Emerging Science Journal (ESJ)
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Air Pollution Forecasting in a Regional Context for Sustainable Management
Get PDFThe aim of this research was to develop and apply a statistical model that can be used to forecast long-term daily maximum particulate matter with a diameter of less than 2.5 microns (PM2.5) concentrations. In order to predict the daily maximum PM2.5 concentrations in the northeastern region of Thailand, the extreme value theory was analyzed, and an appropriate distribution model was identified by employing the Generalized Pareto distribution (GPD). The data of daily maximum PM2.5 concentrations during the years 2021–2023 obtained from six stations was used. These stations are located in Khon Kaen, Loei, Nakhon Ratchasima, Nong Khai, Nakhon Phanom, and Ubon Ratchathani provinces. The results of this study reveal that the GPD is appropriate based on the results of Kolmogorov-Smirnov Statistics Test. Estimating the return levels during the following return periods: 2 years, 5 years, 10 years, 25 years, 50 years, and 100 years showed that the area in the upper northeastern region, particularly Loei and Nakhon Phanom, has daily maximum PM2.5 concentrations above 500 micrograms per cubic meter. These results can also be used as information to support decision-making when conducting response planning in high-risk areas, which can be helpful for efficient resource planning and prevention actions. Doi: 10.28991/ESJ-2024-08-05-024 Full Text: PD
Improving the Reliability of Biometric Authentication Processes Using a Model for Reducing Data Drift
Get PDFModern complexes providing biometric identification face several problems, such as information drift caused by the variability of facial patterns, voice timbres, and current states. Information drift can characteristically exhibit short-term (subjects' states have changed) or long-term changes. Simultaneously, the developed trusted systems should not have the properties of explainable AI to prevent the possibility of intruders, based on understanding the system behavior to perform actions to hack the system. This paper's objective is to improve the reliability of biometric authentication by increasing the informativity of the classified images by transforming the correlations between the information features using the Bayes-Minkowski measure. The paper puts forth the proposition of employing neuroimmune models that are founded upon the principles of both acquired and innate immunity, with an analogy to the natural immune system. In addition, the authors propose to analyze correlations between information features instead of the features themselves. To reduce the influence of data drift, the authors suggest using adaptive learning with a teacher and reinforcement, which helps to work even with small and unrepresentative data samples. The proposed algorithm demonstrates a high degree of accuracy, as evidenced by its equal error rate (EER), and is particularly well-suited to feature recognition tasks due to its adaptive model. The test results have shown that the proposed solutions increase the level of security of personal data and improve the reliability of biometric authentication against fraudulent actions of intruders, including approaches based on adversarial algorithms. The integration of the immune structure into the authentication system enables the algorithm to remain stable even when presented with a limited number of samples. The proposed algorithm mitigates the impact of data drift on the authentication outcome. Doi: 10.28991/ESJ-2024-08-06-018 Full Text: PD
Visitor Experience Map and NFC-Based Scoring for Data-Driven Exhibition Enhancement
Get PDFIn the current exhibition industry, it is crucial for organizers and exhibitors to comprehend and enhance visitor experiences. The objective of this study is to improve the exhibition setting by utilizing Near Field Communication (NFC) technology to capture, monitor, and analyze visitor behavior, engagement, and satisfaction. The main approach entails combining NFC technology with the Visitor Experience Map to fully understand the complexities of the visitor experience. NFC-enabled smartphones facilitate seamless interaction with the system, as users simply need to bring their smartphones close to NFC tags. This enables data collection and triggers the activation of a visitor scoring form for ratings and feedback. The study's findings indicate a mean system usability score of 81.4, which demonstrates successful implementation and great usability. This confirms the effective and easy-to-use nature of the strategy, guaranteeing that visitors can effortlessly provide their ratings and feedback. The originality and enhancement reside in the successful integration of NFC technology with the Visitor Experience Map, providing a strong and user-focused approach for organizers and exhibitors to enhance the exhibition experience. This study creates a favorable situation for both visitors and stakeholders, demonstrating the potential of technological advancements to greatly influence the exhibition industry. Doi: 10.28991/ESJ-2024-08-01-015 Full Text: PD
Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis
Get PDFIn recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant's natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model's efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model's potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PD
Towards the Study of Professional Corporate Education in Terms of Its Thematic Focus and Outcomes
Get PDFAim: The aim of the paper is to characterize how the time for training employees in individual thematic areas is related to the outcomes and changes, facilitated by individual educational activities. Methods: The questionnaire method and interviewing methods were applied for obtaining data from respondents. The starting-point of empirical research was the knowledge and needs of the company, which perceives education as an investment. The respondent sample included 370 lectors/instructors and managers from three countries. The research was conducted in selected companies of section C − Manufacturing, Statistical Classification of Economic Activities. Basic variables include the number of training hours and results of the changes after training activities. Three groups of employee education are analysed: general training, performance-oriented training and digital training. Findings: Our division of educational activities into three groups enables the fulfilment of the basic European Commission targets for the 2021−2027 programme. Recommendations: Invest in developing new skills through future corporate training in the context of innovative transformation of the economy for a smarter Europe. Create conditions for better utilisation of human potential by eliminating the discrepancy between offered and required skills. Novelty of the paper:international comparison of educational activities, focus on the smart Europe and transformation to Industry 4.0. Doi: 10.28991/ESJ-2024-SIED1-04 Full Text: PD
Short and Effective: A Reasoned Proposal for Organizational Climate Measurement
Get PDFGenerally, organizational climate research does not focus on the work environment because the mindset and emotions of employees are often mistaken for organizational culture. Additionally, surveys to evaluate the organizational climate tend to be long, and therefore, organizational climate studies are conducted only once a year”that too if an organization is concerned about its employees. This research proposes a methodology to evaluate organizational climate; the methodology has the following characteristics: it is a short evaluation named "pulse”; it is oriented toward specific elements of culture that influence the organizational climate and its variability; and it considers organizational contexts. The study was conducted in three organizations encompassing three sectors (N=3,331 employees). The survey included three questions regarding employees' feelings and climate perception at the individual, group, and organizational levels. Additionally, it had 56 questions related to the elements of organizational culture, grouped into six components after an exploratory analysis: Structure, Recognition, Leadership, Accountability, Work Team, and Ethics. The results showed significant differences between organizations based on the organizational climate perception, its strength, and the behavior of the variables associated with the organizational culture that impacts the climate. Additionally, cultural elements were reduced because of their relationship with the organizational climate. This research suggests that organizational climate studies should be conducted for specific organizational contexts. Additionally, it proposes a methodology to reduce the duration of organizational climate studies by focusing on specific cultural dimensions associated with the climate, which can be applied longitudinally throughout the year to monitor climate changes. Doi: 10.28991/ESJ-2024-08-05-09 Full Text: PD
Enhancing GI Cancer Radiation Therapy: Advanced Organ Segmentation with ResECA-U-Net Model
Get PDFThis research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the U-Net and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Center, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely. Doi: 10.28991/ESJ-2024-08-03-012 Full Text: PD
Factors Affecting the Ability to Repay Debts of Corporations at Commercial Banks
Get PDFThis study aims to estimate the influence of these factors on the ability of corporations to repay debts at commercial banks in Vietnam, especially in Ho Chi Minh City. Moreover, this study focuses on the period from 2018 to 2022. Hence, the effects of the COVID-19 pandemic were also examined in this study. In addition, this study employs a binary logistic regression method to analyze the extent of the factors' impact on the ability of corporate customers to repay debts. The results reveal that the model includes six statistically significant factors: collateral, loan term, income, firm size, leverage, net sales, and COVID-19. Furthermore, the model can be used to forecast corporations' ability to repay debts, which could help commercial banks plan their loan strategies for corporate customers based on the significant results of the Hosmer-Lemeshow test. The study, on the other hand, only focused on some banks in the biggest city in the south of Vietnam, so further research on the area is needed, such as over Vietnam. Doi: 10.28991/ESJ-2024-08-03-016 Full Text: PD
The Effect of Felt Accountability on User Satisfaction with Accounting Information
Get PDFFelt accountability affects an account-givers' behavior, decisions, and organizational performance. Accounting information (AI) is provided for decision-making and accountability in the public sector. This study investigated the effects of felt accountability on expertise, legitimacy, and AI disclosure level for accountability on users' satisfaction. Survey data included 401 responses across public institutions in Vietnam, and SEM linear structure analysis was used to examine the results. The research findings indicate that felt accountability directly affects users' satisfaction and their expertise and legitimacy, and the level of AI disclosure. The expertise and legitimacy of the account-holder and the level of AI disclosure partially mediate the relationship between felt accountability and users' satisfaction. This implies that AI's needs, purposes, and importance are determined based on hypothetical users that are not useful in reality. In practice, AI must meet accountability requirements to bring satisfaction to users. The satisfaction level of actual users of AI is influenced by the account-givers' perceived accountability regarding the needs, expertise, and legitimacy of the account-holder. Therefore, it is essential to identify the type of information needed, the timing of AI disclosure, and the actual AI users to reduce the gap between the supply and demand of AI. The research results provide evidence supporting agency and social contingency theories in accountability relationships. Doi: 10.28991/ESJ-2024-08-02-023 Full Text: PD
Mobile Device Forensics Framework: A Toolbox to Support and Enhance This Process
Get PDFCybercrime is growing rapidly, and it is increasingly important to use advanced tools to combat it and support investigations. One of the battlefronts is the forensic investigation of mobile devices to analyze their misuse and recover information. Mobile devices present numerous challenges, including a rapidly changing environment, increasing diversity, and integration with the cloud/IoT. Therefore, it is essential to have a secure and reliable toolbox that allows an investigator to thwart, discover, and solve all problems related to mobile forensics while deciphering investigations, whether criminal, civil, corporate, or other. In this work, we propose an original and innovative instantiation of a structure in a forensic toolbox for mobile devices, corresponding to a set of different applications, methods, and best practice information aimed at improving and perfecting the investigative process of a digital investigator. To ensure scientific support for the construction of the toolbox, the Design Science Research (DSR) methodology was applied, which seeks to create new and unique artifacts, drawing on the strength and knowledge of science and context. The toolbox will help the forensic investigator overcome some of the challenges related to mobile devices, namely the lack of guidance, documentation, knowledge, and the ability to keep up with the fast-paced environment that characterizes the mobile industry and market. Doi: 10.28991/ESJ-2024-08-03-011 Full Text: PD