Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Dawod, Ahmad Yahya
Unknown Affiliation
Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Correlation between capital markets and cryptocurrency: impact of the coronavirus Ariya, Kanyawut; Chanaim, Somsak; Dawod, Ahmad Yahya
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6637-6645

Abstract

The objective of the study is to use daily Thai data analysis to strengthen correlations between Bitcoin and conventional asset measurements. The most popular asset prices and indices include gold, oil, the SET50 index, Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), Dashcoin (DASH), Stellar Lumens (XLM), Binance coin (BNB), and Dogecoin (DOGE). We find a significant correlation between cryptocurrencies and the digital economy using a matrix approach to the Pearson correlation coefficient. With the help of a minimal spanning tree model and random matrix theory, we can determine the shortest route between assets. Yet, as predicted, only a small percentage of the greatest eigenvalues diverge. We are also developing a novel technique to find the SET-50 index. In an investment portfolio during the coronavirus period, alternatives to the gold price and the DOGE may offer possibilities for risk diversification.
Multi-channel microseismic signals classification with convolutional neural networks Shu, Hongmei; Dawod, Ahmad Yahya; Tepsan, Worawit; Mou, Lei; Tang, Zheng
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1038-1049

Abstract

Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
TRANSFORMATION OF DIGITAL INNOVATION IN EDUCATION IN THE POST-COVID ERA: AN EXPLORATION CENTERED ON DRONES AND VIRTUAL REALITY Lu, Jiaxin; Dawod, Ahmad Yahya; Ying, Fangli
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 2 (2024): Volume 8, Nomor 2, December 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i2.37133

Abstract

The relevance of this study is underscored by the significant shifts in traditional methods of teaching software disciplines in the post-COVID era. The widespread adoption of remote learning and integrating innovative technologies have necessitated re-evaluating educational practices. This study aims to explore the impact of modern innovative technologies on the effectiveness of education and the quality of learning in the post-COVID period. The primary method employed in this study was a survey, through which questions related to the use of drones and virtual reality (VR) technologies in the educational process were posed to students and educators. The results obtained during this study indicate a high level of acceptance by modern students and their educators of the role of drones and VR technologies in the educational environment of contemporary educational institutions. Participants in the surveys positively assessed the transformation trends of innovative technologies in the educational environment, which applies to almost all groups of students and educators without exception, regardless of their specialties. Respondents highlighted multiple positive effects of integrating drones and VR technologies into the educational space of educational institutions, including a high level of comfort when using these technical devices, improvement in the perception of educational material, and enhancement of the quality of mastering software disciplines. Both educators and students positively view the emerging prospects of practical use of drones and VR technologies in educational institutions' educational space, particularly in the post-COVID period and beyond.
Co-Authors Ariya, Kanyawut Chanaim, Somsak Lu, Jiaxin Mou, Lei Shu, Hongmei Tang, Zheng Tepsan, Worawit Ying, Fangli