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<![CDATA[Mixed Tukey Exponentially Weighted Moving Average-Modified Exponentially Weighted Moving Average Control Chart for Process Monitoring ]]>
<![CDATA[Khanittha Talordphop]]>;
<![CDATA[Saowanit Sukparungsee]]>;
<![CDATA[Yupaporn Areepong]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-014
<![CDATA[The goal of this study is to present the mixed Tukey exponentially weighted moving average-modified exponentially weighted moving average control chart (MEME-TCC) for monitoring process location with symmetric and skewed distributions in an attempt to significantly improve detection ability. With the benefits of nonparametric assumption robustness. The average and median run lengths are supporting measurements for assessing the performance of a monitoring scheme using Monte Carlo simulation. Furthermore, the average extra quadratic loss (AEQL), relative mean index (RMI), and performance comparison index (PCI) can all be used to evaluate overall performance criteria. The proposed chart is compared with existing charts such as; EWMA, MEWMA, TCC, MEME, MMEE, and MMEE-TCC. The comparison result shows that the proposed chart is the best control chart for detecting small to moderate shifts among all distributional settings. Nevertheless, the EWMA chart detects large shifts more effectively than other charts, except in the case of the gamma distribution, where MEWMA performs best. The results of adapting the proposed control chart to two sets of real data corresponded to the research findings. Doi: 10.28991/ESJ-2023-07-03-014 Full Text: PDF]]>
<![CDATA[Implementation of Takagi Sugeno Kang Fuzzy with Rough Set Theory and Mini-Batch Gradient Descent Uniform Regularization ]]>
<![CDATA[Sugiyarto Surono]]>;
<![CDATA[Zani Anjani Rafsanjani Hsm]]>;
<![CDATA[Deshinta Arrova Dewi]]>;
<![CDATA[Annisa Eka Haryati]]>;
<![CDATA[Tommy Tanu Wijaya]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-09
<![CDATA[The Takagi Sugeno Kang (TSK) fuzzy approach is popular since its output is either a constant or a function. Parameter identification and structure identification are the two key requirements for building the TSK fuzzy system. The input utilized in fuzzy TSK can have an impact on the number of rules produced in such a way that employing more data dimensions typically results in more rules, which causes rule complexity. This issue can be solved by employing a dimension reduction technique that reduces the number of dimensions in the data. After that, the resulting rules are improved with MBGD (Mini-Batch Gradient Descent), which is then altered with uniform regularization (UR). UR can enhance the classifier's fuzzy TSK generalization performance. This study looks at how the rough sets method can be used to reduce data dimensions and use Mini Batch Gradient Descent Uniform Regularization (MBGD-UR) to optimize the rules that come from TSK. 252 respondents' body fat data were utilized as the input, and the mean absolute percentage error (MAPE) was used to analyze the results. Jupyter Notebook software and the Python programming language are used for data processing. The analysis revealed that the MAPE value was 37%, falling into the moderate area. Doi: 10.28991/ESJ-2023-07-03-09 Full Text: PDF]]>
<![CDATA[Overview on Case Study Penetration Testing Models Evaluation ]]>
<![CDATA[Ahmad S. Al-Ahmad]]>;
<![CDATA[Hasan Kahtan]]>;
<![CDATA[Yehia I. Alzoubi]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-025
<![CDATA[Model evaluation is a cornerstone of scientific research as it represents the findings' accuracy and model performance. A case study is commonly used in evaluating software engineering models. Due to criticism in terms of generalization from a single case study and testers, deciding on the number of case studies used for evaluation and the number of testers has been one of the researchers’ challenges. Multiple case studies with multiple testers can be difficult in some domains, such as penetration testing, due to the complexity and time needed to prepare test cases. This study aims to review the literature and examine the evaluation methods used pertaining to the number of case studies and testers involved. This study is beneficial for researchers, students, and penetration testers as it provides case study design steps that are useful to determine the appropriate number of test cases and testers required. The paper's findings and novelty highlight that a single case study with a single tester is enough to evaluate a model. It also strikes a balance between what is enough for the evaluation and the need to reduce criticisms of a single case study by using two case studies with a single tester. Doi: 10.28991/ESJ-2023-07-03-025 Full Text: PDF]]>
<![CDATA[Utilizing Machine Learning to Reassess the Predictability of Bank Stocks ]]>
<![CDATA[Hera Antonopoulou]]>;
<![CDATA[Leonidas Theodorakopoulos]]>;
<![CDATA[Constantinos Halkiopoulos]]>;
<![CDATA[Vicky Mamalougkou]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-04
<![CDATA[Objectives: Accurate prediction of stock market returns is a very challenging task due to the volatile and non-linear nature of the financial stock markets. In this work, we consider conventional time series analysis techniques with additional information from the Google Trend website to predict stock price returns. We further utilize a machine learning algorithm, namely Random Forest, to predict the next day closing price of four Greek systemic banks. Methods/Analysis: The financial data considered in this work comprise Open, Close prices of stocks and Trading Volume. In the context of our analysis, these data are further used to create new variables that serve as additional inputs to the proposed machine learning based model. Specifically, we consider variables for each of the banks in the dataset, such as 7 DAYS MA,14 DAYS MA, 21 DAYS MA, 7 DAYS STD DEV and Volume. One step ahead out of sample prediction following the rolling window approach has been applied. Performance evaluation of the proposed model has been done using standard strategic indicators: RMSE and MAPE. Findings: Our results depict that the proposed models effectively predict the stock market prices, providing insight about the applicability of the proposed methodology scheme to various stock market price predictions. Novelty /Improvement: The originality of this study is that Machine Learning Methods highlighted by the Random Forest Technique were used to forecast the closing price of each stock in the Banking Sector for the following trading session. Doi: 10.28991/ESJ-2023-07-03-04 Full Text: PDF]]>
<![CDATA[STEM Talent: A Game Changer in Organizational Digital Transformation ]]>
<![CDATA[Piyawat Jriyasetapong]]>;
<![CDATA[Supaporn Kiattisin]]>;
<![CDATA[Smitti Darakorn Na Ayuthaya]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-020
<![CDATA[Although organizational digital transformation (ODT) is implemented globally, Thailand and the Lao People's Democratic Republic do not possess the right factors for success under the tech-no-socio-economic paradigm. Organizations must modernize their capital resources, particularly their talent, in order to become agile, competitive, and resilient in the digital era. In this research, we identify and validate by proposing talent success factors and a framework for enabling and promoting ODT in Thailand and the Lao People's Democratic Republic. The statistical population consisted of 410 individuals who were observed in their digital businesses. Confirmatory factor analysis (CFA) shows that a four-factor model fits. The most influential factor for ODT was found to be transdisciplinary ontology talent (TOT), followed by mental model talent (MMT), enterprise architecture talent (EAT), and strategic agile talent (SAT). The findings demystified the four factors, entitled "STEM talent," in a comprehensive framework and its artifacts while explaining their respective influences. The article proposes a STEM talent and its framework for ODT with high potential, including but not limited to Thailand and the Lao People's Democratic Republic. Doi: 10.28991/ESJ-2023-07-03-020 Full Text: PDF]]>
<![CDATA[Demystifying Tourists’ Intention to Visit Destination on Travel Vlogs: Findings from PLS-SEM and fsQCA ]]>
<![CDATA[Pantas H. Silaban]]>;
<![CDATA[Wen-Kuo Chen]]>;
<![CDATA[Bernard E Silaban]]>;
<![CDATA[Andri Dayarana K. Silalahi]]>;
<![CDATA[Ixora Javanisa Eunike]]>;
<![CDATA[Hanna Meilani Damanik]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-015
<![CDATA[With the advent of digital technologies (i.e., social media), tourism has evolved its marketing strategies. Even though published literature discusses the importance of tourism content on social media from various consumer perspectives, much more work must be done to examine how consumers make travel decisions based on tourism content. This study proposes a model for analyzing travel intent based on consumer motivations (e.g., novelty, entertainment, and relaxation) to watch social media travel videos. Consumers' travel intentions are influenced by trust and parasocial relationships. Through an online survey, 215 responses were collected and analyzed using a structural equation modeling (SEM) approach using Smart-PLS 3.0 and fuzzy set qualitative comparative analysis (fsQCA). In the study, relaxation ranked most highly among the three motivations for viewers to watch travel videos on YouTube for building parasocial relationships. In contrast, consumers seeking entertainment are more likely to form trust, which will result in consumers' intentions to travel. Based on intermediate solutions generated by the fsQCA, two causal configurations can be used to explain consumer travel decisions influenced by social media tourism content. The study also discusses theoretical and practical guidelines in depth. Doi: 10.28991/ESJ-2023-07-03-015 Full Text: PDF]]>
<![CDATA[Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods ]]>
<![CDATA[Meryem Amane]]>;
<![CDATA[Karima Aissaoui]]>;
<![CDATA[Mohammed Berrada]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-010
<![CDATA[The development of learning objects (LO) and e-pedagogical practices has significantly influenced and changed the performance of e-learning systems. This development promotes a genuine sharing of resources and creates new opportunities for learners to explore them easily. Therefore, the need for a system of categorization for these objects becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight the performance of these LOs and make them very useful for learners. This study consists of two main phases. First, we extract metadata from learning objects, using the algorithm of Web exploration techniques such as feature selection techniques, which are mainly implemented to find the best set of features that allow us to build useful models. The key role of feature selection in learning object classification is to identify pertinent features and eliminate redundant features from an excessively dimensional dataset. Second, we identify learning objects according to a particular form of similarity using Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As a clustering algorithm, Fuzzy C-Means is used to perform classification accuracy according to Euclidean distance metrics as similarity measurement. Finally, to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset were conducted. The findings of this study indicate that the proposed approach exceeds the traditional approach and leads to viable results. Doi: 10.28991/ESJ-2023-07-03-010 Full Text: PDF]]>
<![CDATA[Anticancer Activity Study of Modified Artocarpin Compound from Pudau Plant (Artocarpus kemando Miq.) ]]>
<![CDATA[Tati Suhartati]]>;
<![CDATA[Khalimatus Sa’diah]]>;
<![CDATA[Yandri Yandri]]>;
<![CDATA[Sutopo Hadi]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-05
<![CDATA[This research is a continuation of the successful isolation of artocarpin from the root of Artocarpus kemando Miq reported in our previous study. In the previous study, the artocarpin was characterized with UV-Vis and FTIR techniques. In this follow-up investigation, the artocarpin was subjected to a transesterification reaction using acetic anhydride and pyridine as catalysts, and the product of the reaction was specified as compound 1. The compound 1 was further characterized with different techniques to gain more complete data and then tested for anticancer activity test against P-388 murine leukemia cells. Characterizations of the compound 1 using 1H-NMR and 13C-NMR techniques suggest that the modification reaction resulted in the conversion of the -OH groups at C2' and 4' at the artocarpin molecule to -OOCH3, and based on the MS analysis, the compound was proposed to have the molecular formula of C30H32O8. Another important feature of compound 1 that should be noted is the significant improvement in stability compared to the unmodified artocarpin. Anticancer activity tests against P-388 murine leukemia cells revealed that compound 1 has an IC50of 2.35 µg/mL, confirming that the compound is categorized as an active anticancer agent and suggesting that the compound has promising potential that deserves further investigations. Doi: 10.28991/ESJ-2023-07-03-05 Full Text: PDF]]>
<![CDATA[Concentration of B-CG and sFlt-1 in Rattus Norvegicus Model of Preeclampsia with Swimming Exercise Treatment ]]>
<![CDATA[Oktalia Sabrida]]>;
<![CDATA[Muslim Akmal]]>;
<![CDATA[Sri Wahyuni]]>;
<![CDATA[Khairan Khairan]]>;
<![CDATA[Gholib Gholib]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-021
<![CDATA[Preeclampsia (PE) is a life-threatening pregnancy complication for the mother and fetus. High concentrations of human chorionic gonadotrophin (hCG) and soluble fms-like tyrosine kinase-1 (sFLt-1) during pregnancy may have a role in the pathophysiology of PE. Swimming Exercise (SE) is one of the physical activities recommended for pregnant women and carries a minimal risk. This study is aimed at analyzing the interaction between the conditions of rats (normal and PE), the onset of PE (early onset and late onset), and the time of SE (SE 0 minutes; SE 5 minutes; SE 10 minutes) on the concentrations of B-CG and sFlt-1 in the Rattus norvegicus (R. norvegicus) model of PE. 72 R. norvegicus were included in this study and divided into 12 experimental groups (each group n = 6 individuals). R. norvegicus PE was prepared by inducing L-Nitro-Arginine-Methyl Ester (L-NAME) at a 75 mg/kg BW/day dose. The determination of PE was supported by the observation of differences in the values of urine protein (PU), urine glucose (GU), and urine leukocytes (LU) in R. norvegicus before and after injection of L-NAME. The three-factorial statistical test showed a significant interaction between the concentration of B-CG and the condition of R. norvegicus, the onset of PE, and the time of SE, with a p-value <0.001. The three-factorial statistical test also showed a significant interaction between the sFLt-1 concentration and the condition of R. norvegicus, the onset of PE, and the time of SE with p<0.05. The difference in the concentration of B-CG and sFLt-1 R. norvegicus in each treatment group was influenced by the condition of the rats (normal and PE), the onset of PE (early onset and late onset), and the time of SE (SE 0 minutes; SE 5 minutes; SE 10 minutes). Research related to SE on PE still needs to be continued to decide on recommendations on whether SE can be used as a preventive measure in complementary midwifery care for preventing and reducing symptoms of PE in pregnancy. Doi: 10.28991/ESJ-2023-07-03-021 Full Text: PDF]]>
<![CDATA[An Empirical Analysis of Fintech's Impacts on the Financial Performance of Banks in Kosovo ]]>
<![CDATA[Hiflobina Dermaku]]>;
<![CDATA[Muhamet Hajdari]]>;
<![CDATA[Kastriot Dermaku]]>;
<![CDATA[Liridon Hoti]]>
<![CDATA[ Emerging Science Journal Vol 7, No 3 (2023): June ]]>
Publisher : <![CDATA[Ital Publication ]]>
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DOI: 10.28991/ESJ-2023-07-03-016
<![CDATA[This analysis aims to empirically investigate the impact of different forms of Fintech on the financial performance of banks in Kosovo from 2010 to 2021. The research is based on secondary data, accounting for 48 observations at quarterly frequencies. The model treats bank performance (i.e., net profits of the bank sector) as an endogenous variable of ATMs, POS, and e-payments. The methodology applied in the research is based on the OLS technique and diagnostic tests for evaluating the normality of distribution, multicollinearity, autocorrelation, specification error, and heteroscedasticity. Results show that the variability of ATMs and e-payments determines bank performance variability. In particular, e-payments show a significant positive impact on bank profitability, whereas ATM payments display a negative impact on bank profitability. In addition, an increase in ATM payments by 1% decreases bank profitability by 0.367%. While an increase in e-payments by 1% increases bank profitability by 0.11%. The POS payments were found to have no significant relationship with bank profitability. Doi: 10.28991/ESJ-2023-07-03-016 Full Text: PDF]]>
