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All Journal Journal of Islamic Law and Legal Studies TechComp Innovations: Journal of Computer Science and Technology Societal Serve: Journal of Community Engagement and Services Edu Spectrum: Journal of Multidimensional Education
Tofig Hasanov
Dagestan State Agrarian University named after M.M. Dzhambulatov
Religion Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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 1 
Halal Economy and Consumer Protection Laws: A Legal-Economic Perspective Tofig Hasanov; Muhammad Rafi Thoriq; Sujoko Winanto; Nigar Aliyeva
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.v3i1.148

Abstract

The rapid growth of the global halal economy has positioned halal products and services as important contributors to international trade, economic development, and evolving consumer markets. This study investigates the role of halal consumer protection law in supporting the advancement of the halal economy from both legal and economic perspectives. Employing a qualitative approach with a juridical normative framework, this research examines regulatory structures, halal certification mechanisms, consumer protection principles, and the broader economic implications of halal governance. The findings indicate that effective halal consumer protection frameworks are essential for establishing legal certainty, preserving product authenticity, strengthening consumer trust, and improving market transparency. From an economic perspective, comprehensive halal regulations contribute to enhancing product competitiveness, expanding global market access, encouraging industrial innovation, attracting investment, and promoting sustainable economic growth. Nevertheless, this study highlights several ongoing challenges, including the lack of harmonization among international halal standards, limited regulatory enforcement capacity, the financial burden of certification processes for micro, small, and medium enterprises (MSMEs), and the increasing complexity of digital trade and cross-border e-commerce.
Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems Ravi Kumar Saidala; Umna Iftikhar; Tofig Hasanov; Vüqar Ahmad Mammadli
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 2 (2025): 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.v2i2.124

Abstract

This study examines the emerging paradigm of federated intelligence architectures as a secure, privacy-preserving, and scalable foundation for data-driven innovation across AI, IoT, and cloud ecosystems. With billions of interconnected devices generating massive heterogeneous data, traditional centralized machine-learning models face critical limitations, including privacy risks, regulatory constraints, latency, and single points of failure. Through a qualitative content-analysis approach, this paper synthesizes contemporary research on federated learning, blockchain integration, zero-trust governance, and edge intelligence to formulate a comprehensive understanding of distributed AI infrastructures. The findings highlight that federated learning enables collaborative model training without exposing raw data, significantly enhancing privacy, security, and compliance. Moreover, combining blockchain with federated learning strengthens auditability, model integrity, and trust, while zero-trust principles provide continuous verification and adaptive security enforcement across devices. Edge-AI integration further reduces latency and bandwidth consumption, enabling real-time analytics in resource-constrained IoT environments. Collectively, these elements contribute to the formation of cognitive ecosystems capable of autonomous, interoperable, and context-aware operations. The study underscores the transformative potential of federated intelligence while identifying critical gaps that inform future research trajectories.
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
Digital Innovation Strengthening Community-Centered Health Services through Technology Integration in Romanian Social Welfare Contexts Asma Elmar Mansurzada; Aytan Azizli; Tofig Hasanov
SocietalServe: Journal of Community Engagement and Services Vol 2 No 2 (2025): Societal Serve: Journal of Community Engagement and Services
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/societalserve.v2i2.134

Abstract

This community engagement program aims to strengthen community-centered health services in Romania by integrating digital innovations into existing social welfare systems. The initiative responds to persistent challenges in accessibility, service coordination, and information flow between local health providers and underserved populations. Through a structured intervention—including digital literacy workshops, development of simple data-collection tools, and collaborative planning sessions with community health workers—the program seeks to enhance local capacity for technology-supported service delivery. A mixed set of participatory and data-driven strategies was employed to ensure that technological solutions align with community needs and institutional capacities. Results indicate significant improvements in digital readiness among participants, increased accuracy of health-service reporting, and stronger collaboration between social welfare actors and community members. Feedback collected from participants highlights a high level of satisfaction with the practicality and relevance of the training sessions. Overall, the program demonstrates that integrating accessible technological innovations into community-based health systems can contribute to more responsive, efficient, and inclusive social welfare structures in Romania. The findings further underscore the importance of continued investment in digital competencies and collaborative governance to support sustainable health-service improvements in socioeconomically diverse contexts.
Data-Informed Professional Development for Strengthening Teacher Assessment Literacy in the Digital Era Hazhar Talaat Abubaker Blbas; Asma Elmar Mansurzada; Tofig Hasanov
Edu Spectrum: Journal of Multidimensional Education Vol. 2 No. 2 (2025): Edu Spectrum: Journal of Multidimensional Education
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/eduspectrum.v2i2.129

Abstract

This study examines how data-informed professional development can strengthen teacher assessment literacy amid the growing demands of the digital era. Using a qualitative design, the research combines library investigation and content analysis of scholarly literature published over the past decade related to digital assessment, teacher data literacy, and technology-supported instructional decision-making. Findings reveal that digital transformation in education requires teachers to master data interpretation skills, utilize technology-enhanced assessment tools, and develop ethical awareness regarding privacy and algorithmic bias. However, many teachers continue to face competency gaps, inadequate professional development, and challenges in integrating digital assessment practices effectively. The study highlights that meaningful professional development must be continuous, collaborative, evidence-based, and incorporate data literacy, AI literacy, and reflective pedagogical practice. Such an approach equips teachers to interpret complex data, design valid digital assessments, and make ethically responsible, evidence-driven instructional decisions. This research provides conceptual clarity and a foundation for designing comprehensive professional development frameworks aimed at enhancing teacher assessment literacy within rapidly evolving digital learning environments.
Co-Authors Amirkhan Pashayev Asma Elmar Mansurzada Aytan Azizli Hazhar Talaat Abubaker Blbas Muhammad Rafi Thoriq Nigar Aliyeva Ravi Kumar Saidala Sujoko Winanto Umna iftikhar Vüqar Ahmad Mammadli