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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science Sharia Oikonomia Law Journal Journal Emerging Technologies in Education
Tariq, Usman
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Other

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Deep learning for economic transformation: a parametric review Tariq, Usman; Ahmed, Irfan; Khan, Muhammad Attique; Bashir, Ali Kashif
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp520-541

Abstract

Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.
Bridging biosciences and deep learning for revolutionary discoveries: a comprehensive review Tariq, Usman; Ahmed, Irfan; Khan, Muhammad Attique; Bashir, Ali Kashif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp867-883

Abstract

Deep learning (DL), a pivotal artificial intelligence (AI) innovation, has dramatically transformed biosciences, aligning with the surge in complex data volumes to foster notable progress across disciplines such as genomics, genetics, and drug discovery. DL's precision and efficiency outmatch conventional methods, propelling advancements in biomedical imaging and disease marker identification. Despite its success, DL's integration into broader bioscience areas encounters hurdles including data scarcity, interpretability challenges, computational demands, and the necessity for ethical and regulatory considerations. Overcoming these obstacles is vital for DL to achieve its transformative potential fully. This review explores into DL's expanding role in biosciences, critically examining areas ripe for DL application and highlighting underexplored opportunities. It provides an insightful analysis of the algorithms that form the backbone of DL in biosciences, offering a thorough understanding of their capabilities. Ultimately, this paper aims to equip biotechnologists and researchers with the knowledge to leverage DL effectively, thereby enhancing the analysis of complex bioscience data and contributing to the field's future advancements.
Applying Augmented Reality for History Lessons in Japan Kobayashi, Riko; Sato, Haruka; Suzuki, Ren; Hussain, Sara; Tariq, Usman
Journal Emerging Technologies in Education Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jete.v3i2.2162

Abstract

Background. The integration of Augmented Reality (AR) into history education holds the potential to enhance student engagement and comprehension by providing immersive, interactive learning experiences. A mixed-methods approach was adopted, combining pre- and post-tests with interviews and focus groups. The findings suggest that AR can serve as a transformative tool in history education, bridging the gap between abstract content and lived experience.Purpose. This study investigated the effectiveness of AR-enhanced history lessons in Japanese high schools. A total of 200 students aged 15–18 participated in a quasi-experimental study, with one group receiving AR-based instruction and a control group continuing traditional methods. Method. Quantitative results showed a 25% improvement in historical knowledge among AR users versus 5% in the control group (p < 0.001). Qualitative feedback indicated higher engagement, improved retention, and greater enthusiasm toward history learning. Result. The findings indicate a significant increase in student engagement and understanding of historical events, with 85% of students reporting improved retention and a deeper understanding of history. Teachers noted a positive shift in students’ enthusiasm for learning history.Conclud. AR technology enhances history education by providing immersive and interactive learning experiences, leading to greater student engagement and better knowledge retention.  
Shariah Law and Economic Justice: Analyzing the Impact of Zakat on Income Distribution in Indonesia Flores, Josefa; Santos, Luis; Tariq, Usman
Sharia Oikonomia Law Journal Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/solj.v3i1.2085

Abstract

Zakat, one of the five pillars of Islam, is a mandatory form of almsgiving aimed at redistributing wealth and promoting economic justice. In Indonesia, the world’s largest Muslim-majority country, zakat has the potential to significantly impact income distribution and reduce poverty. However, its effectiveness is often hindered by inefficiencies in collection, distribution, and utilization. This study examines the impact of zakat on income distribution in Indonesia, focusing on its role in reducing economic inequality and promoting social welfare. The research aims to identify the challenges and opportunities associated with zakat management and propose strategies for enhancing its effectiveness. Using a mixed-methods approach, this study combines quantitative analysis of income distribution data with qualitative interviews with zakat institutions, beneficiaries, and policymakers. Data were analyzed to assess the impact of zakat on poverty alleviation, income inequality, and economic empowerment. The findings reveal that zakat has a modest but positive impact on income distribution, particularly in rural areas. However, inefficiencies in collection and distribution, as well as a lack of transparency, limit its potential to achieve broader economic justice. The study concludes that improving zakat management through better governance, transparency, and targeted distribution strategies is essential for maximizing its impact on income distribution. This research contributes to the discourse on Islamic economics by providing practical recommendations for enhancing the role of zakat in promoting economic justice and social welfare in Indonesia.
Co-Authors Ahmed, Irfan Bashir, Ali Kashif Flores, Josefa Hussain, Sara Khan, Muhammad Attique Kobayashi, Riko Santos, Luis Sato, Haruka Suzuki, Ren