Verma / Dasgupta / Pattanaik | Advanced Network Technologies and Intelligent Computing | Buch | 978-3-031-83789-0 | sack.de

Buch, Englisch, 470 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 739 g

Reihe: Communications in Computer and Information Science

Verma / Dasgupta / Pattanaik

Advanced Network Technologies and Intelligent Computing

4th International Conference, ANTIC 2024, Varanasi, India, December 19-21, 2024, Proceedings, Part II
Erscheinungsjahr 2025
ISBN: 978-3-031-83789-0
Verlag: Springer Nature Switzerland

4th International Conference, ANTIC 2024, Varanasi, India, December 19-21, 2024, Proceedings, Part II

Buch, Englisch, 470 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 739 g

Reihe: Communications in Computer and Information Science

ISBN: 978-3-031-83789-0
Verlag: Springer Nature Switzerland


This book constitutes the refereed proceedings of the 4th International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024, held in Varanasi, India, during December 19–21, 2024.

The 95 full papers and 15 short papers included in this book were carefully reviewed and selected from 507 submissions. They were organized in topical sections as follows: Advance Network Technologies; and Intelligent Computing.

Verma / Dasgupta / Pattanaik Advanced Network Technologies and Intelligent Computing jetzt bestellen!

Zielgruppe


Research

Weitere Infos & Material


.- Advance Network Technologies..- Ameliorated Standardization Framework for Post-Quantum Cryptographic Algorithms..- Integrating IoT and Machine Learning for Human Fall Detection  and Activity Monitoring..- A Novel Protocol for Authentication and Group Key Distribution for Secure Data Transmission in VANET Environment Using Theory of Number..- Revolutionizing Urban Mobility: ZoomBikes..- Lane Detection in Autonomous Vehicles Using Unsupervised Machine Learning and Light Detection and Ranging..- Secure Intelligence Development Lifecycle (SIDL) Model for Vulnerability Detection..- Predictive VM Placement Algorithm for Resource Optimization: Leveraging Deep Learning Forecasting and Resource Relationship Modeling..- Analysis of Different Gravity Models to Determine Key Nodes in Social Networks..- PUF-Based Ownership transfer using Blockchain..- Intelligent Computing..- Comprehensive Water Quality Analysis and Prediction Using Ensemble Machine Learning Models..- Efficient Sparse Tensor Core Networks for Real-Time Insect Classification in Agriculture..- Detection model for Pulmonary TB on Augmented X-ray Images Enhanced through Histogram Equalization..- Comparing the Performance of Supervised, Unsupervised and Hybrid Learning on Medical Insurance Fraud Detection..- Deep-MFR: A Deep Learning Ensemble Approach for Improved Masked Face Recognition..- Reduced Kernel Principal Component Analysis approach for Microarray Spot Classification..- Detection of Vitiligo using Ensemble Learning..- Integration of Multi-Feature Analysis with Lightweight CNN Model for Heart Sound Classification..- Vaccine Sentiment Analysis:  A Twitter Study using NLP and ML approach..- COMPUTER-AIDED DIAGNOSTIC SYSTEM FOR ALZHEIMER'S DISEASE USING 3D MRI IMAGES..- Impact of Virtual Reality on Cultural Heritages: Development of Mobile VR Tour of the Museum Site of Chandigarh, India..- Interpretable Liver Fibrosis Classification Using 1D-CNN..- Software Defects Prediction using Generative Adversarial Network based Data Balancing..- CRNet: Convolutive Recurrent Network for Suspect Face Identification..- Integrating Optimized CNN and Deep CNN Model for Enhanced Maize Plant Leaf Disease Classification and Prediction Systems..- Lung Region Segmentation from Chest-Radiographs Using EfficientNetB7..- EnigmaArt: Dual Image Encryption and Compression via Autoencoding and Pixel Conversion..- Artificial neural networks with soft attention: Natural language processing for phishing email detection optimized with modified metaheuristics..- Reddit Sentiment Analysis Using AWS Services..- Enhancing Security in Software-Defined Networks: A Machine Learning-Driven Hybrid Intrusion Detection System with Optimized Feature Selection.



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.