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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Robotics and Control Systems
Al Nasar, Mohammad Rustom
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Control & Systems Engineering Electrical & Electronics Engineering

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A Hybrid Adaptive Gradient-Based Sled Dog Optimizer for Enhanced Robotic Decision-Making in Industrial Applications Al Nasar, Mohammad Rustom
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1788

Abstract

As autonomous robotic systems are increasingly used in industrial applications, there is a growing need to create efficient and automated decision-making capabilities that can work in complex environments with a range of possible actions. RL offers an effective way to train robotic agents. Still, conventional RL techniques tend to have issues with slow and unstable policy learning, poor convergence, and weak exploration-exploitation balance. To solve this problem, this paper develops a Hybrid optimization approach that incorporates reinforcement learning, deep learning, and metaheuristic optimization for more robust robotic control and adaptability. The new approach utilizes a Deep Q-Network with Experience Replay for learning policies. At the same time, an Adaptive Gradient-Based Sled Dog Optimizer is used to improve and optimize decision-making. Epsilon-greedy selection combined with Noisy Network is used for hybrid exploration-exploitation, which helps learning. The effectiveness of the proposed method was validated against five existing methods, which include Conservative Q-Learning, Behavior Regularized Actor-Critic, Implicit Q-Learning, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic, over the three benchmark robotic datasets of MuJoCo, D4RL, and OpenAI Gym Robotics Suite. The vast majority of results provide compelling support for the argument that the proposed approach consistently outperformed the baseline approaches in terms of accuracy, precision, recall, stability, speed of convergence, and degree of generalization. The improvement in performance was confirmed by validation methods such as analyzing confidence intervals and computing results of p-values.
Elliptic curve cryptography based light weight technique for information security Alshar’e, Marwan; Alzu’bi, Sharf; Al-Haraizah, Ahed; Alkhazaleh, Hamzah Ali; Jawarneh, Malik; Al Nasar, Mohammad Rustom
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8587

Abstract

Recent breakthroughs in cryptographic technology are being thoroughly scrutinized due to their emphasis on innovative approaches to design, implementation, and attacks. Lightweight cryptography (LWC) is a technological advancement that utilizes a cryptographic algorithm capable of being adjusted to function effectively in various constrained environments. This study provides an in-depth analysis of elliptic curve cryptography (ECC), which is a type of asymmetric cryptographic method known as LWC. This cryptographic approach operates over elliptic curves and has two applications: key exchange and digital signature authentication. Next, we will implement asymmetric cryptographic algorithms and evaluate their efficiency. Elliptic curve elgamal algorithms are implemented for encryption and decryption of data. Elliptic curve Diffie-Hellman key exchange is used for sharing keys. Experimental results have shown that ECC needs small size keys to provide similar security. ECC takes less time in key generation, encryption and decryption of plain text. Time taken by ECC to generate a 2,048 bit long key is 1,653 milliseconds in comparison to 4,258 millisecond taken by Rivest-Shamir-Adleman (RSA) technique.
Co-Authors Al-Haraizah, Ahed Alkhazaleh, Hamzah Ali Alshar’e, Marwan Alzu’bi, Sharf Jawarneh, Malik