• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.561-568
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• 2022

Most deep learning-based video object segmentation methods perform the segmentation with past prediction information stored in external memory. In general, the more past information is stored in the memory, the better results can be obtained by accumulating evidence for various changes in the objects of interest. However, all information cannot be stored in the memory due to hardware limitations, resulting in performance degradation. In this paper, we propose a method of storing new information in the external memory without additional memory allocation. Specifically, after calculating the attention score between the existing memory and the information to be newly stored, new information is added to the corresponding memory according to each score. In this way, the method works robustly because the attention mechanism reflects the object changes well without using additional memory. In addition, the update rate is adaptively determined according to the accumulated number of matches in the memory so that the frequently updated samples store more information to maintain reliable information.

• Journal of Life Science
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• v.6 no.3
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• pp.178-184
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• 1996

Aloe(Aloe vera LINNE) has been used as a home medicine for the past several thousand in the world, and has been studied on various chronic degenerative diseases such as atherosclerosis, myocardiac infarction and hypertension. SMAP8, learning and memory impairment animal mode, were fed basic or experimental diets with 1.0% of freeze dried(FD)-Aloe powder for 8 months. This study was designed to investigate the effects of Aloe on body weight gain, grading score of senescence(GSS), triglyceride, total and LDL-cholesterol levels, and atherogenic index in serum of SAMP8, and also designed to investigate the effects of Aloe on cholesterol accumultions in mitochondria and microsome fractions of SAMP8 brain. Body weight gain was consistently lower in aloe group than in control group, but no significantly differences between them. Grading score of senescence resulted ina marked decreases pf 20% in 1.0% Aloe group compared with control group. Administrations of 1.0% aloe resulted ina marked decreases in 15% and 20% of triglyceride and cholesterol levels, respectively, and also significantly decreased in 15% of LDL-cholesterol levels and atherogenic index in serum of SAMP8 compared with control group. Cholesterol accumulations were significantly inhibited in 20% and 10% of mitochondria and microsome fractions of SAMP8 brain, respectively, by administration of 1.0% Aloe. These results suggest that administration of Aloe mau not only effectively inhibit chronic degenerative diseases in serum of SAMP8, but may also improve learning and memory impairments of SAMP8 brain.

• Biomolecules & Therapeutics
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• v.27 no.1
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• pp.71-77
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• 2019

Previous studies have shown that spinosin was implicated in the modulation of sedation and hypnosis, while its effects on learning and memory deficits were rarely reported. The aim of this study is to investigate the effects of spinosin on the improvement of cognitive impairment in model mice with Alzheimer's disease (AD) induced by $A{\beta}_{1-42}$ and determine the underlying mechanism. Spontaneous locomotion assessment and Morris water maze test were performed to investigate the impact of spinosin on behavioral activities, and the pathological changes were assayed by biochemical analyses and histological assay. After 7 days of intracerebroventricular (ICV) administration of spinosin ($100{\mu}g/kg/day$), the cognitive impairment of mice induced by $A{\beta}_{1-42}$ was significantly attenuated. Moreover, spinosin treatment effectively decreased the level of malondialdehyde (MDA) and $A{\beta}_{1-42}$ accumulation in hippocampus. $A{\beta}_{1-42}$ induced alterations in the expression of brain derived neurotrophic factor (BDNF) and B-cell lymphoma-2 (Bcl-2), as well as inflammatory response in brain were also reversed by spinosin treatment. These results indicated that the ameliorating effect of spinosin on cognitive impairment might be mediated through the regulation of oxidative stress, inflammatory process, apoptotic program and neurotrophic factor expression,suggesting that spinosin might be beneficial to treat learning and memory deficits in patients with AD via multi-targets.

• Journal of Ginseng Research
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• v.45 no.1
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• pp.108-118
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• 2021

Background: Korean ginseng (Panax ginseng Meyer) contains a variety of ginsenosides that can be metabolized to a biologically active substance, compound K. Previous research showed that compound K could be enriched in the red ginseng extract (RGE) after hydrolysis by pectinase. The current study investigated whether the enzymatically hydrolyzed red ginseng extract (HRGE) containing a notable level of compound K has cognitive improving and neuroprotective effects. Methods: A scopolamine-induced hypomnesic mouse model was subjected to behavioral tasks, such as the Y-maze, passive avoidance, and the Morris water maze tests. After sacrificing the mice, the brains were collected, histologically examined (hematoxylin and eosin staining), and the expressions of antioxidant proteins analyzed by western blot. Results: Behavioral assessment indicated that the oral administration of HRGE at a dosage of 300 mg/kg body weight reversed scopolamine-induced learning and memory deficits. Histological examination demonstrated that the hippocampal damage observed in scopolamine-treated mouse brains was reduced by HRGE administration. In addition, HRGE administration increased the expression of nuclear-factor-E2-related factor 2 and its downstream antioxidant enzymes NAD(P)H:quinone oxidoreductase and heme oxygenase-1 in hippocampal tissue homogenates. An in vitro assay using HT22 mouse hippocampal neuronal cells demonstrated that HRGE treatment attenuated glutamate-induced cytotoxicity by decreasing the intracellular levels of reactive oxygen species. Conclusion: These findings suggest that HRGE administration can effectively alleviate hippocampus-mediated cognitive impairment, possibly through cytoprotective mechanisms, preventing oxidative-stress-induced neuronal cell death via the upregulation of phase 2 antioxidant molecules.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.1-7
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• 2020

With the increase in cyber attacks, automated IDS using machine learning is being studied. According to recent research, the IDS using the recursive learning model shows high detection performance. However, the simple application of the recursive model may be difficult to reflect the associated session characteristics, as the overlapping session environment may degrade the performance. In this paper, we designed the session management module and applied it to LSTM (Long Short-Term Memory) recursive model. For the experiment, the CSE-CIC-IDS 2018 dataset is used and increased the normal session ratio to reduce the association of mal-session. The results show that the proposed model is able to maintain high detection performance even in the environment where session relevance is difficult to find.

• Asia Marketing Journal
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• v.6 no.3
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• pp.1-26
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• 2004

The purpose of this study is to conduct a theoretical and empirical study to identify the appropriate organizational culture for the activation of CRM. The contents of this study are consisted of two parts: First, using the organizational learning theory originated in the organizational behavior area this study proposed which culture related factors are indispensable for the activation of CRM. Second, the propositions in the first part were confirmed by analyzing the survey data from the CRM practitioners in Korean companies. Conclusively the results show the follows: First, for the activation of CRM not only the individual learning(including team learning) but also the enterprise-wide sharing of the information is the crucial element. Second, for the activation of the individual learning, the enterprise-wide participation, the active experimental trials based on the empowerment, and the facilitative leadership of top management must be encouraged. Third, for the activation of the information sharing the active communication among the departments and the possession of organizational memory must be realized.

• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• The Journal of Bigdata
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• v.5 no.1
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• pp.1-9
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• 2020

The study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and in based on artificial intelligence-based HPAI spread analysis and patterning. The model that is actively used in time series and text mining recently is LSTM (Long Short-Term Memory Models) model utilizing deep learning model structure. The LSTM model is a model that emerged to resolve the Long-Term Dependency Problem that occurs during the Backpropagation Through Time (BPTT) process of RNN. LSTM models have resolved the problem of forecasting very well using variable sequence data, and are still widely used.In this paper study, we used the data of the Call Detailed Record (CDR) provided by KT to identify the migration path of people who are expected to be closely related to the virus. Introduce the results of predicting the path of movement by learning the LSTM model using the path of the person concerned. The results of this study could be used to predict the route of HPAI propagation and to select routes or areas to focus on quarantine and to reduce HPAI spread.