• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.15-24
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• 2023

One of the objectives and aspirations of scientists and engineers ever since the development of computers has been to interact naturally with machines. Hence features of artificial intelligence (AI) like natural language processing and natural language generation were developed. The field of AI that is thought to be expanding the fastest is interactive conversational systems. Numerous businesses have created various Virtual Personal Assistants (VPAs) using these technologies, including Apple's Siri, Amazon's Alexa, and Google Assistant, among others. Even though many chatbots have been introduced through the years to diagnose or treat psychological disorders, we are yet to have a user-friendly chatbot available. A smart generative cognitive behavioral therapy with spoken dialogue systems support was then developed using a model Persona Perception (P2) bot with Generative Pre-trained Transformer-2 (GPT-2). The model was then implemented using modern technologies in VPAs like voice recognition, Natural Language Understanding (NLU), and text-to-speech. This system is a magnificent device to help with voice-based systems because it can have therapeutic discussions with the users utilizing text and vocal interactive user experience.

• The Korean Journal of Physiology and Pharmacology
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• 제14권6호
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• pp.371-376
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• 2010

Glycyrrhizae radix (GR) is an herbal medicine that is commonly used in the East Asia for treating a variety of diseases, including stomach disorders. The objective of the present study was to examine the anti-stress effects of GR on repeated stress-induced alterations of anxiety, learning and memory in rats. Restraint stress was administered for 14 days (2 h/day) to the rats in the Control and GR groups (400 mg/kg/day, PO). Starting on the eighth day, the rats were tested for spatial memory on the Morris water maze test (MW) and for anxiety on the elevated plus maze (EPM). We studied the changes of the expressions of cholineacetyl transferase (ChAT) and tyrosine hydroxylase (TH) in the locus coerleus (LC) using immunohistochemistry. The results showed that the rats treated with GR had significantly reduced stress-induced deficits on their learning and memory on the spatial memory tasks. In addition, the ChAT immunoreactivities were increased. Gor the EPM, treatment with GR increased the time spent in the open arms (p<0.001) as compared to that of the control group. Moreover, GR treatment also normalized the increases of the TH expression in the LC (p<0.001). In conclusion, administration of GR improved spatial learning and memory and reduced stress-induced anxiety. Thus, the present results suggest that GR has the potential to attenuate the behavioral and neurochemical impairments caused by stress.

• Animal cells and systems
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• 제16권4호
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• pp.302-312
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• 2012

The purpose of this study was to examine whether Phellodendri Cortex extract (PCE) could improve learning and memory impairments caused by lipopolysaccharide (LPS)-induced inflammation in the rat brain. The effect of PCE on modulating pro-inflammatory mediators in the hippocampus and its underlying mechanism were investigated. Injection of LPS into the lateral ventricle caused acute regional inflammation and subsequent deficits in spatial learning ability in the rats. Daily administration of PCE (50, 100, and 200 mg/kg, i.p.) for 21 days markedly improved the LPS-induced learning and memory disabilities in the Morris water maze and passive avoidance test. PCE administration significantly decreased the expression of pro-inflammatory mediators such as tumor necrosis factor-${\alpha}$, interleukin-$1{\beta}$, and cyclooxygenase-2 mRNA in the hippocampus, as assessed by RT-PCR analysis and immunohistochemistry. Together, these findings suggest that PCE significantly attenuated LPS-induced spatial cognitive impairment through inhibiting the expression of pro-inflammatory mediators in the rat brain. These results suggested that PCE may be effective in preventing or slowing the development of neurological disorders, including Alzheimer's disease, by improving cognitive and memory function because of its anti-inflammation activity in the brain.

• 대한치매학회지
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• 제22권2호
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• pp.61-68
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• 2023

Background and Purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. Methods: A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. Results: The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Conclusions: Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

• 한국멀티미디어학회논문지
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• 제22권12호
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• pp.1491-1499
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• 2019

Insufficient sleep time and bad sleep quality causes many illnesses and it's research became more and more important. The most common method for measuring sleep quality is the polysomnography(PSG). The PSG is a test used to diagnose sleep disorders. The most common PSG data is obtained from the examiner, which attaches several sensors on a body and takes sleep overnight. However, most of the sleep stage classification in PSG are low accuracy of the classification. In this paper, we have studied algorithm for sleep level classification based on machine learning which can replace PSG. EEG, EOG, and EMG channel signals are studied and tested by using CNN algorithm. In order to compensate the performance, a mixed model using both CNN and DNN models is designed and tested for performance.

• 말소리와 음성과학
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• 제4권4호
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• pp.129-138
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• 2012

Reading fluency is an important variable in reading comprehension. However, a limited number of studies on reading fluency are available in Korea. The purpose of this study is to review the articles about reading fluency during last 10 years and to present a direction for future research. Forty research papers from the Journal of Learning Disabilities and Language Speech and Hearing Services in Schools were selected from 2002 to 2012. These papers were analyzed in terms of their subjects and research methods. About 64% of the articles focused on typically developing children and children with dyslexia. About 67% of the research consisted of descriptive studies. Based on these results, suggestions were made for future research on reading fluency.

• The Journal of Korean Physical Therapy
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• 제22권6호
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• pp.91-98
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• 2010

Neurostimulation approaches have been developed and explored to modulate neuroplastic changes of cortical function in human brain. As one of the most primary noninvasive tools, transcranial direct current stimulation (tDCS) was extensively studied in the field of neuroscience. The alternation of cortical neurons depending on the polarity of the tDCS has been used for improving cognitive processing including working memory, learning, and language in normal individuals, as well as in patients with neurological or psychiatric diseases. In addition, tDCS has great advantages: it is a non-invasive, painless, safe, and cost-effective approach to enhance brain function in normal subjects and patients with neurological disorders. Numerous previous studies have confirmed the efficacy of tDCS. However, tDCS has not been considered for clinical applications and research in the field of physical therapy. Therefore, this review will focus on the general principles of tDCS and its related application parameters, and provide consideration of motor behavioral research and clinical applications in physical therapy.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1336-1345
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• 2021

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders in children. The diagnosis of ADHD in children is based on the interviews and observation reports of parents or teachers who have stayed with them. Since this approach cannot avoid long observation time and the bias of observers, another approach based on Electroencephalography(EEG) is emerging. The goal of this study is to develop an assistive tool for diagnosing ADHD by EEG classification. This study explores the frequency bands of EEG and extracts the implied features in them by using the proposed CNN. The CNN architecture has three Convolution-MaxPooling blocks and two fully connected layers. As a result of the experiment, the 30-60 Hz gamma band showed dominant characteristics in identifying EEG, and when other frequency bands were added to the gamma band, the EEG classification performance was improved. They also show that the proposed CNN is effective in detecting ADHD in children.

• BMB Reports
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• 제54권3호
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• pp.149-156
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• 2021

The well-known second messenger cyclic adenosine monophosphate (cAMP) regulates the morphology and physiology of neurons and thus higher cognitive brain functions. The discovery of exchange protein activated by cAMP (Epac) as a guanine nucleotide exchange factor for Rap GTPases has shed light on protein kinase A (PKA)-independent functions of cAMP signaling in neural tissues. Studies of cAMP-Epac-mediated signaling in neurons under normal and disease conditions also revealed its diverse contributions to neurodevelopment, synaptic remodeling, and neurotransmitter release, as well as learning, memory, and emotion. In this mini-review, the various roles of Epac isoforms, including Epac1 and Epac2, highly expressed in neural tissues are summarized, and controversies or issues are highlighted that need to be resolved to uncover the critical functions of Epac in neural tissues and the potential for a new therapeutic target of mental disorders.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.25-28
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• 2020

Automatic segmentation of brain tissues such as WM, GM, and CSF from brain MRI scans is helpful for the diagnosis of many neurological disorders. Accurate segmentation of these brain structures is a very challenging task due to low tissue contrast, bias filed, and partial volume effects. With the aim to improve brain MRI segmentation accuracy, we propose an end-to-end convolutional based U-SegNet architecture designed with multi-scale kernels, which includes cascaded dilated convolutions for the task of brain MRI segmentation. The multi-scale convolution kernels are designed to extract abundant semantic features and capture context information at different scales. Further, the cascaded dilated convolution scheme helps to alleviate the vanishing gradient problem in the proposed model. Experimental outcomes indicate that the proposed architecture is superior to the traditional deep-learning methods such as Segnet, U-net, and U-Segnet and achieves high performance with an average DSC of 93% and 86% of JI value for brain MRI segmentation.