Proceedings of the 3rd International Conference on Emerging Trends & Innovation (3rd ICETI)

AvInam Ullah Khan,Hafiz Muhammad Attaullah

Inbunden, Engelska, 2026

Del i serien Information Systems Engineering and Management

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Beskrivning

This book brings together forward-looking research and practical innovations that address real-world technological and societal challenges. It offers readers actionable insights, modern solutions, and interdisciplinary perspectives that support smarter decision-making, sustainable development, and digital transformation.The contributions highlight emerging trends across artificial intelligence, cybersecurity, data science, intelligent systems, healthcare technologies, and sustainable computing. Each chapter presents applied methodologies, experimental findings, and practical frameworks that can be directly adapted by researchers, practitioners, and industry professionals. By connecting academic research with real implementation challenges, the book serves as a valuable reference for those seeking to translate ideas into impact. It supports collaboration between disciplines and promotes innovation that responds to global needs. Designed for researchers, engineers, and advanced students, this collection reflects the spirit of knowledge exchange and international cooperation fostered through the 3rd International Conference on Emerging Trends & Innovation (ICETI 2025).

Produktinformation

Utgivningsdatum:2026-06-16
Mått:155 x 235 x undefined mm
Format:Inbunden
Språk:Engelska
Serie:Information Systems Engineering and Management
Antal sidor:736
Förlag:Springer Nature Switzerland AG
ISBN:9783032224255

Utforska kategorier

Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

Enhancing Cybersecurity with AI-Based Threat Classification.- An Adaptive Model for Unmasking Zero-Day Threats using Federated Learning.- Deep Architectural Classification for Heart Disease Prediction.- Multi-Dataset Comparative Analysis of Genetic Algorithms for Intrusion Detection in UAV Networks.- Enhancing Breast Cancer Classification: A Comparative Analysis of Five Hybrid Deep Learning and Machine Learning Architectures on Tabular Data.- A Lightweight Continuous Authentication Protocol for Smart Home Environment Under A Zero Trust Architecture.- Unveiling Covert AI: Reverse Engineering Android Apps to Expose Behavioral Tracking.- The Clipboard Conspiracy: How Android Apps Can Steal Your Data Unseen.- Prediction of Vulnerabilities in Banking sector using Machine Learning.- The Reverse Engineer’s Toolkit: A Comprehensive Overview of Essential Tools and Techniques.- Lightweight Intrusion Detection System for UAV-Based Search and Rescue Operations Using Machine and Deep Learning Models.- Disaster-Aware IoT Connectivity using FANET Routing Protocol.- Differential Privacy-Enabled Horizontal Federated Learning for Anti-Money Laundering Detection in Financial Transactions.- A Hybrid Stacked Ensemble of Deep Learning and Machine Learning Models for High-Accuracy Diabetes Prediction.- YOLO-Based Object Detection and Recognition Using Webots Simulation.- Harris' Hawks Optimization for Enhanced Clustering in Medical Diagnosis.- Performance Benchmarking of ROBOTa Optimized Machine and Deep Learning Models for UAV IDS.- A Comparative Study on Optimized Models for Intrusion Detection using CICIDS2017.- Anomaly based Intrusion Detection System for UAVs Network.- Lightweight Intrusion Detection System for UAV-Based Search and Rescue Operations Using Machine and Deep Learning Models.- Disaster-Aware IoT Connectivity using FANET Routing Protocol.- Differential Privacy-Enabled Horizontal Federated Learning for Anti-Money Laundering Detection in Financial Transactions.- A Hybrid Stacked Ensemble of Deep Learning and Machine Learning Models for High-Accuracy Diabetes Prediction.- YOLO-Based Object Detection and Recognition Using Webots Simulation.- Harris' Hawks Optimization for Enhanced Clustering in Medical Diagnosis.- Performance Benchmarking of ROBOTa Optimized Machine and Deep Learning Models for UAV IDS.- A Comparative Study on Optimized Models for Intrusion Detection using CICIDS2017.- Anomaly based Intrusion Detection System for UAVs Network.