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All Journal Computer Science and Information Technologies JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) TechComp Innovations: Journal of Computer Science and Technology
Qasimi, Mehr Ali
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Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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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.
Solving the Travelling Salesman Problem Using the PSO Optimization Qasimi, Mehr Ali
TechComp Innovations: Journal of Computer Science and Technology Vol. 1 No. 1 (2024): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v1i1.20

Abstract

This article examines a potential solution to the well-known Travelling Salesman Problem (TSP), which is classified as an NP-hard problem. We also provide a theoretical synopsis of several approaches that have been employed to tackle this problem. A prominent example of a combinatorial problem is the traveling salesman problem (TSP). To address the fundamental PSO algorithm's premature convergence issue and stagnation behavior on TSP, a scout characteristic-based PSO algorithm is suggested.We address Particle Swarm Optimization (PSO), a member of the evolutionary methods class, and outline the methodology for applying PSO to the TSP. Among population-based metaheuristic optimization methods, Particle Swarm Optimization (PSO) is one of the most widely used. Scientific domains such as engineering, chemistry, medicine, advanced physics, and humanities have all effectively employed PSO. Numerous theoretical and empirical results on the convergence and parameterization of PSO versions have been produced as a result of the method's extensive investigation since its introduction in 1995. Hundreds of PSO versions have been developed. It is well recognized that population size has a significant impact on the effectiveness of metaheuristics; nevertheless, no comprehensive research has been done on the appropriate selection of PSO swarm size to date.Through the application of this approach, we examine the effects of various control settings. The ideal solution and the quality of the solution are contrasted.    
Advances in Parkinson’s disease diagnosis and treatment using artificial intelligence: a review Qasimi, Mehr Ali; Acar, Züleyha Yılmaz
Computer Science and Information Technologies Vol 7, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i1.p121-130

Abstract

Parkinson’s disease (PD) diagnosis and monitoring have significantly improved because to current advancements in artificial intelligence (AI), particularly in the areas of deep learning (DL) and machine learning (ML). Early-stage insensitivity of traditional diagnostic techniques necessitates the use of clever, data-driven alternatives. AI-powered noninvasive diagnostic methods like speech recognition, handwriting analysis, and neuroimaging categorization are the main topic of this technical review. We provide a summary of comparative performance measures from recent models, highlighting their practical usefulness, data modality, and accuracy. Also covered are important issues like data variability, real-world implementation, and model interpretability. Unlike prior surveys that primarily report accuracy metrics, this review explicitly focuses on identifying the gap between experimental AI performance and real-world clinical deployment, emphasizing interpretability, validation, and scalability challenges in PD diagnosis. The purpose of this letter is to provide guidance for researchers creating deployable and clinically valid AI systems for PD detection.
Co-Authors Acar, Züleyha Yılmaz Nashir, Asmatullah