Discovering Future Earnings Patterns through FP-Growth and ECLAT Algorithms with Optimized Discretization

Future earnings indicate whether the trend of earnings is increasing or decreasing in the future of a business. It is beneficial to investors and users in the analysis and planning of investments. Consequently, this study aimed to identify future earnings patterns from financial statements on the Stock Exchange of Thailand. We proposed a novel approach based on FP-Growth and ECLAT algorithms with optimized discretization to identify associated future earnings patterns. The patterns are easy to use and interpret for the co-occurrence of associated future earnings patterns that differ from other studies that have only predicted earnings or analyzed the earnings factor from accounting descriptors. We found four strongly associated increases in earnings patterns and nine strongly associated decreases. Moreover, we also established ten accounting descriptors related to earnings: 1) %∆ in long-term debt, 2) %∆ in debt-to-equity ratio, 3) %∆ in depreciation/plant assets, 4) %∆ in operating income/total assets, 5) %∆ in working capital/total assets, 6) debt-to-equity ratio, 7) issuance of long-term debt as a percentage of total long-term debt, 8) long-term debt to equity, 9) repayment of long-term debt as a percentage of total long-term debt, and 10) return on closing equity. Doi: 10.28991/ESJ-2022-06-06-07 Full Text: PD

Associated Patterns and Predicting Model of Life Trauma, Depression, and Suicide Using Ensemble Machine Learning

This study aimed to find associated patterns by association rule mining and propose a prediction model using ensemble learning methods of high levels of trauma items affecting depression and suicide among primary school students in Thai rural extended opportunity schools. Our proposed methods were different from others that have analysed the relationship of high life trauma leading to depression and suicide by using statistical analysis. We found strongly associated patterns and effects among primary students’ trauma, depression, and suicide. The trauma of psychological abuse and neglect may result in suicide, whereas psychological abuse, neglect, and the experience of self-harm are also likely to result in the increased severity of traumatic events in life. The trauma of physical and sexual abuse, neglect, helplessness, feeling worthless, being weak, and self-harm were associated with depression. Our research discovered new knowledge that the risk of suicide arises from two extreme types of trauma: when children’s safety is frequently threatened and the family communicates frequently using rude or abusive words; these traumas may not merely correlate with depression but may ultimately result in suicide. Moreover, this study discovered 7 highly important trauma items and 4 suicide items for predicting depression and suicide using the Random Forest technique. We found that the Random Forest technique performed well in predicting depression and suicide. The predicted depression results show that the overall accuracy was 85.84%, precision was 89.33%, and recall was 75.28%. The predicted suicide results show that the overall accuracy was 91.28%, precision was 89.05%, and recall was 84.72%. From these results, we identified high life trauma affecting depression and suicide, which are very beneficial to practitioners to use in preliminary screening. In addition, those involved need to be aware and attentive in counselling these people with these symptoms in time. Doi: 10.28991/ESJ-2022-06-04-02 Full Text: PD

An Optimized Machine Learning and Deep Learning Framework for Facial and Masked Facial Recognition

In this study, we aimed to find an optimized approach to improving facial and masked facial recognition using machine learning and deep learning techniques. Prior studies only used a single machine learning model for classification and did not report optimal parameter values. In contrast, we utilized a grid search with hyperparameter tuning and nested cross-validation to achieve better results during the verification phase. We performed experiments on a large dataset of facial images with and without masks. Our findings showed that the SVM model with hyperparameter tuning had the highest accuracy compared to other models, achieving a recognition accuracy of 0.99912. The precision values for recognition without masks and with masks were 0.99925 and 0.98417, respectively. We tested our approach in real-life scenarios and found that it accurately identified masked individuals through facial recognition. Furthermore, our study stands out from others as it incorporates hyperparameter tuning and nested cross-validation during the verification phase to enhance the model's performance, generalization, and robustness while optimizing data utilization. Our optimized approach has potential implications for improving security systems in various domains, including public safety and healthcare. Doi: 10.28991/ESJ-2023-07-04-010 Full Text: PD

Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining

Identifying fraudulent financial statements is important in open innovation to help users analyze financial statements and make investment decisions. It also helps users be aware of the occurrence of fraud in financial statements by considering the associated pattern. This study aimed to find associated fraud patterns in financial ratios from financial statements on the Stock Exchange of Thailand using discretization of the financial ratios and frequent pattern growth (FP-Growth) association rule mining to find associated patterns. We found nine associated patterns in financial ratios related to fraudulent financial statements. This study is different from others that have analyzed the occurrence of fraud by using mathematics for each financial item. Moreover, this study discovered six financial items related to fraud: (1) gross profit, (2) primary business income, (3) ratio of primary business income to total assets, (4) ratio of capitals and reserves to total debt, (5) ratio of long-term debt to total capital and reserves, and (6) ratio of accounts receivable to primary business income. The three other financial items that were different from other studies to be focused on were (1) ratio of gross profit to primary business profit, (2) ratio of long-term debt to total assets, and (3) total assets