The Internet has become an essential part of people’s lives, including e-commerce and online banking. While online transactions are convenient, it also opens up a new opportunity for scammers to easily attack victims. In this paper, data obtained from ChaladOhn, a website that helps prevent Thais from online e-commerce scams, from February 2022 to January 2023 were studied and investigated. According to the data, it has been found that the total loss value from the cases with a loss value of less than 1 million THB each was 3,126.5 million THB. Furthermore, it has been found that the top two banks involved in the scam were KBank and BBL, respectively, accounting for 22,400 or 43% of scam accounts, and 1,485 million THB or 47% of the loss. Since it’s expected that this problem will take a turn for the worst, the investigated results should be informed to the regulators and the related organization, so that an effective solution can be designed, proposed, and implemented to cope with the rapidly growing number of online scamming cases.

Analyzing Bank Account Information of Nominees and Scammers in Thailand: Insights from ChaladOhn Website Data

This paper would provide a comprehensive review of the synergistic integration of measurement science and artificial intelligence (AI) in the realm of smart agriculture. As agriculture undergoes a transformative phase toward precision and data-driven practices, the paper explores the critical role of measurement science in providing accurate data, while AI processes and analyzes this data to facilitate intelligent decision-making in smart agricultural systems.

Synergizing Measurement Science and Artificial Intelligence in Smart Agriculture

This paper presents the development of a deep learning model designed to identify six distinct types of Thai food dishes, using an imbalanced dataset with 2,736 images. To investigate and solve the imbalance issue, three approaches, consisting of undersampling, oversampling, and hybridsampling approaches, were applied before conducting the augmentation process and transformation. The processed data were then used to train three transfer learning models, consisting of ResNet50, MobileNet, and InceptionV3. The results indicate that the hybridsampling method can help improve the performance of each transfer learning model. Especially, the accuracy can be improved from 94% to 96% for the ResNet50 model.

Accuracy Improvement of Transfer Learning Using Hybrid Approach: a Case Study of Imbalanced Thai Food Image Classification

This paper presents the development of a deep learning model designed to identify two classes of object images, the work-in-process of a kind of tap. There were the good parts of316 images and the defective parts of320 images. Both classes of images were processed using the oversampling technique for data augmentation before transformation. Then, the processed data were applied to training three transfer learning models, consisting of ResN et50, MobileN et, and Xception. The results show an accuracy and F1-score of 100% for the ResN e50 model, while the augmentation can help improve the performance of the MobileNet and Xception models. Particularly, when evaluated with the test set, the accuracy can be improved from 98.96% to 99.67% for the MobileNet model, while it can be increased from 95.83% to 97.01 % for the Xception model. The accuracy rates were slightly improved, about 0.72% and 1.23% for MobileNet and Xception, respectively. This means that the augmentation approach can be used to improve the performance of transfer learning models. For the major contributions of this study, it could be extended to other types of taps, and then ResNet50, which is the best deep learning model in this study, can be applied for the development of a new station in a production line, for detection and rejection defects automatically.

Detection of Defects in Work-in-Process Products Using Deep Learning: A Case of a Water Tap Production Line

Scams are fraudulent acts that have become more common in the Philippines as a result of the growth of Ride-Hailing, Delivery, and Food Application Service platforms. Constant loss of profit and consumer services are severely impacted; as a result, both parties are impacted by the Malicious Acts committed by these individuals in order to disrupt and hurt the users of the applications. Rampancy in Scams, particularly in certain specific places, violates the terms and conditions of the apps by committing heinous activities, but also in terms of privacy of information (e.g., Able to Access Customer Address; Access/Contact Customer Phone Number) and criminal acts in the nation. According to the statistics, two-thirds of the dataset interviewers have not yet encountered 

A Quantitative Case Study on Rampant Online Ordering Scams in the Philippines

Online scams have become a new normal risk for online shoppers, including for Thai people. This paper presents a study of online scams analyzed by age, gender and value loss. The dataset for this study is from the ChalaOhn system, a website developed by a state university and the metropolitan police for protection against online shopping scams in Thailand. The dataset was considered, analyzed and visualized before presenting the results. It was found that more than two thirds (69.8 %) of the victims are 20-39 years old, and more than two fifths (42.7 %) are 20-29 years old. 37.1 % of victims lost less than ${\$}$100, and the mode loss of value was ${\$}$28.6 (1,000 THB). These findings should be considered further to help online shoppers and reduce online scams in Thailand. Awareness raising based on these findings should focus on Thai people under 40 years old.

A Study of Online Scams Associated with Age, Gender and Loss of Value in Thailand

In Thailand, monitoring of the water situation is important for agriculture (e.g., rice farming) and flood prevention. Therefore, irrigation management requires a system called the automatic report system for water level measurement that will utilize information from remote closed-circuit television (CCTV) cameras and then create the report automatically. This paper presents a pilot study for water level measurement and report system development that was designed to use images from CCTV cameras monitoring staff gauges obtained from remote sites for water level reporting using digital image processing techniques and machine learning techniques. One of the major differences and contributions of this study is applying an image modification technique for training machine learning. After the evaluation, it was found that the machine learning models show excellent performance for most sites with RMSE values of less than 3.00 when tested with the test sets, particularly the model that was trained through use of a large number of modified images (e.g., 2,000 images). Therefore, this prototype system should be developed further for irrigation management.

Irrigation Management: A Pilot Study for Automatic Water Level Measurement and Report System Development Using Machine Learning Associated with Modified Images

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Papers

A Study of Online Scams Associated with Age, Gender and Loss of Value in Thailand

Irrigation Management: A Pilot Study for Automatic Water Level Measurement and Report System Development Using Machine Learning Associated with Modified Images

Accuracy Improvement of Transfer Learning Using Hybrid Approach: a Case Study of Imbalanced Thai Food Image Classification