Dr. Ali Bou Nassif Profile

Dr. Ali Bou Nassif

at Univ of Sharjah

SPIE Involvement:

Author

Publications (14)

Proceedings Article | 14 June 2023 Presentation + Paper

Design car side impact using machine learning

Mohammad AlShabi, Khaled Obaideen, Ali Bou Nassif, Maamar Bettayeb

Proceedings Volume 12549, 125490G (2023) https://doi.org/10.1117/12.2664467

KEYWORDS: Design and modelling, Particle swarm optimization, Evolutionary algorithms, Machine learning, Mathematical optimization, Monte Carlo methods, Algorithm development, Safety, Manufacturing, Finite element methods

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Proceedings Article | 14 June 2023 Presentation + Paper

Design a speed reducer gearbox using chameleon swarm algorithm

Mohammad AlShabi, Khaled Obaideen, Rami Amin, Ali Bou Nassif, Maamar Bettayeb

Proceedings Volume 12549, 1254905 (2023) https://doi.org/10.1117/12.2664447

KEYWORDS: Particle swarm optimization, Design and modelling, Mathematical optimization, Engineering, Particles, Unmanned vehicles, Simulations, Tongue, Genetic algorithms

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Proceedings Article | 14 June 2023 Presentation + Paper

Path planning and obstacle avoidance utilizing chameleon swarm algorithm

Khaled Awad Ballous, Mohammad AlShabi, Ali Bou Nassif, Maamar Bettayeb

Proceedings Volume 12549, 125490B (2023) https://doi.org/10.1117/12.2664081

KEYWORDS: Particle swarm optimization, Tongue, Monte Carlo methods, Detection and tracking algorithms, Mathematical optimization, Histograms, Eye tracking, Robotics

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Proceedings Article | 14 June 2023 Poster + Paper

Path-planning and obstacle avoidance algorithms for UAVs: a systematic literature review

Khawla Saif Almazrouei, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12549, 125490S (2023) https://doi.org/10.1117/12.2664056

KEYWORDS: Particle swarm optimization, Unmanned aerial vehicles, Robotics, Mathematical optimization, Design and modelling, Databases, Algorithm development, Computer programming, Computer simulations, Autonomous vehicles

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Proceedings Article | 14 June 2023 Poster + Paper

Heart attack prediction using machine learning

Mohamed Wed Eladham, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12548, 125480F (2023) https://doi.org/10.1117/12.2664047

KEYWORDS: Heart, Machine learning, Data modeling, Performance modeling, Cardiovascular disorders, Feature selection, Evolutionary algorithms, Random forests

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Proceedings Article | 14 June 2023 Poster + Paper

Design a wing flange lever mechanism using Giza pyramid algorithm

Mohammad AlShabi, Mahmoud Abouelazm, Ali Bou Nassif, Maamar Bettayeb

Proceedings Volume 12546, 125460U (2023) https://doi.org/10.1117/12.2664459

KEYWORDS: Evolutionary algorithms, Particle swarm optimization, Robotics, Mathematical optimization, Detection and tracking algorithms, Engineering, Design and modelling, Artificial neural networks, Signal filtering, Machine learning

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Proceedings Article | 13 June 2023 Poster + Paper

Face mask detection using machine learning

Mohamed Wed Eladham, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12528, 125280C (2023) https://doi.org/10.1117/12.2672551

KEYWORDS: Machine learning, Education and training, Data modeling, COVID 19, Facial recognition systems, Evolutionary algorithms, Convolutional neural networks, Matrices, Performance modeling, Deep learning

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Proceedings Article | 13 June 2023 Presentation + Paper

Detecting heart diseases through machine learning

Shaikha Ali Alobeidli, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12527, 125270E (2023) https://doi.org/10.1117/12.2674145

KEYWORDS: Heart, Cardiovascular disorders, Feature selection, Machine learning, Support vector machines, Random forests, Data modeling, Blood, Chest, Blood pressure

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Proceedings Article | 12 June 2023 Poster + Paper

Spam SMS detection based on long short-term memory and recurrent neural network

Marya Jehad, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12538, 125381Y (2023) https://doi.org/10.1117/12.2674147

KEYWORDS: Neural networks, Machine learning, Performance modeling, Deep learning, Data modeling, Support vector machines, Feature extraction

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Proceedings Article | 12 June 2023 Poster + Paper

Denial of service detection on industrial control system using BLSTM

Abdalkarim R. M. Seyam, Ali Bou Nassif, Qassim Nasir, Mohammad AlShabi

Proceedings Volume 12538, 125381X (2023) https://doi.org/10.1117/12.2674146

KEYWORDS: Deep learning, Control systems, Education and training, Information technology, Telecommunications, Machine learning, Neural networks, Photonic integrated circuits, Internet of things, Infrared sensors

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Cyber Physical Systems (CPS) security within industrial fields have enforced itself, due to its deployment critical infrastructure. The complexity, and diversity are evolved with these CPS systems. While connectivity demands for these systems to communicate with each other increases, their attack surface expands. The impact of cybersecurity has on business continuity increase. ICS can run real time critical function, where firewall inspection delay can fail the process. Hence a special firewall consideration needs to be implemented. The Uptime requirements for these ICS is extremely high, which means the normal maintenance or security patches is out of discussion. Beside a modification on any ICS due to firmware update, can trigger revalidations for all interconnected ICS. Perimeter defense firewall is one of the common strategies to protect these ICS systems. The firewall will inspect and detect ingress traffic. Internal firewall will be more enhanced way to protect also from internal attacks within the network. Hence, a need for more efficient ways to detect these attacks, based on Deep Learning (DL) approach with a good source of (Industrial Internet of Things) IIoT dataset. This conference paper evaluates Deep learning approach using Bi-directional Long Short-Term Memory (BLSTM) on resent publicly available dataset “Edge-IIoTset”. This dataset has realistic dataset of IIoT applications. With more than 10 types of sensors/devices uses in ICS systems. With fourteen attacks including DoS/DDoS attack. In this research paper, we consider utilizing deep learning algorithms (BLSTM) to detect and protect the service availability of Critical Infrastructure (CI) and Industrial Control Systems (ICS) from Denial of Service (DoS)attack. The research proposal considers most recent dataset with packet compared with flow format to train our module. The benchmarking with common metrics is used as baseline to compare algorithm efficiency, where accuracy of 99.877 was achieved and validation time of 18millisconds.

Proceedings Article | 8 June 2022 Paper

Empirical evaluation of classifiers for breast cancer diagnosis

Hassan Masoud Darya, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12123, 121230D (2022) https://doi.org/10.1117/12.2632637

KEYWORDS: Breast, Breast cancer, Data modeling, Tumors, Machine learning, Evolutionary algorithms, Diagnostics, Cancer

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Proceedings Article | 8 June 2022 Paper

Predicting heart failure disease using machine learning

Yasser Hassan Basha, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12123, 1212308 (2022) https://doi.org/10.1117/12.2632634

KEYWORDS: Heart, Machine learning, Detection and tracking algorithms, Data modeling, Binary data, Feature selection, Performance modeling, MATLAB, Data conversion, Medical research

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