Recent Advances in Computational Methods in Science and Technology

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 Alzheimerâs 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 COâemissions 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 Parkinsonâs 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