Recent Advances in Computational Methods in Science and Technology

Volume 1
1. Auflage 2026
ISBN: 978-1-041-11769-8
Verlag: Taylor & Francis Ltd

Buch, Englisch, 588 Seiten, Format (B × H): 174 mm x 246 mm

This proceedings compilation emerges from the exchange of research insights and innovative ideas among academicians, researchers, practitioners, and students in the field of computer science.

This book gathers peer-reviewed papers covering the most recent advances in Internet of Things (IoT), Cloud Computing, Machine Learning, Networking, System Design and Methodologies, Big Data Analytics and Applications, ICT for Sustainable Environment, and Artificial Intelligence. It presents cutting-edge developments that offer real-time support and enhanced security solutions for advanced learners, researchers, and academicians. This comprehensive resource can help promote translation of basic research into applied investigation and convert applied investigation into practice.

This compilation is expected to be of significant value to a diverse audience, including researchers, academicians, undergraduate and postgraduate students, research scholars, professionals, technologists, and entrepreneurs.

Zielgruppe

Academic, Further/Vocational Education, Postgraduate, Professional Reference, and Undergraduate Advanced

Autoren/Hrsg.

Kaur, Amanpreet

Kaur, Sukhpreet

Kumar, Manish

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme

Inhaltsverzeichnis

AI-driven investment prediction: A machine learning approach for global investment analysis; Real-time event detection for public safety using deep learning: Enhanced model with hybrid CNN-LSTM-transformer and noise reduction; Architectural insights and scheduling in fog techniques computing challenges and future prospects; Advancements in deep learning techniques for diabetic retinopathy detection with statistical insights and future prospects; Intelligent detection of brain hemorrhage using deep learning algorithms: A comprehensive approach; A hybrid ensemble model for intrusion detection in cloud environment with transparent and explainable AI techniques; Real-time intrusion detection system using deep neural networks; Crop classification using deep learning and satellite data for precision agriculture; Hybrid neural network approach using LSTM and XGBoost for network intrusion detection and classification; Detection of fungal infection in dogs using deep learning techniques; Hybrid optimization algorithm for feature extraction with random forest for migraine detection; Face recognition door lock security system; WSHT: A wavelet-swin hybrid transformer for accurate and automated lung nodule segmentation in CT imaging; Exploring speech recognition and pronunciation accuracy detection techniques: A comprehensive review; Optimizing e-commerce insights through synthetic data generation and big data-driven machine learning; Secure and transparent real estate price predictions: Integrating machine learning and blockchain; Air quality monitoring system based on IoT; Intelligent diagnosis of neurological diseases: An ML model for early Alzheimer’s detection; Detecting fake voices: Survey on audio deepfake detection; Enhanced flood prediction and risk assessment using machine and deep learning-based approaches; Early prediction of multi-diseases using AI techniques to promote health and well-being; A deep hybrid approach to thyroid detection: Integrating TabNet and Vision Transformers (ViT); A comprehensive review of sentiment analysis and content prediction in social media: Techniques, trends, and applications; Automated system for predicting cardiovascular disease using machine and deep learning; Object detection for blind persons; Comorbidity forecasting in asthma: A multimodal learning approach with clinical and environmental data; Advancing brain tumor detection with deep learning: A comprehensive review; Implementation and validation of an onset seizure detection model using EEG signals; Hybrid deep learning models for automated fetal health prediction using cardiotocography signals; A comparative study of chaotic and DNA-based lightweight cryptographic schemes for IoT devices; Machine learning-based optimized water assessment and auto-irrigation over Internet of Things framework; MedXTech: Developing a patient condition prediction system; A comparative analysis of two hyperchaotic image encryption schemes: Dynamic DNA encoding vs. ABC-optimized watermarking; Deep learning-based cyber threat detection in big data environments; Deep learning-based plant leaf disease detection using CNNs and hybrid vision transformer architectures for precision agriculture; Deep learning applications in thyroid disease and cancer diagnosis; A proactive security approach for cyber-physical systems using Modbus honeypots; Review on feasibility of underwater wireless optical communication; Multimodal sentiment analysis: Advances in machine learning and deep learning approaches; Predicting smartphone addiction using machine learning: A behavioural and neural approach; Development of IoT-based fruit condition monitoring system; Hybrid machine learning models for the prediction and recommendation of different types of crops disease: A review; Benchmarking artifact-based deepfake detection: Metrics-driven analysis of model performance; A robust multi-class text classification framework using TF-IDF features and soft voting ensembles; Heart attack risk prediction using ensemble machine learning models on clinical data; Blockchain-based data sharing framework for multi-party collaboration in distributed system; EchoText: Integrating whisper and YAMNet models for comprehensive audio analysis and subtitle generation; Comparison of text vectorization techniques: TF-IDF, Word2Vec, BERT, and FastText; An optimized deep learning-based intrusion detection system for IoT botnets using hybrid feature selection; Leveraging machine learning for advanced cyber threat detection in network traffic: A comprehensive review; A novel LSTM-based method for identifying epileptic seizures from EEG data; Hybrid AI models for real-time intrusion detection in large-scale big data; Predictive analysis of network-based cybersecurity threats using ensemble and discriminant learning approaches; Machine learning-based crop recommendation system using agro-environmental parameters; Analyzing and predicting CO2emissions and their impact on global warming using linear regression techniques; The digital field: Integrating IoT and deep learning for agricultural excellence; Automated transcription and summarization of video content using whisper and transformers: A hybrid deep learning approach; Enhancing cardiovascular illness prediction with machine learning algorithm: A broad review; Detection of Parkinson’s disease using machine learning and deep learning techniques: A review; A bibliometric analysis on deep learning and IoT-based mushroom cultivation; Advances in AI-driven optical sensing for aflatoxin detection in rice: A review of UV fluorescence and hyperspectral imaging; Applications and monitoring system in IoT-driven healthcare technologies; Credit card fraud detection using machine learning: A comprehensive survey and analysis; Transformers and capsule networks for accurate cardiomegaly detection in medical imaging; Harnessing YOLOv5 for real-time object detection: A cloud-based approach; Generative AI application for the elderly with dementia; Deep learning-based data security in mobile networks; Role of data augmentation in medical image analysis; Comparative analysis of deep learning architectures for image classification in gastric cancer detection; CNN-based detection of image splicing: A deep learning approach; Detecting mental health influences on night eating syndrome with clustering methods amongst university students; Predictive maintenance of vehicles: A comparative study of predictive model; A sequential CNN framework for accurate brain tumor detection and subtyping; Affective deep learning approach to personalized music recommendation; Breast cancer detection using deep learning on medical imaging modalities: A comprehensive review; Machine learning-based decision tree for hotspot prediction in cloud computing environment; A comparative study of federated learning model weight aggregation algorithms for privacy-preserving IoMT applications; Transfer learning pipeline from VGGNet To ViT in hybrid image processing; Analysis of energy-efficient task scheduling in IoT-fog-cloud environments using nature-inspired optimization algorithms; Towards precision agriculture: A review of image segmentation techniques and their future prospects; A hybrid deep learning model for robust detection of foliar diseases in cultivated plants; Predicting friendship and follower growth in social networks using ML-based link prediction

Über Autor(innen)

Sukhpreet Kaur is a Professor at the Computer Science and Engineering, Chandigarh Engineering College - CGC Landran, Mohali. She has 18 years of experience in teaching and research. She earned her Ph. D in CSE from I K Gujral Punjab Technical University, Jalandhar and her master’s in technology in CSE from GNDEC, Ludhiana. Her research interests include Image Processing, Artificial Intelligence and Computer Vision. She has published more than 60 research papers in reputable Scopus-indexed international journals. She has also actively contributed to the academic community by organizing and conducting several international conferences, fostering collaborations and knowledge exchange in emerging areas of computer science.

Amanpreet Kaur is a Professor at the Department of Information Technology, Chandigarh Engineering College - CGC Landran, Mohali. She earned her Ph.D. in Computer Science & Engineering from I.K. Gujral Punjab Technical University, Punjab, in 2020. She holds an M. Tech. in Information Technology with distinction from Guru Nanak Dev University, Amritsar, and B.Tech. in Computer Science & Engineering with honours and distinction. She has over  21 years of teaching experience at undergraduate and postgraduate levels. Dr. Kaur has been supervising many M. Tech. dissertations and Ph.D. research scholars. Her research contributions span multiple areas of computer science, with 40+ research publications in reputed international journals and 20+ papers presented at international conferences. She continues to contribute actively to academia through her teaching, mentorship, and research activities.

Manish Kumar is a Professor at the Department of Computer Science and Engineering, Chandigarh Engineering College - CGC, Landran, Mohali. His academic career spans 20 years, with experience in teaching, research, and academic and administrative outreach activities. He completed his B.Tech., M. Tech. and Ph.D. degree in Computer Science and Engineering. His research specialization domains include wireless sensor network, data mining and machine learning. He has over 50 publications to his credit in widely circulated journals of national and international repute. He has also played a key role in the academic community by organizing several national and international conferences, fostering collaborations and advancing research in emerging domains of computer science.