• 유통과학연구
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• 제19권9호
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• pp.91-100
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• 2021

Purpose: This study investigates the use intention of artificial intelligence (AI) food service stores for senior customers, which are becoming a trend in the service industry. Research design, data and methodology: For the study, the extended technology acceptance model (TAM) and motivated consumer innovativeness (MCI) variables, proven by existing researchers, were used. In addition to the effect of motivated consumer innovativeness on customer value, we investigated the effect of customer value on trust and use intention. For the study, 520 questionnaires were distributed online by an expert survey agency. Data was verified through validity and reliability. Results: The analysis results of the research hypothesis verified that functionally motivated consumer innovativeness (fMCI), hedonically motivated consumer innovativeness (hMCI), and socially motivated consumer innovativeness (sMCI) all had positive effects on usefulness and enjoyment. Furthermore, usefulness had a statistically significant positive effect on trust, but perceived enjoyment did not; trust was found to positively affect the intention to use. Conclusions: We compared the moderating effects of seniors' gender and age (at 60) between groups. Although there was no moderating effect of age, it was verified that regarding the effect of usefulness on trust, the male group showed a greater influence than the female group.

• Korean Journal of Radiology
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• 제23권1호
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• pp.89-100
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• 2022

Objective: To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). Materials and Methods: A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer's disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. Results: The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer's continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer's disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. Conclusion: We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• 산경연구논집
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• 제5권2호
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• pp.5-12
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• 2014

Purpose - The purpose of this thesis is to assess the product design digital learning status of universities that are currently involved in learning environment projects in manufacture and commerce integration (MCI). Thus, enterprises must keep learning and creating new inventions with revolutionary progress. Research design, data, and methodology - This study not only emphasizes the analysis of technical ability, course concepts, conducting models, and learning environments of every aspect, but also systematically probes the planning of learning, system framework, web learning, environmental activities, data statistics, and digitalized learning, among other aspects. Results - The results of this study help in finally understanding each school's manufacture and commerce integration situation, in order to evaluate product design learning. Consequently, it is essential to evaluate computer learning at schools, thereby affecting communication and the requirements of business education training. Conclusions - It is essential to focus on MCI to promote web teaching to preserve and enhance knowledge disseminating technologies, and immediately share knowledge with learners, while improving work efficiency and cultivating the talent needed by industry.

• Genomics & Informatics
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• 제21권3호
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

• Journal of information and communication convergence engineering
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• 제9권6호
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• pp.671-675
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• 2011

DVC (Distributed Video Coding) and FRUC (Frame Rate Up Conversion) techniques need to have an efficient motion compensated frame interpolation algorithms. Conventional works of these applications have mainly focused on the performance improvement of overall system. But, in some applications, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame matches the original frame. For this aim, this paper deals with the modeling methods for evaluating the block-based matching cost. First, several matching criteria, which have already been dealt with the motion compensated frame interpolation, are introduced and then combined to make estimate models for the size of MSE (Mean Square Error) noise of the MCI frame to original one. Through computer simulations, it is shown that the block-based matching criteria are evaluated and the proposed model can be effectively used for estimating the MSE noise.

• 한국통신학회논문지
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• 제30권11B호
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• pp.744-749
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• 2005

본 논문에서는 기존의 QoS 라우팅 알고리즘이 가지고 있는 문제점인 네트워크 상태 정보 오버헤드를 최소화하면서 네트워크 상태의 정확성을 유지하기 위한 예측 기반 QoS 라우팅 기법인 PSS (Prediction Safety-Shortest) 라우팅 알고리즘 모델을 제안하였다. QoS 라우팅의 상태 정보 갱신 주기에 따른 가용 대역폭의 부정확한 정보를 극복하기 위하여 네트워크 상태를 적용할 수 있는 시계열 예측 알고리즘을 적용하였고, 알고리즘의 성능 평가를 위하여 실제 네트워크와 유사한 MCI 네트워크상에서 시뮬레이션 수행하였으며 라우팅 실패율, 라우팅 대역폭 실패율, 그리고 라우팅 부정확율의 비교를 통하여 본 알고리즘의 우수성을 확인하였다.

• 생물정신의학
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• 제27권2호
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• pp.94-100
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• 2020

Objectives Amyloid β positron emission tomography (Aβ PET) is widely used as a diagnostic tool in patients who have symptoms of cognitive impairment, however, this diagnostic examination is too expensive. Thus, predicting the positivity of Aβ PET before patients undergo the examination is essential. We aimed to analyze clinical predictors of patients who underwent Aβ PET retrospectively, and to develop a predicting model of Aβ PET positivity. Methods 468 patients who underwent Aβ PET with cognitive impairment were recruited and their clinical indicators were analyzed retrospectively. We specified the primary outcome as Aβ PET positivity, and included variables such as age, sex, body mass index, diastolic blood pressure, systolic blood pressure, education, dementia family history, Mini Mental Status Examination (MMSE), Clinical Dementia Rating (CDR), Clinical Dementia Rating-Sum of Box (CDR-SB), hypertension (HTN), diabetes mellitus (DM) and presence of apolipoprotein E (ApoE) E4 as potential predictors. We developed three final models of amyloid positivity prediction for total subjects, mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia using a multivariate stepwise logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed and the area under curve (AUC) value was calculated for the ROC curve. Results Aβ PET negative patients were 49.6% (n = 232), and Aβ PET positive patients were 50.4% (n = 236). In the final model of all subjects, older age, female sex, presence of ApoE E4 and lower MMSE are associated with Aβ PET positivity. The AUC value was 0.296. In the final model of MCI subjects (n = 244), older age and presence of ApoE E4 are associated with Aβ PET positivity. The AUC value was 0.725. In the final model of AD subjects (n = 173), lower MMSE scores, the presence of ApoE E4 and history of HTN are associated with Aβ PET positivity. The AUC value was 0.681. Conclusions The cerebral amyloid positivity model, which was based on commonly available clinical indicators, can be useful for prediction of amyloid PET positivity in MCI or AD patients.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.205-215
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• 2017

The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.

• 한국콘텐츠학회논문지
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• 제9권3호
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• pp.49-57
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• 2009

본 논문에서는 기존에 제시된 QoS 라우팅의 링크 상태 갱신 알고리즘의 성능을 향상시킬 수 있는 플로우 유지 시간을 기반으로 한 LSU 알고리즘을 제안하였다. 기존에 제시된 LSU 알고리즘은 네트워크 트래픽의 통계 정보를 기반으로 하여 LSU 메시지 전송 여부를 결정하는 반면, 제안하는 알고리즘은 플로우 유지 시간을 기반으로 하여 LSU 메시지의 개수를 감소시킬 수 있기 때문에 기존에 제안된 알고리즘에 모두 적용하여 사용할 수 있다. 제안하는 알고리즘은 짧은 시간 안에 반복적으로 발생되는 LSU 메시지의 개수를 감소시키기 위해 플로우의 유지 시간에 대한 통계적 정보를 사용하였다. 알고리즘의 성능 평가를 위해 기존에 제시된 다양한 LSU 알고리즘을 구현하여 본 논문에서 제안하는 알고리즘을 적용하였고 실제 네트워크와 유사한 MCI 네트워크상에서 라우팅 Blocking 확률과 링크 당 평균 LSU 메시지의 개수를 성능 평가 항목으로 하여 시뮬레이션을 수행하여 제시하는 알고리즘의 우수성을 확인하였다.