Mohammad AlShabi

• PhD
• Professor (Associate) at University of Sharjah

The increasing production of disposable plastic products contributes greatly to marine pollution and its impact on the marine ecosystem and organisms consuming ocean-derived food. To address this issue, this paper proposes a new customized convolutional neural network (CNN) model for categorizing the level of marine pollution in underwater ocean re...

Object detection and tracking are pivotal tasks in machine learning, particularly within the domain of computer vision technologies. Despite significant advancements in object detection frameworks, challenges persist in real-world tracking scenarios, including object interactions, occlusions, and background interference. Many algorithms have been p...

Due to their nonlinear behavior and the harsh environments to which batteries are subjected, they require a robust battery monitoring system (BMS) that accurately estimates their state of charge (SOC) and state of health (SOH) to ensure each battery’s safe operation. In this study, the interacting multiple model (IMM) algorithm is implemented in co...

The combustion of natural gas consisted of methane CH4, ethane C2H6, and propane C3H8 is theoretically investigated to obtain the optimum adiabatic flame temperature (AFT). The investigation includes the development of combustion equations that take into consideration different compositions of natural gas. The final equation to calculate the AFT is...

This paper proposes an enhanced fusion technique to improve the accuracy of the state estimation of a navigational system. The Smooth Variable Structure Filter (SVSF) is examined to estimate the system’s state under model uncertainty. Its combination with the Unscented Kalman Filter (UKF) to acquire better navigational accuracy while being robust t...

This work develops a novel formulation of the lattice Kalman filter (LKF) for enhanced robustness. This novel approach initially integrates the concept of sliding innovation to refine the measurement update phase of the LKF, ensuring that the filter’s innovation is constrained within predetermined bounds; the resultant robust filter is designated a...

This paper proposes a novel estimator for the purpose of fault detection and diagnosis. The interacting multiple model (IMM) strategy is effective for estimating the behaviour of systems with multiple operating modes. Each mode corresponds to a distinct mathematical model and is subject to a filtering process. This paper applies various model-based...

High entropy alloys (HEAs) attract many researchers due to their unique and desirable properties in comparison to conventional alloys, and their potential for advanced applications. Because of the complexity of designing HEAs, several attempts have been conducted to integrate experimental and computational studies with machine learning (ML) algorit...

Epilepsy is a neurological condition caused by sudden onsets of electrical activity in the brain. This results in frequent, uncommon seizures, which can lead to severe physical consequences. In a clinical setting, data recorded using EEG (Electroencephalogram) is used to help diagnose the condition. This research focuses on the use of Short-Term Fo...

Eng. Khawla Almazrouei currently works at the autonomous robotics research center in the Technology innovation institute. Khawla does research in Computer Vision, Autonomous Robotics, and path planning . Khawla holds a BSc in Computer Engineering and Artificial Intelligence minor major from the United Arab Emirates University. Khawla is doing her m...