• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.1-10
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• 2012

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and the life distribution applied exponential and log shaped type hazard function. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model could be confirmed. This paper, a failure data analysis of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and coefficient of determination.

• East Asian Economic Review
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• v.19 no.3
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• pp.243-273
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• 2015

The purpose of this paper is to identify learning in games in experimental economic settings, and apply their results to real multilateral trade negotiations, such as the Doha Development Agenda (DDA) in the World Trade Organizations (WTO). This paper argues that the structure of games including a veto player (Veto games) is similar to the WTO/DDA negotiations in that the players do not possess identical power. This paper's main contribution to the literature involves showing that learning about power is dominant over learning from simple repetition in Veto games. Additionally, this paper shows that players are concerned about how much they have gained in previous games in Veto games, although their memories generally do not last beyond the next game, and thus they tend to be selfish as they have less shares. Based on these results, there is a possibility to be more generous in the distribution of benefits by allowing players without veto power to retain special rights so that they would not be totally powerless. It also shows the necessity of having "respite" in the process of negotiations and policy options for choosing partners for winning coalitions.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.1-7
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• 2024

Comparing text features involves evaluating the "similarity" between texts. It is crucial to use appropriate similarity measures when comparing similarities. This study utilized various techniques to assess the similarities between newspaper articles, including deep learning and a previously proposed method: a combination of Pointwise Mutual Information (PMI) and Word Pair Matching (WPM), denoted as PMI+WPM. For performance comparison, law data from medical research in Japan were utilized as validation data in evaluating the PMI+WPM method. The distribution of similarities in text data varies depending on the evaluation technique and genre, as revealed by the comparative analysis. For newspaper data, non-deep learning methods demonstrated better similarity evaluation accuracy than deep learning methods. Additionally, evaluating similarities in law data is more challenging than in newspaper articles. Despite deep learning being the prevalent method for evaluating textual similarities, this study demonstrates that non-deep learning methods can be effective regarding Japanese-based texts.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.107-114
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• 2023

The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.519-528
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• 2005

Computers have been increasingly recognized as tools for teaming, in addition to supporting industrial works. Such advantages e-Learning have as teaming at any time and place, distribution and management of standardized contents, mentoring with learners, immediate feed-backs, and dynamic learning have been applied in a variety of divisions. Despite of the researches and interests, the study on the different views between teachers who design and operate e-learning and students who receive lessons hasn't been enough. So it studied the recognition of middle school teachers and students on the e-Learning. <중략>The research result showed that there were similarity in the views between teachers and students on the concept of e-Learning. Many teachers and students have experienced the e-Learning directly or indirectly. Teachers and students showed similar opinions on the beforehand education and preferred subjects of the e-Learning. But the students required fast and immediate feedback of the teachers. Teachers and students showed similar opinions on the utilization of multimedia components to achieve the goal of education. But teachers thought that immediate feedback was important. The students thought it important to control the degree of difficulty. It suggests a way to activate the e-Learning of middle school efficiently with the research result.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.637-645
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• 2021

Mobile devices have become attractive learning devices for education. The digitalization of the higher education system in Sri Lanka by 2020 is part of the government's effort to modernize and enhance the country's overall education system particularly in view of the COVID-19 pandemic. Theoretically, this study contributes to the M-Learning model in higher education institutions via the integration of literature on technology adoption (TAM and UTAUT) with the variables of Perceived Usefulness, Perceived Ease of Use, Attitude, Effort Expectancy, Social Influence, and Facilitating Condition. The attitude towards M-Learning amongst higher education students was gauged via an online questionnaire survey. The convenience sample comprised 344 students from the Advanced Technological Institutes (ATI) in Batticaloa District, Sri Lanka. Descriptive statistics, a measurement, and structural model, and hypotheses testing were used to analyze the derived data. The findings indicate that mobile learning is significantly affected by perceived ease of use, social influence, effort expectancy, and facilitating condition, but negatively affected by attitude and perceived usefulness. The exhaustive literature review revealed that there are very few M-Learning studies related to digital learning in the context of higher education in the Batticaloa district.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.6
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• pp.89-96
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• 2021

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.35-43
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• 2021

This study aims to investigate factors affecting eBook acceptance and learning outcomes among students experiencing online distance learning. As conventional textbooks are now switched into eBooks, the effects of contextual factors including lecturer, student computer competency, content and design of the course, access ability, infrastructure, and university support on eBook acceptance and learning outcome needs to be evaluated. The sample of this study is represented by students at the Universiti Teknologi MARA, City Campus Melaka, undertaking 'strategic management course'. Non-probability random sampling was selected as the sampling technique and a purposive sampling method was chosen to select the samples. The samples comprised 171 students randomly selected through Google Form. The questionnaire data was analyzed by using PLS-SEM. The results indicated that these factors contributed 62.3% variations in the eBook acceptance and 67.1% variations in the learning outcomes. The strongest factor affecting both dependent variables was content and design of course. Managerial implication suggested that the content for all courses taught through the eBook platform needs to be revisited and improved in accordance with the mode of online deliverance. Tutorial on how to navigate the eBook platform is important to all users as this would enhance acceptance and produce better learning outcomes among students.

• Genomics & Informatics
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• v.18 no.1
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• pp.7.1-7.7
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• 2020

In this paper, we present few technical notes about the distance distribution paradigm for Mosaab-metric using 1, 2, and 3 grams feature extraction techniques to analyze composite data points in high dimensional feature spaces. This technical analysis will help the specialist in bioinformatics and biotechnology to deeply explore the biodiversity of influenza virus genome as a composite data point. Various technical examples are presented in this paper, in addition, the integrated statistical learning pipeline to process segmented genomes of influenza virus is illustrated as sequential-parallel computational pipeline.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.769-779
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• 2020

Self-efficacy has been extensively evaluated, but no studies have investigated the effect of self-efficacy on the self-reliance of women in poor-households economic productivity. This study analyzes self-efficacy as a personal factor, learning processes, and social support as an environmental factor towards the achievement of self-reliance in women from poor-households in productive economy businesses. Despite the dominant logic of this scheme, there is a need for field-based data regarding whether the variable really supports the sustainable empowerment of poor-households women. This study used the quantitative method through the survey technique. The samples of this study included 250 people collected from five regencies in Indonesia by using a multiple-stage random sampling. The data were analyzed with structural equation modeling. The results show that social support has a significant positive impact on the learning process; social support has a direct negative impact on self-efficacy. The learning process has a direct positive influence on self-efficacy, while social support has a non-significant impact on self-reliance. The learning process has a direct influence on self-reliance. Social support and the learning process both have significant positive impact on self-efficacy. Social support, learning process, and self-efficacy simultaneously have a positive impact on self-reliance in productive economic activities.