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This Author published in this journals
All Journal Nusantara Hasana Journal International Journal of Management Science and Information Technology (IJMSIT) Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert
Amir Kror Shahidzay
Kabul University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Nursing

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Evaluation of High-Tech Projects Failure Factors in Afghanistan Mohammad Hanif Gharanai; Amir Kror Shahidzay; Faqeed Ahmad Sahnosh
International Journal of Management Science and Information Technology Vol. 5 No. 1 (2025): January - June 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v5i1.3318

Abstract

Afghanistan ranks among the unfortunate low-income countries lacking viable and consistent electronic government systems, policies, and infrastructure for project execution. Despite the country's adoption of technology, digital systems and ICT projects remain inefficient and prone to vulnerabilities, hindering the delivery of reliable services to both the Afghan government and its citizens. This research assesses various technology-based projects to uncover the underlying causes of failures. The study’s main goals include pinpointing these root causes, exploring the link between policies and ICT initiatives, evaluating platforms and services, and offering recommendations to enhance the environment for the effective and timely execution of ICT projects. A focus on user-centered design is maintained throughout the research process, which employs a mixed-methods approach. This study provides valuable insights for both private and public sectors to implement projects more effectively. We aim for this research to shed light on the factors leading to failures during the planning, design, and implementation phases of projects, encouraging a reflective approach moving forward.
A COMPREHENSIVE REVIEW OF BIAS IN AI ALGORITHMS Abdul Wajid Fazil; Musawer Hakimi; Amir Kror Shahidzay
Nusantara Hasana Journal Vol. 3 No. 8 (2024): Nusantara Hasana Journal, January 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v3i8.1052

Abstract

This comprehensive review aims to analyze and synthesize the existing literature on bias in AI algorithms, providing a thorough understanding of the challenges, methodologies, and implications associated with biased artificial intelligence systems.Employing a narrative synthesis and systematic literature review approach, this study systematically explores a wide array of sources from prominent databases such as PubMed, Google Scholar, Scopus, Web of Science, and ScienceDirect. The inclusion criteria focused on studies that distinctly defined artificial intelligence in the education sector, were published in English, and underwent peer-review. Five independent reviewers meticulously evaluated search results, extracted pertinent data, and assessed the quality of included studies, ensuring a rigorous and comprehensive analysis. The synthesis of findings reveals pervasive patterns of bias in AI algorithms across various domains, shedding light on the nuanced aspects of discriminatory practices. The systematic review highlights the need for continued research, emphasizing the intricate interplay between bias, technological advancements, and societal impacts. The comprehensive analysis underscores the complexity of bias in AI algorithms, emphasizing the critical importance of addressing these issues in future developments. Recognizing the limitations and potential consequences, the study calls for a concerted effort from researchers, developers, and policymakers to mitigate bias and foster the responsible deployment of AI technologies. Based on the findings, recommendations include implementing robust bias detection mechanisms, enhancing diversity in AI development teams, and establishing transparent frameworks for algorithmic decision-making. The implications of this study extend beyond academia, informing industry practices and policy formulations to create a more equitable and ethically grounded AI landscape.
IoT-Based Remote Patient Monitoring Systems in Healthcare: A Systematic Literature Review Baz Mohammad Saifi; Amir Kror Shahidzay
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 3 (2026): Journal of Advanced Computer Knowledge and Algorithms - July 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i3.27715

Abstract

The Internet of Things (IoT) has emerged as a transformative technology in modern healthcare systems, particularly in enabling Remote Patient Monitoring (RPM). IoT-based RPM systems facilitate continuous real-time monitoring of patients through wearable sensors, communication networks, and cloud-based platforms, thereby improving healthcare accessibility and quality of care. This study aims to systematically review and synthesize the existing literature on IoT-based Remote Patient Monitoring systems, focusing on their architectures, application domains, benefits, challenges, research trends, and future directions. A systematic literature review methodology was adopted to identify relevant studies from major scientific databases, including IEEE Xplore, SpringerLink, PubMed, ScienceDirect, and Google Scholar. The search covered publications from 2020 to 2025. A total of 573 articles were initially identified, and 35 studies meeting the predefined inclusion criteria were selected for final analysis. The selected studies were analyzed across multiple dimensions, including system architectures, application domains, benefits, challenges, and emerging research trends. The findings indicate that IoT-based RPM systems significantly improve healthcare delivery by enabling continuous real-time monitoring, enhancing patient outcomes, and reducing healthcare costs. However, several challenges remain, including interoperability issues, wearable device energy consumption, data security and privacy concerns, and sensor reliability. Emerging trends include the integration of artificial intelligence, edge computing, and blockchain technologies into healthcare IoT systems. This review identifies several important research gaps, including the lack of standardized frameworks, limited real-world implementation, and insufficient focus on low-resource healthcare environments. The findings provide valuable insights for researchers and practitioners and offer guidance for future research aimed at developing secure, scalable, and efficient IoT-based healthcare systems.
Exploring the Opportunities and constraint of E-Commerce Technology implementation Among Afghan Entrepreneurs Barialay Raufi; Amir Kror Shahidzay
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 3 (2026): Journal of Advanced Computer Knowledge and Algorithms - July 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i3.28189

Abstract

This study investigates the opportunities and barriers associated with e-commerce technology adoption among Afghan entrepreneurs operating in a fragile, infrastructure-constrained economy. A quantitative, descriptive research design was employed using a structured, Technology Acceptance Model-based questionnaire distributed through Google Forms to online traders and technology professionals in Kabul City and several provincial centers. Of 410 individuals contacted, 210 valid responses were obtained (N = 210), and the instrument demonstrated high internal consistency (Cronbach's alpha = 0.812). Descriptive statistics, multiple linear regression, ANOVA and Pearson correlation were computed using SPSS. The results show near-universal recognition of technology as an opportunity for business growth (99.1%), competitiveness (97.2%) and time savings (96.2%), with social media platforms serving as the dominant commercial channel (93.3%). At the same time, respondents reported severe structural barriers: absence of internet in remote areas (94.7%), weak cybersecurity (91.9%), high connectivity costs (91.9%), low public trust (88.6%) and limited digital awareness (88.1%). Regression analysis showed that perceived technology benefits explained only 19.0% of the variance in business growth (R² = 0.190), while barrier factors explained 10.1% (R² = 0.101). Correlation analysis identified technology impact as the strongest predictor of growth (r = 0.670), while technical weaknesses and technology-skill gaps were very strongly correlated (r = 0.814). The findings suggest that infrastructure investment, cybersecurity legislation and digital-literacy programs are prerequisites for translating entrepreneurial enthusiasm into measurable e-commerce growth in Afghanistan.
Adopting Big Data Technologies in Telecommunications: A Case Study of ATOMA, Afghanistan Sadiq Aminzai; Amir Kror Shahidzay
Gameology and Multimedia Expert Vol. 3 No. 2 (2026): Gameology and Multimedia Expert - April 2026
Publisher : Department of Informatics Faculty of Engineering Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/game.v3i2.26811

Abstract

The telecommunications sector generates vast and rapidly growing data volumes, rendering traditional Relational Database Management Systems (RDBMS) insufficient for modern analytical demands. This paper investigates ATOMA's (Advanced Telecom Operations and Mobility of Afghanistan) strategic transition from a legacy Oracle-based data warehouse to a distributed Big Data ecosystem comprising Hadoop, Apache Spark, and Apache Kafka. Drawing on qualitative case study methodology, data were collected from 15 purposively selected IT professionals across eight functional teams using a structured questionnaire. Thematic analysis was conducted through the Technology-Organization-Environment (TOE) framework and the Migration Lifecycle Model. Findings reveal that ATOMA's primary migration drivers include Oracle scalability limitations, batch-reporting inefficiencies, missing Call Detail Records (CDRs), absence of real-time analytics, and cost reduction imperatives. Participants identified data migration complexity, skill gaps, system integration challenges, and change management as the most significant barriers. Anticipated benefits across all teams consistently highlighted real-time reporting, improved fraud detection, enhanced customer analytics, and open-source cost optimization. The paper proposes a Phased Big Data Adoption Framework (PBAF) tailored to telecom operators in fragile, resource-constrained environments comprising five stages: Assessment, Pilot, Hybrid Operation, Full-Scale Deployment, and Optimization. Findings are directly applicable to telecom operators in emerging markets facing analogous legacy-system migration challenges.
Co-Authors Abdul Wajid Fazil Barialay Raufi Baz Mohammad Saifi Faqeed Ahmad Sahnosh Mohammad Hanif Gharanai Musawer Hakimi Sadiq Aminzai