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All Journal Journal of Robotics and Control (JRC) Journal of Applied Data Sciences
Shehab, Rami
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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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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.
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.
SECRE-MEN: A Lightweight Quantum-Resilient Authentication Framework for IoT-Edge Networks Faleh, May Adnan; Abdulsada, Ali M.; Alaidany, Ali A.; Al-Shareeda‬‏, ‪Mahmood A.; Almaiah, Mohammed Amin; Shehab, Rami
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26006

Abstract

The wide 6G-IoT and Mobile Edge Computing (MEC) deployments give rise to severe concerns in authentication, revocation and protection against quantum-post and side channel attacks. In this paper, SECRE-MEN (Secure and Efficient Cryptographic Revocable Authentication for MEC enabled Networks) is presented to be a lightweight and scalable authentication architecture specifically designed for the resource limited IoT systems. SECRE-MEN consists of three main parts: (1) Masked Cryptographic Techniques that are used to randomise elliptic curve operations, thereby mitigate side-channel attacks, (2) VCs, providing support for digitally-signed, lightweight authentication, without requiring the use of bulky certificates, and (3) a Bloom filter-based RDB, which is distributed across multiple MEC nodes, to allow for fast, memory-efficient revocation checks. To enable future-proof security post-quantum cryptography (PQC) is included in SECRE-MEN by lattice-based schemes, such as Kyber and Dilithium, which may incur additional computational cost on ultra-low-power platforms according to the trade-off introduced in this paper. Effort experiments show that the proposed RAM-MENAMI decreases 29.3% the computation cost, and reduces 21.8% the communication budget and improve 20.3% of power efficiency in comparison with the RAM-MEN. In addition, SECRE-MEN is resistant against impersonation, MITM, replay and quantum attacks, as well as allows for dynamic revocation and secure synchronization among MEC nodes. This places SECREMEN as an effective toolkit for cybersecurity of massive IoT-MEC networks in the era of the evolving 6G.
Artificial Intelligence-Driven and Secure 5G-VANET Architectures for Future Transportation Systems Saare, Murtaja Ali; Abdulhamed, Mohamed Abdulrahman; Al-Shareeda‬‏, ‪Mahmood A.; Almaiah, Mohammed Amin; Shehab, Rami
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26295

Abstract

The advent of 5G has opened a new era of intelligent, adaptive and secure VANETs that is envisaged to serve as the backbone network architecture for next generation of vehicular transportation systems. In this work, we present a connected 5G VANETs-to-Edge Computing systems with Artificial Intelligence (AI) infrastructure to improve system adaptability, anomaly detection, trust management, and real-time decisionmaking. Crucial enabling technologies like Software-Defined Networking (SDN). Mobile Edge Computing (MEC), and millimeterwave communication are investigated in detail. We examine key security threats such as identity forgery, data interception, and denial-of-service attacks, and assess the AI-enhanced defense measures such as intrusion detection systems and blockchainbased trust models. Applications, like autonomous platooning, and collaborative vehicle authentication provide additional examples of AI technologies’ added value in the context of vehicular communications and safety. The paper concludes by providing open issues and future directions, including quantum-resistant protocols, lightweight AI models and cognitive networking in the context AI-driven 5G-VANET ecosystems.
CA-HBCA: A Software Engineering Framework for Secure, Scalable, and Adaptive Healthcare Blockchain Systems Qasim, Mustafa Moosa; Altmemi, Jalal M. H.; Ali, Akram Hussain Abd; Al-Shareeda‬‏, ‪Mahmood A.; Almaiah, Mohammed Amin; Shehab, Rami
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26643

Abstract

Secure, scalable, and compliant solutions are becoming a requirement for healthcare systems handling sensitive medical data. Blockchain presents unique opportunities to create transparency and trust that is decentralized, yet has inherent challenges posed by scalability, sustainability and regulation. This study presents CA-HBCA, a Cognitive and Adaptive Software Engineering Framework for intelligent healthcare blockchain applications. The novel contribution of the research is the combination of four sledging modules, such as an AIbased cognitive security layer that triggers real-time anomaly detection, an adaptive sustainability engine that optimises energyperformance, a DevSecOps-based continuous delivery pipline, and a HL7/FHIR-compliant interoperability and consent management layer. Methodologically, the FEACAN was realized with Solidity, TensorFlow, and Ethereum/Hyperledger testnets, and tested by simulating healthcare scenarios such as EHR exchange, and adversary search. We obtained 93.2% precision of anomaly detection, 17.6% reduction of energy consumption, 42 transactions per second throughput in Hyperledger, and 98.7% of success rate of HL7-FHIR transformation, etc. The framework also demonstrated 100% smart contract–based consent compliance under test cases. The results indicate that CA-HBCA can be employed for the establishment of secure, sustainable and regulation-compliant blockchains in digital health infrastructures. In the future, we will also carry out validation with clinical real data sets and investigate the scalability in a variety of healthcare settings.
Co-Authors Abdulhamed, Mohamed Abdulrahman Abdulsada, Ali M. Ahmed, Saadaldeen Rashid Al-Shareeda‬‏, ‪Mahmood A. Alaidany, Ali A. AlAli, Rommel Ali, Akram Hussain Abd Alkhdour, Tayseer Almaiah, Mohammad Almaiah, Mohammed Amin Alsharaiah, Mohammad A. Alsmadi, Fares Altmemi, Jalal M. H. Faleh, May Adnan Hashim, Wassan Adnan Mahmood, Mohammed Thakir Mekimah, Sabri Qasim, Mustafa Moosa Saare, Murtaja Ali zighed, Rahma