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Computational Intelligence and Network Systems: First International Conference, CINS 2023, Dubai, United Arab Emirates, October 1820, 2023, Proceedings 1st ed. 2024 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 143 pages, height x width: 235x155 mm, weight: 248 g, 51 Illustrations, color; 21 Illustrations, black and white; XII, 143 p. 72 illus., 51 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1978
  • Izdošanas datums: 16-Dec-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031489837
  • ISBN-13: 9783031489839
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Computational Intelligence and Network Systems: First International Conference, CINS 2023, Dubai, United Arab Emirates, October 1820, 2023, Proceedings 1st ed. 2024Palielināt
  • Formāts: Paperback / softback, 143 pages, height x width: 235x155 mm, weight: 248 g, 51 Illustrations, color; 21 Illustrations, black and white; XII, 143 p. 72 illus., 51 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1978
  • Izdošanas datums: 16-Dec-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031489837
  • ISBN-13: 9783031489839
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031489839.html
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.
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.