E-raamat: Recent Advances in Computational Methods in Science and Technology: Volume 2 [Taylor & Francis e-raamat]

Mental disorder detection using Neuralink; Neuralink and the future of
mental disorder diagnostics; Towards intelligent attendance management system
with face recognition using LBPH algorithm; Vedcure: Towards intelligent
ayurvedic drug recommendation and disease prediction; Intelligent anomaly
detection in big data environments using unsupervised machine learning;
Automated social media bot detection using metadata and machine learning;
Monitoring malnutrition with machine learning: An in-depth review; Smart risk
scoring: Mapping urban behaviour, pollution, and insurance costs using
explainable machine learning; Comparative evaluation of LLM-based error
correction on tesseract OCR output; Real-time malware detection using
convolutional neural networks; Applying machine learning algorithms for the
classification of sleep disorders; Deep learning-based approaches for
diagnosis and detection of different types of brain cancer; Enhancing
computer vision systems through integration of attention mechanisms and
transformer networks for object detection; Adaptive region-based image
segmentation and classification using transfer learning for agricultural
disease detection systems; Implementation of advanced machine learning
algorithms for predictive maintenance in industrial Internet of Things
networks; Lung cancer detection model using ML algorithms by various
parameters of patient; Artificial intelligence-based approaches for the early
prediction and classification of pancreatic cancer conditions; Intrusion
detection system using Random Forest for real-time network security; Machine
learning in cybersecurity: A predictive model for threat detection; Advanced
personal security application for women using AI, blockchain, and real-time
monitoring; Precision aflatoxin detection in rice: A systematic review of
optical sensing and a farmer-centric UV-HSI solution for smallholder
agriculture; Enhancing cardiovascular disease prediction using optimized
machine learning technique; Task management application with AI-powered task
prioritization deployed on multi-cloud platform; Fine-tuned BERT-based
sentiment analysis model for Twitter data and its deployment on AWS Fargate;
Quantum-enhanced cloud paradigms: Emerging architectures and integration
challenges; Harnessing hybrid intelligence: A CNN-SVM approach for precise
neem leaf disease classification; Application of DCT domain-based image
improvement for vision impaired people; Multivariate statistical exploration
of post-pandemic behavior in Bareilly: An integrated PCA; AI therapist: A
RAG-enhanced mental health platform integrating Google Gemini for
personalised support; Explainable multimodal deep learning for breast cancer
diagnosis; Integrated vision and sensor data framework for fire severity
forecasting; A hybrid evaluation framework for machine learning and deep
learning models in semantic sentiment analysis of Twitter data streams;
Global supply chain disruption analysis using trade and logistics big data; A
hybrid KNN and tri-ensemble approach for predicting HMPV infection; Analysing
data concealment techniques in NTFS file system using computer file
forensics; Multi-modal imaging integration through deep learning; Enriching
lives: Utilizing ML algorithms for prediction of cardiovascular disease;
Dimensionality reduction and XAI approaches for analyzing multivariate
socioeconomic shifts during COVID-19; A comprehensive review of brain tumor
detection techniques using generative models; Brain tumor detection using
generative AI; Forest-based anomaly detection and isolation for cyber
security applications; HEDGE: A sustainable framework for energy efficient
optimized proof of work in block chain systems; Secure IoT environments with
hybrid deep CNN and BiLSTM-based intrusion detection; Edge-enhanced
communication protocols for real-time data processing in UAV systems;
Environmental impacts of cyber threats: An emerging dimension of digital
security; A holistic approach to Yamuna River rejuvenation through green
technology and community participation; A shift-left performance engineering
approach using LoadRunner DevWeb protocol in agile development pipelines;
Literature survey on techniques and challenges to diagnose the
gastrointestinal tract; Machine learning aspects to measure and predict UHI
using urban morphology features; Enhanced oral cancer detection using CNN
optimized by hybrid grey wolf-particle swarm algorithm; Analyzing the effects
of model architecture on feature importance using explainable AI techniques;
Unboxing the black box: A survey on explainable artificial intelligence
approaches in ML; Comparative evaluation of transformer-based models for
sentiment analysis of e-commerce reviews using PyTorch; Comparative analysis
of fundamental edge detection techniques for medical images; Continual
learning in neural networks: Addressing catastrophic forgetting through
scalable and robust methods; A comprehensive review of synthetic data
generation: Models, metrics, and industry use cases; Leveraging machine
learning techniques for enhanced skin cancer detection; An approach to reduce
carbon footprint on environment: A comparative analysis; DeepFake detection
using deep learning techniques; Neural architecture search: Automating deep
learning model optimization for enhanced performance and scalability;
Comprehensive review of machine learning and deep learning frameworks for
robust classification of multiclass leaf diseases under variable conditions;
A study of machine learning and deep learning models for PCOS diagnosis;
Mitigating AI bias and advancing fairness: A systematic survey of techniques,
tools, and ethical implications in machine learning; Reducing carbon
footprint in AI workloads using serverless cloud architectures; Reinforcement
learning with deep Q-networks for predictive trading in the NSE market;
Investigation of cloud computing-based capacity building for successful IoT
application implementations; Intelligent rollback mechanisms for Kubernetes
deployments using reinforcement learning; Plant leaf classification using
machine learning and deep learning: A review of CNN-based approaches; Machine
learning approaches for the detection and classification of plant leaf
diseases; From simulated to real: Integrating deep learning for efficient
medical image analysis solutions; Empowering SOCs: A conceptual analysis of
agentic AIs transformative potential in cybersecurity operations; Deep
reinforcement learning to improve interactive virtual reality training
environments: An inclusive strategy for personalized learning, skill
development and mental health; AI-powered urban planning: Predictive
analytics for sustainable development; Trust calibration in AI systems:
Bridging the gap between automation and intuition; Modern road management
framework: Development approaches; Distributed intelligence in 6G: The role
of federated learning and edge computing; Hybrid stacking ensemble for
multi-class diagnosis of pancreatic conditions using clinical features;
Self-organizing particle swarm optimization for multi-objective resource
allocation in massive MIMO-enabled 5G wireless communication networks;
Diagnosis and tau burden prediction in Alzheimers disease via non-invasive
biomarkers and gradient boosting models; Vocal biomarkers in medicine: A new
era for chronic disease diagnosis; Reading between the vessels: A clear AI
method for finding both diabetic retinopathy and cardiovascular risk; Hybrid
ML forecasting model for fertilizer and crop yield

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 masters 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.