Innovations in Computing

Sushil Kamboj, Pardeep Singh Tiwana

• Publisher: CRC Press
• Year: 2025
• eBook ISBN: 9781040694190
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

This book presents cutting-edge research, emerging trends, and groundbreaking innovations in the field of computing. It offers insights into real-world applications that leverage computational methods and the ever-evolving landscape of machine learning.

This title comprises a selection of papers that reflect a dynamic exchange of ideas in the field of Internet of things, Cloud Computing, Machine Learning, Networks, System Design and Methodologies, Big Data Analytics and Applications, ICT for Sustainable Environment, and Artificial Intelligence. With practical solutions illustrated through case studies and examples, this book will be a useful technical guide for solving real-world challenges.

This book serves as a gateway for researchers, academicians, undergraduate and post graduate students, and professionals to explore the forefront of computational innovation and the dynamic advancements transforming computer science.

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Preface
• Acknowledgements
• Editor biographies
• Advisory committee
• Organizing committee
• Iris image authentication by the detection of parallel eyelid blinks on both eyes together
• Unsupervised machine learning for E-learning data: A case study
• Comparative study of traditional and hybrid models in short-term financial forecasting using machine learning
• Enhancing conversational AI with memory-augmented networks for context-aware dialogue systems
• Effects of DoS and DDoS attacks on IoT networks
• Machine learning techniques for analysing and detecting human sentiments
• Experimental analysis of smart sensors with arduino uno interfacing
• Fusion of NLP and ML in categorizing medical transcription classification by disease
• Comparative study on identifying human-written and AI-generated text using machine learning algorithms
• An automata based adaptive framework for autism prediction
• Tracing a supply chain blockchain: A comprehensive investigation
• Real-time faculty directory and availability system using Python and AI/ML
• AIS-driven smart oil spill detection for maritime safety
• Decrypting the cipher: A CNN-FNN pipeline for cryptographic algorithm detection
• Driving the future: IOT-Powered V2X communication for autonomous transportation
• Classification of drone landing field for drone landing using ReLUCNN-GIST features
• An electricity billing system using Java and MySQL
• A WoS bibliometric survey on dynamic resources allocation in cloud computing
• Federated learning approaches for DDoS detection in IoMT: Challenges, models, and performance evaluation
• Exploring United States agriculture: Cropland data layer program for crop classification and validation with satellite imagery
• Optimized feature reduction for autism spectrum disorder classification using PSO-enhanced ResNet-50 model
• Intelligent resume screening and evaluation using large language models: An AI-driven approach for enhanced recruitment processes
• AI-powered vehicle detection: Enhancing traffic management and video analysis with deep learning
• Blockchain technology for enhancing transparency and efficiency in supply chain management
• Enhanced performance assessment of AI models for heart disease prediction
• Cloud security meets artificial intelligence: Advancing threat detection system
• Deep learning applications in breast cancer diagnosis: Enhancing mammography evaluation through convolutional neural networks
• AI-powered smart eyeglasses for dyslexia: A real-time assistive reading device
• Serverless architecture in full stack development
• Transforming image compression and dynamic content delivery with seam carving techniques
• Collaborative task management system for teams: A scalable and efficient approach using Node.js and NoSQL/SQL databases
• Estimation of gamma ray shielding parameters of zirconium substituted barium lanthanum titanate ceramics
• Comprehensive review of driver fatigue detection: Methods, technologies and future perspectives
• Enhancing privacy-preserving AI in mobile keyboards: Federated learning and differential privacy approaches
• Lung disease prediction system using machine learning
• Comparative evaluation of machine learning algorithms for accurate breast cancer classification using diagnostic imaging features
• Improving network resource allocation in SDN via deep learning-based load balancing
• Deepfake technology: Emerging threats and countermeasures for military security using AI
• Deep learning framework for lung image classification with GUI: Utilizing CNNs for medical imaging diagnosis
• Challenges & innovations in digital forensics and cyber investigations
• AHRS: An enhanced hybrid and knowledge-driven recommendation system for movie dataset
• NDVI vegetation study and prediction for urban areas with XAI and LLM
• Artificial intelligence in breast cancer detection: Techniques and trends
• Comparative study of different deep learning models for cotton plant disease detection
• Trust evolution in healthcare with artificial intelligence
• Deep learning-powered apple leaf disease diagnosis
• A comparative study of AI-generated and human-written content
• Human activity recognition in real-time: An accessible framework for learning and application
• A hybrid deep learning framework with meta-learning to predict genetic disorder
• Deep learning meets genomics: Enhancing genetic disorder detection and interpretation
• Enhancing rare genetic disorder detection using few-shot learning and explainable AI
• Cyber deception in online dating: Analyzing scamming tactics and cybercrime mitigation strategies
• Ensemble methods for predicting polygenic disorders: An integrated machine learning approach
• Computer vision-based method to evaluate K-nearest neighbors (KNN) for tomato disease detection
• Advancing agricultural monitoring: Efficient farmland mapping using machine learning techniques
• Artificial intelligence based learning approaches for brain cancer detection
• Effects of different substrates for wearable patch antennas at 2.45 GHz
• Cyber-physical systems: Bridging the gap between the virtual and real worlds
• Workload prediction in cloud computing: A machine learning-based approach
• Height enhancement via bone physiotherapy: Insights from fracture-induced techniques
• Genetic algorithm-based optimization techniques for workflow scheduling in cloud computing environments
• A study and comparison of different methods of weather forecasting system: Approach and accuracy
• The significance of AI, ML, IoT, and robotics in a sustainable future for electric vehicles
• Role of renewable energy in electric vehicles
• Enhanced breast cancer detection using CNN-LSTM-FC-RF: A hybrid deep learning approach for mammography study
• A comparative study of transfer learning models for lung cancer detection using medical imaging
• Opinion mining in practice: Insights into challenges and tools
• Sentiment assessment using transformer models: Advancing textual emotion recognition through deep learning
• Driver drowsiness detection using machine learning techniques
• Deep learning-enabled approach for pneumonia classification for chest X-ray images
• Comparative analysis of LSTM and CNN models for sentiment analysis on Twitter data
• Customer sentiment study via Twitter
• Innerrooh: A new era in mental health support through technology and user-focused design
• A gas leakage detector using IoT
• A modified rectangular multiband dielectric resonator antenna for satellite applications
• Similarity computation model using convolution neural network classification model
• Sentiment recognition using convolution neural network model
• Optimizing network performance: A study of load balancing and resource allocation in 5G communication networks
• Evaluation of crop classification using remote sensing and machine learning
• Advancements and challenges in next-generation cryptographic techniques: A security and performance perspective
• Harnessing artificial intelligence for disaster management
• Predicting no-show appointments in hair salons using machine learning
• A deep learning enabled approach to predict the energy consumption
• A comparative study of Indian electric vehicles using mathematical modelling
• AI-IoT-blockchain synergy: A next-gen framework for smart cities
• A framework for server performance analysis in cloud datacenter: A systematic study using memoryless open queuing system
• Role of machine learning in medical operations and cancer treatment: Trends and challenges
• Automated fetal health prediction and classification using machine learning and deep learning approaches on cardiotocograms
• Leveraging machine learning for early disease detection: Revolutionizing medical diagnosis with AI
• The role of machine learning in genetic disorder prediction: A bibliometric and systematic review
• Recurrent neural networks in genomics: Enhancing disease identification and mutation analysis
• Analysis of performance of scheduling algorithm in multi cloud architecture
• Harnessing IOT technologies for sustainable urban development: A smart cities perspective
• Global trends and challenges in early lung cancer detection: A bibliometric and systematic approach
• Transfer learning approaches for lung cancer detection using histopathological images: A comparative study of CNN models
• Advancements in Parkinson's disease detection: Machine learning and deep learning techniques
• File fusion: An AI integrated file
• HARQ-IR based rate-compatible QC-LDPC code design for efficient AWGN transmission
• An email-based paradigm for the visually impaired based on text-to-speech and speech-to-text model
• Machine learning for kidney health: Classification models for chronic kidney disease detection
• Developing and evaluating a hybrid machine learning framework for genetic disorder classification
• Human–AI collaboration in sustainable cyber-physical systems
• Research paper on detection and diagnosis of Parkinson's disease using machine learning approaches
• Detection and diagnosis of Parkinson's disease using machine learning approaches
• Author index