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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
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Search results for , issue "Vol 8, No 6 (2024): December" : 25 Documents clear
Enhancing Efficiency: The Impact of Cloud Computing Adoption on Small and Medium Enterprises Performance Abdalla, Reem A.; Ramayah, T.; Sankar, Jayendira P.; Hidaytalla, Lamya A.; John, Jeena Ann
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-017

Abstract

This study investigated the factors influencing cloud computing adoption (CCA) and its impact on organizational performance (OP) among SMEs employees in Bahrain. The study used an online survey approach, which includes Likert scale questions to assess attitudes and views, multiple-choice questions for categorical data, and open-ended questions to obtain qualitative insights. The target audience comprises 300-350 small and medium-sized enterprises (SMEs) in Bahrain currently utilizing cloud computing technology, and 314 useful responses were received. A mixed two-step sampling technique was initiated by convenience sampling. Then, snowball sampling was used to guarantee the inclusion of various SME categories, thus ensuring representativeness. The measurements are derived from validated instruments used in academic research, with the questionnaire incorporating elements adapted from the studies conducted. Participants' responses to the Likert scale are analyzed using SmartPLS 4 to understand their perspectives. Full collinearity was used to assess common method bias, and VIF values below 3.3 indicated no bias. The measuring model's validity and reliability were evaluated by loadings, AVE, CR, and discriminant validity tests (HTMT), which ensured all constructs fulfilled thresholds. Path coefficients, standard errors, t-values, and p-values were used to evaluate the structural model using 10,000-sample bootstrapping. The research findings indicate that both Perceived Ease of Use (PEU) and Perceived Usefulness (PU) have a substantial impact on Cloud Computing Adoption (CCA), which in turn improves the performance of Bahraini SMEs. PEU and PU directly impact CCA while indirectly improving Organizational Performance (OP) by increasing cloud computing usage. These findings emphasize the importance of user-friendly and beneficial cloud solutions in increasing cloud computing adoption and enhancing business outcomes for SMEs. Doi: 10.28991/ESJ-2024-08-06-017 Full Text: PDF
Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis Almeida, Fernando Pedro Silva; Castelli, Mauro; Côrte-Real, Nadine
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-01

Abstract

Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices. Doi: 10.28991/ESJ-2024-08-06-01 Full Text: PDF
The Impact of Artificial Intelligence on Digital Marketing: Leveraging Potential in a Competitive Business Landscape Hendrayati, Heny; Achyarsyah, Mochamad; Marimon, Frederic; Hartono, Ulil; Putit, Lennora
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-012

Abstract

This study aims to thoroughly investigate how Artificial Intelligence (AI) is strategically integrated into digital marketing practices and its consequential effects on Indonesia’s fiercely competitive business environment. Employing a quantitative research approach, this study meticulously examines Indonesian enterprises’ prevailing strategies for AI utilization. The research method employed in this study is quantitative, with the unit of analysis being Indonesian companies. The sample size comprises 100 companies selected through the stratified random sampling technique. Analysis of the data is conducted using the SPSS statistical package. Through detailed analysis of survey data and advanced statistical techniques, the research reveals a significant positive correlation between the integration of AI in digital marketing and improved marketing effectiveness. The study highlights a noticeable increase in customer engagement metrics and noteworthy enhancements in conversion rates among businesses proficient in leveraging AI technologies, further reinforcing this correlation. Additionally, the findings suggest that companies embracing AI demonstrate significantly heightened adaptability to the constantly evolving market dynamics, strengthening their competitive positioning. These insightful discoveries underscore the critical importance of harnessing AI’s transformative capabilities within digital marketing strategies to sustain and bolster a competitive edge in the marketplace. Furthermore, the study discusses its contributions to existing knowledge and provides practical implications for marketers and business policymakers in Indonesia. Doi: 10.28991/ESJ-2024-08-06-012 Full Text: PDF
Development of a Cloud Service for Comprehensive Research of Polymer Synthesis Processes Miftakhov, Eldar; Mustafina, Sofya; Kashnikova, Anastasiya; Akimov, Andrey
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-023

Abstract

The issue of digitalization in chemical technology production is currently quite pressing, and the available computational infrastructure is insufficient for assessing the technological properties of the products obtained through mathematical modeling tools. This problem is particularly relevant for polymer synthesis processes, where standard empirical evaluations require enormous computational resources, and existing methods and algorithms prove ineffective when organizing multiple computational trials to select optimal production scenarios. The aim of this study is to develop a cloud-based digital service that enables comprehensive research into complex physicochemical processes occurring via polymerization mechanisms. The implementation of all algorithms is based on the use of kinetic and statistical approaches to modeling, and the embedded calculation methods are adapted to the specifics of polymerization processes. The conceptual framework of the developed cloud service is represented by a three-tier network architecture, and the established mechanism of network interaction allows the service to operate in a 24-hour multi-user mode. Task execution in the remote environment and the distribution of computational resources are handled using Docker containerization technology, which provides software-level virtualization within the operating system. The storage subsystem is managed by the MongoDB database management system, which supports distributed information storage functions. The organization of test computational experiments in evaluating the detailed properties of polymer products allowed for the assessment of the system’s core logic in web interface mode and the adequacy of the obtained calculation results. Doi: 10.28991/ESJ-2024-08-06-023 Full Text: PDF
Managerial Recommendations for Enhancing Green Consumption Behavior and Sustainable Consumption Nga, Lu Phi; Tam, Phan Thanh
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-07

Abstract

The green consumerism movement is gaining steam in emerging nations with middle-income or higher populations, such as Vietnam, and is particularly well-liked in affluent countries. In addition, the importance of green consumerism is gaining significant traction, alongside efforts to promote ecologically friendly production and consumption. As the economy progressed, people's living standards improved, leading to a growing need for high-quality, safe products and services. This is particularly true for items that directly serve people and contribute to their everyday lives. Therefore, the article aims to evaluate the factors affecting green consumer behavior and sustainable consumption based on the structural equation model with the least squares method to test their hypotheses. The data were applied in the study through a survey of 360 consumers in 04 big cities in Vietnam. Research results showed that seven factors impact green consumption behavior, including (1) environmental awareness, (2) green product characteristics, (3) green marketing, (4) perceptions about green product prices, (5) social influence, (6) environmental policy, and (7) green consumption policy with significance 0.01. The finding explores green consumption behavior influencing sustainable consumption with a significance of 0.01. The practical implication helps managers, policymakers, and manufacturers consider applications to improve humanity and behavior green consumption in the global context of moving towards sustainable green development. The theory implication is to change behavior to improve greening production, reduce pollution and greenhouse emissions, and move towards sustainable development, bringing many practical economic and social benefits and intangible value for businesses. Simultaneously, the novelty of the study aids enterprises in staying abreast of this trend, enabling them to seize possibilities for fast growth, extend their market presence, and capitalize on governmental backing for businesses. Doi: 10.28991/ESJ-2024-08-06-07 Full Text: PDF
Improving the Reliability of Biometric Authentication Processes Using a Model for Reducing Data Drift Kuklin, Vladimir Zh.; Ivanov, Naur Z.; Muranov, Alexander N.; Alexandrov, Islam A.; Linskaya, Elena Yu.
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-018

Abstract

Modern 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: PDF
Bio-Mechatronics Development of Robotic Exoskeleton System With Mobile-Prismatic Joint Mechanism for Passive Hand Wearable-Rehabilitation Vargas, Mariela; Mayorga, J.; Oscco, B.; Cuyotupac, V.; Nacarino, A.; Allcca, D.; Gamarra-Vásquez, L.; Tejada-Marroquin, G.; Reategui, M.; Maldonado-Gómez, R. R.; Vasquez, Y.; de la Barra, Daira; Tapia-Yanayaco, P.; Charapaqui, Sandra; V. Rivera, Milton; Palomares, R.; Ramirez-Chipana, M.; Cornejo, Jorge; Cornejo, José; De La Cruz-Vargas, Jhony A.
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-02

Abstract

The World Health Organization (WHO) estimates that 15 million people are affected by stroke each year, causing deterioration of the upper limb, which is reflected in 70-80% of them, decreasing the performance of daily activities and quality of life, mainly affecting hand functions. Thus, the purpose of this study is to present a high-quality alternative to recover muscle tone and mobility, consisting of a hand-exoskeleton for passive rehabilitation. It covers a motion protocol for each finger and pressure sensors to give a safety pressure range during the gripping function. The bio-design method covers standards (ISO 13485 and VDI 2206) based on biomechanic and anthropometric fundamentals, where Fusion 360 was used for mechanical development and electrical-electronic circuit schematics. The prototyping process was based on 3D printing using polylactic acid (PLA); also, the actuators were servomotors DS3218, the pressure sensors were RP-C7.6-LT, and the microcontroller was Arduino Nano. The system has been validated by the Institute of Research in Biomedical Sciences (INICIB) at the Ricardo Palma University, where the novelty of this work lies in the introduction of a new mobile-prismatic joint mechanism. In conclusion, favorable results were achieved regarding the complete flexion and extension of the fingers (91.6% acceptance rate, tested in 100 subjects), so the next step proposes that the wearable device will be used in the Physical Medicine and Rehabilitation Departments of Medical Centers. Doi: 10.28991/ESJ-2024-08-06-02 Full Text: PDF
Enterprise Innovation Decision-Making Towards Green and Sustainability from the Perspective of Cognitive Innovation Tran, Thi Anh Phuong; Hou, Xinshuo
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-013

Abstract

Although numerous current studies on green consumption and sustainable enterprise development have been carried out, the majority of them examined the issues from customers' viewpoints. This study aims to explore the mechanism that shapes firm innovation decision-making in the context of green and sustainable development from the perspective of business awareness under the impact of customer expectations. The study conducted an online survey (via Google Forms) with the participation of 301 employees from different enterprises in the Mekong Delta, Vietnam. To restrict the common method biases, Cronbach’s alpha was checked by using SPSS to ensure the reliability of the initial scales. Based on a deductive approach and testing hypotheses through evaluating the measurement model and structural model using SmartPLS software, the research results determined the mechanism of forming firm innovation decisions in this study via the impact of customer expectations as a stimulating factor leading to awareness of innovation. Customer expectations were positively associated with perceived marketing innovation. Perceived marketing innovation was not only positively associated with perceived process innovation but also related to firm innovation. Similarly, perceived process innovation was significantly positively associated with firm innovation. In alignment with research findings, significant practical and academic contributions were also proposed. Doi: 10.28991/ESJ-2024-08-06-013 Full Text: PDF
PM2.5 IoT Sensor Calibration and Implementation Issues Including Machine Learning Srisang, Wacharapong; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Khongthong, Chonthicha; Piamonte, John Rex P.; Sparrow, Elena B.
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-08

Abstract

Affordable 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: PDF
SlowFast-TCN: A Deep Learning Approach for Visual Speech Recognition Ha, Nicole Yah Yie; Ong, Lee-Yeng; Leow, Meng-Chew
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-024

Abstract

Visual Speech Recognition (VSR), commonly referred to as automated lip-reading, is an emerging technology that interprets speech by visually analyzing lip movements. A challenge in VSR where visually distinct words produce similar lip movements is known as homopheme problem. Visemes are the basic visual units of speech that are produced by the lip movements and positions. Furthermore, visemes are typically having shorter durations than words. Consequently, there is less temporal information for distinguishing between different viseme classes, leading to increased visual ambiguity during classification. To address this challenge, viseme classification must not only extract lip image spatial features, but also to handle visemes of varying durations and temporal features. Therefore, this study proposed a new deep learning approach SlowFast-TCN. SlowFast network is used as the frontend architecture to extract the spatio-temporal features of the slow and fast pathways. Temporal Convolutional Network (TCN) is used as the backend architecture to learn the features from the frontend to perform the classification. A comparative ablation analysis to dissect each component of the proposed SlowFast-TCN is performed to evaluate the impact of each component. This study utilizes a benchmark dataset, Lip Reading in Wild (LRW), that focuses on English language. Two subsets of the LRW dataset, comprising of homopheme words and unique words, represent the homophemic and non-homophemic dataset, respectively. The proposed approach is evaluated on varying lighting conditions to assess its performance in real-world scenarios. It was found that illumination can significantly affect the visual data. Key performance metrics, such as accuracy and loss are used to evaluate the effectiveness of the proposed approach. The proposed approach outperforms traditional baseline models in accuracy while maintaining competitive execution time. Its dual-pathway architecture effectively captures both long-term dependencies and short-term motions, leading to better performance in both homophemic and non-homophemic datasets. However, it is less robust when dealing with non-ideal lighting scenarios, indicating the need for further enhancements to handle diverse lighting scenarios. Doi: 10.28991/ESJ-2024-08-06-024 Full Text: PDF

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