• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.159-166
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• 2022

Artificial Intelligence has stimulated every aspect of today's life. Human thinking quality is trying to be involved through digital tools in all research areas of the modern era. The education industry is also leveraging artificial intelligence magical power. Uses of digital technologies in pedagogical paradigms are being observed from the last century. The widespread involvement of artificial intelligence starts reshaping the educational landscape. Adaptive learning is an emerging pedagogical technique that uses computer-based algorithms, tools, and technologies for the learning process. These intelligent practices help at each learning curve stage, from content development to student's exam evaluation. The quality of information technology students and professionals training has also improved drastically with the involvement of artificial intelligence systems. In this paper, we will investigate adopted digital methods in the education sector so far. We will focus on intelligent techniques adopted for information technology students and professionals. Our literature review works on our proposed framework that entails four categories. These categories are communication between teacher and student, improved content design for computing course, evaluation of student's performance and intelligent agent. Our research will present the role of artificial intelligence in reshaping the educational process.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.2
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• pp.183-203
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• 2023

This study was conducted to analyze the characteristics of the artificial intelligence chatbot service of the library and compare the usability of the chatbot service applied to the library, and to propose a plan to improve the usability of the artificial intelligence chatbot service in the library. In order to achieve this research purpose, usability comparison factors were extracted through previous studies on the usability evaluation of artificial intelligence chatbot services, and based on case studies, artificial intelligence chatbot services applied to libraries were classified into their own website-based and SNS-based chatbot services according to the platform. Experiments, questionnaires, and interviews were conducted to evaluate the usability of website-based and SNS-based chatbot services applied to the library. Based on the results of the usability evaluation, implications and improvement plans for the artificial intelligence chatbot service of the library were derived.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.6
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• pp.650-658
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• 2020

The research of the AI-based command decision support system was insufficient both quantitatively and qualitatively. In particular, in Korea, there was no research on concrete concept design at the current concept research level. This paper proposed the conceptual design of a tactical echelon command decision support system based on artificial intelligence(AI) according to the current army's doctrine of the operation process. The suggested conceptual design clarified the problem and proposed an appropriate process for design, and applied the function analysis method among rational techniques that enable conceptual design systematically.

• Journal of the Korean School Mathematics Society
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• v.23 no.2
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• pp.223-237
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• 2020

Artificial intelligence technology, which represents the era of the 4th Industrial Revolution, is now deeply involved in our lives, and future education places great emphasis on building students' capabilities for the principles and uses of artificial intelligence. Therefore, the purpose of this study is to develop the contents of AI related education in mathematics, which the relationship is closely connected to each other. To this end, I propose establishing two novel AI-related contents in mathematics education. One subject is related to learning the principle of machine learning based on mathematics foundation. In addition, I draw the core math contents dealt in following subject called 'Basic Mathematics for AI and Data Science.'

• Journal of Engineering Education Research
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• v.25 no.3
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• pp.35-44
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• 2022

Artificial intelligence is a core technology in the era of digital transformation, and as the technology level is advanced and used in various industries, its influence is growing in various fields, including social, ethical and legal issues. Therefore, it is time to raise social awareness on ethics of artificial intelligence as a prevention measure as well as improvement of laws and institutional systems related to artificial intelligence development. In this study, we analyzed unstructured data, typically text, such as online news articles and comments to confirm the degree of social awareness on ethics of artificial intelligence development. The analysis showed that the public intended to concentrate on specific issues such as "Human," "Robot," and "President" in 2018 to 2019, while the public has been interested in the use of personal information and gender conflics in 2020 to 2021.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.461-467
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• 2020

In the field of nuclear medicine, errors are sometimes generated because the assessment of the uniformity of gamma cameras relies on the naked eye of the evaluator. To minimize these errors, we created an artificial intelligence model based on CNN algorithm and wanted to assess its usefulness. We produced 20,000 normal images and partial cold region images using Python, and conducted artificial intelligence training with Resnet18 models. The training results showed that accuracy, specificity and sensitivity were 95.01%, 92.30%, and 97.73%, respectively. According to the results of the evaluation of the confusion matrix of artificial intelligence and expert groups, artificial intelligence was accuracy, specificity and sensitivity of 94.00%, 91.50%, and 96.80%, respectively, and expert groups was accuracy, specificity and sensitivity of 69.00%, 64.00%, and 74.00%, respectively. The results showed that artificial intelligence was better than expert groups. In addition, by checking together with the radiological technologist and AI, errors that may occur during the quality control process can be reduced, providing a better examination environment for patients, providing convenience to radiologists, and improving work efficiency.

• Journal of Distribution Science
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• v.22 no.6
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• pp.83-93
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• 2024

Purpose: The present study is developed with the aim of mapping the trends in scientific production related to the implementation of artificial intelligence systems for agro-food supply chains. Research design, data and methodology: The methodological approach of the research shows a descriptive documentary process based on bibliometric techniques for mapping the main indicators of the area of knowledge through the establishment of a search equation in Scopus. Results: The research results show a total of 633 documents published between 2019 and 2023, with a great annual growth rate of 61.68%; In addition to a notable participation of countries such as India, China, the United Kingdom and the United States in the generation of new knowledge related to artificial intelligence applied to food distribution systems. Conclusions: It is concluded that the rise of new artificial intelligence technologies has shown extremely important results in the development of industries worldwide, with increasingly accelerated steps; which certainly translates into the creation of spaces and incentives in the production of research aimed at understanding these dynamics and in turn to propose new alternatives and proposals for the reduction of the large technological gaps that are present within the agro-food sector.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.77-78
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• 2007