Computational Intelligence and Network Systems (eBook, PDF)

First International Conference, CINS 2023, Dubai, United Arab Emirates, October 18-20, 2023, Proceedings
Redaktion: Muthalagu, Raja; Fiorentino, Michele; Prasad, Neeli Rashmi; R, Elakkiya; Pawar, Pranav M.; P S, Tamizharasan

• Format: PDF

Produktbeschreibung

This book constitutes the proceedings of the First International Conference, CINS 2023, held in Dubai, United Arab Emirates, from October 18 to 20, 2023.

The 11 full papers included in this volume were carefully reviewed and selected from 130 submissions. This volume discusses contemporary challenges within computing systems and the utilization of intelligent approaches to improve computing methodologies, data processing capabilities, and the application of these intelligent techniques. The book also addresses several topics pertaining to networks, including security, network data processing, networks that transcend boundaries, device heterogeneity, and advancements in networks connected to the Internet of Things, software-defined networks, cloud computing, and intelligent networks.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 143
• Erscheinungstermin: 15. Dezember 2023
• Englisch
• ISBN-13: 9783031489846
• Artikelnr.: 69699385

Inhaltsangabe

SemVidRec: A Semantic Approach to Annotations Driven Video Recommendation Model Incorporating Machine Intelligence.- HeartBeatNet: Unleashing the Power of Attention in Cardiology.- EEG-based Identification of Schizophrenia using Deep Learning Techniques.- MLSM: A Metadata Driven Learning Infused Semantics Oriented Model for WebImage Recommendation Via Tags.- Protocol Security in 6th Generation (6G) Networks.- Efficient Method for Video Sentiment Analysis. -Deep Learning-Based Solution for Intrusion Detection in the Internet of Things.- Plant Protein Classification using K-mer Encoding.- Deep CNN based Alzheimer Analysis in MRI using Clinical Dementia Rating.- Disaster Tweets Classification for Multilingual Tweets using Machine Learning Techniques.- Enhancing Mitotic Cell Segmentation: A.- Transformer based U-Net Approach.