Fahd N. Al-Wesabi

TL;DR: A weighted voting based ensemble model is employed for the multimodal fusion process using recurrent neural network (RNN), bi-directional long short term memory (Bi-LSTM), and deep belief network (DBN) and depicts the novelty of the work.

TL;DR: An automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra- light Deep Learning Architecture for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM) forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM).

TL;DR: The proposed mechanisms outperforms in identifying accurately multi-variant sophisticated bot attacks by achieving 99.94% detection rate and the proposed technique attains 0.066(ms) time that shows the promising results in terms of speed efficiency.

TL;DR: This study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique, which exploits BC and ML techniques to accomplish security in the smart city environment.

TL;DR: This study develops a novel deep learning-based small object detection and classification model for garbage waste management (DLSODC-GWM) technique that mainly focuses on detecting and classifying small garbage waste objects to assist intelligent waste management systems.