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This two-volume set, CCIS 2382 and CCIS 2383, constitutes the refereed proceedings of the First International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024, held in Punjab, India, during April 5-6, 2024.
The 38 full papers and 6 short papers included in this book were carefully reviewed and selected from 153 submissions. The papers are organized in the following topical sections:
Part I: Advanced Network Technologies; Computational Intelligence.
Part II: Computational Intelligence; Computer Technology Trends.
List of contents
- Advanced Network Technologies Advanced Network Technologies.
.- Robust Network Intrusion Detection System Using VGG16, Autoencoder,and Random Forest For Enhanced Cybersecurity In IOT.
.- Impact of Divergent Vehicle s Speed on Vehicular Ad-Hoc Network Routing Protocols Analysis.
.- Revolutionizing GST Collection: A Blockchain-Backed Platform for Security and Efficiency.
.- SCADA Aided Architecture for Remote Monitoring in Solar Irrigation Systems.
.- A Comprehensive Approach for Heart Patient Monitoring and Prevention Using IOT and Blockchain Technology.
.- A Critical Study for Efficient and Reliable Routing Protocols for WBAN-integrated Health Monitoring Systems.
.- Blockchain-Enhanced Energy-Efficient Architectures for Sustainable Internet of Things Ecosystems.
.- Post-Quantum Secure Hardware and Infrastructure for AR/VR Metaverse Application.
.- Systematic Advancements in IoT: Integrating Edge Computing for Enhanced Architectures in Next-Generation Devices.
.- Detection of Knee Osteoarthritis From Magnetic Resonance Imaging Using A 3-D Independent Component Analysis Method In Machine Learning.
.- Quantum-Resistant Digital Rights Management (DRM) for Protecting Intellectual Property in ARVR Metaverse Content.
.- Computational Intelligence.
.- Machine Learning-Driven Anomaly Detection in Blockchain Transactions for High-Security Digital Banking.
.- Innovative Integration of Machine Learning Predictive Models within Blockchain Frameworks for Supply Chain Fault Tolerance .
.- Quality Model for Cloud Service Providers Using ANFIS Method.
.- Machine Learning in the Nick of Time for Sophisticated Cybersecurity Threat Detection.
.- Cognitive Computation through Machine Learning Models for Real-Time Traffic Management.
.- Transfer Learning-based Semantic Segmentation of Hippocampus in Magnetic Resonance Brain Image.
.- Blockchain Enhanced Security and Exchange of Electronic Health Records.
.- Computational Intelligence Approach for an Intrusion Detection System.
.- Performance Evaluation of Existing Deep Learning Models for the Detection of Man-In-The-Middle Attacks on IoT Network.
.- Malaria Detection with Multi-Stage Recognition using Neighbor Sample Joint Learning and Deep Learning Techniques.
.- Early Classification of Lung Cancer Based On Cell Morphology Features.
.- A Logical Language for Reasoning about Democratic Decision-Making.
.- Selecting an Academic Cloud Scheme Based on the Investment Model of a Differential Game of Quality.
.- Hybrid Time-Frequency Domain Analysis for Cardiovascular Disease Forecasting over ECG Data.
.- Esophageal Cancer Diagnosis with a Bilinear Pooling and Attention-Based Convolutional Neural Network.
.- Data Science in Healthcare- A Bibliometric Study and Analysis.
.- Graph Convolutional Networks for Improved Motor Imagery Recognition.
.- A Survey of Quantum Algorithms For Computer Science.
.- A Review of Obstacles and Emerging Solutions in Computer Vision.
.- Spear or Shield: Mastering the Art of Gen-AI in Face Recognition.
.- A Synthesis of Approaches in Sign Language Communication Research: Trends and Future Directions.
.- Quantitative Measurements of Renal Obstruction Using Image Extraction Approaches for 99mTc-MAG3 Renal Radiotracer.
.- A Review of Location Prediction Approaches in Ubiquitous Computing: Applications, Challenges.
.- Develop a genetic algorithm to optimize intracranial electroencephalography (IEEG) using neural network architectures.
.- A Comparative Study of Various Human Activity Recognition Techniques Using Deep Learning.
