• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.56-67
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• 2019

The aim of this study was to investigate the influence of rosemary oil inhalation on memory, attention and autonomic nervous system according to the concentration difference in the aged. The research design was non-equivalent control group non-synchronized design. Participants were 89 individuals aged 65 or older who live in the community. Participants inhaled almond carrier oil(control group), 10%(experimental group A) and 100%(experimental group B) rosemary oil. Memory, attention, and autonomic nervous system responses were measured. Data were analyzed by SPSS win 24.0. The differences of the group and time were analyzed through repeated measure ANOVA. There were no significant differences in immediate recall (F=.42, p =.656), delayed recall (F=.45, p=.639), recognition (F=1.45, p=.242), digit span-forward (F=1.53, p=.223), digit span-backward (F=.46, p=.636), activities of sympathetic nerve system (LF)(F=.19, p=.828), activities of parasympathetic nerve system (HF)(F=.37, p=.694), LH/HF(F=1.39, p=.256), systolic blood pressure (F=.37, p=.694), diastolic blood pressure (F=1.25, p=.291). The inhalation of 10% and 100% rosemary oil for five minutes showed no significant effects on memory, attention and automatic nervous system in the aged.

• BMB Reports
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• v.49 no.12
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• pp.671-680
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• 2016

Accumulating evidence indicates many brain functions are mediated by epigenetic regulation of neural genes, and their dysregulations result in neuronal disorders. Experiences such as learning and recall, as well as physical exercise, induce neuronal activation through epigenetic modifications and by changing the noncoding RNA profiles. Animal models, brain samples from patients, and the development of diverse analytical methods have broadened our understanding of epigenetic regulation in the brain. Diverse and specific epigenetic changes are suggested to correlate with neuronal development, learning and memory, aging and age-related neuronal diseases. Although the results show some discrepancies, a careful comparison of the data (including methods, regions and conditions examined) would clarify the problems confronted in understanding epigenetic regulation in the brain.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.134-140
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• 1994

In this paper, an algorithm of CBAM(Combination Bidirectional Associative Memory) model proposes, analyzes and tests CBAM model `s performancess by simulating with recalls and recognitions of patterns. In learning-procedure each correlation matrix of training patterns is obtained. As each correlation matrix's some elements correspond to juxtaposition, all correlation matrices are merged into one matrix (Combination Correlation Matrix, CCM). In recall-procedure, CCM is decomposed into a number of correlation matrices by spiliting its elements into the number of elements corresponding to all training patterns. Recalled patterns are obtained by multiplying input pattern with all correlation matrices and selecting a pattern which has the smallest value of energy function. By using a CBAM model, we have some advantages. First, all pattern having less than 20% of noise can be recalled. Second, memory capacity of CBAM model, can be further increased to include English alphabets or patterns. Third, learning time of CBAM model can be reduced greatly because of operation to make CCM.

• Korean Journal of Biological Psychiatry
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• v.13 no.2
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• pp.117-120
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• 2006

Localized amnesia is characterized by a failure to recall events that occurred during a circumscribed period of time. Localized amnesia is the most common type of dissociative amnesia. It is assumed that this is a disorder of memory retrieval. Recent neuroimaing studies reported that posterior cingulate cortex may play a important role in memory(autobiographical) retrieval. The authors reported a case of localized amnesia with mass on left posterior cingulate cortex.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Korean Journal of Biological Psychiatry
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• v.15 no.2
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• pp.92-100
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• 2008

Objectives : The purpose of this study was to investigate 1) the neuropsychological deficits with major depressive disorder(MDD) in depressed state and 2) the changes of neuropsychological dysfunctions during depressed episodes and remitted periods in the MDD group. Methods : 12 patients with MDD and 70 normal controls who were diagnosed and classified by DSM-IV and SCID-IV interview participated in this study. The psychopathology was measured using the Hamilton rating scale for depression(HAM-D) and Brief Psychiatric Rating Scale(BPRS). The memory function, executive function, and sustained attention were measured by a trained psychologist using the Korean version of Memory Assessment Scale(K-MAS), Wisconsin Card Sorting Test(WCST), and Vigilance(VIG) and Cognitrone (COG) in Vienna Test System. After 6 weeks of treatment, we retested the cognitive tests in order to measure the cognitive functions in remitted states. Results : Patients with MDD achieved significantly lower score in sentence immediately recall, verbal memory score and total memory score of the K-MAS, total errors of the WCST, response time of Vigilance and response time at "Yes" response of Cognitrone than normal controls at baseline. After 6 weeks of medication, the psychiatric symptoms in the patient group were attenuated, and most of the neuropsychological functions including attention, memory, and frontal/executive function were improved except for response time of Cognitrone. Conclusions : This study provides evidence for distinct neuropsychological deficits in patients with MDD on their depressed states and remitted periods. The impairment on response time remains after remission, and this would be a trait marker of major depressive disorder.

• Therapeutic Science for Rehabilitation
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• v.9 no.4
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• pp.95-106
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• 2020

Objective : The purpose of this study was to systematically review sex differences in episodic memory. Methods : We searched previous studies published in all electronic databases between 2010 and 2019. The key terms used in the search were 'sex differences' or 'gender differences' and 'episodic memory' or 'autobiographical memory'. 8 studies were finally extracted for analysis. Results : The 8 studies had evidence levels of II (67.5%) and III (37.5%), which are quite high. Healthy younger adults or healthy adults were recruited to examine sex differences in episodic memory. Assessment methods for episodic memory were mainly divided into cognitive tasks or standardized tests using visual or auditory stimuli. Subjects were instructed to memorize the stimuli and asked to recall them after some time. Overall females outperformed male. In particular, there were significant sex differences in verbal episodic memory. In contrast, there was no significant sex difference in visual episodic memory. Conclusion : To identify sex differences in episodic memory, a variety of test methods were used in various ways. Overall, females showed higher episodic memory than males. These findings suggest a need for cognitive intervention considering sex differences in the clinic. In the future, episodic memory tests with high ecological validity should be conducted to investigate sex differences in episodic memory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2612-2633
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• 2020

Due to the explosive growth of multimedia speech data, how to protect the privacy of speech data and how to efficiently retrieve speech data have become a hot spot for researchers in recent years. In this paper, we proposed an encrypted speech retrieval scheme based on long short-term memory (LSTM) neural network and deep hashing. This scheme not only achieves efficient retrieval of massive speech in cloud environment, but also effectively avoids the risk of sensitive information leakage. Firstly, a novel speech encryption algorithm based on 4D quadratic autonomous hyperchaotic system is proposed to realize the privacy and security of speech data in the cloud. Secondly, the integrated LSTM network model and deep hashing algorithm are used to extract high-level features of speech data. It is used to solve the high dimensional and temporality problems of speech data, and increase the retrieval efficiency and retrieval accuracy of the proposed scheme. Finally, the normalized Hamming distance algorithm is used to achieve matching. Compared with the existing algorithms, the proposed scheme has good discrimination and robustness and it has high recall, precision and retrieval efficiency under various content preserving operations. Meanwhile, the proposed speech encryption algorithm has high key space and can effectively resist exhaustive attacks.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.21-27
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• 2019

The study empirically analyzed the effects of content attitudes and recall on digital signage advertising appeal (information appeal vs. image appeal) and interactivity level (low vs. high). As a result, first, it was found that a moderately low level of interactivity had a positive effect on content attitudes and recall than when the level of digital signage was extremely high. In addition, at moderately low levels of interactivity, information appeals had higher content attitudes and recalls than image appeals. Second, the content of image appeal has a positive effect on attitude when the digital signage level of interactivity is high, and the image recall ad and information appeal ad have negative effects on recall. Third, the low level of interactivity of digital signage has a positive effect on the content attitude and recall of information appeal. With the advent of digital media in recent years, concerns about how to construct the level of interactivity and information content on a strategic level are increasing in practice. The results of this study are expected to suggest the direction of the strategic grounds for this.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.39-46
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• 2021

AI is deeply applied to various algorithms that assists us, not only daily technologies like translator and Face ID, but also contributing to innumerable fields in industry, due to its dominance. In this research, we provide convenience through AI categorization, extracting the only data that users need, with objective classification, rather than verifying all data to find from the internet, where exists an immense number of contents. In this research, we propose a model using LSTM(Long-Short Term Memory Network), which stands out from text classification, and compare its performance with models of RNN(Recurrent Neural Network) and BiLSTM(Bidirectional LSTM), which is suitable structure for natural language processing. The performance of the three models is compared using measurements of accuracy, precision, and recall. As a result, the LSTM model appears to have the best performance. Therefore, in this research, text classification using LSTM is recommended.