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All Journal Journal of Advanced Computer Knowledge and Algorithms JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Nashir, Asmatullah
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Computer Science & IT

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The Role of Deep Learning in Advancing Computer Vision Applications: A Comprehensive Systematic Review Khadem, Najibullah; Nashir, Asmatullah; Rahmatyar, Shamsullah
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 1 (2026): Journal of Advanced Computer Knowledge and Algorithms - January 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i1.24732

Abstract

Deep learning has emerged as a transformative technology in computer vision, enabling significant advancements in tasks such as image classification, object detection, segmentation, and anomaly detection across diverse domains, including healthcare, agriculture, robotics, and industrial automation. Despite these advancements, challenges related to model interpretability, data scarcity, generalization, computational demands, and real-time deployment remain significant barriers. This study aims to systematically review and analyze recent developments in deep learning techniques applied to computer vision, identify associated challenges and research gaps, and propose potential directions to enhance the efficiency, robustness, and applicability of these systems. A comprehensive literature search was conducted across multiple reputable databases, including ScienceDirect, SpringerLink, IEEE Xplore, MDPI, and Wiley Online Library, focusing on peer-reviewed articles published between 2018 and 2025. Thematic analysis and descriptive synthesis were applied to extract insights regarding deep learning architectures, application domains, datasets, key findings, and limitations. Results indicate that Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformer-based architectures, and hybrid models have significantly advanced computer vision applications. However, issues such as interpretability, data scarcity, and computational complexity persist. Future directions include lightweight architectures, transfer learning, federated learning, explainable AI, and multi-modal approaches. In conclusion, while deep learning has substantially improved computer vision capabilities, addressing current limitations is essential for broader real-world adoption and multi-domain applicability.
Simulasi Pengendalian Lift Menggunakan Manajemen Logika Fuzzy Qasimi, Mehr Ali; Nashir, Asmatullah
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 3 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i3.8974

Abstract

The use of a permanent magnet synchronous motor as an actuator in a ropeless elevator presents a number of difficulties that must be overcome for the system to be secure and stable. Detent force, one issue with stabilization systems, will be examined in terms of how well it functions under a fuzzy logic controller using a nonlinear test like changes in load and distance to obtain a policy suitable for application in the industrial sector or other human endeavors. The elevator technologies are designed to provide the necessary passenger floors while taking into account the highest standards of elevator performance and passenger pleasure. This work addresses the problem by developing an elevator group controller using a fuzzy algorithm. This project is designed to handle the necessary passenger traffic density while maintaining acceptable passenger waiting times by integrating a fuzzy controller into an elevator system. Within a set of fuzzy rules, three important linguistic variables are added to improve the performance of the elevator group. These consist of load capacity, priority, distance, and average waiting time (AWT). The necessity of floor priority is lessened when there is a great volume of passenger traffic; instead, the expected arrival time should be decreased. While the real elevator prototype is being programmed using a PIC microcontroller acting as a controller, the simulation was completed to visually verify the fuzzy system's priority. Thus, a set of ambiguous guidelines was developed based on real-world issues, primarily the reduction of waiting times and energy usage. The elevator controller will select which elevator will service which incoming hall request when a few are registered. In order to maximize efficiency for financial reasons, high-rise buildings and the ensuing large number of elevators they require provide a significant logistical challenge in terms of time and space conservation. In order to run the elevators properly, complex elevator group control systems are built.
Co-Authors Khadem, Najibullah Qasimi, Mehr Ali Rahmatyar, Shamsullah