Recent Advances in Computational Methods in Science and Technology

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.

• 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 AI’s 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 Alzheimer’s 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