• Animal cells and systems
• /
• v.12 no.1
• /
• pp.11-14
• /
• 2008

Finding a way to strengthen human cognitive functions, such as learning and memory, has been of great concern since the moment people realized that these functions can be affected and even altered by certain chemicals. Since then, plenty of endeavors have been made to look for safe ways of improving cognitive performances without adverse side-effects. Unfortunately, most of these efforts have turned out to be unsuccessful until now. In this study, we examine the effect of a natural compound, idesolide, on hippocampus-dependent recognition memory. We demonstrate that idesolide is effective in the enhancement of recognition memory, as measured by a novel object recognition task. Thus, idesolide might serve as a novel therapeutic medication for the treatment of memoryrelated brain anomalies such as mild cognitive impairment(MCI) and Alzheimer's disease.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.12
• /
• pp.983-987
• /
• 2009

One of the critical themes in the language acquisition is its exposure to linguistic environments. Linguistic environments, which interact with infants, include not only human beings such as its parents but also artificially crafted linguistic media as their functioning elements. An infant learns a language by exploring these extensive language environments around it. Based on such large linguistic data exposure, we propose a machine learning based method on the cognitive mechanism that simulate flexibly and appropriately infant's language learning. The infant's initial stage of language learning comes with sentence learning and creation, which can be simulated by exposing it to a language corpus. The core of the simulation is a memory-based learning model which has language hypernetwork structure. The language hypernetwork simulates developmental and progressive language learning using the structure of new data stream through making it representing of high level connection between language components possible. In this paper, we simulates an infant's gradual and developmental learning progress by training language hypernetwork gradually using 32,744 sentences extracted from video scripts of commercial animation movies for children.

• Journal of Auto-vehicle Safety Association
• /
• v.8 no.1
• /
• pp.19-25
• /
• 2016

As new car technology is developing, temporal interaction is needed in automotive. Rhythmic pattern is one of the practical examples of temporal interaction in vehicle. To recognize rhythmic pattern and its input medium, dictionary learning is applicable algorithm. In this paper, performance and memory requirement of the learning algorithm is tested and is sufficiently good for use this acoustic sound.

• ETRI Journal
• /
• v.32 no.4
• /
• pp.596-602
• /
• 2010

This paper proposes a computationally efficient learning-based super-resolution algorithm using k-means clustering. Conventional learning-based super-resolution requires a huge dictionary for reliable performance, which brings about a tremendous memory cost as well as a burdensome matching computation. In order to overcome this problem, the proposed algorithm significantly reduces the size of the trained dictionary by properly clustering similar patches at the learning phase. Experimental results show that the proposed algorithm provides superior visual quality to the conventional algorithms, while needing much less computational complexity.

• Journal of Ginseng Research
• /
• v.18 no.1
• /
• pp.39-43
• /
• 1994

This study was performed to investigate the effect of ginseng saponin from Korean red ginseng on the learning and memory. Total (50, 100 mg/kg, bw) and panaxadiol saponin (15, 30 mg/kg, bw) treated groups did not show the difference of the time score and the number of error in comparison with control group. Panaxatriol saponin (15, 30 mg/kg, bw) significantly decreased both the time score and the number of error in water maze test. These results indicate that panaxatriol saponin from Korean red ginseng may improve the learning ability of rat in water multiple T-maze.

• Journal of Pharmacopuncture
• /
• v.20 no.1
• /
• pp.10-17
• /
• 2017

Objectives: Boswellic acid (BA), a compound isolated from the gum-resin of Boswellia carterii, is a pentacyclic terpenoid that is active against many inflammatory diseases, including cancer, arthritis, chronic colitis, ulcerative colitis, Crohn's disease, and memory impairment, but the mechanism is poorly understood. This study investigated the effects of boswellic acid on spatial learning and memory impairment induced by trimethyltin (TMT) in Wistar rats. Methods: Forty male Wistar rats were randomly divided into 5 groups: Normal group, TMT-administrated rats (8.0 mg/kg, Intraperitoneally, i.p.) and TMT + BA (40, 80 and 160 mg/kg, i.p.)-administrated rats. BA was used daily for 21 days. To evaluate the cognitive improving of BA, we performed the Morris water maze test. Moreover, to investigate the neuroprotective effect of BA, we determined the acetylcholinesterase (AchE) activity, the malondialdehyde (MDA) level as a marker of lipid peroxidation, and the glutathione (GSH) content in the cerebral cortex. Results: Treatment with TMT impaired learning and memory, and treatment with BA at a dose of 160 mg/kg produced a significant improvement in learning and memory abilities in the water maze tasks. Consistent with behavioral data, the activity of AChE was significantly increased in the TMT-injected rats compared to the control group (P < 0.01) whereas all groups treated with BA presented a more significant inhibitory effect against AChE than the TMT-injected animals. In addition, TMT reduced the GSH content and increased the MDA level in the cerebral cortex as compared to the control group) P < 0.01). On the other hand, treatment with BA at 160 mg/kg slightly increased the GSH content and reduced the MDA level in comparison to the TMT-administered group (P < 0.01). Conclusion: The above results suggest that the effect of BA in improving the cognitive function may be mediated through its antioxidant activity.

• Preventive Nutrition and Food Science
• /
• v.19 no.4
• /
• pp.343-347
• /
• 2014

The present study investigated the effects of Vaccinium uliginosum L. (bilberry) on the learning and memory impairments induced by amyloid-${\beta}$ protein ($A{\beta}P$) 1-42. ICR Swiss mice were divided into 4 groups: the control ($A{\beta}40$-1A), control with 5% bilberry group ($A{\beta}40$-1B), amyloid ${\beta}$ protein 1-42 treated group ($A{\beta}1$-42A), and $A{\beta}1$-42 with 5% bilberry group ($A{\beta}1$-42B). The control was treated with amyloid ${\beta}$-protein 40-1 for placebo effect, and Alzheimer's disease (AD) group was treated with amyloid ${\beta}$-protein 1-42. Amyloid ${\beta}$-protein 1-42 was intracerebroventricular (ICV) micro injected into the hippocampus in 35% acetonitrile and 0.1% trifluoroacetic acid. Although bilberry added groups tended to decrease the finding time of hidden platform, no statistical significance was found. On the other hand, escape latencies of $A{\beta}P$ injected mice were extended compared to that of $A{\beta}40$-1. In the Probe test, bilberry added $A{\beta}1$-42B group showed a significant (P<0.05) increase of probe crossing frequency compared to $A{\beta}1$-42A. Administration of amyloid protein ($A{\beta}1$-42) decreased working memory compared to $A{\beta}40$-1 control group. In passive avoidance test, bilberry significantly (P<0.05) increased the time of staying in the lighted area compared to AD control. The results suggest that bilberry may help to improve memory and learning capability in chemically induced Alzheimer's disease in experimental animal models.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.5
• /
• pp.187-194
• /
• 2021

A person's behavioral recognition is the recognition of what a person does according to joint movements. To this end, we utilize computer vision tasks that are utilized in image processing. Human behavior recognition is a safety accident response service that combines deep learning and CCTV, and can be applied within the safety management site. Existing studies are relatively lacking in behavioral recognition studies through human joint keypoint extraction by utilizing deep learning. There were also problems that were difficult to manage workers continuously and systematically at safety management sites. In this paper, to address these problems, we propose a method to recognize risk behavior using only joint keypoints and joint motion information. AlphaPose, one of the pose estimation methods, was used to extract joint keypoints in the body part. The extracted joint keypoints were sequentially entered into the Long Short-Term Memory (LSTM) model to be learned with continuous data. After checking the behavioral recognition accuracy, it was confirmed that the accuracy of the "Lying Down" behavioral recognition results was high.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.159-168
• /
• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• Journal of Korea Multimedia Society
• /
• v.21 no.12
• /
• pp.1526-1533
• /
• 2018

This study aims to provide implications for mobile foreign language vocabulary learning app by analyzing the effects of mobile vocabulary learning app based on character animation. For this purpose, we applied the learning application designed with character animation and text, and the application designed with text only to two groups of learners, and analyzed the effect. As a result, we found that application designed with character animation and text was useful in recognition frequency and duration concerning learning. Regarding learning outcomes, we found that it is useful not only in memory but also in learning interest and motivation. This study provides implications for learning method and design development of mobile-based foreign language vocabulary learning application which actively using recently.