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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 20 Documents
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Search results for , issue "Vol 7, No 1 (2023): February" : 20 Documents clear
Management of Continuous Professional Development through Competency-Based Training Model for Junior High School Teachers . Sherly; Syawal Gultom; Eka Daryanto; . Nasrun
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-2023-07-01-014

Abstract

One of the strategies to improve teachers' skills and professionalism is the practice of continuous professional development. To fulfill the demands of students and learning in the 21st century, teachers must have a strong foundation and knowledge of ongoing professional development. However, a-business-as-usual continuous professional development does not contribute to any significant improvements for teachers of junior high school in Pematangsiantar. The objective of this research was to develop a management model for teachers’ continuous development. This is carried out through competency-based training with a heutagogy approach. A development model called ADDIE, which stands for Analysis, Design, Develop, Implementation, and Evaluation was used to develop the management model. The subject of this research was 80 junior high school students in Pematangsiantar. They were divided into 30 and 50 people for the limited trial and the broad trial, respectively. Questionnaires were distributed as the instrument of data collection. The data was then analyzed using a statistical descriptive analysis technique. The research found that the effectiveness of the competency-based training management model was measured by the N-Gain score, in which the G-value was 0.79 and 0.82 for limited trial and broad trial, respectively, which was in the high category. The results of the assessment of the effectiveness of the model obtained an average value of 94%, which was in the "very good" category. The effectiveness of the training program was assessed from the aspects of reactions, learning, behavior, and results, and the results show that the assessment of the classroom action research training program obtained an average score of 92%, or a very good category, which means that the classroom action research training program is very effective in improving teachers’ competence. The significance of this model has been proven to give an innovative solution to teachers’ continuous professional development. Doi: 10.28991/ESJ-2023-07-01-014 Full Text: PDF
Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition I Made Agus Wirawan; Retantyo Wardoyo; Danang Lelono; Sri Kusrohmaniah
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-2023-07-01-09

Abstract

The convolution process in the Capsule Network method can result in a loss of spatial data from the Electroencephalogram signal, despite its ability to characterize spatial information from Electroencephalogram signals. Therefore, this study applied the Continuous Capsule Network method to overcome problems associated with emotion recognition based on Electroencephalogram signals using the optimal architecture of the (1) 1st, 2nd, 3rd, and 4th Continuous Convolution layers with values of 64, 128, 256, and 64, respectively, and (2) kernel sizes of 2×2×4, 2×2×64, and 2×2×128 for the 1st, 2nd, and 3rd Continuous Convolution layers, and 1×1×256 for the 4th. Several methods were also used to support the Continuous Capsule Network process, such as the Differential Entropy and 3D Cube methods for the feature extraction and representation processes. These methods were chosen based on their ability to characterize spatial and low-frequency information from Electroencephalogram signals. By testing the DEAP dataset, these proposed methods achieved accuracies of 91.35, 93.67, and 92.82% for the four categories of emotions, two categories of arousal, and valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 94.23, 96.66, and 96.05% for the four categories of emotions, the two categories of arousal, and valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 96.20, 97.96, and 97.32% for the four categories of emotions, the two categories of arousal, and valence, respectively. Doi: 10.28991/ESJ-2023-07-01-09 Full Text: PDF
Assessment of the Concentration and Structure of the Bioeconomy: The Regional Approach Aina Muska; Dina Popluga; Irina Pilvere
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-2023-07-01-05

Abstract

The bioeconomy is seen as crucial for achieving a climate-neutral Europe by 2050; therefore, it is important to monitor and illustrate the performance and trends of the bioeconomy development not only at state level but also in regions. The research aims to develop a methodology for the identification of bioeconomy concentration and the structure of bioeconomy enterprises at a regional level. The methodology of the research is based on four main steps: (1) defining the framework of bioeconomy enterprises; (2) setting data sources and research limitations; (3) estimating the bio-based share of bioeconomy industries; (4) estimating a location quotient which provide data serving to assess the level of concentration of the factor analysed. The research is based on the analysis of 119 municipalities and 30 387 bioeconomy enterprises by using a location quotient. The research results revealed that the municipalities could be classified into three groups according to the concentration of the bioeconomy. Such a classification of municipalities allowed us to identify the strengths and weaknesses of each municipality in the field of bioeconomy and potential development possibilities. The novelty of the research provides a methodological background for municipal-level monitoring of the bioeconomy and suggestions for improving the uneven development of the bioeconomy. Doi: 10.28991/ESJ-2023-07-01-05 Full Text: PDF
A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering Yousra Mahmoudi; Nadjet Zioui; Hacène Belbachir; Mohamed Tadjine; Abdelmounaam Rezgui
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-2023-07-01-020

Abstract

Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PDF
Development and Implications of Controlling in the Public Sector Margarita L. Vasyunina; Mikhail E. Kosov; Nataliya S. Shmigol; Inna V. Lipatova; Eli A. Isaev; Irina S. Medina; Natalya A. Guz
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-2023-07-01-015

Abstract

Controlling is essential for public organizations to deliver optimal performance. However, the existing literature lacks sufficient knowledge to help organizations implement better strategies to enhance control. Therefore, this study examined the concept of control in the public sector, its impact on organizational efficiency, and a key focus on implementation. This study adopted a mixed approach (qualitative and quantitative) to study control in the public sector. The literature review was used to gather qualitative data, and a survey was conducted among the managers working in Russian public organizations to determine their responses to controlling practices. The results were compared and analyzed to provide implications and recommendations. It was noted through the results that control in public organizations depends on various factors like controlling approaches and tools, organizational culture, the autonomy of management, and functional control of organizations. Each of these aspects contributes positively toward control and improves public organizations’ efficiency. Therefore, these aspects should be the core focus of public organizations to ensure greater control and efficiency. This research targeted this area to bridge the gap and determine the concept of controlling the Russian public sector. However, this research also has a limitation in that it has surveyed only 102 managers from different Russian public organizations. Doi: 10.28991/ESJ-2023-07-01-015 Full Text: PDF
A New Efficiency Improvement of Ensemble Learning for Heart Failure Classification by Least Error Boosting Ployphan Sornsuwit; Phimkarnda Jundahuadong; Siwarit Pongsakornrungsilp
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-2023-07-01-010

Abstract

Heart failure is a very common disease, often a silent threat. It's also costly to treat and detect. There is also a steadily higher incidence rate of the disease at present. Although researchers have developed classification algorithms. Cardiovascular disease data were used by various ensemble learning methods, but the classification efficiency was not high enough due to the cumulative error that can occur from any weak learner effect and the accuracy of the vote-predicted class label. The objective of this research is the development of a new algorithm that improves the efficiency of the classification of patients with heart failure. This paper proposes Least Error Boosting (LEBoosting), a new algorithm that improves adaboost.m1's performance for higher classification accuracy. The learning algorithm finds the lowest error among various weak learners to be used to identify the lowest possible errors to update distribution to create the best final hypothesis in classification. Our trial will use the heart failure clinical records dataset, which contains 13 features of cardiac patients. Performance metrics are measured through precision, recall, f-measure, accuracy, and the ROC curve. Results from the experiment found that the proposed method had high performance compared to naïve bayes, k-NN,and decision tree, and outperformed other ensembles including bagging, logitBoost, LPBoost, and adaboost.m1, with an accuracy of 98.89%, and classified the capabilities of patients who died accurately as well compared to decision tree and bagging, which were completely indistinguishable. The findings of this study found that LEBoosting was able to maximize error reductions in the weak learner's training process from any weak learner to maximize the effectiveness of cardiology classifiers and to provide theoretical guidance to develop a model for analysis and prediction of heart disease. The novelty of this research is to improve original ensemble learning by finding the weak learner with the lowest error in order to update the best distribution to the final hypothesis, which will give LEBoosting the highest classification efficiency. Doi: 10.28991/ESJ-2023-07-01-010 Full Text: PDF
Immobilization and Stabilization of Aspergillus Fumigatus α-Amylase by Adsorption on a Chitin Yandri Yandri; Ezra Rheinsky Tiarsa; Tati Suhartati; Bambang Irawan; Sutopo Hadi
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-2023-07-01-06

Abstract

In this research, immobilization of A. fumigatus α-amylase on chitin was studied with the main purpose to improve the characteristics of the enzyme. A series of experiments were carried out to study stability improvement, thermodynamic parameters, include ki, ΔGi, and t½, and reusability of the immobilized enzyme. The experimental results indicate that significant thermal stability was achieved, as indicates by the ability of the enzyme to retain its relative activity above 39% after 80 min of incubation at 60oC. Thermodynamic parameters, include ki, ΔGi, and t½, indicate that the immobilized enzyme is more rigid, stable, and less flexible in the water, resulting in increased stability up to 1.5 times compared to that of the native enzyme. Furthermore, the immobilized enzyme was able to retain over 46% of its initial activity after six consecutive applications for starch hydrolysis, confirming the potential of chitin for the production of immobilized enzymes on an industrial scale. Doi: 10.28991/ESJ-2023-07-01-06 Full Text: PDF
A Comparative Study of Collaborative Filtering in Product Recommendation Agori Argyro Patoulia; Athanasios Kiourtis; Argyro Mavrogiorgou; Dimosthenis Kyriazis
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-2023-07-01-01

Abstract

Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PDF
The Quality of E-Government Management, Information Security and Quality Aferdita Qekaj-Thaçi; Ledri Thaçi
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-2023-07-01-016

Abstract

The purpose of the presented paper was to present the state of e-government services in the Republic of Kosovo within the management of electronic services and the role of ICT in data security and quality. E-government is an important sphere in Kosovo, which has been shown to have positively influenced the provision of services to all stakeholders, taking care of the security and quality of these services. This paper belongs to the qualitative and quantitative types, where a total of 115 subjects, experts, and users of e-government services participated and gave their opinions against these services. A literature review was used to process qualitative data, while a questionnaire was used to process quantitative data with experts and users of e-government services. From the results of the research, we understood that the level of use of e-government services in the Republic of Kosovo is high and that there is a high level of data security and quality of services, where the role of ICT in the management of e-data-government is essential and very important. It is recommended that, according to the results of this study, direct access to the management of these services should be considered and offered to ensure the sustainability of e-government services in the Republic of Kosovo. Doi: 10.28991/ESJ-2023-07-01-016 Full Text: PDF
Evaluation of Machine Learning Algorithms for Emotions Recognition using Electrocardiogram Chy Mohammed Tawsif Khan; Nor Azlina Ab Aziz; J. Emerson Raja; Sophan Wahyudi Bin Nawawi; Pushpa Rani
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-2023-07-01-011

Abstract

In recent studies, researchers have focused on using various modalities to recognize emotions for different applications. A major challenge is identifying emotions correctly with only electrocardiograms (ECG) as the modality. The main objective is to reduce costs by using single-modality ECG signals to predict human emotional states. This paper presents an emotion recognition approach utilizing the heart rate variability features obtained from ECG with feature selection techniques (exhaustive feature selection (EFS) and Pearson’s correlation) to train the classification models. Seven machine learning (ML) models: multi-layer perceptrons (MLP), Support Vector Machine (SVM), Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Logistic Regression, Adaboost and Extra Tree classifier are used to classify emotional state. Two public datasets, DREAMER and SWELL are used for evaluation. The results show that no particular ML works best for all data. For DREAMER with EFS, the best models to predict valence, arousal, and dominance are Extra Tree (74.6%), MLP and DT (74.6%), and GBDT and DT (69.8%), respectively. Extra tree with Pearson’s correlation are the best method for the ECG SWELL dataset and provide 100% accuracy. The usage of Extra tree classifier and feature selection technique contributes to the improvement of the model accuracy. Moreover, the Friedman test proved that ET is as good as other classification models for predicting human emotional state and ranks highest. Doi: 10.28991/ESJ-2023-07-01-011 Full Text: PDF

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