Article Per Year (5 Year)
p-Index From 2021 - 2026
1.318
P-Index
This Author published in this journals
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 Journal of Moeslim Research Technik Journal of Selvicoltura Asean Journal of Loomingulisus ja Innovatsioon Techno Agriculturae Studium of Research
Tariq, Usman
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Other

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
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.
Application of Model Predictive Control (MPC) in Industrial Automation Robotic Systems Aslam, Bilal; Tariq, Usman; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1566

Abstract

The industrial automation sector is rapidly evolving, with a growing need for advanced control strategies to enhance the efficiency and precision of robotic systems. Model Predictive Control (MPC) has emerged as a promising approach due to its ability to handle multivariable control problems and constraints effectively. However, its application in robotic automation remains underexplored. This research aims to implement Model Predictive Control in industrial robotic systems to improve performance, adaptability, and operational efficiency. The study focuses on evaluating the effectiveness of MPC in real-time robotic applications, specifically in tasks requiring high precision and dynamic response. A simulation-based approach was employed, using a robotic arm model as a testbed for implementing MPC. The control algorithm was designed to predict future states of the system based on current measurements and optimize control inputs accordingly. Performance metrics, including tracking error and response time, were evaluated under various operational scenarios. The implementation of MPC resulted in a significant reduction in tracking error and improved response times compared to traditional control methods. The robotic arm demonstrated enhanced adaptability to changes in the environment and task requirements, showcasing the robustness of the MPC approach. The findings indicate that Model Predictive Control is an effective strategy for enhancing the performance of robotic systems in industrial automation. The successful application of MPC not only improves operational efficiency but also provides a framework for future research into more complex robotic applications. This study contributes to the growing body of knowledge on advanced control methods in automation.  
Analysis of Factors that Influence the Implementation of Technological Innovation in the Indonesian Public Sector Aslam, Bilal; Tariq, Usman; Ahmad, Omar
Journal of Loomingulisus ja Innovatsioon Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v1i6.1713

Abstract

The implementation of technological innovation in the public sector is a critical driver of efficiency, transparency, and service delivery in many countries, including Indonesia. Despite the increasing importance of technology in public administration, the adoption and effective implementation of technological innovations in the Indonesian public sector face various challenges, including organizational resistance, inadequate infrastructure, and limited digital skills. This study aims to analyze the factors that influence the successful implementation of technological innovations in Indonesian public institutions. Using a mixed-methods approach, the research integrates qualitative interviews with key public sector managers and quantitative surveys of public sector employees to identify the critical factors that facilitate or hinder the adoption of technology. The findings highlight that leadership commitment, organizational culture, availability of resources, and training programs are significant drivers of successful technological implementation. Conversely, barriers such as budget constraints, political factors, and a lack of technical expertise were found to limit the effectiveness of technological innovation. The study concludes that for successful technological innovation in the Indonesian public sector, it is crucial to focus on strengthening leadership, fostering a supportive organizational culture, and investing in infrastructure and training programs. These findings provide practical recommendations for policymakers and public sector managers in Indonesia.
Innovative Water-Saving Irrigation Technology for Agriculture in Arid Regions of South Africa Tariq, Usman; Franchitika, Rizky; Minho, Kim
Techno Agriculturae Studium of Research Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i1.1988

Abstract

Agriculture in the arid regions of South Africa faces major challenges related to water scarcity, which worsens the sustainability of the sector. Water-efficient irrigation technology has emerged as a potential solution to reduce water use and increase agricultural productivity. This study aims to evaluate the impact of water-saving irrigation technology on water use efficiency and crop yields in arid regions of South Africa. Quantitative and qualitative approaches were used in this study, involving 150 farmers as a sample, as well as questionnaire data analysis and in-depth interviews. The results of the study show that this technology is able to increase water use efficiency by up to 30%, increase crop yields by 20%, and reduce average operating costs by 15%. The conclusion of the study is that water-efficient irrigation technology plays an important role in improving the sustainability of agriculture in dry regions and can contribute to food security in South Africa. The adoption of this technology needs to be encouraged more widely through government support and training for farmers.
USING ARTIFICIAL INTELLIGENCE AND LIDAR DATA FOR HIGH-RESOLUTION FOREST INVENTORY AND ABOVE-GROUND BIOMASS ESTIMATION IN A SUMATRAN RAINFOREST Nofirman, Nofirman; Shah, Ahmed; Tariq, Usman
Journal of Selvicoltura Asean Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i4.2483

Abstract

Accurate quantification of forest carbon stocks is critical for global climate change mitigation initiatives like REDD+. Traditional forest inventory methods are often labor-intensive, costly, and limited in scale, particularly in complex tropical ecosystems such as the Sumatran rainforest. The integration of advanced remote sensing technologies and artificial intelligence (AI) offers a transformative potential for overcoming these limitations. This study aimed to develop and validate a high-resolution model for individual tree detection and above-ground biomass (AGB) estimation in a Sumatran rainforest by synergizing airborne LiDAR data with machine learning algorithms. High-density LiDAR data was acquired over a 10,000-hectare study area. Concurrently, extensive field inventory data from 150 plots were collected to serve as ground truth. A deep learning model, specifically a Convolutional Neural Network (CNN), was trained to perform individual tree crown delineation (ITCD) from the LiDAR-derived canopy height model. Tree-level metrics were then used as predictors in a Random Forest algorithm to estimate AGB, which was calibrated against field-measured biomass. The CNN model successfully identified individual trees with an accuracy of 92.4%. The subsequent Random Forest model demonstrated high predictive power for AGB estimation, yielding a strong coefficient of determination ( = 0.89) and a low Root Mean Square Error (RMSE) of 25.8 Mg/ha. The approach generated a high-resolution (1-meter) AGB map, revealing detailed spatial variations in carbon stock across the landscape. The fusion of AI and LiDAR data provides a highly efficient methodology for forest inventory and AGB mapping in dense tropical rainforests. This approach significantly enhances our capacity to monitor carbon dynamics, forest conservation and climate policy.
Co-Authors Ahmed, Irfan Arnes Yuli Vandika Aslam, Bilal Bashir, Ali Kashif Flores, Josefa Franchitika, Rizky Hussain, Sara Khan, Muhammad Attique Kobayashi, Riko Minho, Kim Nofirman, Nofirman Omar Ahmad Santos, Luis Sato, Haruka Shah, Ahmed Suzuki, Ren