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All Journal International Journal of Accounting & Finance in Asia Pasific Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Athavale, Vijay Anant
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Touch n Go e-Wallet: The New Payment Style Existed when COVID-19 Hits Izaidin, Muhammad Daniel Bin Ahmad; Athavale, Vijay Anant; Razak, Muhammad Danial Bin Abdul; Zain, Nafisa Hani Binti Mohamed; Ajiby, Najla Awatif Binti Aqimu’; Singh, Sakshi; Katkar, Yash Rajendra
International Journal of Accounting & Finance in Asia Pasific (IJAFAP) Vol 5, No 3 (2022): October 2022
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/ijafap.v5i3.1933

Abstract

Touch ‘n Go e-wallet is a smartphone application that has recently gained users since the pandemic of COVID-19 hits Malaysia. Touch ‘n Go is an e-wallet, an electronic card that can make online payments using a smartphone. It is a secure way to pay using a smartphone because it is convenient to use and reduces physical touch, which can spread diseases and germs to other people. The pandemic and the imposition of Movement Control Orders (MCO) and Home Quarantine have encouraged e-wallet usage, as people will choose cashless payments during that period. This study examines how e-wallets help consumers throughout the COVID-19 pandemic in Malaysia. A total of 150 consumers completed an online survey via Google Forms, and the data were analyzed using SPSS. We found that perceived ease of use and trust impacted consumer satisfaction. This research provides new insights on e-wallet perceptions of Touch n Go and how this perception may promote consumer satisfaction.Keywords: COVID-19, E-wallet, MCO, Pandemic, Physical touch, Smartphone, Touch n Go
Implementation of the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) Method to Address Class Imbalance in Alzheimer’s Disease Magnetic Resonance Imaging (MRI) Datasets Alamudin, Muhammad Faiq; Mazdadi, Muhammad Itqan; Nugroho, Radityo Adi; Saragih, Triando Hamonangan; Muliadi, Muliadi; Athavale, Vijay Anant
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.109

Abstract

Class imbalance in medical imaging datasets often leads to biased machine learning models, particularly in Alzheimer’s disease (AD) diagnosis using MRI. This study proposes the use of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to mitigate class imbalance in AD MRI datasets. Realistic MRI images were synthesized for underrepresented AD stages, and the quality of the generated data was quantitatively validatedusing the Fréchet Inception Distance (FID), with the lowest FID score recorded at 31.84, indicating a high degree of realism and diversity. The synthetic images were used to augment a dataset of 6,400 T1-weighted scans for training four Convolutional Neural Network (CNN) architectures: ResNet-50, AlexNet, VGG-16, and VGG-19. Results demonstrated statistically significant improvements in balanced accuracy across all models (p < 0.01 for all comparisons). The AlexNet + WGAN-GP combination achieved the highest accuracy of 98.54%, representing a mean improvement of 4.76% (95% CI: 2.45% to 6.98%) over its baseline. Significant gains were also observed for ResNet-50, VGG-16, and VGG-19. These enhancements were consistent across multiple evaluation metrics, including precision, recall, F1-score, and AUC. These findings confirm that WGAN-GP is a highly effective and statistically validated strategy for boosting the diagnostic accuracy of CNN models in Alzheimer's disease classification
Co-Authors Ajiby, Najla Awatif Binti Aqimu’ Alamudin, Muhammad Faiq Izaidin, Muhammad Daniel Bin Ahmad Katkar, Yash Rajendra Muhammad Itqan Mazdadi Muliadi Radityo Adi Nugroho Razak, Muhammad Danial Bin Abdul Saragih, Triando Hamonangan Singh, Sakshi Zain, Nafisa Hani Binti Mohamed