• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.43-54
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• 2019

Currently, a majority of artificial intelligence is used to secure big data; however, it is concentrated in a few of major companies. Therefore, automatic data augmentation and efficient learning algorithms for small-scale data will become key elements in future artificial intelligence competitiveness. In addition, it is necessary to develop a technique to learn meanings, correlations, and time-related associations of complex modal knowledge similar to that in humans and expand and transfer semantic prediction/knowledge inference about unknown data. To this end, a neural memory model, which imitates how knowledge in the human brain is processed, needs to be developed to enable knowledge expansion through modality cooperative learning. Moreover, declarative and procedural knowledge in the memory model must also be self-developed through human interaction. In this paper, we reviewed this essential methodology and briefly described achievements that have been made so far.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.235-242
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• 2019

Recently, the use of artificial intelligence technology including deep learning has become active in various fields. In particular, several algorithms showing superior performance in object recognition and detection based on deep learning technology have been presented. In this paper, we propose the proper direction for the implementation of mobile healthcare application that user's convenience is effectively reflected. By effectively analyzing the current state of use satisfaction research for the existing fitness applications and the current status of mobile healthcare applications, we attempt to secure survival and superiority in the fitness application market, and, at the same time, to maintain and expand the existing user base.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.4
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• pp.145-152
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• 2019

Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.230-235
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• 2022

This study is to select an optimal object detection algorithm for designing a self-checkout counter to improve the inconvenience of payment systems for products without existing barcodes. To this end, a performance comparison analysis of YOLO v2, Tiny YOLO v2, and the latest YOLO v5 among deep learning-based object detection algorithms was performed to derive results. In this paper, performance comparison was conducted by forming learning data as an example of 'donut' in a bakery store, and the performance result of YOLO v5 was the highest at 96.9% of mAP. Therefore, YOLO v5 was selected as the artificial intelligence object detection algorithm to be applied in this paper. As a result of performance analysis, when the optimal threshold was set for each donut, the precision and reproduction rate of all donuts exceeded 0.85, and the majority of donuts showed excellent recognition performance of 0.90 or more. We expect that the results of this paper will be helpful as the fundamental data for the development of an automatic payment system using AI self-service technology that is highly usable in the non-face-to-face era.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• Journal of Information Technology Services
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• v.22 no.4
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• pp.1-19
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• 2023

This study is a study on the effect of technical readiness factors on counselors' intention to use when applying AICC. AICC counselors experience improved customer service and emotional stability by receiving various monitor notification window services based on artificial intelligence algorithms such as customer counseling history, prohibited word control system, and customized counseling system. Accordingly, this study tried to verify using factors derived from technology readiness theory and technology acceptance theory among the factors affecting the intention to continue using AICC provided to counselors. To verify the research hypothesis, the causal relationship between variables such as Optimism, Innovativeness, Discomfort, Insecurity, and Technology Acceptance Theory, such as Team Support, Ease of Usage, and Innovation Resistance, was verified. As a result of empirical analysis, first, it was verified that Optimism has a positive (+) effect on Team Support and Ease of Usage, and Discomfort and Insecurity have a negative (-) effect on Ease of Usage and Team Support. Second, it was confirmed that Team Support and Ease of Usage had a positive effect on the Intention to use AICC. Based on the above empirical analysis results, the concepts of Technical Readiness were clearly proved, and in practical terms, AICC helped inquiry, quality evaluation, recording, and management of counseling history, ultimately increased corporate work efficiency.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.50-61
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• 2022

This work demonstrates the design and evaluation of Aqua-Aware, a lightweight miniaturized light emitting diode (LED) based underwater compact sensor node which is used to obtain different characteristics of the underwater environment. Two optical sensor nodes have been designed, developed, and evaluated for a short and medium link range called as Aqua-Aware short range (AASR) and Aqua-Aware medium range (AAMR), respectively. The hardware and software implementation of proposed sensor node, algorithms, and trade-offs have been discussed in this paper. The underwater environment is emulated by introducing different turbulence effects such as air bubbles, waves and turbidity in a 4-m water tank. In clear water, the Aqua-Aware achieved a data rate of 0.2 Mbps at communication link up to 2-m. The Aqua-Aware was able to achieve 0.2 Mbps in a turbid water of 64 NTU in the presence of moderate water waves and air bubbles within the communication link range of 1.7-m. We have evaluated the luminous intensity, packet success rate and bit error rate performance of the proposed system obtained by varying the various medium characteristics.

• Biomedical Science Letters
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• v.29 no.2
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• pp.53-57
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• 2023

The main objective of pathologists is to achieve accurate lesion diagnoses, which has become increasingly challenging due to the growing number of pathological slides that need to be examined. However, using digital technology has made it easier to complete this task compared to older methods. Digital pathology is a specialized field that manages data from digitized specimen slides, utilizing image processing technology to automate and improve analysis. It aims to enhance the precision, reproducibility, and standardization of pathology-based researches, preclinical, and clinical trials through the sophisticated techniques it employs. The advent of whole slide imaging (WSI) technology is revolutionizing the pathology field by replacing glass slides as the primary method of pathology evaluation. Image processing technology that utilizes WSI is being implemented to automate and enhance analysis. Artificial intelligence (AI) algorithms are being developed to assist pathologic diagnosis and detection and segmentation of specific objects. Application of AI-based digital pathology in biomedical researches is classified into four areas: diagnosis and rapid peer review, quantification, prognosis prediction, and education. AI-based digital pathology can result in a higher accuracy rate for lesion diagnosis than using either a pathologist or AI alone. Combining AI with pathologists can enhance and standardize pathology-based investigations, reducing the time and cost required for pathologists to screen tissue slides for abnormalities. And AI-based digital pathology can identify and quantify structures in tissues. Lastly, it can help predict and monitor disease progression and response to therapy, contributing to personalized medicine.