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All Journal Journal of Robotics and Control (JRC) Journal of Applied Data Sciences
AlAli, Rommel
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering

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Applying Structural Equation Modelling for Examining the Impact of Quality Dimensions in Improving the Adoption of Digital-Learning Platforms Alkhdour, Tayseer; Almaiah, Mohammed Amin; Shishakly, Rima; AlAli, Rommel
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.518

Abstract

Although a number of studies have proved the significance of quality characteristics in improving Digital-learning platforms success, there is few research about the impact of quality dimensions in increasing system adoption and usage. As a result, our research investigated the impact of quality indicators such as Quality of Service, quality of learning content and information, and quality of system on Digital-learning platforms usability. Quality of Service, quality of learning content and information, and quality of system characteristics were determined to be the essential components impacting Digital-learning platforms adoption among learners. The study revealed that system quality was the most critical factor influencing the perceived ease of use and usefulness of Digital-learning platforms. Information quality also had a significant impact on both perceived ease of use and usefulness. Additionally, service quality affected these usability factors as well. The findings indicate that system quality significantly influenced usability factors, specifically perceived ease of use and perceived usefulness (H1: β = 0.321; H2: β = 0.366). Additionally, service quality is found to have a significant effect on both usability factors, ease of use and usefulness (H5: β = 0.371; H6: β = 0.366). Furthermore, the results are essential in determining the importance of those quality components that can be utilized by developers in institutions of higher education to enhance their Digital-learning platforms experiences.
Optimizing Mobile Robot Path Planning with a Hybrid Crocodile Hunting and Falcon Optimization Algorithm Hashim, Wassan Adnan; Ahmed, Saadaldeen Rashid; Mahmood, Mohammed Thakir; Almaiah, Mohammed Amin; Shehab, Rami; AlAli, Rommel
Journal of Robotics and Control (JRC) Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i2.25586

Abstract

Thorough path planning is critical in unmanned ground vehicle control to reduce path length, computational time, and the number of collisions. This paper aims to introduce a new metaheuristic method called the Hybrid Crocodile Hunting-SearcH and Falcon Optimization (CHS-FO) algorithm. This method combines CHS's exploration and exploitation abilities with FO's rapid convergence rate. In this way, the use of both metaheuristic techniques limits the disadvantage of the individual approach, guaranteeing a high level of both global and local search. We conduct several simulations to compare the performance of the CHS-FO algorithm with conventional algorithms such as A* and Genetic Algorithms (GA). It is found The results show that the CHS-FO algorithm performs 30–50% better in terms of computation time, involves shorter path planning, and improves obstacle avoidance. Eristic also suggests that the path generation algorithm can adapt to environmental constraints and be used in real-world scenarios, such as automating product movement in a warehouse or conducting search and rescue operations for lost vehicles. The primary The proposed CHS-FO architecture makes the robot more independent and better at making choices, which makes it a good choice for developing the next generation of mobile robotic platforms. Goals will encompass the improvement of the algorithm's scalability for use in multiple robots, as well as the integration of the algorithm in a real environment in real time.
The Impact of Industrial Security Risk Management on Decision-Making in SMEs: A Confirmatory Factor Analysis Approach Almaiah, Mohammad; Mekimah, Sabri; zighed, Rahma; Alkhdour, Tayseer; AlAli, Rommel; Shehab, Rami
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.543

Abstract

This study focuses on the importance of industrial risk management for small and medium-sized enterprises (SMEs) in Algeria, particularly given the administrative, economic, and financial challenges they face, as well as their limited experience in this field. Risk management serves as a strategic tool that aids institutions in achieving safety and sustainability by identifying potential risks that may lead to industrial disasters, such as chemical incidents and technical malfunctions, then analyzing, assessing, and responding to these risks in ways that minimize their impact on the safety of individuals, property, and the environment. The study aims to analyze the impact of risk management on SMEs' ability to make accurate and timely decisions during critical moments while fostering a culture of safety and proactive risk handling. To achieve these objectives, a survey was conducted on a sample of 390 Algerian industrial SMEs. The study employed the Confirmatory Factor Analysis methodology (CB-SEM) to analyze data from these SMEs, which helped in identifying core risk management processes such as risk description, analysis, and conclusion, and evaluating their effectiveness in supporting decision-making. The findings indicate that the impact of the risk description process on decision-making is positive but weak at 14.7%, while the impact of the risk analysis process on decision-making is also positive and weak at 18.9%. However, the effect of the risk conclusion process on decision-making was positive and moderate, at 64.8%. The results further reveal that SMEs that adopt a comprehensive and sustainable approach to risk management have a greater ability to manage disasters and ensure operational safety. The study highlights the importance of regularly reviewing safety protocols, providing training and simulations for employees, improving risk response strategies, and enhancing organizational performance. However, it was observed that some SMEs lack reliance on modern systems for risk avoidance. The study recommends the importance of allocating an independent budget to address potential risks, activating proactive systems for risk prediction, and employing internal and external experts for risk analysis. The study recommends that SMEs focus on developing mechanisms for describing and analyzing risks and collaborating with specialized entities to implement modern systems that support safety and sustainability. Additionally, it advises organizations to raise employees' awareness and provide training on risk handling to enhance the effectiveness of risk management and ensure business continuity.
Investigating Quantum-Resilient Security Mechanisms for Flying Ad-Hoc Networks (FANETs) Abbood, Abdulnasser AbdulJabbar; AL-Shammri, Faris K.; Alzamili, Zainab Marid; Al-Shareeda‬‏, ‪Mahmood A.; Almaiah, Mohammed Amin; AlAli, Rommel
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25351

Abstract

Flying Ad Hoc Networks (FANETs) are indispensable in applications such as Surveillance, Disaster response missions, and Military operations. Both security and communication efficiency must meet certain requirements. However, their effectiveness is hobbled by dynamic topologies, resource constraints, and cyber threats. Therefore, Post-Quantum Cryptography (PQC) is necessary. Classical algorithms and current PQC schemes for FANETs have been discussed in this thesis, including cryptographic solutions that are lightweight enough for resourceconstrained environments. The numerical results of the experiment show that while lattice-based cryptography involves minimal risk of breaches, its power consumption is 25% higher than that for other systems and its processing time 30% slower. In contrast, multivariate polynomial cryptography is better on metrics like usage of electricity: only 10% more power consumed energywise and 15% more CPU cycles needed for processing. The introduction of PQC algorithms and architectures resulted in a 5–10% reduction in network throughput and increased latency to 20% in some scenarios. The results show that hybrid cryptographic systems—combining classical with PQC techniques— have the potential to achieve both high efficiency and long-term security. Case studies have validated the feasibility of tailored quantum-safe algorithms in FANETs, which can offer considerable security benefits while standing rigorous scrutiny in terms of scalability and computational performance on dynamic, missioncritical operations.
Assimilate Grid Search and ANOVA Algorithms into KNN to Enhance Network Intrusion Detection Systems Alsharaiah, Mohammad A.; Almaiah, Mohammed Amin; Shehab, Rami; Alkhdour, Tayseer; AlAli, Rommel; Alsmadi, Fares
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.604

Abstract

The recent progress of operational network intrusion detection systems (NIDS) has become increasingly essential. Herein, a fruitful attempt to introduce an innovative NIDS methodology that integrates the grid search optimization algorithm and ANOVA techniques with the K nearest neighbor (KNN) algorithm to analyze both spatial and temporal characteristics of data for network traffic. We employ the UNSW-NB15 benchmark dataset, which presents various patterns and a notable imbalance between the training and testing data, with 257674 samples. Therefore, the Synthetic Minority Oversampling Technique has been used since this method is effective in handling imbalanced datasets. Further, to handle the overfitting issue the K folds cross-validation method has been applied. The feature sets within the dataset are meticulously selected using ANOVA mechanisms. Subsequently, the KNN classifier is fine-tuned through hyperparameter tuning using the grid search algorithm. This tuning process includes adjusting the number of K neighbors and evaluating various distance metrics such as 'euclidean', 'manhattan', and 'minkowski'. Herein, all attack types in the dataset were labeled as either 1 for abnormal instances or 0 for normal instances. Our model excels in binary classification by harnessing the strengths of these integrated techniques. By conducting extensive experiments and benchmarking against cutting-edge machine learning and deep learning models, the effectiveness and advantages of our proposed approach are thoroughly demonstrated. Achieving an impressive performance of 99.1%. Also, several performance metrics have been applied to assess the proposed model's efficiency.
Co-Authors Abbood, Abdulnasser AbdulJabbar Ahmed, Saadaldeen Rashid AL-Shammri, Faris K. Al-Shareeda‬‏, ‪Mahmood A. Alkhdour, Tayseer Almaiah, Mohammad Almaiah, Mohammed Amin Alsharaiah, Mohammad A. Alsmadi, Fares Alzamili, Zainab Marid Hashim, Wassan Adnan Mahmood, Mohammed Thakir Mekimah, Sabri Shehab, Rami Shishakly, Rima zighed, Rahma