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

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