Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal ARMADA : Jurnal Penelitian Multidisiplin International Journal of Social Sciences and Humanities
Alireza Khalilipour
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Neural Networks for Fault Detection and Diagnosis in Electronic Circuits Shamsi, Sayed Ehsan; Muhammadi, Muhammad Babur; Abdurahman Hakimi; Alireza Khalilipour
ARMADA : Jurnal Penelitian Multidisiplin Vol. 3 No. 11 (2025): ARMADA : Jurnal Penelitian Multidisplin, November 2025
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/armada.v3i11.1800

Abstract

The continuous development of electronic systems has made the analog, digital, and mixed-signal circuits more sophisticated, thus posing great difficulties to the existing fault detection and diagnosis (FDD) methods. Traditional methods are mostly non-scalable, cannot be adapted to different situations and cannot even sometimes recognize the same fault among various conditions. The present work is to compare the fault diagnosing performance of various models based on neural networks (NNs) in electronic circuits and to point out the NN architectures, optimizations and hybrid learning techniques that the FDD performance of the NN models. A thorough literature review study was done for 28 papers attesting the use of NNs in the circuit fault diagnosis written between the years 2016 and 2025 published in the scientific journals of IEEE Xplore, Springer, Elsevier, and MDPI. The types of neural network architectures, fault classification accuracy, noise and dynamics robustness, and benefits from optimization and feature extraction methods were the main aspects of the papers under review. The findings show that multi-valued neuron networks, conditional variational NNs, convolutional neural networks, denoising autoencoders, and optimized backpropagation models continuously outperform the traditional methods by acquiring higher accuracy, faster convergence and robust fault detection even in the most complex and demanding real-time environments. In addition, the training process is made easier and fault identification is made wider by optimization and hybrid learning approaches through improved training efficiency and multi-fault classification. Generally, neural network-based FDD offers an intelligent, adaptive, and resilient solution that has the power to revolutionize the development of future electronic systems with the characteristic of being smart and robust.
Impact of Key Factors on Student Satisfaction in Private Universities of Balkh Province: A Multivariate Linear Regression Analysis Qarizada, Abdulkhaliq; Mustafa Qader; Farhad Bahrangi; Alireza Khalilipour
International Journal of Social Sciences and Humanities Vol. 4 No. 1 (2026): International Journal of Social Sciences and Humanities
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/ijssh.v4i1.1778

Abstract

This study investigates the impact of key factors on overall student satisfaction in private universities in Balkh Province, Afghanistan. Five primary factors teaching quality, educational facilities, administrative management, social environment, and costs were examined as independent variables. Data were collected from 350 students using a standardized questionnaire and analyzed through multivariate linear regression in SPSS.  The statistical analysis revealed that the model explains 97.7% of the variance in overall student satisfaction, indicating high explanatory power. Findings suggest that teaching quality, educational facilities, and administrative management have a significant positive impact on student satisfaction, while social environment and costs showed no significant effect. Among these, teaching quality emerged as the strongest predictor, playing a pivotal role in enhancing students’ academic experience and motivation. These results align with prior research and underscore the importance of improving educational processes and efficient management to elevate student satisfaction. Given the local context of Afghanistan and post-COVID-19 challenges, this study introduces novelty by exploring the influence of digital technology on satisfaction factors. Based on the findings, it is recommended that universities prioritize faculty development, upgrade educational facilities, and streamline administrative systems to enhance educational quality and student experience. Future research should adopt a broader approach to examine the role of costs, financial equity in education, and digital factors in student satisfaction.
Co-Authors Abdurahman Hakimi Farhad Bahrangi Muhammadi, Muhammad Babur Mustafa Qader Qarizada, Abdulkhaliq Shamsi, Sayed Ehsan