• Journal of Life Science
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• v.17 no.10
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• pp.1400-1405
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• 2007

The effect of supplementation with docosahexaenoic acid into n-3 fatty acid deficient diet on improvement of loaming related brain function was investigated. On the second day after conception, Sprague Dawley strain dams were subjected to a diet containing either n-3 fatty acid deficient (Def) or n-3 fatty acid deficient + docosahexaenoic acid (Def+DHA). After weaning, male pups were fed on the same diet of their respective dams until adulthood. Motor activity and Morris water maze tests were measured at 10 weeks old. In motor activity test, there were no statistically significant differences in moving time and moving distance between the Def and Def+DHA diet groups. The n-3 fatty acid deficient with DHA (Def+DHA) group exhibited a shorter escape latency, swimming time and swimming distance (P<0.05) compared to the n-3 fatty acid deficient group (Def) but there was no difference in resting time and swimming speed between the experimental diet groups. In memory retention trial, the number of crossing of the platform position (region A) was significantly greater than those of other regions for the Def+DHA group. However, the Def group swam randomly without preference for the provisions platform location, indicating poorer memory retention. From those results, supplementation with DHA into the n-3 fatty acid deficient diet improved the spatial loaming ability in rats as assessed by Morris water maze test.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1456-1464
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• 2007

The research of skin-tone detection has been conducting continuously to enlarge the importance in security, surveillance and administration of the information and 'Password Control System' for using face and skin recognition in airports, harbors and general companies. As well as tile rapid diffusion of the application range in image communications and an electron transaction using wide range of communication network, the importance of the accurate detection of skin color has been augmenting recently. In this paper, it will set up the boundaries of skin colors using the information of Cb and Cr in YCbCr color model of human skin color which is from hundreds compiled portrait images for each race, and suggest a efficient yet simple structure about the skin detection which has been followed by whether the comprehension of the boundaries of skin or not with adaptive skin-range set. With the possibility of the 1D Processes which does not use any memory, it is able to be applied to relatively small-sized hardware and system such as mobile apparatuses. To add the selective mode, it is not only available the improvement of tie skin detection, but also showing the correspondent results about previous face recognition technologies using complicated algorithm.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.213-226
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• 2011

This study was performed to investigate the effect of essential oils and ethanolic extracts of approximately 650 plant species on acetylcholine esterase (AChE) enzyme activity using Ellman's colorimetric method in 96-well microplates. The results showed that the ethanolic extracts from twig of Sophora subprostrata, twig of Phellodendron amurense, seed of Corylopsis coreana, and essential oil (EO) from Citrus paradisi, Cupressus sempervirens, Ocimum basilicum, Pinus sylvestris and Rosmarinus officinalis inhibited more than 80% of AChE activity. Among these, EO from Pinus sylvestris, C. sempervirens and C paradisi exhibited higher values of AChE inhibitory activity, which were 75, 84 and 99% at a concentration of 50 ug/ml, respectively. Finally, EO from C paradisi (grapefruit, GEO) showed the highest inhibitory activity towards AChE, which showed 91% of inhibition at a concentration of 20 ug/ml. We also examined the anti-dementia effects of GEO in mouse by passive avoidance test and Morris water maze test. The model mouse (male, ICR) of dementia (negative control) was induced by administration of scopolamine (1 mg/kg body weight). The latency time of sample group administrated with GEO (100 mg/kg, p.o.) increased significantly as compared with negative control on passive avoidance test. There were significant recovery from the scopolamine-induced deficits on learning and memory in water maze test through daily administrations with GEO (100 mg/kg, p.o.). From these results, we conclude that GEO treatment might enhance the cognitive function, suggesting that the EO of C. paradis may be a potential candidate for improvement of perceptive ability and dementia.

• 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.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.210-216
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• 2019

The purpose of this study is to investigate the effect of the multimodal cognitive intervention focusing on instrumental daily life on the cognitive function, depression and quality of life of the elderly with high-risk of dementia. This study was conducted on 24 elderly people with high-risk of dementia who participated in cognitive rehabilitation program from March to June, 2018 in Chungbuk A region. The intervention was applied to cognitive training and creative activities related to instrumental daily life. MMSE-DS, Subjective Memory Complaints Questionnaire, Short Geriatric Depression Scale-Korean version and Geriatric quality of life - Dementia were performed before and after the intervention. We confirmed that the subjects showed significant improvement in Subjective Memory Complaints and Quality of Life, but showed no significant changes in cognitive function and depression after the intervention program. Through this study, it was confirmed that this program which can affect the real life of the elderly can be usefully applied in the community. In the future, it will be necessary to develop a program that utilizes more diverse instrumental activities of daily living.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.127-137
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• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Internet Computing and Services
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• v.14 no.1
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• pp.63-69
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• 2013

Approximate Frequent pattern mining is to find approximate patterns, not exact frequent patterns with tolerable variations for more efficiency. As the size of database increases, much faster mining techniques are needed to deal with huge databases. Moreover, it is more difficult to discover exact results of mining patterns due to inherent noise or data diversity. In these cases, by mining approximate frequent patterns, more efficient mining can be performed in terms of runtime, memory usage and scalability. In this paper, we study the characteristics of an approximate mining algorithm based on probabilistic technique and run performance evaluation of the efficient approximate frequent pattern mining algorithm. Finally, we analyze the test results for more improvement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2323-2329
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• 2013

Web-services employ an XML to represent data and an XML parser is needed to use data. The DOM(Document Object Model) is widely used to parse an XML, but it is not suitable for any systems with limited resources because it requires a preprocessing to create the DOM and additional memory space. In this paper, we propose the StreXTree(Streaming XML Tree) with failure transitions and without any preprocessing tasks in order to improve the system performance. Compared to other works, our StreXTree parser achieves 2.39x and 3.02x improvement in system performance in Search and RBStreX, respectively. In addition, our StreXTree parser supports Well-Formed checking to verify the syntax and structure of XML.