• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• The Korean Journal of Archival Studies
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• no.20
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• pp.77-114
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• 2009

The actual conditions of the labor unions are primitive. First, there is no good records management regulation. At this research, I found it that most regulations of the labor unions were all the same. I think they have been copied a kind of one of originality. Second, the definition of records were very narrow, like documentary evidence. Third, the classification, filing, disposal regulations are the below level of the public institution in 1970s. Fourth, there are no standards of the records scheduling for the labor records. What kind of labor records have the historical values? I could not find, only the documentary evidence value. So, I think The actual conditions of the labor unions are primitive. I investigated the collections of the Southern Labor Archives in USA. There were many kind of records. For example, the records of regional labor unions also central labor unions, pamphlets, journals, photos, personal records, oral history, organizational records like protocols article of associations internal rules, minute books etc. Like this the collections of the Southern Labor Archives in USA are very various. But our actual conditions of the labor unions is far from that. Rather, we just have tried collected records for publishing the white papers. But this habitual practice would not be desirable. Because they must manage the records from the producing time. Mostly, 'laborer history HANNAE' were organised, and they are trying the collecting and management of the labor records. Also They are trying the computerizing, compilation. 'HANNAE' has the condition for the transformation of the labor archives. But if they want to be really, they must make the records management infra and so, should normalize the record management firstly. For example, They must be keep the standardized records management regulations, records scheduling redesigned. the developing standard model for the records management. And they have the vision for the hub of the labor archives. When coming to this, it will be realized the labor archives Now the records for the working class are disappearing. The managing the records for the labor is another labor movement. All together should join it. But I think the supporting of the archival science research colleagues will be the essential part.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Journal of Smart Tourism
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• v.1 no.1
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• pp.53-64
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• 2021

Upselling is often a critical factor in revenue generation for businesses in the tourism and travel industry. Utilizing passenger data from a major international airline company, we develop the PAX (Passenger, Airline, eXternal) model to predict passengers that are most likely to accept an upgrade offer from economy to premium. Formulating the problem as an extremely unbalanced, cost-sensitive, supervised binary classification, we predict if a customer will take an upgrade offer. We use a feature vector created from the historical data of 3 million passenger records from 2017 to 2019, in which passengers received approximately 635,000 upgrade offers worth more than $422,000,000 U.S. dollars. The model has an F1-score of 0.75, outperforming the airline's current rule-based approach. Findings have several practical applications, including identifying promising customers for upselling and minimizing the number of indiscriminate emails sent to customers. Accurately identifying the few customers who will react positively to upgrade offers is of paramount importance given the airline 'industry's razor-thin margins. Research results have significant real-world impacts because there is the potential to improve targeted upselling to customers in the airline and related industries.

• Animal cells and systems
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• v.2 no.2
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• pp.209-216
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• 1998

The invasion of Ambrosia artemisiifolia in the Ukrainian Carpathians Mts. and the Transcarpathian Plain in Central Europe was reconstructed on the basis of floristic records. The first spontaneous occurrence was dated from the beginning of the 1940s. Within the next 55 year period, the distributional spread speed of the species was of 67.6 $km^2/y$ (by the average data). The occupied area by A. artemisiifolia in the range of the studied areas is about $3716.5km^2$ now. The features of behavior of the invader and the habitat preference were determined. The frequency of occurrence by sociologic-ecological classification was carried out. The generalized model of correlations among the gravitation, the active temperature sum and the disturbance gradients and the frequency of occurrence of the species was presented. The scheme of the invasion stages of A. artemisiifolia is reflected in the population status changes of the species during the areal dynamics.

• Journal of Engineering Education Research
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• v.23 no.2
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• pp.24-30
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• 2020

This paper proposes the approaches to the evaluation of learning using concepts of artificial intelligence. Among various techniques, deep learning algorithm is employed to achieve quantitative results of evaluation. In particular, this paper focuses on the process-based evaluation instead of the result-based one using face expression. The expression is simply acquired by digital camera that records face expression when students solve sample test problems. Face expressions are trained using convolutional neural network (CNN) model followed by classification of expression data into three categories, i.e., easy, neutral, difficult. To substantiate the proposed approach, the simulation results show promising results, and this work is expected to open opportunities for intelligent evaluation system in the future.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.271-272
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• 2020

Data mining is known useful in medical area when no availability of evidence favoring a particular treatment option is found. Huge volume of structured/unstructured data is collected by the healthcare field in order to find unknown information or knowledge for effective diagnosis and clinical decision making. The data of 5,179 records considered for analysis has been collected from Korean National Health and Nutrition Examination Survey(KHANES) during 2-years. Data splitting, referred as the training and test sets, was applied to predict to fit the model. We analyzed to predict chronic kidney disease (CKD) using data mining method such as naive Bayes, logistic regression, CART and artificial neural network(ANN). This result present to select significant features and data mining techniques for the lifestyle factors related CKD.

• Journal of the Korea Safety Management & Science
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• v.13 no.2
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• pp.195-202
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• 2011

The study focuses on the Repeated Measurements Design (RMD) which observations are periodically made for identical subjects within definite time periods. One of the purposes of this design is to monitor and keep track of replicated records within regular period over years. This paper also presents the classification models of RMD that is developed according to the number of factors in Between-Subject (BS) variates and Within-Subject (WS) variates. The types of models belong to each number of factors: One factor is 0BS 1WS. Two factors are 1BS 1WS and 0BS 2WS. Three factors are 1BS 2WS and 2BS 1WS. Lastly, the four factors include model of 2BS 2WS In addition, the study explains the generation mechanism of models for RMD using Generalizability Design (GD). GD is a useful method for practitioners to identify linear model of experimental design, since it generates a Venn diagram. Lastly, the research develops three types of 1BS 2WS RMDs with crossed factors and nested factors. Those are random models, mixed models and fixed models and they are presented by using Generalizability Design, $(S:A{\times}B){\times}C$. Moreover, the example of applications and its implementation steps of models developed in the study are presented for better comprehension.

• Journal of Korean Academy of Nursing
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• v.40 no.4
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• pp.463-472
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• 2010

Purpose: This study was done to develop a web-based education program for nurses working in nursing homes. The focus was on the rights of older adults. Methods: The program was designed based on the Network-Based Instructional System Design (NBISD) model and was operated and evaluated between July 2007 and June 2008. Results: Out of nursing records of 40 residents from a nursing home, the final 7 cases were deducted through classification using the Resource Utilization Group (RUG)-III. The data on needs for education was collected from 28 nurses working in 15 nursing homes located in Seoul and Gyeonggi Province, who agreed to complete a self-report questionnaire. A comprehensive review of the literature and two focus groups interviews were used to search for risk factors and guidelines for protection of human rights. The education program was developed based on Kolb's experiential learning model and composed of 5 units, which included content on types of human rights and rights to death with dignity, elder abuse, physical liberty, and self-determination. The program was positively evaluated showing a score of 3.35 (SD=0.37) out of 4. Conclusion: The educational program developed in this study should promote nurses' sensitivity to the rights of elders and improve nurses' behaviors in protecting the rights of elders residing in nursing homes.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.