Computer Sciences

A study of Bayesian and ensemble methods for intrusion detection, with restored figures, reported UNSW-NB15 performance and discussion of implementation limitations.

A hybrid IoT anomaly detection model combining GRU, ResNet and LeNet-5 with dimensionality reduction and Salp Swarm–Seagull feature selection.

A review of machine learning methods, healthcare applications and evaluation measures, drawing on 70 papers from 2018 to 2023.

IntelliNoiseFilter combines ConvRecGRU neural noise estimation with optimized Kalman filtering, preserving the supplied model comparisons and evaluation limits.

A literature review of routing, load balancing, encryption and traffic reduction for multimedia IoT, with a comparison of ten published approaches.

An edited thesis comparing UKF and Elman networks, ANN with BWO-MA, and HRFSVM for fish disease detection, with datasets, methods and reported results.

A comparison of MATLAB tools, Fish-Pak and other datasets, training splits and evaluation metrics used in three fish disease detection studies.

Eye-image classification and OpenCV video processing underpin a MobileNet-based application that detects driver drowsiness and triggers a warning.

Multi-metric clustering, reinforcement learning and EVSSCS optimisation are combined to select cluster heads and transmission paths in wireless sensor networks.

CNN and DBN predictions combine with Savannah Fusion Optimisation to manage hand-offs and routing in mobile RPL IoT networks.

A cascaded U-Net and dense GRU research framework for segmenting and classifying Alzheimer MRI images.

A crop leaf disease detection framework integrating semantic segmentation, hybrid feature selection and the VersaNet deep learning classifier.

An FPGA implementation study of Montgomery multiplication using a serial to parallel multiplier for elliptic curve cryptography.

An image-analysis framework for chromosome structure detection using segmentation, texture descriptors and ensemble models, with the original visual material and qualified results.

A network data-mining workflow linking information fusion and feature selection to anomaly detection, with restored source material and explicit evaluation qualifications.

A customized neural framework for network attack detection, tracing preprocessing and optimization through classification and explaining inconsistencies in the reported model comparison.

An EEG channel and feature selection manuscript combining MothCray optimization with spatiotemporal EEGNet, accompanied by notes on dataset and accuracy inconsistencies.

A proposed three-stage outbreak prediction approach using optimized SVM, KNN and XGBoost, published with its original results and an explicit note about unresolved error statistics.

Abstract Cloud computing services are increasing day by day in recent times. Especially users from all sectors turn to use cloud computing to fast accessing of required services by investing less amount of money in their applications. As the increase of retrieving

Abstract autonomous vehicles (AV) or self-driving cars got more and more into the focus of the automotive industry. Object detection and tracking is essential in AV. To overcome the challenges of various methods used for the object detection and tracking in autonomous