Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Nuchitprasitchai, Siranee
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Impact of electric vehicle demand forecasting on charging station infrastructure development Kronghinlad, Chartrin; Nilsiam, Yuenyong; Bhumpenpein, Nalinpat; Nuchitprasitchai, Siranee; Tangprasert, Sakchai
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1010-1019

Abstract

This research addresses the challenge of forecasting electric vehicle (EV) demand in Thailand and its influence on the development of charging infrastructure. To improve predictive capability in environments with restricted historical data, we employed the grey model (GM) and genetic algorithms (GA) both independently and in combination. Using EV registration records from 2019 to 2023 obtained from the Automotive Information Center of Thailand, the optimized GM-GA hybrid model achieved markedly superior accuracy, with a mean absolute error (MAE) of 0.0016 and root mean squared error (RMSE) of 0.0031. These results demonstrate the model’s capacity to deliver precise forecasts despite data limitations, making it a valuable decision-making tool for charging station planning and deployment. The outcomes underscore the importance of forward-looking infrastructure strategies to support the growth of Thailand’s EV market and its transition toward sustainable mobility.
Data analytics and prediction of cardiovascular disease with machine learning models: a systematic literature review Sonthana, Ravipa; Tangprasert, Sakchai; Nilsiam, Yuenyong; Bhumpenpein, Nalinpat; Nuchitprasitchai, Siranee
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp914-923

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of death globally, underscoring the need for effective early risk prediction. This systematic literature review analyzes research published between 2013 and 2023 on the application of machine learning (ML) in CVD risk prediction. Key areas examined include feature selection, data preprocessing, algorithm choice, and model evaluation. Studies were selected from ACM Digital Library, IEEE Xplore, ScienceDirect, and Scopus based on predefined research questions. Common challenges include limited or low-quality datasets, inconsistent preprocessing methods, and the need for clinically interpretable models. Widely used algorithms include random forest (RF), support vector machine (SVM), decision tree (DT), logistic regression (LR), naïve Bayes (NB), k-nearest neighbor (K-NN), and extreme gradient boosting (XGBoost). The review highlights that robust preprocessing, optimal feature selection, and thorough model validation significantly improve predictive accuracy. It also emphasizes the importance of balancing performance with interpretability for clinical adoption. Finally, the study proposes a structured framework to guide future research and practical implementation, including the integration of genetic and behavioral data to support more personalized and effective cardiovascular care.
Co-Authors Bhumpenpein, Nalinpat Kronghinlad, Chartrin Nilsiam, Yuenyong Sonthana, Ravipa Tangprasert, Sakchai