Computer Sciences

An FPF encoder and decoder map three-bit data words to four-bit codewords to reduce crosstalk in deep submicron buses, with Verilog simulations and reported latency results.

A GloVe and LSTM model classifies disaster-related tweets using preprocessing, spatial dropout and sigmoid activation, with reported validation accuracy of 83.33%.

A study of multi-objective routing in delay-tolerant networks that combines whale and teaching-learning optimisation and compares performance across five network sizes.

An MRI classification framework combines image enhancement, optimized SegNet, hybrid feature selection and a voting ensemble of three neural architectures.

A KIMORE-based framework explores skeleton augmentation, hybrid optimisation and neural models for assessing five rehabilitation exercises.

A hybrid sentiment-analysis framework combines tweet preprocessing, Chicken Royale feature selection and a voting classifier built from three neural models.

A conceptual discussion of emotional AI explores cognition, values, decision-making and proposed applications in recruitment, security and interactive systems.

A layered IoT healthcare framework combines optimised hybrid cryptography, medical-image watermarking and blockchain-based retrieval controls.

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.