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All Journal International Journal of Engineering, Science and Information Technology International Journal of Sharia and Law
Haider Mahmood Jawad
Al-Rafidain University College
Religion Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Integrating Islamic Principles with Modern Criminal Justice: Re-Evaluating Hudud Laws in the Context of Digital Evidence and Procedural Fairness Haider Mahmood Jawad
International Journal of Sharia and Law Vol. 1 No. 2 (2025)
Publisher : Qiyam Islamic Studies Center Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65211/ijsl.v1i2.24

Abstract

The rapid adoption of digital forensics in Muslim jurisdictions poses doctrinal and procedural dilemmas for the enforcement of hudud, the fixed punishments regulated by Islamic criminal law. Although classical jurists demanded near-absolute certainty, statutes now admit blockchain logs, DNA profiles, and geolocation data whose epistemic status is contested. This study investigates whether authenticated digital evidence, evaluated through a maqāṣid-aligned reliability matrix, preserves both procedural fairness and the deterrent mission of hudud. A convergent mixed-methods design combined doctrinal analysis with empirical testing of 210 criminal case files from Malaysia, Aceh, and Saudi Arabia (2015-2024). Reliability indices were computed for five evidence types; Bayesian updating estimated posterior guilt probabilities; interviews with 67 justice actors contextualised findings; cost–benefit metrics assessed restorative settlements. DNA profiles (mean RI = 0.91) and blockchain logs (0.87) achieved high evidentiary reliability, producing shubha deflection rates below 10 %. Geolocation data (0.74) and digital confessions (0.79) generated significantly higher doubt and conversion to taʿzīr. Restorative settlements delivered cost–benefit ratios above 1.1 and victim-satisfaction scores exceeding 78/100, particularly in Aceh, were digital monitoring enhanced compliance. Jurisdictions employing multidisciplinary verification panels recorded wrongful-conviction reversals below 4 %. The findings demonstrate that modern forensic artefacts can coexist with classical proof doctrines when governed by transparent authentication and probabilistic evaluation. Implementing a maqāṣid-based reliability matrix offers courts a principled route to align divine mandates, technological progress, and human-rights safeguards, thereby modernising Islamic criminal justice without compromising its ethical foundations, in diverse contexts worldwide.
Deep Reinforcement Learning-Based Control Architectures for Autonomous Maritime Renewable Energy Platforms Sura Sabah; Refat Taleb Hussain; Ismail Abdulaziz Mohammed; Haider Mahmood Jawad; Intesar Abbas; Taqwa Hariguna
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1209

Abstract

Autonomous vessels driven by renewable energy are increasingly envisioned as vital for sustainable ocean?operations such as environmental monitoring, offshore power generation, and long-haul unmanned surface vehicles. Implementing fine-scale control of these systems has proven challenging however,?due to time-varying sea-state dynamics, sporadic energy inputs, the possibility of failure at the component level, and the requirement for coordination between multiple agents. In the article, an end-to-end deep reinforcement learning-based hierarchical control solution with real-time navigation and?its synthesis for energy optimization is proposed. It combines high-level energy regulation with low-level actuator scheduling so as to react to the variations of?the environment and internal perturbations. Simulations using actual wave realizations, sensor failures, actuator outages, and network communication variation were used?to demonstrate the performance of the control system in the following 5 performance aspects: energy saving, navigation accuracy, communication reliability, fault tolerant and multi-agent coordination. Results indicate that the architecture sustained over 80% of the performance and achieved energy efficiencies up to 54.5% in the?best case under failure scenarios. Performance-measures demonstrated reasonable scalability?up to 5–7 agents without significant communication overhead. The findings support the applicability of deep reinforcement learning for real-time maritime control under uncertainty, offering a viable alternative to conventional rule-based or predictive control strategies. The framework’s modular design allows for future integration with federated learning, hybrid control models, or autonomous deployment. The article contributes to the growing field of intelligent marine systems by providing a robust and adaptable control strategy for sustainable and scalable operations in autonomous maritime environments.
Co-Authors Intesar Abbas Ismail Abdulaziz Mohammed Refat Taleb Hussain Sura Sabah Taqwa Hariguna