• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.385-395
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• 2018

With the development of Internet, cyber attack has become a major threat. To detect cyber attacks, intrusion detection system(IDS) has been widely deployed. But IDS has a critical weakness which is that it generates a large number of false alarms. One of the promising techniques that reduce the false alarms in real time is machine learning. However, there are problems that must be solved to use machine learning. So, many machine learning approaches have been applied to this field. But so far, researchers have not focused on features. Despite the features of IDS alerts are important for performance of model, the approach to feature is ignored. In this paper, we propose new feature set which can improve the performance of model and can be extracted from a single alarm. New features are motivated from security analyst's know-how. We trained and tested the proposed model applied new feature set with real IDS alerts. Experimental results indicate the proposed model can achieve better accuracy and false positive rate than SVM model with ordinary features.

• Journal of the Korean Geographical Society
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• v.46 no.6
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• pp.707-723
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• 2011

The purpose of this study was to quantitatively evaluate the effectiveness of learning management systems (LMS) in the physical geography class. The study adopted the experimental design and three classes participated in this study. The first class was controlled using only classroom lectures, the second class used PPT slides along with the classroom lectures, and the third class used online video clips along with the lectures. The experiments were conducted from the Spring Semester 2007 to the Spring Semester 2008 for the introductory physical geography course. The study results showed that online learning tools help students improve academic performance and their attitudes towards the class and the instructor. Compared to simple PowerPoint slides, voice recording attached to the visual lecture slide materials enhanced students' motivation. Class lectures with lecture slides did not improve students' scores. However, when the visual materials were combined with voice recording, the number of internet access to online class materials increased, and class attendance and students' final grades were improved. Based on the results, the instructional design model that combines classroom and online learning was suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

• Proceedings of the Technology Innovation Conference
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• 1998.06a
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• pp.116-127
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• 1998

The Necessity of Cyber University. Within the rapidly changing environment of global economics, the environment of higher education in the universities, also, has been, encountering various changes. Popularization on higher education related to 1lifetime education system, putting emphasis on the productivity of education services and the acquisition of competitiveness through the market of open education, the breakdown of the ivory tower and the Multiversitization of universities, importance of obtaining information in the universities, and cooperation between domestic and oversea universities, industry and educational system must be acquired. Therefore, in order to adequately cope wi th these kinds of rapid changes in the education environment, operating Cyber University by utilizing various information technologies and its fixations such as Internet, E-mail, CD-ROMs, Interact ive Video Networks (Video Conferencing, Video on Demand), TV, Cable etc., which has no time or location limitation, is needed. Using informal ion and telecommunication technologies, especially the Internet is expected to Or ing about many changes in the social, economics and educational area. Among the many changes scholars have predicted, the development and fixations of Distant Learning or Cyber University was the most dominant factor. In the case of U. S. A., Cyber University has already been established and in under operation by the Federate Governments of 13 states. Any other universities (around 500 universities has been opened until1 now), with the help of the government and private citizens have been able to partly operate the Cyber University and is planning on enlarging step-by-step in the future. It could be seen not only as U. S. A. trying to elevate its higher education through their leading information technologies, but also could be seen as their objective in putting efforts on subordinating the culture of the education worldwide. UTRA University in U. S. A., for example, is already exporting its class lectures to China, and Indonesia regions. Influenced by the Cyber University current in the U.S., the Universities in Korea is willing .to arrange various forms of Cyber Universities. In line with this, at JUNAM National University, internet based Cyber University, which has set about its work on July of 1997, is in the state of operating about 100 Cyber Universities. Also, in the case of Hanam University, the Distant Learning classes are at its final stage of being established; this is a link in the rapid speed project of setting an example by the Korean Government. In addition, the department of education has selected 5 universities, including Seoul Cyber Design University for experimentation and is in the stage of strategic operation. Over 100 universities in Korea are speeding up its preparation for operating Cyber University. This form of Distant Learning goes beyond the walls of universities and is in the trend of being diffused in business areas or in various training programs of financial organizations and more. Here, in the hope that this material would some what be of help to other Universities which are preparing for Cyber University, I would 1ike to introduce some general concepts of the components forming Cyber University and Open Education System which has been established by JUNAM University. System of Cyber University could be seen as a general solution offered by tile computer technologies for the management on the students, Lectures On Demand, real hour based and satellite classes, media product ion lab for the production of the multimedia Contents, electronic library, the Groupware enabling exchange of information between students and professors. Arranging general concepts of components in the aspect of Cyber University and Open Education, it would be expressed in the form of the establishment of Cyber University and the service of Open Education as can be seen in the diagram below.

• Journal of Information Technology Applications and Management
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• v.19 no.4
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• pp.227-242
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• 2012

It is a daunting task to theorize the process effectiveness of IT convergence based service model. Despite the criticalness of investigating process enhancement impact of IT-convergence based service model, the theoretical research in this field is relatively scarce, possibly due to the too wide and comprehensiveness of research scope. In this vein, we conducted exploratory study to understand the contributional impact of IT convergence based service model on resolving service process limitations. We first identified five IT convergence based service models in the area of typical service industry, which include entertainment, learning, location based services, tourism, and healthcare. Our research model classified value creation factors of the IT convergence model in twofold. The one is defined as basic value creation factor of the IT convergence, which is treated as the second-order factor that consists of two first-order factors of mobile functionality and Internet with digital contents merging functionality. The other is defined as service process limitations resolving factor which are comprised with the two first-order factors of simultaneousity and perishability. Both the second-order factors are modeled, each respectively, with the two first-order factors in formative manner. Using PLS, empirical validation is executed to analyze each value creating factor's contribution impact on the relative advantage, as well as the mediating effect of basic value creation factor on resolving service process limitations. On the basis of the insights revealed from this paper, further theory building research could be elaborated in the area of IT convergence applications for service industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.81-98
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• 2013

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.15-23
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• 2016

In this paper we presented 4G security models in an open environment. 4G networks have still more security holes and open issues for expert to notice. In here. we show that a number of new security threats to cause unexpected service interruption and disclosure of information will be possible in 4G due mainly to the fact that 4G is an IP-based, heterogeneous network. it tells about the security issues and vulnerabilities present in the above 4G standards.. we try to present several architectures behind 4G infrastructure for example WiMAX and 3GPP LTE architecture. Finally, we point to potential areas for future vulnerabilities and evaluate areas in 4G security which warrant attention. When you make a big dada program, If you use this network architecture then you can improve your learning speed.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.103-111
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• 2020

Liver cancer is the most fatal cancer that occurs worldwide. In order to diagnose liver cancer, the patient's physical condition was checked by using a CT technique using radiation. Segmentation was needed to diagnose the liver on the patient's abdominal CT scan, which the radiologists had to do manually, which caused tremendous time and human mistakes. In order to automate, researchers attempted segmentation using image segmentation algorithms in computer vision field, but it was still time-consuming because of the interactive based and the setting value. To reduce time and to get more accurate segmentation, researchers have begun to attempt to segment the liver in CT images using CNNs, which show significant performance in various computer vision fields. The pixel value, or numerical value, of the CT image is called the Hounsfield Unit (HU) value, which is a relative representation of the transmittance of radiation, and usually ranges from about -2000 to 2000. In general, deep learning researchers reduce or limit this range and use it for training to remove noise and focus on the target organ. Here, we observed that the range of HU values was limited in many studies but different in various liver segmentation studies, and assumed that performance could vary depending on the HU range. In this paper, we propose the possibility of considering HU value range as a hyper parameter. U-Net and ResUNet were used to compare and experiment with different HU range limit preprocessing of CHAOS dataset under limited conditions. As a result, it was confirmed that the results are different depending on the HU range. This proves that the range limiting the HU value itself can be a hyper parameter, which means that there are HU ranges that can provide optimal performance for various models.