• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.939-945
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• 2023

This study analyzes the 'performance efficiency' of AI-based reading assistance systems in the field of radiology by measuring their 'time behavior' properties. Due to the increase in medical images and the limited number of radiologists, the adoption of AI-based solutions is escalating, stimulating a multitude of studies in this area. Contrary to the majority of past research which centered on AI's diagnostic precision, this study underlines the significance of time behavior. Using 50 chest X-ray PA images, the system processed images in an average of 15.24 seconds, demonstrating high consistency and reliability, which is on par with leading global AI platforms, suggesting the potential for significant improvements in radiology workflow efficiency. We expect AI technology to play a large role in the field of radiology and help improve overall healthcare quality and efficiency.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.135-141
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• 2024

The article examines the key constants of reengineering the modern educational cluster, associated with the processes of digital transformation of all spheres of modern socio-cultural space. The first constant is the strategic rethinking of the educational process organization and awareness of the new roles of all participants (tutors, applicants, controlling elements, etc.). The other constant involves practical re-design of the system of educational services, which consists in the reorientation from the traditional model of education functioning for society to the implementation of the educational format in the form of new projects (structural, target, business). Consequently, the purpose of the study is to highlight the attitudes relevant to the modern realities of information and technological support of education in the context of socio-economic interactions of society. The criteria for the reengineering of educational concepts and the structural organization of the educational sphere are defined. The modern world is going through a period of complete digital transformation of all spheres of public activity. The scientific intelligence notes that education is no exception in these processes, as the dependence of educational realities on information and computer technologies is now noted. The COVID-19 pandemic, for all its tragedy, was also a kind of trigger, clearly marking the new components that have become defined in the organization of the educational process. The conclusion is made that the use of digital technologies in the organization of the educational institution or in the organization of the educational process has become not an auxiliary element, but a dominant factor. Mobility, dynamism, interdisciplinarity, synergy - all these aspects are relevant for socio-economic interactions of society and should be provided by educational programs. The results of the study can be used in the reorganization processes of educational institutions and institutions. Further research requires aspects of the analysis of the foreign experience of reengineering in education, carried out taking into account digital transformations of modern sociocultural space.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.270-272
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• 2022

Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

• Journal of Korean Society for Quality Management
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• v.52 no.3
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• pp.557-574
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• 2024

Purpose: This study investigates whether adoption of AI-based systems and technologies improve operational efficiency in healthcare organizations through a systematic review of the literature and real-world examples. Methods: In this study, we divided the AI application cases into care services and administrative functions, then we explored opportunities and challenges in each area. Results: The analysis results indicate that the care service field primarily uses AI-based systems and technologies for quick disease diagnosis and treatment, surgery and disease prediction, and the provision of personalized healthcare services. In the administrative field, AI-based systems and technologies are used to streamline processes and automate tasks for the following functions: patient monitoring through virtual care support systems; automating patient management systems for appointment times, reservations, changes, and no-shows; facilitating patient-medical staff interaction and feedback through interaction support systems; and managing admission and discharge procedures. Conclusion: The results of this study provide valuable insights and significant implications about the application of AI-based systems or technologies for various innovation opportunities in healthcare organizations. As digital transformation accelerates across all industries, these findings provide valuable information to managers of hospitals that are interested in AI adoption, as well as for policymakers involved in the formulation of medical regulations and laws.

• International journal of advanced smart convergence
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• v.3 no.2
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• pp.10-13
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• 2014

In oriental medicine, the pulse beats are important signals that may let us know the conditions of one's health and disease. In other words, doctors of oriental medicine can simply analyze pulse waves anywhere and anytime to treat patients without using high-priced medical appliances. However, they are largely subjective in interpreting the pulse rates and hence their reliability is far from being perfect. The current paper aims to solve this problem by using fuzzy inference rules in judging patients' health status and to develop a software kit of intelligent electronic needles.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.585-590
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• 2000

Recently, It is gradually raised necessity that thickness of thin film is measured accuracy and managed in industrial circles and medical world. Ultrasonic Signal processing method is likely to become a very powerful method for NDE method of detection of microdefects and thickness measurement of thin film below the limit of Ultrasonic distance resolution in the opaque materials, provides useful information that cannot be obtained by a conventional measuring system. In the present research, considering a thin film below the limit of ultrasonic distance resolution sandwiched between three substances as acoustical analysis model, demonstrated the usefulness of ultrasonic Signal processing technique using information of ultrasonic frequency for NDE of measurements of thin film thickness, sound velocity, and step height, regardless of interference phenomenon. Numeral information was deduced and quantified effective information from the image. Also, pattern recognition of a defected input image was performed by neural network algorithm. Input pattern of various numeral was composed combinationally, and then, it was studied by neural network. Furthermore, possibility of pattern recognition was confirmed on artifical defected input data formed by simulation. Finally, application on unknown input pattern was also examined.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.91-109
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• 1995

This paper compared four knowledge acquisition methods (namely, neural network, case-based reasoning, discriminant analysis, and covariance structure modeling) for allergic rhinitis. The data were collected from 444 patients with suspected allergic rhinitis who visited the Otorlaryngology Deduring 1991-1993. Among four knowledge acquisition methods, the discriminant model had the best overall diagnostic capability (78%) and the neural network had slightly lower rate(76%). This may be explained by the fact that neural network is essentially non-linear discriminant model. The discriminant model was also most accurate in predicting allergic rhinitis (88%). On the other hand, the CSM had the lowest overall accuracy rate (44%) perhaps due to smaller input data set. However, it was most accuate in predicting non-allergic rhinitis (82%).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.37-47
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• 2024

This study presents the results of designing a system that determines the location of a person in an indoor environment based on a single IMU sensor attached to the tip of a person's shoe in an area where GPS signals are inaccessible. By adjusting for human footfall, it is possible to accurately determine human location and trajectory by correcting errors originating from the Inertial Measurement Unit (IMU) combined with advanced machine learning algorithms. Although there are various techniques to identify stepping, our study successfully recognized stepping with 98.7% accuracy using an artificial intelligence model known as Long Short-Term Memory (LSTM). Drawing upon the enhancements in our methodology, this article demonstrates a novel technique for generating a 200-meter trajectory, achieving a level of precision marked by a 2.1% error margin. Indoor pedestrian navigation systems, relying on inertial measurement units attached to the feet, have shown encouraging outcomes.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.21 no.3
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• pp.161-167
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• 2010

Objectives: In assessing behavioral/emotional problems in school-aged children, the importance of multi-informant reporting has been well documented. However, in clinical settings obtaining multiple informants' opinions has proven difficult. For that reason, we researched the agreement and predictive validity of the Child Problem-Behavior Screening Questionnaire (CPSQ) in order to reveal how accurate parents' assessments reflected teachers'opinions. Methods: We conducted the first screening for second- and third-grade children from 3 elementary schools in Seoul from 2003 to 2007 using the CPSQ. There were 1178 children included in the analysis. We then administered the Korean version of the Child Behavior Checklist (K-CBCL) as a second screening tool and subsequently, the ADHD Diagnostic System (ADS) and the Korean Educational Development Institute version of the Wechsler Intelligence Scale for Children (KEDI-WISC) was administered by a psychiatrist. We examined each item on the CPSQ and the subscale's agreement between parent and teacher as well as the predictive validity of the CPSQ in children diagnosed with emotional/behavioral problems. Results: The agreement rates between parents and teachers appeared high for questions 18 (0.433), 1 (0.385), and 2 (0.325). Among the subscales, a relatively high correlation was found for externalizing problems, attention deficit hyperactivity disorder, and cognitive problems. For all diagnosed children, their parents revealed a higher sensitivity and lower specificity than teachers. Conclusion: From these results, we confirmed that the CPSQ can be useful for sorting out externalizing and cognitive problems. There is a need for further study, however, with a larger sample size.