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
 35   36   37   38   39 
Fitting Multi-Layer Feed Forward Neural Network and Autoregressive Integrated Moving Average for Dhaka Stock Exchange Price Predicting Maksuda Akter Rubi; Shanjida Chowdhury; Abdul Aziz Abdul Rahman; Abdelrhman Meero; Nurul Mohammad Zayed; K. M. Anwarul Islam
Emerging Science Journal Vol 6, No 5 (2022): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-05-09

Abstract

The stock market plays a vital role in the economic development of any country. Stock market performance can be measured by the market capitalization ratio as well as many other factors. The primary purpose of this study is to predict the movement of the stock market based on the total market capitalization of the Dhaka Stock Exchange (DSE) using autoregressive integrated moving average (ARIMA) models as well as artificial neural networks (ANN). The data set covers monthly time series data of total market capitalization from November 2001 to December 2018. This study also shows the best model for forecasting the movement of DSE market capitalization. The ARIMA (2,1,2) model is chosen from among the several ARIMA model combinations. From several artificial neural networks (ANN) models as a modern tool, a three-layer feed-forward topology using a backpropagation algorithm with five nodes in the hidden layer, one lag, and a learning rate equal to 0.01 is selected as the best model. Finally, these selected two models are compared based on the Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Theil’s U statistic. The results showed that the estimated error of ANN is less than the estimated error of the traditional method. Doi: 10.28991/ESJ-2022-06-05-09 Full Text: PDF
Detection Sensitivity of a Modified EWMA Control Chart with a Time Series Model with Fractionality and Integration Piyatida Phanthuna; Yupaporn Areepong
Emerging Science Journal Vol 6, No 5 (2022): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-05-015

Abstract

Among the many statistical process control charts, the modified exponentially weighted moving average (EWMA) control chart has been designed to swiftly detect a small shift in a process parameter. Herein, we propose two explicit formulas for the average run length (ARL) for integrated moving average (IMA) and fractional integrated moving average (FIMA) models combined with the modified EWMA control chart for time series prediction. The application of the suggested control chart procedures depends on the residuals of the IMA and FIMA models. The performance of the control chart with both models is evaluated by using the ARL. Explicit formulas for the ARL for the two models with the modified EWMA statistic are derived and their precision is compared with the numerical integral equation method. The explicit formulas could accurately predict the true ARL while markedly decreasing the computational processing time compared to the numerical integration method. The capabilities of the IMA and FIMA models with the modified EWMA control chart were studied by varying g times the last term and exponential smoothing parameter λ, with the relative mean index being used to evaluate these situations. The results show that the modified EWMA control chart with either model performed better than the original EWMA control chart. Furthermore, the modified EWMA control chart with either model was highly efficient when g increased and λ was small. Two applications involving energy commodity prices are used to illustrate the efficacies of the proposed approaches, the results of which were in accordance with the experimental study. Doi: 10.28991/ESJ-2022-06-05-015 Full Text: PDF
Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks Islam Alexandrov; Aslan Tatarkanov; Vladimir Kuklin; Maxim Mikhailov
Emerging Science Journal Vol 6, No 5 (2022): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-05-010

Abstract

The relevance of this type of network is associated with the development and improvement of protocols, methods, and tools to verify routing policies and algorithmic models describing various aspects of SDN, which determined the purpose of this study. The main purpose of this work is to develop specialized methods to estimate the maximum end-to-end delay during packet transmission using SDN infrastructure. The methods of network calculus theory are used to build a model for estimating the maximum transmission delay of a data packet. The basis for this theory is obtaining deterministic evaluations by analyzing the best and worst-case scenarios for individual parts of the network and then optimally combining the best ones. It was found that the developed method of theoretical evaluation demonstrates high accuracy. Consequently, it is shown that the developed algorithm can estimate SND performance. It is possible to conclude the configuration optimality of elements in the network by comparing the different possible configurations. Furthermore, the proposed algorithm for calculating the upper estimate for packet transmission delay can reduce network maintenance costs by detecting inconsistencies between network equipment settings and requirements. The scientific novelty of these results is that it became possible to calculate the achievable upper data delay in polynomial time even in the case of arbitrary tree topologies, but not only when the network handlers are located in tandem. Doi: 10.28991/ESJ-2022-06-05-010 Full Text: PDF
Virtual Reality in Festivals: A Systematic Literature Review and Implications for Consumer Research Daisy Lee; Peggy M.L. Ng; Tai Ming Wut
Emerging Science Journal Vol 6, No 5 (2022): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-05-016

Abstract

Technological advancements in virtual reality have influenced festivalgoers, performers, and festival organizers. Due to the COVID-19 pandemic, organizers of cultural and tourism festivals have sought to deliver festivals online using virtual reality to provide an immersive experience from home. However, despite growing interest in virtual reality for festivals, there is no current systematic review to synthesize knowledge from academic papers within the festival context. This paper aims to provide a structured understanding of extant virtual reality research regarding festivals by using a systematic literature review. After a comprehensive review of extant literature from major databases, 19 relevant articles were extracted and synthesized according to the types, venues, roles, and objectives of the virtual reality applications. This study is the first systematic literature review to examine the current landscape of consumer research on virtual reality in festivals. Our results show that the limited numbers of extant literature concerning virtual reality in festivals indicates that this is an important yet significantly under-researched topic for future research. Current literature on virtual reality in festival contexts also lacks an in-depth understanding of consumer engagement and experiences. This paper recommends incorporating the application of theory and robust consumer research methods into future virtual festival research. Doi: 10.28991/ESJ-2022-06-05-016 Full Text: PDF
Comparison of Machine Learning Approach for Waste Bottle Classification Abdul Fadlil; Rusydi Umar; . Sunardi; Arief Setyo Nugroho
Emerging Science Journal Vol 6, No 5 (2022): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-05-011

Abstract

The use of machine learning for the image classification process is growing all the time. Many methods can be used to classify an image with good accuracy. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are popular methods for this case. The two approaches have differences in the data training process to achieve classification objectives. Although there are some differences between these approaches, there are some advantages to both of them. This research explores the comparison of the two CNN and SVM methods by comparing the training process carried out and the accuracy results of the classification. The process stages are divided into pre-processing, training, and testing. The objects used are ten waste plastic bottles with different brands of medium size with a total data of 1100 images. Based on the observations, both methods have advantages and disadvantages in the data training and classification process. However, from the results, CNN's accuracy is better than SVM. The accuracy of both networks is 99% for CNN and 74% for SVM, respectively. So, from the results of experiments that have been carried out in the study, it was found that CNN was still better than SVM. Doi: 10.28991/ESJ-2022-06-05-011 Full Text: PDF
A Systematic Review on Emotion Recognition System Using Physiological Signals: Data Acquisition and Methodology Tawsif K.; Nor Azlina Ab. Aziz; J. Emerson Raja; J. Hossen; Jesmeen M. Z. H.
Emerging Science Journal Vol 6, No 5 (2022): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-05-017

Abstract

Emotion recognition systems (ERS) have become a popular research field to contribute to human-machine interaction in different areas. Different kinds of applications on ERS can serve different purposes. Artificial intelligence (AI) and the internet of things (IoT) are the technologies behind such applications. The main objective of this study is to enable researchers and developers to search for the most suitable options to develop an emotional state recognition system. More specifically, this paper presents work on ERS, which is built using physiological signals extracted from biosensors. It also presents details of how the extracted physiological signals are used to identify the user's emotional state. In this review, the sensors are categorized based on their modality: contact-based sensors and contactless sensors. Next, the ERS process is presented together with the reported results for each described technique. Articles from four different research databases were reviewed, of which 147 articles from 2009 to 2021 were referred to that are related to ERS using physiological signals. This paper should be significant for researchers developing systems that integrate human emotion recognition capability. The findings reported here can guide them in choosing suitable methods for their systems. Doi: 10.28991/ESJ-2022-06-05-017 Full Text: PDF
Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition I Made Agus Wirawan; Retantyo Wardoyo; Danang Lelono; Sri Kusrohmaniah
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-2022-06-06-03

Abstract

Participants' emotional reactions are strongly influenced by several factors such as personality traits, intellectual abilities, and gender. Several studies have examined the baseline reduction approach for emotion recognition using electroencephalogram signal patterns containing external and internal interferences, which prevented it from representing participants’ neutral state. Therefore, this study proposes two solutions to overcome this problem. Firstly, it offers a modified weighted mean filter method to eliminate the interference of the electroencephalogram baseline signal. Secondly, it determines an appropriate baseline reduction method to characterize emotional reactions after the smoothing process. Data collected from four scenarios conducted on three datasets was used to reduce the interference and amplitude of the electroencephalogram signals. The result showed that the smoothing process can eliminate interference and lower the signal's amplitude. Based on the three baseline reduction methods, the Relative Difference method is appropriate for characterizing emotional reactions in different electroencephalogram signal patterns and has higher accuracy. Based on testing on the DEAP dataset, these proposed methods achieved accuracies of 97.14, 99.70, and 96.70% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 89.71, 97.63, and 96.58% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 99.59, 98.20, and 99.96% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Doi: 10.28991/ESJ-2022-06-06-03 Full Text: PDF
Data Mining Applications in Banking Sector While Preserving Customer Privacy Özge Doğuç
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-2022-06-06-014

Abstract

In real-life data mining applications, organizations cooperate by using each other’s data on the same data mining task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving data mining (PPDM) practices involve rules and techniques that allow parties to collaborate on data mining applications while keeping their data private. The objective of this paper is to present a number of PPDM protocols and show how PPDM can be used in data mining applications in the banking sector. For this purpose, the paper discusses homomorphic cryptosystems and secure multiparty computing. Supported by experimental analysis, the paper demonstrates that data mining tasks such as clustering and Bayesian networks (association rules) that are commonly used in the banking sector can be efficiently and securely performed. This is the first study that combines PPDM protocols with applications for banking data mining. Doi: 10.28991/ESJ-2022-06-06-014 Full Text: PDF
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
Mathematics and Mother Tongue Academic Achievement: A Machine Learning Approach Catarina Nunes; Ana Beatriz-Afonso; Frederico Cruz-Jesus; Tiago Oliveira; Mauro Castelli
Emerging Science Journal Vol 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-SIED-010

Abstract

Academic achievement is of great interest to education researchers and practitioners. Several academic achievement determinants have been described in the literature, mostly identified by analyzing primary (sample) data with classic statistical methods. Despite their superiority, only recently have machine learning methods started to be applied systematically in this context. However, even when this is the case, the ability to draw conclusions is greatly hampered by the "black-box" effect these methods entail. We contribute to the literature by combining the efficiency of machine learning methods, trained with data from virtually every public upper-secondary student of a European country, with the ability to quantify exactly how much each driver impacts academic achievement on Mathematics and mother tongue, through the use of prototypes. Our results indicate that the most important general academic achievement inhibitor is the previous retainment. Legal guardian's education is a critical driver, especially in Mathematics; whereas gender is especially important for mother tongue, as female students perform better. Implications for research and practice are presented. Doi: 10.28991/ESJ-2022-SIED-010 Full Text: PDF

Page 37 of 88 | Total Record : 874

 35   36   37   38   39 

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