• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.63-72
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• 2022

The study applied the non-financial dimensions of the Balanced Scorecard (customer dimension, internal processes dimension, learning, and growth dimension). It was done to evaluate performance and measure the effectiveness of these dimensions on performance evaluation at College of Science and Humanities Studies: Al Aflaj, Prince Sattam Bin Abdulaziz University. The researchers used the descriptive analytical approach to conduct the study to find the effect of these dimensions. Data was collected from the college staff and administrators; 120 questionnaires were distributed, out of which 112 were collected. The questionnaire data were analyzed using exploratory (EFA) and confirmatory factor analysis (CFA), hypotheses were tested using the structural equation modeling (SEM) through the (Spss) and (Amos) software. The study finding showed that the balanced scorecard had a positive contribution in evaluating the performance of the College of Science and Humanities Studies: Al Aflaj, Prince Sattam Bin Abdulaziz University through the dimensions of customers and internal processes, and the study finding revealed that the balanced scorecard has no contribution at performance evaluating the College of Science and Humanities Studies: Al Aflaj, Prince Sattam Bin Abdulaziz University through the dimension of learning and growth.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.263-274
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• 2013

As the numbers of Internet users and the growth of education market along with the generalization of distance study increase, e-Learning industry in China is growing rapidly more than 20% each year. However, domestic e-Learning industry for entry to the Chinese market is showing inadequate result even though its potential growth in China and their scale of industry is near about 3 trillion won. A type of this industry is combined with Information Technology (IT) and education industry and their complex factors need to be considered because of the country's education policy and ICT infrastructure. In addition to these factors, sometimes main agents can be the government or a private organization and they form different circumstances each other. Therefore, it is required to have an in-depth study of the entering the Chinese market based on an accurate analysis for Chinese education and culture. In this research, it will focus on the current state of e-Learning market in Korea and China after studying the e-Learning system through the existing reference research. Moreover, this research will propose a method of the entry for the Chinese e-Leaning market through a case study from domestic and foreign companies.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.153-168
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• 2018

Purpose: The balanced scorecard (BSC) has been adopted to evaluate factors affecting performances in organizational dealer marketing in Chinese cosmetics market. Four performance measures in BSC: learning & growth, internal business processes, customer performance, and financial performance are employed in our empirical study. Methods: We conducted surveys of dealers in a Chinese cosmetics company and used total 463 samples for analysis. Confirmatory factor analysis and structural equation model analysis were employed using AMOS 20.0. Results: This study found that internal business process had a positive relation with customer performance and learning and growth. Also, customer performance and learning & growth positively affected financial performances. Conclusion: This study has some academic and practical contributions in that the revised BSC model reflects the special aspects of Chinese cosmetics market and it can be used as a guide for companies in the Chinese cosmetics market to understand which factors are affecting performances.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.275-279
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• 2022

The growth of technology nowadays has made many things easy for humans. These things are from everyday small task to more complex tasks. Such growth also comes with the illegal activities that are perform by using technology. These illegal activities can simple as displaying annoying message to big frauds. The easiest way for the attacker to perform such activities is to convenience user to click on the malicious link. It has been a great concern since a decay to classify URLs as malicious or benign. The blacklist has been used initially for that purpose and is it being used nowadays. It is efficient but has a drawback to update blacklist automatically. So, this method is replace by classification of URLs based on machine learning algorithms. In this paper we have use four machine learning classification algorithms to classify URLs as malicious or benign. These algorithms are support vector machine, random forest, n-nearest neighbor, and decision tree. The dataset that is used in this research has 36694 instances. A comparison of precision accuracy and recall values are shown for dataset with and without preprocessing.

• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.71-90
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• 2010

Electronic learning(or e-learning) has created hype for companies, universities, and other educational institutions. It has led to the phenomenal growth in the use of web-based learning and experimentation with multimedia, video conferencing, and internet-based technologies. Many researchers are interested in the factors that affect to the performance of e-learning or e-learning services. In this sense, this study is aimed at proposing e-learning system reuse prediction models in which e-learner intention to reuse influence factors(i.e., system accessibility, system stability, information clarity, information validity, self-regulated efficacy, computer self-efficacy, perceived usefulness, perceived ease of use, flow, and parental expectation) affect e-learner intention to reuse positively. A web survey was conducted for the full members of the e-learning education institute A in Seoul, Republic of Korea, an exclusive e-learning company that provides real time video lectures via the desktop conferencing system. The web survey was conducted for 20 days from November 5, 2009, through the e-learning web site of the company A. In this study, three data mining techniques were used : the multivariate discriminant analysis, CART, and C5.0 algorithm. This study was conducted to provide the e-learning service providers, e-learning operators, and contents developers with marketing and management strategies for improving the e-learning service companies, based on the data mining analysis results.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• Journal of Industrial Technology
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• v.30 no.B
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• pp.49-54
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• 2010

E-learning has the potentials to provide educational publicity and equality, and has been deployed rapidly in many areas of our society. Growth of e-learning market is also rapid and sustained. However, for e-learning to be a good solution, enough contents should be provided, for which sharing limited contents in the early deployment stage should be necessary. In this paper, we survey some activities about contents sharing. On the other hand, there have been many complaint from e-learning about its effectiveness. Therefore, in this paper, based on the poll results on concentration and satisfaction of e-learning, we also study practical ways to improve studying efficiency of e-learners.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.9-18
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• 2022

Purpose: The government around the world is still highlighting the effect of the new variant of Covid-19. The government continues to make efforts to restore the economy through several programs, one of them is National Economic Recovery. This program is expected to increase public and investor confidence in handling Covid-19. This study aims to capture public sentiment on the economic growth rate in Indonesia, especially during the third wave of the omicron variant of the covid-19 virus, that is at the time in the fourth quarter of 2021. Research design, data, and methodology: The approach used in this research is to collect crowdsourcing data from twitter, in the range of 1st to 10th October 2021. The analysis is done by building model using Deep Learning Neural Network method. Results: The result of the sentiment analysis is that most of the tweets have a neutral sentiment on the Economic Growth discussion. Several central figures who discussed were Minister of Coordinating for the Economy of Indonesia, Minister of State-Owned Enterprises. Conclusions: Data from social media can be used by the government to capture public responses, especially public sentiment regarding economic growth. This can be used by policy makers, for example entrepreneurs to anticipate economic movements under certain conditions.

• International Journal of Quality Innovation
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• v.9 no.2
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• pp.11-30
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• 2008

The purpose of this study is to develop BSC model of social enterprise. Performance analysis tool of BSC have been brought over from the business world, designed and created from the perspectives of profit-based businesses. The BSC is a strategic performance measurement and management tool designed for the private sector acting as a communication/information and learning system, to measure 'where we are now' and 'where to aim for next'. It prescribes a plan for translating 'vision' and 'strategy' into concrete action across four perspectives at different stages, depending on the business. These perspectives are 'financial', 'customer', 'internal processes' and 'learning and growth', each of which is connected by cause-and-effect relationships that reflect the firm's strategy. Social aims of social enterprise are to accomplish desired outcomes which are to employ vulnerable people and to provide social services. The measurement factors of financial perspective are stable funding, efficiency of budgeting, stakeholders' financial supports, and trade profit. The measurement factors of customer perspective are government, social service users, employees, local communities, sup plier, social activity company, and partnership with external organizations. The measurement factors of internal process perspective are organizational culture, organizational structure/management, internal/external communication, quality of products and services, information sharing. The measurement factors of learning and growth perspective are training and development, management participation, knowledge sharing, leadership of CEO and manager, and learning culture.