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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Civil Engineering Journal Indonesian Journal of Electrical Engineering and Computer Science
Ahmed, Irfan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering

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Macroscopic Traffic Characterization Based on Distance Headway Iftikhar, Amir; Khan, Zawar H.; Aaron Gulliver, T.; Khattak, Khurram S.; Ahmed, Irfan
Civil Engineering Journal Vol 10, No 12 (2024): December
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-12-016

Abstract

Accurate traffic characterization is essential for congestion mitigation. In this paper, a traffic model is proposed that incorporates distance headway in the well-known Lighthill, Whitham, and Richards (LWR) model. Velocity is influenced by the headway distance between vehicles. When this distance is small, the velocity is low, and when it is large, the velocity is high. The proposed and LWR models are implemented in MATLAB, and the performance is evaluated for different values of distance headway. The results show that traffic with the proposed model evolves with smaller changes that are more accurate and realistic than with the LWR model. Doi: 10.28991/CEJ-2024-010-12-016 Full Text: PDF
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.
Co-Authors Aaron Gulliver, T. Bashir, Ali Kashif Iftikhar, Amir Khan, Muhammad Attique Khan, Zawar H. Khattak, Khurram S. Tariq, Usman