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All Journal Journal of Community Service and Society Empowerment Journal of Advanced Computer Knowledge and Algorithms EDUTREND: Journal of Emerging Issues and Trends in Education
Shahbazi, Hafizullah
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Exploring the Impact of Artificial Intelligence on Women's Empowerment: A Comprehensive Survey Shahbazi, Hafizullah; Hakimi, Musawer; Ulusi, Helena; Rahimi, Behnaz; Quraishi, Tamanna
Journal of Community Service and Society Empowerment Том 2 № 03 (2024): Journal of Community Service and Society Empowerment
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/jcsse.v2i03.856

Abstract

Artificial intelligence (AI) has the capacity to greatly empower women and promote gender equality on a global scale. However, in order to effectively utilise AI to promote women's empowerment, it is crucial to have a comprehensive comprehension of its influence, possibilities, and difficulties. This study examines the various aspects of how AI contributes to the advancement of women's empowerment. It explores the extent to which AI is integrated into initiatives aimed at empowering women, the perceived impact of AI on women's empowerment on a global scale, and the obstacles women face in accessing AI opportunities. An integrated research methodology, including of surveys and literature evaluation, was utilised to collect data from a diverse sample of 88 people. The results indicate a substantial degree of AI incorporation in projects aimed at empowering women, with varying perspectives on the impact of AI. Additionally, the study revealed difficulties in accessing AI opportunities and observed differing levels of knowledge among women. This study highlights the significance of ethical issues and inclusive policies in utilising AI to promote women's empowerment. The findings provide significant knowledge for policymakers, researchers, and practitioners who aim to utilise AI's revolutionary capacity to promote gender equality and empower women on a global scale.
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
Exploring the Impact of Artificial Intelligence on Women's Empowerment: A Comprehensive Survey Shahbazi, Hafizullah; Hakimi, Musawer; Ulusi, Helena; Rahimi, Behnaz; Quraishi, Tamanna
EDUTREND: Journal of Emerging Issues and Trends in Education Vol. 1 No. 2 (2024): EDUTREND: Journal of Emerging Issues and Trends in Education
Publisher : Lembaga Riset dan Inovasi Masyarakat Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59110/edutrend.333

Abstract

Artificial intelligence (AI) can significantly empower women and promote gender equality globally. However, to effectively utilize AI to promote women's empowerment, it is crucial to comprehend its influence, possibilities, and difficulties comprehensively. This study examines the various aspects of how AI contributes to the advancement of women's empowerment. It explores the extent to which AI is integrated into initiatives aimed at empowering women, the perceived impact of AI on women's empowerment on a global scale, and the obstacles women face in accessing AI opportunities. An integrated research methodology, including surveys and literature evaluation, was utilized to collect data from a diverse sample of 88 people. The results indicate a substantial degree of AI incorporation in projects aimed at empowering women, with varying perspectives on the impact of AI. Additionally, the study revealed difficulties in accessing AI opportunities and observed differing levels of knowledge among women. This study highlights the significance of ethical issues and inclusive policies in utilizing AI to promote women's empowerment. The findings provide significant knowledge for policymakers, researchers, and practitioners who aim to utilize AI's revolutionary capacity to promote gender equality and empower women globally.
Co-Authors Ezam, Zakirullah Hakimi, Musawer Quraishi, Tamanna Rahimi, Behnaz Rahmani, Khoshal Rahman Shahidi, Shahwali Ulusi, Helena Wahid Samadzai, Abdul