Recent Advances in Computational Methods in Science and Technology

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 AIâs 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 Alzheimerâs 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