Institute of Technology, Agartala, India, during 23-24 November 2024. The conference proceedings is a collection of high-quality research articles in the field of intelligent vision and computing. The topics covered in the book are artificial intelligence, machine learning, deep learning, internet of things, information security, embedded systems, cloud computing, quantum computing, bio-inspired intelligence, cyber-physical systems, hybrid systems, intelligence for security, data mining, evolutionary optimization, swarm intelligence, signal processing, blockchain technology, big data applications, natural language processing, data acquisition, storage and retrieval for big data, data representation, and processing, imaging sensors technology, features extraction, image segmentation, deep learning, convolutional neural network, biometrics recognition, biomedical imaging, intelligent transport systems, and human-computer interaction.

Contents

1. Python-driven Mapping of Technological Proficiency with AI to Simplify Transfer Applications in Education.- 2. Optimizing Categorical Boosting using Modified Metaheuristic for Review Sentiment Analysis.- 3. Comprehensive Analysis on Sentiment Analysis using Bidirectional Sequential Models.- 4. Optimizing Brain Tumor Detection through Deep Learning.- 5. Efficient Shard Allocation and Security-Driven Optimization in Metaverse Blockchain Frameworks.- 6. Unified NeRF: Integrating Explicit and Implicit Techniques for Advanced 3D Scene Modeling.- 7. Multi sensors setup and placement with simulation using ROS and GAZEBO.- 8. OWM-DM: Optimal Weighted Model for Decision-Making-Based Intrusion Detection in IoT Environment for Handling Cyber-Attacks.- 9. Leveraging Hypergraph Neural Network for Fake News Detection on UPFD Dataset.- 10. Efficient Intrusion Detection in Social IoT: A Hybrid CNN-LSTM Model.- etc.