Emerging Science Journal (ESJ)
Not a member yet
960 research outputs found
Sort by
Adopting ISO 20022: Opportunities, Challenges, and Success Factors for Corporations in Payment Processing
Get PDFThis research explores the adoption of ISO 20022, a standard that corporations can leverage to instruct payments to their partner financial institutions. Due to the complexity and case-specific variables involved, the adoption process may be complex and require significant effort from financial institutions and customers over an extended period. This research analyzes the opportunities and challenges for corporate users posed by ISO 20022 and identifies the success factors that must be considered during the adoption process. The research key findings indicate that an implementation approach incorporating flexibility, custom extensions, the use of a markup language for creating and managing messages, pilot testing, and user feedback can be an effective adoption model for ISO 20022. Design Science Research Methodology is employed in designing, building, and evaluating a solution proposal to develop a structured, customized, and flexible solution complying with the ever-changing requirements and landscape. This research contributes to the payment processing field by providing a comprehensive adoption model for ISO 20022 that considers critical factors and challenges. The proposed customized and flexible solution can assist corporations in successfully adopting ISO 20022 and contribute to creating a common language and model for payment data worldwide. The initiative's success depends on the effective adoption by all players, including corporations. Doi: 10.28991/ESJ-2024-08-04-010 Full Text: PD
PM2.5 IoT Sensor Calibration and Implementation Issues Including Machine Learning
Get PDFAffordable IoT PM2.5 sensors, enabled by the Internet of Things, offer new ways to monitor air quality. However, concerns exist about their data accuracy. This study aimed (1) to investigate the low-cost PM sensor's performance under various outdoor ambient circumstances and (2) to evaluate seven calibration methods, which include decision trees, gradient-boosted trees, linear regression, nearest neighbors, neural networks, random forests, and the Gaussian Process. The Davis AirLink was used as a reference to compare the Plantower PMS3003 sensor's performance. The data from the Plantower PMS3003 sensor were then compared to the Davis AirLink values using calibration curves created by machine learning algorithms. Calibration curves were generated using machine learning algorithms trained on sensor measurements collected in two Thai cities (Nakhon Si Thammarat and Phuket). Our results show that all machine learning methods outperformed traditional linear regression, with decision trees and neural networks demonstrating the most significant improvement. This research highlights the need for sensor calibration and the limitations of current calibration methods and paves the way for advancements in cloud-based calibration and machine learning for improved data accuracy in IoT PM2.5sensor technology. Doi: 10.28991/ESJ-2024-08-06-08 Full Text: PD
The Effect of EAP on Job Performance Based on Psychological Contract and Perceived Organizational Support
Get PDFIn order to study whether employee assistance programs have a significant impact on job performance, whether psychological contract and perceived organizational support play a mediating role in job performance, and thus provide practical operational strategies for relevant enterprises to propose human resource management suggestions to promote job performance, the implementation of employee assistance programs has been the subject of practical research. The findings indicate that addressing issues related to high turnover, job burnout, and absences cannot be separated from the importance of the psychological contract, perceived organizational support, and employee performance in organizational change. By applying structural equation modeling (SEM) to the data from front-line employees of several units in China, this research tested the relationships among employee assistance programs, psychological contracts, perceived organizational support, and job performance using SPSS and AMOS. The results indicate that employee assistance programs positively affect job performance; psychological contracts and perceived organizational support play a mediating role between employee assistance programs and employee job performance (JOP). Our research suggests that an employee assistance program can optimize frontline employee assistance work, build a mechanism to stimulate frontline employees' psychological contracts, and create an organizational environment full of perceived organizational support. This study innovatively uses the structural equation model for quantitative research. In addition, most previous studies on EPA were based on a single variable, psychological contract, and POS were used as the main intermediary variables to explore the mechanism of their impact on job performance so as to enhance the explanatory power of employee job performance. Doi: 10.28991/ESJ-2024-08-05-018 Full Text: PD
Open Government Data Intention-Adoption Behavioural Model for Public Sector Organisations: A Technological Innovation Perspective
Get PDFThe objective of this research is to examine an open government data (OGD) intention-adoption behavioural model for the public sector organisations (PSOs), since examining the model is expected to lead to a better understanding of how to realise this technological innovation among PSOs on a large scale to excavate its innovative value. In this respect, we proposed a theoretical model to explore the factors that affect OGD adoption behaviour based on three dimensions of the TOE (technology, organisation, and environment) framework. The model was then analysed after collecting the survey data from 249 PSOs in Pakistan using a purposive sampling technique. The findings unfolded that the factors, except centralisation and civil society participation, framed in technology dimension (data resource, dataset quality, perceived benefits), organisation dimension (data-driven culture, digitisation capacity, need for transparency), and environment dimension (compliance pressure, political leadership commitment) affect the PSOs' OGD adoption intention. Cumulatively, the intention to adopt OGD was found to have a significant positive impact on OGD adoption behaviour. Based on the TOE framework, the model, with the addition of adoption intention as a significant positive factor in adoption behaviour, bears a crucial theoretical and practical contribution in the context of OGD. Doi: 10.28991/ESJ-2024-08-05-04 Full Text: PD
An Explainable Deep Learning Approach for Classifying Monkeypox Disease by Leveraging Skin Lesion Image Data
Get PDFAccording to the World Health Organization's (WHO) external situation report on the multi-country outbreak of Monkeypox in 2023, from 11 countries in Southeast Asia Regions, Thailand recorded the highest reported cases, totaling 461. The ongoing Monkeypox outbreak has raised significant public health concerns due to its rapid spread across several nations. Early detection and diagnosis are imperative for effectively treating and controlling Monkeypox. Given this context, this study aimed to determine the most efficient model for detecting Monkeypox by employing interpretable deep learning techniques. This study utilizes deep learning techniques to diagnose Monkeypox based on images of skin lesions. We evaluate based on four models”convolutional neural network (CNN), gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short term memory (BiLSTM)”using a publicly available dataset. Additionally, we incorporate Local Interpretable Model-Agnostic Explanations (LIME) and techniques for explainable AI, facilitating visual interpretation of model predictions for healthcare practitioners. The CNN model's performance and LSTM model's performance have an accuracy of 100%, while the GRU model's performance and BiLSTM model's performance have an accuracy of 99.88% and 99.45%. Our findings demonstrate the effectiveness of deep learning models, including the suggested CNN model leveraging the pre-trained MobileNetV2 and LSTM. These models can play a pivotal role in combating the Monkeypox virus. Doi: 10.28991/ESJ-2024-08-05-013 Full Text: PD
Effective Forecasting of Insurer Capital Requirements: ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH Approaches
Get PDFThis research paper presents a comprehensive analysis of three prominent volatility and dependence models for financial time series: ARMA-GARCH, GARCH-EVT, and DCC-GARCH. These models are employed to assess and forecast capital requirements for life and non-life insurer investments. This study evaluates the models' performance in forecasting Value-at-Risk, using daily data on key Thai financial indicators (representing permissible insurer investment assets) from March 2009 to March 2024. Specifically, 1-day and 10-day VaR forecasts are generated using the ARMA-GARCH and DCC-GARCH models, while the ARMA-GARCH-EVT model is employed for 1-day VaR forecasting. Our findings indicate that the ARMA-GARCH model effectively captures time-varying volatility, while the GARCH-EVT approach enhances tail risk estimation, particularly relevant for stress testing. Additionally, the DCC-GARCH model allows for the examination of dynamic conditional correlations between assets, providing insights into portfolio diversification benefits. Rigorous backtesting procedures, employing Kupiec and Christoffersen tests with a rolling window of 1,000 out-of-sample observations, confirm that the majority of models accurately forecast VaR at their respective horizons, with only a very small subset of 10-day VaR models exhibiting limitations. These results highlight that ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH models offer insurers robust tools for estimating minimum capital requirements, forecasting investment risk, and guiding strategic asset allocation decisions. This research underscores the effectiveness of these models for practical application in the insurance industry while also emphasizing the importance of continued model validation, particularly for extended forecasting horizons. Doi: 10.28991/ESJ-2024-08-06-03 Full Text: PD
Employees' Perceptions of Workplace Safety Culture: A Case Study of a Polyester Company
Get PDFSafety culture is a crucial component of ensuring workplace safety and preventing accidents. This study aimed to assess the safety leadership and safety culture of a polyester company in Thailand. Data gathering was conducted utilizing survey forms developed by the study team, including the 360-degree safety leadership survey and the 36-question safety culture survey. The sample group, comprising 1,286 individuals, consisted of management, employees, and independent contractors from a polyester company with four business units. Both the safety leadership and the safety culture perception surveys were provided with average values, thorough descriptions, graphical representations, and visuals. The study revealed that the perceptions of safety leadership and safety culture among the employees aligned with the standard level. However, certain issues, including off-the-job safety, communicating about safety, empowering other employees, setting safety standards and expectations, and promoting safety improvements and sharing, placed the organization's safety culture at the foundational level, requiring management commitment and awareness development. For further actions, the company should emphasize the value of strategic workers' involvement in programs that empower employees, such as visible safety leadership for all management levels, notification and reporting programs, and safety sharing among business partners, to establish a world-class safety culture as the company's goal. Doi: 10.28991/ESJ-2024-08-01-017 Full Text: PD
EFL Instructors' Perspective on Using AI Applications in English as a Foreign Language Teaching and Learning
Get PDFThis study aimed to explore the perspectives of EFL instructors working in a variety of universities in the UAE on the effectiveness of AI applications in the EFL classroom. EFL teachers need to use AI applications in ways that are aligned with instructional goals and support student learning. A quantitative approach was used, and data was gathered from a survey of 46 EFL instructors. The results showed that the instructors strongly relied on AI applications to facilitate tasks, offer data-driven insights to improve instructional strategies and customize the learning process for each student. They also positively valued the benefits that AI applications bring to their classrooms for improving the teaching process. Notably, the results showed that the years of teaching experience had a statistically significant impact on the means of EFL instructors' perspectives regarding the benefits of adopting AI apps in EFL classrooms. The results also showed that, despite teaching experience, there were no significant differences in perceptions regarding the challenges of utilizing AI apps. This is probably because EFL students are accustomed to using technology in their lectures. Due to their benefits in English language instruction, the study suggests incorporating AI applications into the EFL teaching process. Doi: 10.28991/ESJ-2024-SIED1-05 Full Text: PD
The Role of Product Visual Appeal and Sale Promotion Program on Consumer Impulsive Buying Behavior
Get PDFThe study aims at investigating the impact on relationships among visual appeals of products, sales promotion programs, and instant gratification factors on consumers' impulsive buying behavior. The study comprises two stages using both qualitative and quantitative research methods. In detail, qualitative research is applied in the first stage to explore the factors that might influence consumers' online impulsive buying behavior and examine the relationship among the factors. The quantitative research is conducted in Vietnam. The 362 Vietnam-collected valid questionnaires were analyzed using Cronbach alpha, exploring factor analysis (EFA), confirmed factor analysis (CFA), and structural equal model (SEM) to check the measurement values and test the proposed hypotheses. The result shows that product visual appeal and sales promotion programs have a positive impact on instant gratification factors and impulsive buying behaviors. Moreover, the research proved the direct influence of instant gratification factors on consumers' impulsive buying behavior. The findings contribute to expanding measured values and provide several suggested solutions for the practical management of business strategy. Doi: 10.28991/ESJ-2024-08-01-021 Full Text: PD
Comparison of Activation Functions in Convolutional Neural Network for Poisson Noisy Image Classification
Get PDFDeep learning, specifically the Convolutional Neural Network (CNN), has been a significant technology tool for image processing and human health. CNNs, which mimic the working principles of the human brain, can learn robust representations of images. However, CNNs are susceptible to noise interference, which can impact classification performance. Choosing the right activation function can improve CNNs performance and accuracy. This research aims to test the accuracy of CNN with ResNet50, VGG16, and GoogleNet architectures combined with several activation functions such as ReLU, Leaky ReLU, Sigmoid, and Tanh in the classification of images that experience Poisson noise. Poisson noise is applied to each test data to evaluate CNN accuracy. The data used in this study consists of three scenarios of different numbers of classes, namely 3 classes, 5 classes, and 10 classes. The results showed that combining ResNet50 with the ReLU activation function produced the best performance in class recognition in each scenario of the number of classes experiencing Poisson noise interference. The model achieved 97% accuracy for 3-class data, 95% for 5-class data, and 90% for 10-class data. These results show that using ResNet50 with the ReLU activation function can provide excellent resistance to Poisson noise in image processing. It was found that as the number of classes increases, the accuracy of image recognition tends to decrease. This shows that the more complex the image classification task is with a larger number of classes, the more difficult it is for CNNs to distinguish between different classes. Doi: 10.28991/ESJ-2024-08-02-014 Full Text: PD