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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 903 Documents
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Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering Ilya S. Lebedev; Mikhail E. Sukhoparov
Emerging Science Journal Vol 8, No 1 (2024): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-01-025

Abstract

This paper presents a solution for building and implementing data processing models and experimentally evaluates new possibilities for improving ensemble methods based on multilevel data processing models. This study proposes a model to reduce the cost of retraining models when transforming data properties. The research objective is to improve the quality indicators of machine learning models when solving classification problems. The novelty is a method that uses a multilevel architecture of data processing models to determine the current data properties in segments at different levels and assign algorithms with the best quality indicators. This method differs from the known ones by using several model levels that analyze data properties and assign the best models to individual segments of data and training. The improvement consists of using unsupervised clustering of data samples. The resulting clusters are separate subsamples for assigning the best machine-learning models and algorithms. Experimental values of quality indicators for different classifiers on the whole sample and different segments were obtained. The findings show that unsupervised clustering using multilevel models can significantly improve the quality indicators of “weak” classifiers. The quality indicators of individual classifiers improve when the number of data clusters is increased to a certain threshold. The results obtained are applicable to classification when developing models and machine learning methods. The proposed method improved the classification quality indicators by 2–9% due to segmentation and the assignment of models with the best quality indicators in individual segments. Doi: 10.28991/ESJ-2024-08-01-025 Full Text: PDF
Comparison of Activation Functions in Convolutional Neural Network for Poisson Noisy Image Classification Khang Wen Goh; Sugiyarto Surono; M. Y. Firza Afiatin; K. Robiatul Mahmudah; Nursyiva Irsalinda; Mesith Chaimanee; Choo Wou Onn
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-014

Abstract

Deep learning, specifically the Convolutional Neural Network (CNN), has been a significant technology tool for image processing and human health. CNNs, which mimic the working principles of the human brain, can learn robust representations of images. However, CNNs are susceptible to noise interference, which can impact classification performance. Choosing the right activation function can improve CNNs performance and accuracy. This research aims to test the accuracy of CNN with ResNet50, VGG16, and GoogleNet architectures combined with several activation functions such as ReLU, Leaky ReLU, Sigmoid, and Tanh in the classification of images that experience Poisson noise. Poisson noise is applied to each test data to evaluate CNN accuracy. The data used in this study consists of three scenarios of different numbers of classes, namely 3 classes, 5 classes, and 10 classes. The results showed that combining ResNet50 with the ReLU activation function produced the best performance in class recognition in each scenario of the number of classes experiencing Poisson noise interference. The model achieved 97% accuracy for 3-class data, 95% for 5-class data, and 90% for 10-class data. These results show that using ResNet50 with the ReLU activation function can provide excellent resistance to Poisson noise in image processing. It was found that as the number of classes increases, the accuracy of image recognition tends to decrease. This shows that the more complex the image classification task is with a larger number of classes, the more difficult it is for CNNs to distinguish between different classes. Doi: 10.28991/ESJ-2024-08-02-014 Full Text: PDF
Innovative Technology for Managing Biofuel Production from Timber Industry Waste Evgeniy V. Kostyrin; Anna E. Machina
Emerging Science Journal Vol 8, No 3 (2024): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-03-03

Abstract

The relevance of this study is determined by the growing worldwide interest in renewable energy sources against the backdrop of depleting fossil fuel reserves. This study aims to develop an innovative technology for managing biofuel production from wood waste, including a set of interrelated economic and mathematical models focused on maximizing the fuel and energy efficiency of biofuels depending on the location of waste generation, feedstock moisture content, and distance to the biofuel production site. This technology should also combine the main directions of international research in the field of environmental responsibility of countries in terms of carbon dioxide (CO2) emissions and the Paris Climate Agreement. The methodological basis of the research comprises the authors’ innovative technology based on a set of interconnected economic and mathematical models and managerial decision-making systems, methods for nonlinear programming, system analysis, an information approach to the analysis of systems, accepted technological processes, norms, and standards established in the international practice of the timber industry. This innovative technology was implemented in practice using the capabilities of the MathCad and MS Excel software products. The article determines the optimal operating parameters of timber industry enterprises at which the specific thermal energy of the produced biofuel exceeds by at least 15% the thermal energy spent on processing this biofuel as an energy carrier. Wood waste biofuel production is profitable if the distance for feedstock transportation to the production site does not exceed 80 km and the relative humidity of the raw materials does not exceed 60%. Doi: 10.28991/ESJ-2024-08-03-03 Full Text: PDF
Continuous Integration of Risk Management in a Business Process Reengineering: Towards Optimization through Machine Learning Hicham Raffak; Abdellah Lakhouili; Mohamed Mansouri
Emerging Science Journal Vol 8, No 3 (2024): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-03-019

Abstract

To improve their market position and competitiveness, organizations aim to shorten production times, cut costs, and boost the quality and variety of their products. While Business Process Reengineering (BPR) is a good method to achieve these goals, implementing it can be risky: any change to a task in the initial process directly affects the final process's performance in terms of time, cost, and quality. This article, drawing on design science, a literature review, and a field study with responses from executives and managers of two companies in the aerospace and automotive sectors in Morocco to measure their satisfaction and identify their risk management needs, provides an overview of the BPR method, its implementation frameworks and methodologies, and explains the importance of risk management in such projects. It suggests an improved continuous risk management process for BPR projects that enhances the gathering and use of risk management data through machine learning. Doi: 10.28991/ESJ-2024-08-03-019 Full Text: PDF
Analyzing Socio-Academic Factors and Predictive Modeling of Student Performance Using Machine Learning Techniques Romel Al-Ali; Khadija Alhumaid; Maha Khalifa; Said A. Salloum; Rima Shishakly; Mohammed Amin Almaiah
Emerging Science Journal Vol 8, No 4 (2024): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-04-05

Abstract

Understanding the factors that influence student performance is crucial for improving educational outcomes. Thus, this study aims to examine the impact of socio-economic and psychological factors on student performance, less is known about how students' personal attitudes and behaviors across different departments and activities correlate with their academic success. This study employs exploratory data analysis (EDA) to identify trends and relationships within the dataset. Machine learning techniques, such as K-means clustering and Long Short-Term Memory (LSTM) networks, are utilized to model and predict student performance based on their reported behaviors and preferences. The dataset is reduced using Principal Component Analysis (PCA) to enhance the clustering process. The findings suggest significant variations in academic performance based on departmental affiliation, gender, and engagement in certification courses. The LSTM model achieved an accuracy of 91% on the test set, demonstrating substantial predictive capability. However, the classification report reveals that while the model was highly effective in identifying the majority class (label 1), achieving a precision of 91% and a recall of 100%, it failed to correctly predict any instances of the minority class (label 0). The insights from this study could help educators tailor interventions to address the specific needs of students based on their behaviors and departmental affiliations, leading to more personalized education strategies and potentially improving academic outcomes. Doi: 10.28991/ESJ-2024-08-04-05 Full Text: PDF
Developing a Linked Open Data Platform for Folktales in the Greater Mekong Subregion Treepidok Ngootip; Paiboon Manorom; Wirapong Chansanam; Marut Buranarach
Emerging Science Journal Vol 7, No 6 (2023): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-06-06

Abstract

This research paper presents the development of a linked open data (LOD) platform that aims to organize and facilitate access to valuable knowledge about folktales and ethnic groups in the Greater Mekong Subregion countries. The study’s methodology involved the creation of a linked open data platform, structuring folktales’ knowledge, and evaluating its performance through expert assessment. The LOD platform was constructed through Google OpenRefine to establish connections with external data sources, and the RDF files (N-Triples) were deployed on Fuseki Server (Apache Jena) to serve as the SPARQL endpoint for querying the linked open data. The Pubby web app was chosen for further development to provide a user-friendly interface, which customized with the Bootstrap framework, featuring an intuitive homepage and a search box function for simplified data retrieval. For the expert evaluation, the study confirmed that the platform performs a high suitability in terms of congruence, reliability, integrity, understandability, collaboration, accessibility, and connectedness. The developed LOD platform exhibits significant potential for expanding its application to various content domains, offering a valuable resource for accessing and exploring the rich cultural heritage of folktales in the Greater Mekong Subregion countries. Doi: 10.28991/ESJ-2023-07-06-06 Full Text: PDF
Generative Artificial Intelligence and Web Accessibility: Towards an Inclusive and Sustainable Future Patricia Acosta-Vargas; Belén Salvador-Acosta; Sylvia Novillo-Villegas; Demetrios Sarantis; Luis Salvador-Ullauri
Emerging Science Journal Vol 8, No 4 (2024): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-04-021

Abstract

This study examines the accessibility of Generative Artificial Intelligence (AI) tools for people with disabilities, using WCAG 2.2 success criteria as a reference. Significant accessibility issues were identified in the evaluated applications, highlighting barriers mainly affecting disabled users. Integrating accessibility considerations from the beginning of application development and adopting a proactive approach are emphasized. Although challenges are faced, such as the shortage of inclusive training data and opacity in AI decision-making, the need to continue addressing various aspects of accessibility in the field of generative AI tools is acknowledged. These efforts are based on regulatory compliance and ethical principles to ensure equal societal participation, regardless of individual abilities. The fundamental role of accessibility in realizing this vision is highlighted, aligning with the United Nations Sustainable Development Goals, particularly those related to equality, education, innovation, and inclusion. Improving accessibility meets regulatory requirements and contributes to a broader global agenda for a more equitable and sustainable future. Doi: 10.28991/ESJ-2024-08-04-021 Full Text: PDF
Towards Energy Analysis and Efficiency for Sustainable Buildings Tri B. Kurniawan; Deshinta A. Dewi; Fathoni Usman; Fadly Fadly
Emerging Science Journal Vol 7, No 6 (2023): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-06-022

Abstract

Energy analysis that leads to energy efficiency becomes one of the most important factors in the building design process, especially considering the current energy crisis and the effects of global warming. Building designers greatly benefited from the review and analysis to optimize energy usage for the building in the design stage. While the current design approach is mostly done manually, this paper presents the automated version using the developed BIM plugin. It eases the designer’s choice of alternative plans that yield an effectively designed building. The development of energy analysis in the application aims to promote energy efficiency by calculating the energy consumption estimation based on energy codes MS2680 and MS1525. This application is improved by a simulation that uses the Building Information Modeling (BIM) platform and extracts the necessary parameters from the BIM model with the aid of the created plugins. This study measured energy consumption and efficiency using the two primary parameters of Overall Thermal Transfer Value (OTTV) and Roof Thermal Transfer Value (RTTV). According to the results, OTTV reaches 42.72% and RTTV reaches 8.02%, both of which respectively meet Malaysian Energy Code limits of less than 50% and 25%. Doi: 10.28991/ESJ-2023-07-06-022 Full Text: PDF
Cycloartobiloxanthone, a Flavonoid with Antidiabetic, Antibacterial and Anticancer Activities from Artocarpus kemando Miq. Tati Suhartati; Antin S. Prihatin; Armidla N. Kurniati; Hendri Ropingi; Yandri Yandri; Sutopo Hadi
Emerging Science Journal Vol 8, No 1 (2024): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-01-04

Abstract

In this present work, a cycloartobiloxanthone compound was isolated from the stem wood and root bark of the Pudau plant (Artocarpus kemando Miq.). The purity of the compound was determined using thin-layer chromatography with three eluent systems and melting point tests. The sample was then analyzed using UV-Vis, IR, and NMR spectroscopy, ensuring that the compound is cycloartobilox-anthone. The cycloartobiloxanthone compound was obtained in a yellow crystalline form with a melting point of 285.1-294°C. The compound was then investigated for antidiabetic, anticancer, and antibacterial properties, showing that the compound has an anti-diabetic effect by reducing the activity of the α-amylase enzyme, with the highest percentage of inhibition of 48.53 ± 1.84% achieved with the use of 1000 ppm of the compound. Cycloartobiloxanthone isolated has an IC50 value of 9.21 µg/mL for anticancer activity against MCF-7 cells, indicating that the compound shows active cytotoxic actions. Staphylococcus aureus was very strongly inhibited by the compound in the antibacterial test at all doses, whereas for Salmonellasp., the activity was categorized as moderate at concentrations of 0.4 and 0.3 mg/disc and strong at 0.5 mg/disc. The anti-diabetic, anti-cancer, and antibacterial bioactivity studies indicated that the cycloartobiloxanthone compound isolated has a broad spectrum of pharmacological actions, indicating that the compound has promising potential. Doi: 10.28991/ESJ-2024-08-01-04 Full Text: PDF
Design and Study the Performance of a CMOS-Based Ring Oscillator Architecture for 5G Mobile Communication Abdul Rahman; Siddharth Kishore; A. R. Abdul Rajak
Emerging Science Journal Vol 8, No 1 (2024): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-01-020

Abstract

Oscillator circuits are used to make accurate and reliable clock signals for systems as simple as a wristwatch and as complicated as satellites, which are important for long-distance communication. There are many ways to build an oscillator circuit, using either passive or active parts. Each option has pros and cons, but at the current level of mobile communication development, the most important things are interoperability and low power use. This need has driven the development of compact, battery-operated electronics, and Very Large-Scale Integration (VLSI)-based ring oscillators provide the ideal solution. These oscillators ought to dissipate less power, have a large tuning range, and be compact. The paper presents a novel Complementary Metal Oxide Silicon (CMOS) ring oscillator that serves as a Voltage Controlled Oscillator. The suggested architecture utilizes the advantages of both a current-starved ring oscillator and a negative-skewed delay by combining their constituent parts. The proposed architecture has a control voltage of 1.15 V and a supply voltage of 2 V, generating a 9.35 GHz dominant frequency with a 13.82% harmonic distortion between the inputs and outputs. The proposed architecture can implement 5G-based applications that require high frequency and low power by carefully selecting the passive components within the design. Doi: 10.28991/ESJ-2024-08-01-020 Full Text: PDF

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