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All Journal International Journal of Engineering, Science and Information Technology International Journal of Sharia and Law
Mahmood Jawad Abu-AlShaeer
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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From Qiyas to Quantification: Reimagining Evidentiary Standards in Islamic Law through Statistical Methodologies Mahmood Jawad Abu-AlShaeer
International Journal of Sharia and Law Vol. 1 No. 1 (2025)
Publisher : Qiyam Islamic Studies Center Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65211/ybkj5j81

Abstract

Islamic jurisprudence traditionally relies on textual interpretation, analogical reasoning (qiyās), and scholarly consensus to derive legal judgments. However, in contemporary legal systems, particularly in domains such as forensic science, financial litigation, and family law, the need for empirical and objective evidentiary standards is increasing. This necessitates a reconsideration of classical epistemological tools in Islamic law. This article aims to explore how statistical reasoning and probabilistic inference can serve to modernize and complement traditional Islamic evidentiary principles. It aims to identify whether these tools can offer a more precise, replicable, and just framework without compromising the ethical integrity of Shariʿah. A doctrinal and comparative analysis was conducted, incorporating classical legal maxims and statistical inference models. Empirical case studies from Islamic courts and hybrid legal systems were evaluated alongside predictive models such as Bayesian probability, error rate thresholds, and likelihood ratios. The methodology also utilized textual hermeneutics to explore maqāṣid al-Sharīʿah compliance. Integration of statistical inference mechanisms—particularly in the domain of hudūd, tazīr, and personal status cases—indicates a measurable enhancement in judicial consistency and reduction in evidentiary ambiguity. Courts that applied forensic and data-driven models exhibited lower reversal rates and increased public confidence, while remaining compliant with foundational Sharīʿah values when guided by juristic oversight. Incorporating statistical methodologies into Islamic legal procedures does not replace traditional methods but rather reinforces them with quantifiable validity. This evolution can provide a coherent framework for contemporary challenges while remaining aligned with the core objectives of justice, fairness, and social welfare as enshrined in Islamic jurisprudence.
Context-Aware Systems for Proactive Energy Efficiency Services Maan Hameed; Nabaa Ahmed Noori; Aghaid Khudr Suleiman; Mahmood Jawad Abu-AlShaeer; Ahmed Sabah; M. Batumalay
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1728

Abstract

Static energy control systems are increasingly unable to meet the demands of modern built environments, where dynamic occupancy and fluctuating conditions lead to significant inefficiencies. This paper presents a context-aware system for proactive energy management that integrates real-time data acquisition, machine learning-based forecasting, and autonomous control. A multi-tiered architecture was developed and deployed across diverse settings residential, commercial, and industrial—to gather contextual data on temperature, occupancy, lighting, and equipment usage. The system uses predictive forecasting to anticipate short-term energy needs and reinforcement learning to optimize control strategies, ensuring both energy savings and user comfort. Results from the deployment demonstrate significant power reduction, high system responsiveness, and strong user satisfaction. Application-specific benchmarks revealed major efficiency gains in HVAC, lighting, and industrial machinery, while scalability tests confirmed stable performance under increasing sensor loads. This research validates the effectiveness of combining contextual intelligence with adaptive control to create sustainable, responsive, and human-centered energy systems. We provide a practical, modular framework for intelligent energy infrastructure in smart buildings and industrial parks. Future work will focus on enhancing model interpretability, integrating economic incentives, and exploring federated learning for distributed intelligence in support of energy efficiency.
Co-Authors Aghaid Khudr Suleiman Ahmed Sabah M. Batumalay Maan Hameed Nabaa Ahmed Noori