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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advanced Science Computing and Engineering
Nureize Arbaiy, Nureize
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Items Searching in Factory Warehouse Using Arduino Module Ching, Tan Liong; Arbaiy, Nureize
International Journal of Advanced Science Computing and Engineering Vol. 1 No. 1 (2019)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (915.265 KB) | DOI: 10.62527/ijasce.1.1.1

Abstract

The smart store system (F3 Storage System) provides an inventory system function, and is supported by voice recognition for items searching purpose in the warehouse. This system is aimed to improve effectiveness in item searching process for the warehouse management. An inventory system structures is employed in this system to enable items management. Voice recognition facility helps the worker to search item in an effective way. Worker can use voice recognition function to search the item in the warehouse, and searched information of the item will be displayed in the liquid crystal display (LCD) screen. Meanwhile, the location of the item will be physically indicated by the light emitting diode (LED) light function. The developed system also contains a barcode system to enhance the process of scheduling warehouse activity. Such facilities will enhance the capabilities of existing inventory management systems in warehouses. Prototyping model is used to assist project development. Arduino technology is used to enable integrated hardware and software to read data or input. With Arduino technology, traditional search items by using text and search functionality are enhanced to allow speech functionality. This functionality makes the search process faster and more efficient.
The effects of data imbalance on fraud detection model accuracy Ruslan, Rusma Anieza; Arbaiy, Nureize; Lin, Pei-Chun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1402-1408

Abstract

Machine learning (ML) model performance is often assessed by accuracy, but the quality and balance of data also play crucial roles. Imbalanced datasets, where the minority class has fewer samples than the majority class, can lead to biased predictions favoring the majority class. This study addresses the issue of class imbalance through resampling techniques, including random undersampling (RUS) and random oversampling (ROS), specifically applied to a fraud detection dataset. We classify the resampled datasets using random forest (RF) and gradient boosting (GB) models. Our findings indicate that the RF model, when combined with ROS, achieves an accuracy of 97.4%, surpassing the 96.1% accuracy of the GB model with RUS. This approach demonstrates the importance of addressing class imbalance to improve prediction accuracy in ML.
Co-Authors Ching, Tan Liong Lin, Pei-Chun Ruslan, Rusma Anieza