Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal Journal of Islamic Law and Legal Studies TechComp Innovations: Journal of Computer Science and Technology
Amirkhan Pashayev
Azerbaijan University
Religion Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Law, Crime, Criminology & Criminal Justice

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A Sharia-Based Digital Farming Platform: Integrating Digital Technology for Efficient Zakat Distribution in Agriculture Masnur Putra Halilintar; Aulia Istiana Hidayat; Amirkhan Pashayev; Vicente Pironti
Journal of Islamic Law and Legal Studies Vol 3 No 1 (2026): Journal of Islamic Law and Legal Studies
Publisher : Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/jills.v3i1.138

Abstract

This study contributes to the advancement of Islamic social finance discourse by developing a conceptual framework for a digitally integrated zakat governance model within the agricultural sector. The study addresses a critical gap between productive zakat practices and the emerging landscape of digital agricultural transformation. While previous research has largely focused on the redistributive function of zakat as a mechanism for poverty alleviation, limited scholarly attention has been directed toward its potential transformation into a technology-enabled and productivity-oriented instrument that supports sustainable development objectives. Employing a qualitative research approach through content analysis, this study synthesizes interdisciplinary perspectives from Islamic economics, zakat governance, agricultural technology innovation, and digital sustainability studies. The study proposes a Digital Farming Zakat Platform framework consisting of five interconnected dimensions: geospatial-based beneficiary identification, smart farming empowerment, sharia-compliant financial mechanisms, agricultural market integration, and data-driven monitoring systems.
AI-Driven Cybersecurity Threat Detection Framework for Next-Generation Network Environments Ravi Kumar Saidala; Amirkhan Pashayev; Tofig Hasanov
TechComp Innovations: Journal of Computer Science and Technology Vol. 3 No. 1 (2026): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v3i1.184

Abstract

This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems
Co-Authors Aulia Istiana Hidayat Masnur Putra Halilintar Ravi Kumar Saidala Tofig Hasanov Vicente Pironti