cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 874 Documents
 39   40   41   42   43 
Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX Ahmad Aljaafreh; Ahmad Abadleh; Saqer S. Alja'Afreh; Khaled Alawasa; Eqab Almajali; Hossam Faris
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-05

Abstract

This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PDF
Wind Turbine Blade Dynamics Simulation under the Effect of Atmospheric Turbulence Amr Ismaiel
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-012

Abstract

Wind energy is one of the fastest growing sources of renewable energy because of its cleanliness and sustainability. Due to the turbulent nature of wind, a wind turbine experiences severe dynamic loading and faces the danger of fatigue failure. In addition, severe blade deflections imply failure by tower strikes. For this reason, the study of blade deflections under different turbulence conditions is of high importance. In this work, a wind turbine’s blade is simulated under different turbulent conditions. Four different wind fields are generated with a mean wind velocity of 12 m/s and turbulence intensities of 1, 10, 25, and 50%. The blade deflections are calculated in the out-of-plane and in-plane directions as a time-marching series with different blade azimuth positions. The higher the turbulence intensity, the severer the fluctuations of the deflections around its mean value. For the 50% turbulence intensity, the standard deviation of the out-of-plane deflection is 600% larger than that of the 1% turbulence intensity case. The maximum deflections increase significantly as well. A maximum of 3.78 m of out-of-plane tip deflection leads to the danger of a tower strike. And a positive tip deflection of 0.07 m in the in-plane direction indicates that the blade goes against its natural behavior and against the inertial loads while rotating. Continuous monitoring of wind conditions is a must, to put the turbine on brake in cases of gusts and severe turbulence. In areas of high turbulence, downwind turbines can provide a better alternative to allow blade deflections without the danger of tower strikes. Doi: 10.28991/ESJ-2023-07-01-012 Full Text: PDF
Sustainability of Micro Business Actors during the COVID-19 Pandemic Yohanes Susanto; Djatmiko Noviantoro; Sari Sakarina; Yusro Hakimah; Irwan Pancasila
Emerging Science Journal Vol 6 (2022): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2022-SPER-020

Abstract

This study aims to investigate the effect of leadership, business motivation, and compensation during the Covid-19 pandemic on business existence. This study was conducted in a traditional market in Lubuklinggau City, South Sumatra, Indonesia, with a population of 120 respondents from 1200 micro-enterprises. The validity and reliability tests of 36 samples were conducted using SPSS statistics and inferential analysis using the Amos 8.8 Structural Equation Model (SEM). It was found that leadership has a significant effect on business existence by 0.83, compensation has an effect on business existence by 0.17, and business motivation also affects business existence by 0.24. An important finding from this study is that these three variables together affect the existence of businesses during the Covid-19 pandemic. However, the leadership factor of microbusiness actors is more dominant in influencing the business's existence. Implementing awareness-based health protocols will also have a positive impact on tackling the Covid-19 pandemic. In addition, entrepreneurs can also continue to run their businesses and can foster economic stability, especially in traditional markets. Doi: 10.28991/esj-2022-SPER-020 Full Text: PDF
Corporate Social Responsibility and Bank’s Performance under the Mediating Role of Customer Satisfaction and Bank Reputation Kim Quoc Trung Nguyen
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-012

Abstract

This paper aims to examine the indirect linkage between corporate social responsibility (CSR) and firm performance via the effects of customer satisfaction and bank reputation. The study applies Structural Equation Modelling (SEM) to a sample of top managers, finance managers, chief accountants, and employees in Vietnamese state-owned commercial banks. The findings explore the statistically significant effect of CSR on bank performance under the mediating role of customer satisfaction and bank reputation, which are not concerned by previous studies. Because CSR activities assist banks in maintaining their reputation by complying with a long-term commitment to stakeholders' interests and providing valuable customer benefits to increase their satisfaction. So, the research results show that customer satisfaction and the bank's reputation promote a positive relationship between CSR and bank performance. Doi: 10.28991/ESJ-2022-06-06-012 Full Text: PDF
A Comparative Performance Analysis of Hybrid and Classical Machine Learning Method in Predicting Diabetes Kalaiarasi Sonai Muthu Anbananthen; Mikail Bin Muhammad Azman Busst; Rajkumar Kannan; Subarmaniam Kannan
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-08

Abstract

Diabetes mellitus is one of medical science’s most important research topics because of the disease’s severe consequences. High blood glucose levels characterize it. Early detection of diabetes is made possible by machine learning techniques with their intelligent capabilities to accurately predict diabetes and prevent its complications. Therefore, this study aims to find a machine learning approach that can more accurately predict diabetes. This study compares the performance of various classical machine learning models with the hybrid machine learning approach. The hybrid model includes the homogenous model, which comprises Random Forest, AdaBoost, XGBoost, Extra Trees, Gradient Booster, and the heterogeneous model that uses stacking ensemble methods. The stacking ensemble or stacked generalization approach is a meta-classifier in which multiple learners collaborate for prediction. The performance of the homogeneous hybrid models, Stacked Generalization and the classic machine learning methods such as Naive Bayes and Multilayer Perceptron, k-Nearest Neighbour, and support vector machine are compared. The experimental analysis using Pima Indians and the early-stage diabetes dataset demonstrates that the hybrid models achieve higher accuracy in diagnosing diabetes than the classical models. In the comparison of all the hybrid models, the heterogeneous model using the Stacked Generalization approach outperformed other models by achieving 83.9% and 98.5%. Doi: 10.28991/ESJ-2023-07-01-08 Full Text: PDF
Design of Formative-Summative Evaluation Model Based on Tri Pramana-Weighted Product I Putu Wisna Ariawan; Wayan Sugandini; I Made Ardana; I Made Suwastika Dharma Arta; Dewa Gede Hendra Divayana
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-016

Abstract

Quality in the implementation of e-learning in health universities can be measured both during the learning process and after the learning ends. Measurement can be performed through evaluation activities. The evaluation model needed was an innovative evaluation model that can look for the dominant aspects that determined the quality of e-learning implementation. One breakthrough evaluation model produced in this research to answer this need was the Formative-Summative evaluation model based on the Tri Pramana-Weighted Product. The main objective of this research was to show the quality of the Formative-Summative evaluation model design based on the Tri Pramana-Weighted Product. This research approach was based on the Borg & Gall development model, which focused on the design development stage. The subjects involved in conducting trials of the evaluation model design were 34 respondents. The technique for determining the subject was carried out using a purposive sampling technique. In addition, the tool used to conduct the trial was a questionnaire. The location for filling out the questionnaire was in health universities and colleges in the province of Bali. The technique used to analyze the data from the test results was descriptive quantitative by comparing the average percentage of the quality of the test results with the percentage of quality that refers to the five-scale categorization. The results showed that the percentage of the design quality of the Formative-Summative evaluation model based on the Tri Pramana-Weighted Productof 88.02%. This means that the quality of the evaluation model design was included in the good category. The impact of the results of this research on the field of education was to present an innovation in evaluation activities that made it easier for educational evaluators to measure the quality of e-learning, especially in health universities and colleges. Doi: 10.28991/ESJ-2022-06-06-016 Full Text: PDF
Discovering Future Earnings Patterns through FP-Growth and ECLAT Algorithms with Optimized Discretization Putthiporn Thanathamathee; Siriporn Sawangarreerak
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-07

Abstract

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: PDF
Protection and Exchange of Personal Data on the Web in the Registry of Civil Status Liridon Hoti; Kastriot Dermaku; Selami Klaiqi; Hiflobina Dermaku
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-03

Abstract

Personal data are widely consumed by the central civil registry using interconnection systems as web services and by other institutions that use the data to deliver services. Therefore, the risks of misuse of personal data are major concerns, thus the need for protection and security by improving personal data reliability. This paper analyzes the links between the Central Registry of Civil Status and other institutions and the security of the links by providing data protection and security during communication with web services. This paper emphasizes privacy in information technology and explores modern challenges for every legal entity and natural person. The protection of personal data in related institutions is discussed. The case study concerns the interconnections between the Central Registry of Civil Status in the Republic of Kosovo and other systems in data exchange institutions, such as the modern part of e-government. For data collection, a questionnaire was administered at the institution responsible for the central civil registry to evaluate the protection of personal data and the connection with other institutions from which such personal data originate and are consumed. This paper has also studied the level of applying cryptographic security methods to protect personal data. Doi: 10.28991/ESJ-2023-07-01-03 Full Text: PDF
Modified EDA and Backtranslation Augmentation in Deep Learning Models for Indonesian Aspect-Based Sentiment Analysis . Natasya; Abba Suganda Girsang
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-018

Abstract

In the process of developing a business, aspect-based sentiment analysis (ABSA) could help extract customers' opinions on different aspects of the business from online reviews. Researchers have found great prospective in deep learning approaches to solving ABSA tasks. Furthermore, studies have also explored the implementation of text augmentation, such as Easy Data Augmentation (EDA), to improve the deep learning models’ performance using only simple operations. However, when implementing EDA to ABSA, there will be high chances that the augmented sentences could lose important aspects or sentiment-related words (target words) critical for training. Corresponding to that, another study has made adjustments to EDA for English aspect-based sentiment data provided with the target words tag. However, the solution still needs additional modifications in the case of non-tagged data. Hence, in this work, we will focus on modifying EDA that integrates POS tagging and word similarity to not only understand the context of the words but also extract the target words directly from non-tagged sentences. Additionally, the modified EDA is combined with the backtranslation method, as the latter has also shown quite a significant contribution to the model’s performance in several research studies. The proposed method is then evaluated on a small Indonesian ABSA dataset using baseline deep learning models. Results show that the augmentation method could increase the model’s performance on a limited dataset problem. In general, the best performance for aspect classification is achieved by implementing the proposed method, which increases the macro-accuracy and F1, respectively, on Long Short-Term Memory (LSTM) and Bidirectional LSTM models compared to the original EDA. The proposed method also obtained the best performance for sentiment classification using a convolutional neural network, increasing the overall accuracy by 2.2% and F1 by 3.2%. Doi: 10.28991/ESJ-2023-07-01-018 Full Text: PDF
The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool Mauro Castelli; Diego Costa Pinto; Saleh Shuqair; Davide Montali; Leonardo Vanneschi
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-02

Abstract

The manuscript presents a tool to estimate and predict data accuracy in hospitality by means of automated machine learning (AutoML). It uses a tree-based pipeline optimization tool (TPOT) as a methodological framework. The TPOT is an AutoML framework based on genetic programming, and it is particularly useful to generate classification models, for regression analysis, and to determine the most accurate algorithms and hyperparameters in hospitality. To demonstrate the presented tool’s real usefulness, we show that the TPOT findings provide further improvement, using a real-world dataset to convert key hospitality variables (customer satisfaction, loyalty) to revenue, with up to 93% prediction accuracy on unseen data. Doi: 10.28991/ESJ-2022-06-06-02 Full Text: PDF

Page 41 of 88 | Total Record : 874

 39   40   41   42   43 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol. 10 No. 1 (2026): February Vol. 9 No. 6 (2025): December Vol. 9 No. 5 (2025): October Vol. 9 No. 4 (2025): August Vol. 9 No. 3 (2025): June Vol 9, No 1 (2025): February Vol. 9 No. 1 (2025): February Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education" Vol 8, No 6 (2024): December Vol 8, No 5 (2024): October Vol. 8 No. 5 (2024): October Vol 8, No 4 (2024): August Vol 8, No 3 (2024): June Vol 8, No 2 (2024): April Vol 8, No 1 (2024): February Vol 8 (2024): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 7 (2023): Special Issue "COVID-19: Emerging Research" Vol 7, No 6 (2023): December Vol 7, No 5 (2023): October Vol 7, No 4 (2023): August Vol 7, No 3 (2023): June Vol 7, No 2 (2023): April Vol 7, No 1 (2023): February Vol 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 6 (2022): Special Issue "COVID-19: Emerging Research" Vol 6, No 6 (2022): December Vol 6, No 5 (2022): October Vol 6, No 4 (2022): August Vol 6, No 3 (2022): June Vol 6, No 2 (2022): April Vol 6, No 1 (2022): February Vol 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 5 (2021): Special Issue "COVID-19: Emerging Research" Vol 5, No 6 (2021): December Vol 5, No 5 (2021): October Vol 5, No 4 (2021): August Vol 5, No 3 (2021): June Vol 5, No 2 (2021): April Vol 5, No 1 (2021): February Vol 4 (2020): Special Issue "IoT, IoV, and Blockchain" (2020-2021) Vol 4, No 6 (2020): December Vol 4, No 5 (2020): October Vol 4, No 4 (2020): August Vol 4, No 3 (2020): June Vol 4, No 2 (2020): April Vol 4, No 1 (2020): February Vol 3, No 6 (2019): December Vol 3, No 5 (2019): October Vol 3, No 4 (2019): August Vol 3, No 3 (2019): June Vol 3, No 2 (2019): April Vol 3, No 1 (2019): February Vol 2, No 6 (2018): December Vol 2, No 5 (2018): October Vol 2, No 4 (2018): August Vol 2, No 3 (2018): June Vol 2, No 2 (2018): April Vol 2, No 1 (2018): February Vol 1, No 4 (2017): December Vol 1, No 3 (2017): October Vol 1, No 2 (2017): August Vol 1, No 1 (2017): June More Issue