Current issue: Vol. 4 2023

Yang Zhang, Thaned Rojsiraphisal

Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 1-11

In the financial market analysis field, machine learning techniques for stock price prediction have garnered considerable interest. This study investigates the effectiveness of Long Short-Term Memory (LSTM) models in predicting stock prices for growth stocks and the CSI 300 index in the Chinese A-share market. The study also explores the influence of various technical in-dicators of the LSTM model and models on forecasting accuracy. The exper-imental results demonstrate that the LSTM model is the most effective in predicting stock prices in the A-share market, while other algorithms such as WMA and ARIMA are not as successful in forecasting long-term stock market data. This study proposes some modifications further to enhance the accuracy and dependability of the prediction model.

Chanchanok Aramrat, Pruet Boonma

Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 12-35

Bacterial data are under-utilized in Maharaj Nakorn Chiang Mai hospital. Bacterial data contains information regarding the bacteria that are isolated from various biological samples collected in routine clinical cares. The data can be used to create bacterial profiles and antibiotics susceptibility pro-files which help doctor decide on the most appropriate antibiotics agent to be given to patients with infection. The aims of this study were to develop an application which create bacterial profiles and antibiotics susceptibility profiles by utilizing the hospital bacterial data. To do this, the study was sub-divided into 4 parts 1. Development of ETL process to prepare data for utilization, 2. Data quality assessment, 3. Development of pilot application utilizing prepared data to create bacterial profiles and antibiotics suscepti-bility profiles, and 4. Feasibility assessment of the pilot application. All data was extracted from Maharaj Nakorn Chiang Mai hospital database from 2017 to 2018 with an assistance from hospital information technolo-gy (IT) personnel. All extracted data was explored and compile into one ta-ble to be utilized by the pilot application. The pilot application was written in Google Collaboratory. Overall, the data quality was good. There was some missing data but should barely affect reliability and performance of the application. For feasibility assessment, the pilot application was given to 6 doctors conveniently selected from all doctors working in the hospital for test uses. Later, the doctors were interviewed and asked to provide feed-backs on the pilot application. The application received positive review overall. Improvement points were addressed focusing on data cleaning and preprocessing, minimizing any potential bias. This study provides insight into the development processes of the pilot application that provide bacterial profiles and antibiotics susceptibility profiles to doctors. Modifications are required before such an application can be used in clinical practice.

Supaporn Thankham, Pruet Boonma

Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 36-51

This independent study is a comparative study on privacy impact assessment metrics on multi-domain transactional processing: case study of Registration Office, Chiang Mai University. a privacy impact assessment should be conducted on which personal data, and what the high-risk data are, in order to guide other entities that have multi-domain linkage for doing a DPIA (Data Protection Impact Assessment) on high-risk data to ensure the security of personal infor-mation Including the storage and management of various per-sonal info-mation appropriately. The researcher used the three tools, which include GS1 tool, iPIA tool, and SPIA tool, and conducted a DPIA using the ISO-IEC-27001-2013 Standard Framework and NIST Cybersecurity Framework to be guidelines for designing the specified DPIA.

Preeyanoot Moontee, Trasapong Thaiupathump

Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 52-55

Complaints are expressions of dissatisfaction or grievances about a service that can be used to improve and develop the quality of service. However, there are several levels of complaints according to the level of severity, such as dividing the severity into two levels: the level that did not require warn-ing and the level to be notified to corresponding personnel and require im-mediate actions to solve the problems. The purpose of this independent study was to study the analysis and notification of complaints in health care services by using complaints from health care recipients and the severi-ty of complaints assessed by experts. The researcher obtained the infor-mation from the study to develop a system for analyzing complaints and notifying when there are complaints that are categorized as having to be no-tified through the LINE application. The study found that Multinomial Na-ïve Bayes had the highest efficiency in complaint classification compared to the Accuracy value of 71%.