• The Journal of Bigdata
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• v.6 no.1
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• pp.51-62
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• 2021

In the field of medical artificial intelligence, there have been a lot of researches on disease prediction and classification algorithms that can help doctors judge, but relatively less interested in artificial intelligence that can help medical consumers acquire and judge information. The fact that more than 150,000 questions have been asked about which hospital to go over the past year in NAVER portal will be a testament to the need to provide medical information suitable for medical consumers. Therefore, in this study, we wanted to establish a classification model that classifies 8 medical subjects for symptom text directly described by patients which was collected from NAVER portal to help consumers choose appropriate medical subjects for their symptoms. In order to ensure the validity of the data involving patients' subject matter, we conducted similarity measurements between objective symptom text (typical symptoms by medical subjects organized by the Seoul Emergency Medical Information Center) and subjective symptoms (NAVER data). Similarity measurements demonstrated that if the two texts were symptoms of the same medical subject, they had relatively higher similarity than symptomatic texts from different medical subjects. Following the above procedure, the classification model was constructed using a ridge regression model for subjective symptom text that obtained validity, resulting in an accuracy of 0.73.

• The Journal of Bigdata
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• v.6 no.1
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• pp.91-113
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• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• The Journal of Bigdata
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• v.6 no.1
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• pp.179-196
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• 2021

With the spread of untact culture resulting from the Covid-19 pandemic, the size of the car camping market has expanded to minimize contact with others. As a result, SUVs have exceeded sales of sedans, and sales of recreational vehicles (RVs) have increased by 101% compared to the same period last year. Despite the explosive increase in demand for car camping, research on car camping has not matched this increase. Therefore, in this study, we intended to conduct a study focused on car camping users. According to a survey of Naver's famous car camping cafe, it was difficult to find articles, maps, and websites with car camping places. Analysis of car camping websites showed that most only post information about the camping itself, so details of car camping places were not available. Furthermore, according to a survey derived from related prior studies and literature surveys, most users urged solutions to the problem of unauthorized garbage dumping in the car camping locations. In addition, car camping users wanted to receive information on amenities near the car camping places. Therefore, we aimed to establish a car camping website that provides basic information on car camping places and nearby convenience facilities. Moreover, to solve the problem of garbage dumping, we provided a category wherein users can post pictures of clean camping campaigns. We also developed a business model utilizing the certification process of clean camping. The business model is designed with a structure wherein car camping users are rewarded through the clean camping certification process. Compensation for clean camping certification was proposed to be provided through partnerships with domestic automakers, Korea Tourism Organization, and Small Business Market Promotion Agency.

• The Journal of Bigdata
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• v.6 no.1
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• pp.37-49
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• 2021

The number of dementia patients in Korea is estimated to be over 800,000, and the severity of dementia is becoming a social problem. However, no treatment or drug has yet been developed to cure dementia worldwide. The number of dementia patients is expected to increase further due to the rapid aging of the population. Currently, early detection of dementia and delaying the course of dementia symptoms is the best alternative. This study presented a methodology for early diagnosis of dementia by measuring and analyzing amyloid plaques. This vital protein can most clearly and early diagnose dementia in the retina through AI-based image analysis. We performed binary classification and multi-classification learning based on CNN on retina data. We also developed a deep learning algorithm that can diagnose dementia early based on pre-processed retinal data. Accuracy and recall of the deep learning model were verified, and as a result of the verification, and derived results that satisfy both recall and accuracy. In the future, we plan to continue the study based on clinical data of actual dementia patients, and the results of this study are expected to solve the dementia problem.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• The Journal of Bigdata
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• v.5 no.2
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• pp.121-133
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• 2020

This study was carried out to set the direction of the new industry policy of Busan city by analyzing the changing trend of artificial intelligence technology that has recently developed rapidly and predicting the direction of future development. The company wanted to draw up support measures to utilize artificial intelligence technology, which has been rapidly emerging in the market, in the region's specialized industry. Artificial intelligence is a key keyword in the fourth industrial revolution and artificial intelligence-based data utilization technology can be used in various fields from manufacturing processes to services, and is entering an era of super-fusion in which barriers between technologies and industries will be broken down. In this study, the direction of promotion for fostering Busan as an artificial intelligence city was derived based on the comparison and analysis of artificial intelligence-related ecosystems among major local governments. In this study, we wanted to present a plan to create an artificial intelligence industrial ecosystem that can be called a key policy to foster Busan as an 'AI City'. Busan's plan to foster the AI industry ecosystem is aimed at establishing a policy direction to ultimately nurture the artificial intelligence industry as Busan's future food source.

• The Journal of Bigdata
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• v.8 no.1
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• pp.83-97
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• 2023

Project evaluation is the process of evaluating the progress and results of a project. Smart city projects can be divided into system components (infrastructure, services, platforms), or projects can run simultaneously for multiple services. In addition, services are developed and expanded through additional projects. In order to ensure that the smart city, which is composed of various projects, proceeds in accordance with the goals and strategies, periodic project evaluation is required during the project implementation process. The smart city project evaluation system proposed in this paper is designed to provide comprehensive and objective indicators by reflecting various factors that must be considered for projects occurring in all stages of planning, design, construction, and operation of smart cities. The indicators derived from the evaluation system can be used by decision makers to determine the direction of smart city project development. In addition, it is designed so that the performance of the project can be evaluated interim before the end of the project and the feedback obtained from it can be reflected. To introduce the application method of the smart city project evaluation system proposed in this study, the evaluation system developed in this study was applied to the smart city project case of Incheon Free Economic Zone (IFEZ). Based on the evaluation results, items that can maximize the improvement effect of each smart city project item were presented, and the direction of smart city project implementation was suggested. By utilizing a smart city project evaluation system that reflects the characteristics of smart city projects that are composed of multiple projects, comprehensive planning and management of smart city projects will be possible, and this study will serve as a reference for identifying priority improvement factors for projects.

• The Journal of Bigdata
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• v.8 no.1
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• pp.131-156
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• 2023

The traditional retail industry significantly changed over the past decade due to the mobile and online technologies. This change has been accompanied by a shift in consumer behavior regarding purchasing patterns. Despite the rise of online shopping, there are still specific categories of products, such as "Processed food" in Mongolia, for which traditional shopping remains the preferred purchase method. To prepare for the inevitable future of retail businesses, firms need to closely analyze the performance of their offline stores to plan their further actions in a new multi-channel environment. Retailers must integrate diverse channels into their operations to stay relevant and adjust to the shifting market. In this research, we have analyzed the performance data such as sales, profit, and amount of sales of offline stores by using clustering approach. From the clustering, we have found the several distinct insights by comparing the circumstances and performance of retail stores. For the certain retail stores, we have proposed three different strategies: a fulfillment hub store between online and offline channels, an experience store to elongate customers' time on the premises, and a merge between two non-related channels that could complement each other to increase traffic based on the store characteristics. With the proposed strategies, it may enhance the user experience and profit at the same time.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.720-734
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• 2023

To understand the spatiotemporal distribution pattern of zooplankton and the environmental factors influencing zooplankton abundance in Gomso Bay, major harvesting area of Manila clam (Venerupis philippinarum) in South Korea, zooplankton sampling was conducted four times in autumn (October 2022), winter (January 2023), early spring (March 2023), and spring (May 2023). Among the environmental factors of Gomso Bay, water temperature, chlorophyll a concentration (Chl-a), dissolved oxygen (DO), and pH observed different patterns, while salinity and suspended particulate matter(SPM) showed no significant statistical differences between the survey periods. The zooplankton in Gomso Bay occurred 33, 29, 27, and 29 taxonomic groups during each respective survey period. In October 2022 and May 2023, arthropod plankton were dominated, while in January and March 2023, protozoa were primarily dominant. Among the Arthropods, copepods including Acartia hongi, Paracalanus parvus s. l., Corycaeus spp., and Oithona spp. commonly found along Korean coastal areas of the Yellow Sea, were dominated. Cluster analysis based on zooplankton abundance indicated a single community (stable condition) in each season, attributed to low dissimilarity distances, while three distinct clusters (autumn, winter-early spring, spring) between seasons indicated a highly seasonal environment in Gomso Bay.