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All Journal Journal of Advanced Computer Knowledge and Algorithms
Wahid Samadzai, Abdul
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Computer Science & IT

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Journal : Journal of Advanced Computer Knowledge and Algorithms

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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
Adoption of Cloud-Based Accounting Software in Afghanistan Medium-Sized Enterprises Hasas, Ansarullah; Wahid Samadzai, Abdul
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.v2i4.23156

Abstract

Cloud-based accounting software adoption has emerged as a critical factor in enhancing financial management and operational efficiency among medium-sized enterprises (MSEs) worldwide. In Afghanistan, where digital transformation is still evolving, understanding the factors influencing cloud accounting adoption is essential for driving sustainable business growth. This study investigates the awareness, motivations, barriers, and adoption patterns of cloud-based accounting software within Afghan MSEs across diverse sectors. Employing a quantitative research design grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology–Organization–Environment (TOE) frameworks, data were collected via structured online questionnaires from 50 key decision-makers in five representative companies. Descriptive and inferential statistical analyses were conducted using SPSS to examine adoption drivers and challenges. Findings reveal that while awareness and perceived benefits such as cost-efficiency and real-time access positively influence adoption, significant barriers including security concerns, inadequate infrastructure, and limited technical expertise hinder broader uptake. Sectoral differences further highlight variability in adoption readiness. The study underscores the importance of tailored strategies to enhance infrastructure, provide targeted training, and develop supportive regulatory frameworks to foster cloud accounting adoption in Afghanistan. These insights offer practical recommendations for policymakers and business leaders aiming to accelerate digital financial transformation within the country’s MSE sector. Limitations related to sample size and geographic focus are acknowledged, with suggestions for future research to explore rural contexts and longitudinal adoption trends.
Co-Authors Hasas, Ansarullah Shahbazi, Hafizullah Shahidi, Shahwali