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 25 Documents
 1   2   3 
Search results for , issue "Vol 7, No 4 (2023): August" : 25 Documents clear
Impact of Continuing Education on Employee Productivity and Financial Performance of Banks Muhamet Hajdari; Fidan Qerimi; Arbëresha Qerimi
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-09

Abstract

Objectives: This research aims to measure the impact of continuing education on employee productivity and that of the latter on the financial performance of commercial banks in Kosovo. Methods: A quantitative approach was employed to achieve the research objectives and questions. The statistical population comprised 3636 employees working at commercial banks operating in Kosovo. We obtained data from the Central Bank of Kosovo (CBK). A sample of 360 employees was then determined using Slovin's formula to include the representative sample. Findings: The Ordinary Least-Squares (OLS) model demonstrated that continuing education affects employee productivity, and the latter affects the financial performance of commercial banks in Kosovo. The findings indicated that 40.2% of employee productivity is explained by continuing education, while 20.4% of financial performance is explained by employee productivity. Novelty/improvement:This research showed that commercial banks could receive feedback on the importance of employees’ continuing education in increasing their productivity and, subsequently, the bank's financial performance. This can improve effectiveness and productivity at work and the organization's financial results, especially cost optimization and income generation. Doi: 10.28991/ESJ-2023-07-04-09 Full Text: PDF
Industrial, Collaborative and Mobile Robotics in Latin America: Review of Mechatronic Technologies for Advanced Automation Jose Cornejo; S. Barrera; C. A. Herrera Ruiz; F. Gutierrez; M. O. Casasnovas; Leonardo Kot; M. A. Solis; R. Larenas; F. Castro-Nieny; M. R. Arbulú Saavedra; R. Rodríguez Serrezuela; Y. Muñoz Londoño; Alejandro Serna; D. Ortega-Aranda; S. Aranda-Miramontes; I. Chang; M. Cardona; A. Carrasquilla-Batista; R. Palomares; R. Rodriguez; Ruben Parisuaña; Miguel Bórquez; Oscar Navarro; Fernando Sanchez; I. A. Bonev; Jonathan Coulombe; F. Martín Rico; B. L. Treviño-Elizondo; H. García-Reyes; A. Sollazzo; A. Dubor; A. Markopoulou; C. De Marinis; Marco Chacin; Andres Mora; M. Pérez-Ruiz; A. Ribeiro; E. A. L'Huillier
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-025

Abstract

Mechatronics and Robotics (MaR) have recently gained importance in product development and manufacturing settings and applications. Therefore, the Center for Space Emerging Technologies (C-SET) has managed an international multi-disciplinary study to present, historically, the first Latin American general review of industrial, collaborative, and mobile robotics, with the support of North American and European researchers and institutions. The methodology is developed by considering literature extracted from Scopus, Web of Science, and Aerospace Research Central and adding reports written by companies and government organizations. This describes the state-of-the-art of MaR until the year 2023 in the 3 Sub-Regions: North America, Central America, and South America, having achieved important results related to the academy, industry, government, and entrepreneurship; thus, the statistics shown in this manuscript are unique. Also, this article explores the potential for further work and advantages described by robotic companies such as ABB, KUKA, and Mecademic and the use of the Robot Operating System (ROS) in order to promote research, development, and innovation. In addition, the integration with industry 4.0 and digital manufacturing, architecture and construction, aerospace, smart agriculture, artificial intelligence, and computational social science (human-robot interaction) is analyzed to show the promising features of these growing tech areas, considering the improvements to increase production, manufacturing, and education in the Region. Finally, regarding the information presented, Latin America is considered an important location for investments to increase production and product development, taking into account the further proposal for the creation of the LATAM Consortium for Advanced Robotics and Mechatronics, which could support and work on roboethics and education/R+D+I law and regulations in the Region. Doi: 10.28991/ESJ-2023-07-04-025 Full Text: PDF
Mapping of Sensing Performance of Concentric and Non-Concentric Silver Nanoring Mulda Muldarisnur; Ilham Perdana; E. Elvaswer; Dwi Puryanti
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-04

Abstract

Sensors play a critical role in improving overall human quality of life. They have been employed in most aspects of our lives. A recently emerging sensing platform is based on plasmonic resonance at the boundary of metals and dielectrics. Localized surface plasmon resonances–based sensors offer miniaturization, a simple setup, and relatively high sensitivity for real-time measurements. The reported figure of merit (FOM) of the LSPR-based sensor is generally limited, primarily due to its broad resonance peak. Nanorings composed of metal nanoparticles are known for their broad-range resonance tunability, high field localization, and large sensing area. Asymmetry of the nanoring with the introduction of core offset relaxes the selection rule for mode mixing, thus resulting in a narrower resonance peak. This may overcome broad resonance peak restriction. Concentric and non-concentric nanorings were simulated using the boundary element method implemented with the MNPBEM toolbox. We map the performance of nanoring sensors over a wide range of geometrical variations, namely, diameter, ring shell thickness, and the offset of the inner ring to the center of the outer ring wall (core offset). Sensitivity and FOM were found to rely substantially on the nanoring size parameters. The sensing performance map helps to obtain optimized nanoring parameters for the intended spectral range region. The obtained high sensitivity and FOM are much higher than the data available in the literature over visible and NIR ranges. The findings demonstrate the potential of nanorings for biosensing applications. Doi: 10.28991/ESJ-2023-07-04-04 Full Text: PDF
The Effect of Physical Cues on Customer Loyalty: Based on the Mediating Effect of Customer Engagement and Value Co-creation Xin-Mei Ye; Hira Batool; Shi-Zheng Huang
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-020

Abstract

As a new business model, e-commerce live broadcasting has great value in the commercial field. Based on value co-creation theory and the stimulus-organic-response model, this study explores the influence of physical cues in e-commerce live broadcast scenes on customer loyalty. Using the audience of China's e-commerce live broadcasting platform as the research object, 404 valid data points were collected through a questionnaire survey, and a structural equation analysis model was adopted to explore the relationship among the physical clues of the e-commerce live broadcasting scene, customer engagement, value co-creation, and customer loyalty and to verify the mediating effect of customer engagement and value co-creation. The research shows that aesthetic appeal, layout, and function have a positive impact on customer engagement, but financial security has no positive impact on customer engagement. In addition, value co-creation has an intermediary effect, and customer engagement and value co-creation have a double intermediary effect on physical cues and customer loyalty in e-commerce live broadcast scenes. The research not only expands the theory of value co-creation and scene but also provides practical reference value for e-commerce live broadcasting platforms and enterprises and promotes the design of physical cues in e-commerce live broadcasting scenes to improve customer loyalty. Doi: 10.28991/ESJ-2023-07-04-020 Full Text: PDF
Stand up Against Bad Intended News: An Approach to Detect Fake News using Machine Learning Nafiz Fahad; K. O. Michael Goh; Md. Ismail Hossen; K. M. Shahriar Shopnil; Israt Jahan Mitu; Md. A. Hossain Alif; Connie Tee
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-015

Abstract

The purpose of this approach is to find out the effects and efficiently detect fake news by using a publicly available dataset. However, it is difficult for human beings to judge an article's truthfulness manually, which is why This paper mainly wanted to cure the effect and to found out an automated fake news detection system with benchmark accuracy by using a machine learning classifier, which must be higher than other recent research works. In essence, this work’s target is to find out an efficient way to detect fake and real news, and it also the target is to compare with existing work where researchers used machine learning classifiers and deep learning architecture. The proposed approach depended on a systematic literature review and a publicly available dataset where 7796 news data are recorded with 50% real and 50% fake news. The best and benchmark accuracy is 93.61%, achieved by the Support Vector Machine (SVM) among the used Random Forest, Decision Tree, KNN, and Logistics Regression classifiers, and the achieved accuracy is better than the exciting recent research works. Moreover, fake news is detected, people are able to differentiate between fake or real news, and effects are cured when people used SVM. Doi: 10.28991/ESJ-2023-07-04-015 Full Text: PDF
An Optimized Machine Learning and Deep Learning Framework for Facial and Masked Facial Recognition Putthiporn Thanathamathee; Siriporn Sawangarreerak; Prateep Kongkla; Dinna Nina Mohd Nizam
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-010

Abstract

In this study, we aimed to find an optimized approach to improving facial and masked facial recognition using machine learning and deep learning techniques. Prior studies only used a single machine learning model for classification and did not report optimal parameter values. In contrast, we utilized a grid search with hyperparameter tuning and nested cross-validation to achieve better results during the verification phase. We performed experiments on a large dataset of facial images with and without masks. Our findings showed that the SVM model with hyperparameter tuning had the highest accuracy compared to other models, achieving a recognition accuracy of 0.99912. The precision values for recognition without masks and with masks were 0.99925 and 0.98417, respectively. We tested our approach in real-life scenarios and found that it accurately identified masked individuals through facial recognition. Furthermore, our study stands out from others as it incorporates hyperparameter tuning and nested cross-validation during the verification phase to enhance the model's performance, generalization, and robustness while optimizing data utilization. Our optimized approach has potential implications for improving security systems in various domains, including public safety and healthcare. Doi: 10.28991/ESJ-2023-07-04-010 Full Text: PDF
Improving the Theoretical and Methodological Framework for Implementing Digital Twin Technology in Various Sectors of Agriculture Alexander Semin; Denis Mironov; Mikhail Kislitskiy; Alexander Zasypkin; Valery Ivanov
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-05

Abstract

The aim of this study is to systematize and improve the theoretical and methodological framework for implementing digital twin technology. The study focuses on digital twins in agriculture. This paper is designed to solve the scientific problem associated with the development of a methodological framework for the implementation of digital twins in the work of agricultural organizations. Using methods of analysis of socio-economic phenomena and processes on the basis of a set of scientific approaches, economic-statistical analysis, and others, the study considers the importance of digital twins of agricultural machinery and equipment, identifies trends in agriculture determined by digitalization, and suggests promising areas for digital twins of agricultural machinery and equipment. This paper also examines the theoretical basis for the implementation of digital twin technology in the agricultural sector of production. New research results complement the theoretical provisions on the essence of digital twin technology; develop the methodological provisions of digital twin technology, represented by the study of their significance, principles, and features of operation. The study may be seen as academically novel as it reveals the prerequisites for implementing digital technology in agriculture as well as clarifies and improves the theoretical and methodological provisions of the application of digital twin technology in various sectors of agriculture. Doi: 10.28991/ESJ-2023-07-04-05 Full Text: PDF
Advanced Genetic Programming vs. State-of-the-Art AutoML in Imbalanced Binary Classification Franz Frank; Fernando Bacao
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-021

Abstract

The objective of this article is to provide a comparative analysis of two novel genetic programming (GP) techniques, differentiable Cartesian genetic programming for artificial neural networks (DCGPANN) and geometric semantic genetic programming (GSGP), with state-of-the-art automated machine learning (AutoML) tools, namely Auto-Keras, Auto-PyTorch and Auto-Sklearn. While all these techniques are compared to several baseline algorithms upon their introduction, research still lacks direct comparisons between them, especially of the GP approaches with state-of-the-art AutoML. This study intends to fill this gap in order to analyze the true potential of GP for AutoML. The performances of the different tools are assessed by applying them to 20 benchmark datasets of the imbalanced binary classification field, thus an area that is a frequent and challenging problem. The tools are compared across the four categories average performance, maximum performance, standard deviation within performance, and generalization ability, whereby the metrics F1-score, G-mean, and AUC are used for evaluation. The analysis finds that the GP techniques, while unable to completely outperform state-of-the-art AutoML, are indeed already a very competitive alternative. Therefore, these advanced GP tools prove that they are able to provide a new and promising approach for practitioners developing machine learning (ML) models. Doi: 10.28991/ESJ-2023-07-04-021 Full Text: PDF
On the Locating Rainbow Connection Number of Trees and Regular Bipartite Graphs Ariestha W. Bustan; A. N. M. Salman; Pritta E. Putri; Zata Y. Awanis
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-016

Abstract

Locating the rainbow connection number of graphs is a new mathematical concept that combines the concepts of the rainbow vertex coloring and the partition dimension. In this research, we determine the lower and upper bounds of the locating rainbow connection number of a graph and provide the characterization of graphs with the locating rainbow connection number equal to its upper and lower bounds to restrict the upper and lower bounds of the locating rainbow connection number of a graph. We also found the locating rainbow connection number of trees and regular bipartite graphs. The method used in this study is a deductive method that begins with a literature study related to relevant previous research concepts and results, making hypotheses, conducting proofs, and drawing conclusions. This research concludes that only path graphs with orders 2, 3, 4, and complete graphs have a locating rainbow connection number equal to 2 and the order of graph G, respectively. We also showed that the locating rainbow connection number of bipartite regular graphs is in the range of r-⌊n/4⌋+2 to n/2+1, and the locating rainbow connection number of a tree is determined based on the maximum number of pendants or the maximum number of internal vertices. Doi: 10.28991/ESJ-2023-07-04-016 Full Text: PDF
Architectural Model and Modified Long Range Wide Area Network (LoRaWAN) for Boat Traffic Monitoring and Transport Detection Systems in Shallow Waters Diaz Saputra; Ford Lumban Gaol; Edi Abdurachman; Dana Indra Sensuse; Tokuro Matsuo
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-011

Abstract

Monitoring the movement of boats in shallow waters requires a real-time monitoring system. However, for small-size wooden boats, they are still monitored manually, and data is unavailable in real time, which makes it difficult to effectively monitor them. The integration of IoT platforms with the boat monitoring system is a challenging task, especially in the transport system. This paper has the objective of developing an architectural model of a modified LoRaWAN-based boat monitoring system that is connected to a GPS-based mobile device and base station. The proposed architectural model is an integration of Bluetooth Low Energy (BLE) and LoRaWAN networks, which are also tested in real time to solve the boat traffic monitoring issues. The field tests with parameters of signal transmission, location coordinates, and position of the boats are also presented. The analysis result shows the proposed model is suitable for waters with high noise levels, especially in shallow water and delta rivers. The signal noise can be reduced by extracting the real-time data. In addition, signal interference can be minimized. The performance of this system is also compared to the reference system in real conditions, which shows an adequate correlation result. This proof of concept forms an important basis for deploying it for large-scale applications and commercialization capabilities. Doi: 10.28991/ESJ-2023-07-04-011 Full Text: PDF

Page 1 of 3 | Total Record : 25

 1   2   3 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue 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 (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