• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.595-617
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• 2010

This paper proposes a novel of a personalized Computer Assisted Language Learning (CALL) system based on learner's cognitive abilities related to foreign language proficiency. In this CALL system, a strategy of retrieval learning, a method of learning memory cycle, and a method of repeated learning are applied for effective vocabulary memorization. The system is designed to offer personalized learning based on cognitive abilities related to the human language process. For this, the proposed CALL system has a cognitive diagnosis module which can measure five types of cognitive abilities. The results of this diagnosis are used to create dynamic learning scenarios for personalized learning and to evaluate user performance in the learning. This system is also designed in order to have users be able to create learning word lists and to share them simply with various functions based on open APIs. Additionally, through experiments, it has shown that this system helps students to learn English vocabulary effectively and enhances their foreign language skills.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.533-539
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• 2003

Reinforcement learning (RL) has been widely used as a learning mechanism of an artificial life system. However, RL usually suffers from slow convergence to the optimum state-action sequence or a sequence of stimulus-response (SR) behaviors, and may not correctly work in non-Markov processes. In this paper, first, to cope with slow-convergence problem, if some state-action pairs are considered as disturbance for optimum sequence, then they no to be eliminated in long-term memory (LTM), where such disturbances are found by a shortest path-finding algorithm. This process is shown to let the system get an enhanced learning speed. Second, to partly solve a non-Markov problem, if a stimulus is frequently met in a searching-process, then the stimulus will be classified as a sequential percept for a non-Markov hidden state. And thus, a correct behavior for a non-Markov hidden state can be learned as in a Markov environment. To show the validity of our proposed learning technologies, several simulation result j will be illustrated.

• Herbal Formula Science
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• v.16 no.1
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• pp.131-145
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• 2008

With tablets and powder, decoction has been a widely-used method of medicine formula. However, for these formulas have unique bitter tastes and flavors of herbal component materials as it is, the compliance of herbal medicine is severly decreased especially for female and younger patients. Consequently, expected treatment effects can't be acquired completely. If loathsome tastes and flavors of decoction were effectively reduced while pharmacological activity were kept intact, the compliance could be promoted Chong-Myung-Tang has been widely prescribed for student patients with memory This study shows that Chong-Myung-Tang+chocolate have no difference from Chong-Myung-Tang in terms of pharmacological activity. Sensory difference with net chocolate was also surved. In order to observe the difference of Chong-Myung-Tang+chocolate and Chong-Myung-Tang, memory impairment was induced by intraventricular injection of $A{\beta}_{25-35}$ peptides on mice and Chong-Myung-Tang and Chong-Myung-Tang+chocolate were administered orally for 14 days. In water maze task, improvement of learning ability during acquisition period and significant increase of memory score during retention period resulted from the treatment of Chong-Myung-Tang and Chong-Myung-Tang+chocolate with respect to the $A{\beta}-injected$ control animals. Furthermore, the $A{\beta}_{25-35}$ toxicity on the hippocampus was assessed with immunohistochemistry (Bax, TUNEL), and differences in antioxidant activity was observed through TBARS and DPPH test. We employed sensory tests using chocolate flavor, herb flavor, and bitter taste & hardness as standards to show sensory differences with net chocolate. In this study, it is demonstrated that Chong-Myung-Tang+chocolate do not disturb the pharmacological activity of Chong-Myung-Tang, and have no sensory difference with net chocolate. Chong-Myung-Tang+chocolate can be used to enhance the compliance remarkably and thought of as an effective, functional formula to maximize expected treatment.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.309-316
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• 2018

Objectives: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. Methods: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. Results: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. Conclusions: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

• Biomolecules & Therapeutics
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• v.15 no.2
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• pp.83-88
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• 2007

The roots of Liriope platyphylla (Liliaceae) are widely used in traditional Chinese medicine. In the present study, we investigated the effects of ethanol (70%) extract of the roots of Liriope platyphylla (ELP70) on learning and memory using behavioral and immunohistochemical methods in mice. Control animals were treated with vehicle (10% Tween 80). With sub-chronic treatments of ELP70 (p.o.) for 14 days, the latency time was significantly increased compared with that of the vehicle-treated control group (50, 100 and 200 mg/kg; P<0.05). Moreover, immunopositive cells for brain derived neurotrophic factor (BDNF) were significantly increased in the hippocapmpal dentate gyrus and CA1 regions after ELP70 treatments for 14 days (50, 100 and 200 mg/kg; P < 0.05). In addition, those cells for nerve growth factor (NGF) were also increased in the hippocapmpal dentate gyrus region (50, 100 and 200 mg/kg; P<0.05). These results suggest that the sub-chronic administration of ELP70 improves learning and memory, and that their beneficial effects are mediated, in part, by the enhancement of BDNF or NGF expression.

• Journal of Physiology & Pathology in Korean Medicine
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• v.16 no.2
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• pp.303-310
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• 2002

Ancient Korean physicians have used several oriental herbs to cure dementia and these effects were described in the Korean herbal books. Some cognitive-enhancing oriental herbs have been widely used as a herbal medicine against dementia. However, few of studies have proved their efficacy in treatment for dementia. In the present study, we investigated the effects of herbal compounds, which are mainly consisted of Uncaria sinensis, Corydalis yanhusuo and Acorus gramineus on learning and memory in Mortis water maze task and the central cholinergic system of the rats with neurotoxic medial septum lesion. In water maze test, the animals were trained to find a platform in a fixed position during 6 days and then received a 60-s probe trial in which the platform was removed from the pool on the 7th day. Ibotanic and 192 saporin lesion of the medial septum (MS) impaired the performance of maze test and degenerated choline acetyltransferase (ChAT) in the brain, which is a marker of the central cholinergic system. Daily administrations of herbal medicine (100mg/kg, p.o.) for 21 consecutive days produced significant reversals of the neurotoxic-induced deficit in learning and memory. These treatments also reduced the loss of cholinergic immunoreactive neurons in the brain induced by neurotoxin. These results demonstrated that herbal compounds ameliorated learning and memory deficits through effects on the central nervous system, partly through effect on the acetylcholine system. Our studies suggest an evidence of these herbs as treatment of Alzheimer's disease.

• Biomolecules & Therapeutics
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• v.24 no.3
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• pp.298-304
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• 2016

Dianthus superbus (D. superbus) is a traditional crude drug used for the treatment of urethritis, carbuncles and carcinomas. The objective of this study was to confirm the cognitive enhancing effect of D. superbus in memory impairment induced mice and to elucidate the possible potential mechanism. Effect of D. superbus on scopolamine induced memory impairment on mice was evaluated using the Morris water maze and passive avoidance tests. We also investigated acetylcholinesterase (AChE) activity and brain-derived neurotropic factor (BDNF) expression in scopolamine-induced mice. HPLC-DAD analysis was performed to identify active compounds in D. superbus. The results revealed that D. superbus attenuated the learning and memory impairment induced by scopolamine. D. superbus also inhibited AChE levels in the hippocampi of the scopolamine-injected mice. Moreover, D. superbus increased BDNF expression in the hippocampus. Eight compounds were identified using HPLC-DAD analysis. The content of 4-hydroxyphenyl acetic acid was higher than contents of other compounds. These results indicated that D. superbus improved memory functioning accompanied by inhibition of AChE and upregulation of BDNF, suggesting that D. superbus may be a useful therapeutic agent for the prevention or treatment of Alzheimer's disease.

• Korean Journal of Biological Psychiatry
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• v.21 no.2
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• pp.65-73
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• 2014

Objectives This study aimed to identify the differences and the profiles of cognitive deficits in remitted patients with schizophrenia and first-degree relatives of schizophrenic probands. Methods A total of 26 remitted states of schizophrenia patients were included in the study and the same number of unaffected first-degree relatives of schizophrenic probands and healthy controls were matched for age, sex, years of education. Cognitive function of all participants was measured by using the Digit span test, the Continuous performance test, the Rey auditory & visual learning test, the Complex figure test, the Verbal fluency test, the Wisconsin card sorting test and the Finger tapping test. The effects of subsyndromal symptomatology and general intelligence score were controlled. Results Schizophrenia patients' group showed more significant impairment than other groups in verbal memory (learning, immediate recall, delayed recall), visual memory (copy, immediate recall, delayed recall) and cognitive flexibility domains. The family group and the patient group commonly performed significantly worse than healthy controls in working memory and verbal fluency (category) tests. There were no differences in sustained attention, psychomotor performance. Conclusions Our research shows that the deficit in working memory and verbal fluency could be strong candidates of endophenotypic marker in schizophrenia.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Korean System Dynamics Review
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• v.9 no.1
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• pp.57-71
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• 2008

This research paper describes a complex and vague settings in which organization makes a decision and explains a role of decision maker's learning process. The original paper, written by Cohen, March, Olsen in 1972, said that all members of organization depended on the technology taken through trials and errors, which is the 'learning' process literally. But they intended to exclude the learning process in their simulation model because their PORTRAN model couldn't replicate the learning concept. As a result, they couldn't explain how all agents of garbage can simulation model resolve the problem dynamically. To overcome this original paper's limitations, we try to rebuild a learning process simulation model using by system dynamics approach that can capture the linkage between organization leanings and agents-based decision-makings. Our learning simulation results reveal two points. First, decision maker's leanings process improves the efficiency of decision making in complex situation. Second, group learning shows a superior efficiency to an individual learning because group members share organizational memory and energy.