Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal Journal of Social Science Utilizing Technology Journal of Advanced Computer Knowledge and Algorithms Journal of Social Science Utilizing Technology
Shahidi, Shahwali
Unknown Affiliation
Religion Arts Humanities Computer Science & IT Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : journal of advanced computer knowledge and algorithms

 1 
Effective Data Preprocessing in Data Science: From Method Selection to Domain-Specific Optimization Shahidi, Shahwali; Wahid Samadzai, Abdul; Shahbazi, Hafizullah
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i4.22886

Abstract

In the era of big data and artificial intelligence, data preprocessing has emerged as a critical step in the data science pipeline, influencing the quality, performance, and reliability of machine learning models. Despite its importance, the diversity of techniques, challenges, and evolving practices necessitate a structured understanding of this domain. This study conducts a systematic literature review (SLR) to explore current data preprocessing techniques, their domain-specific applications, associated challenges, and emerging trends. A total of 21 peer-reviewed articles from 2016 to 2024 were analyzed using well-defined inclusion and exclusion criteria, with a focus on machine learning and big data contexts. The results reveal that normalization, data cleaning, feature selection, and dimensionality reduction are the most commonly applied techniques. Key challenges identified include handling missing values, high dimensionality, and imbalanced data. Moreover, recent trends such as automated preprocessing (AutoML), privacy-preserving methods, and scalable preprocessing for distributed systems are gaining momentum. The review concludes that while traditional methods remain foundational, there is a shift toward adaptive and intelligent preprocessing strategies to meet the growing complexity of data environments. This study offers valuable insights for researchers and practitioners aiming to optimize data preparation processes in modern data science workflows
Challenges and Opportunities of Implementing Augmented Reality (AR) and Virtual Reality (VR) in Public Universities of Afghanistan Shahidi, Shahwali; Ali Frugh, Qurban; Kror Shahidzay, Amir
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i4.22962

Abstract

This study explores the challenges and opportunities related to the implementation of Augmented Reality (AR) and Virtual Reality (VR) technologies in public universities in Afghanistan, with a focus on Kabul University. The research aims to assess the levels of awareness, perceived usefulness, readiness to adopt, and barriers affecting AR/VR integration in higher education. Using a quantitative research design, data were collected from 392 respondents comprising students, faculty, and administrative staff through a structured questionnaire. Descriptive analysis showed moderate awareness and positive perceptions of AR/VR’s potential benefits for enhancing learning. Inferential statistics revealed significant associations between respondents’ roles and their willingness to adopt AR/VR, as well as a strong positive relationship between digital literacy and perceived usefulness. Regression analysis identified awareness, digital literacy, and institutional support as key predictors of adoption readiness. The study highlights existing infrastructural and digital literacy challenges but emphasizes the promising potential of AR/VR to transform educational experiences in Afghan universities. The findings provide valuable insights for stakeholders aiming to promote innovative educational technologies in similar contexts.
Co-Authors Ali Frugh, Qurban Amiri, Ghulam Ali Darmel, Farid Ahmad Jalalzai, Safiullah Kror Shahidzay, Amir Shahbazi, Hafizullah Wahid Samadzai, Abdul