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All Journal ARMADA : Jurnal Penelitian Multidisiplin Current Educational Review
Shamsi, Sayed Ehsan
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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.
Internet Consumption Patterns and Their Influence on Emotional and Cognitive Well-Being Among Young Individuals in University Ayobi, Faridoon; Zada, Ahmad Farhad Rajab; Shamsi, Sayed Ehsan
Current Educational Review Vol. 2 No. 1 (2026): March
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/cer.v2i1.707

Abstract

The proliferation of internet connectivity has fundamentally transformed social, academic, and recreational behaviors among university students worldwide. This study investigates the nature of internet consumption patterns and their multifaceted influence on emotional and cognitive well-being among young university students. A cross-sectional survey was conducted with 646 undergraduate students (mean age = 20.6 years, SD = 1.82) recruited from four major Afghanistan universities. Validated instruments were administered, including the Warwick–Edinburgh Mental Well-Being Scale (WEMWBS), the Generalized Anxiety Disorder Scale (GAD-7), the Patient Health Questionnaire (PHQ-9), and a researcher-developed Internet Usage Pattern Inventory (IUPI). Data were analyzed using descriptive statistics, Pearson correlation, one-way ANOVA, and multiple linear regression. Results demonstrated that 61.1% of participants exceeded six hours of daily internet use, primarily for social media and entertainment. Significant negative correlations were found between daily usage duration and emotional well-being (r = −0.451, p < .001), cognitive concentration (r = −0.389, p < .001), and academic GPA (r = −0.389, p < .001). Conversely, purposeful educational use was positively associated with academic outcomes (β = +.214, p < .001). Nighttime screen exposure emerged as a critical mediator of sleep quality and subsequent mood disturbance. The regression model accounted for 46.7% of variance in well-being scores (R² = 0.467, F(5, 640) = 112.4, p < .001). These findings underscore the urgency of developing digital literacy interventions and university-level policies to cultivate healthier, more intentional internet consumption habits among young adults
Co-Authors Abdurahman Hakimi Alireza Khalilipour Ayobi, Faridoon Muhammadi, Muhammad Babur Zada, Ahmad Farhad Rajab