• Asia pacific journal of information systems
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• v.16 no.4
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• pp.217-238
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• 2006

In this study, we propose a virtual community recommendation model based on user behavioral models. It is designed to recommend optimal virtual communities for an active user by applying case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extensions. Also, it is designed to filter its case-base by considering the user's needs type before applying CBR. To test the usefulness of our model, we conduct two-step validation - experimental validation for the collected data, and survey validation for investigating the actual satisfaction level. Experimental results show that our model presents effective recommendation results in an efficient way. In addition, they also show that the information on the user's needs type may generate opportunities for cross-selling other commercial items.

• Journal of Fisheries and Marine Sciences Education
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• v.23 no.1
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• pp.13-22
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• 2011

The purpose of this study was to development and validation of the 'course evaluation' scale for learner-centered in university. The research collected preliminary data from 1,567 university students's responses for item and scale quality analyses, and collected 2,539 university students's for item and scale quality analyses, and 300 university professors's responses for validation. Data were analyzed to obtain item quality, reliability, and validity analysis. The results of the study were as follows; The 'course evaluation' scale for learner-centered in university was defined by 5 factors. The 5 factors were structure and sincerity of lecture, suitability of report and test, level of consulting for student, application of educational media, communication. The results of the confirmatory factor analysis confirmed five sub-scales in the 'course evaluation' scale for learner-centered in university scale. Criterion-related validity evidence was obtained from the correlation analysis as the criterion measures. Cross validity evidence was obtained from the confirmatory factor analysis in university professors.

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2397-2404
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• 2011

Variable selection k nearest neighbor QSAR modeling approach was applied to a data set of 80 3-arylisoquinolines exhibiting cytotoxicity against human lung tumor cell line (A-549). All compounds were characterized with molecular topology descriptors calculated with the MolconnZ program. Seven compounds were randomly selected from the original dataset and used as an external validation set. The remaining subset of 73 compounds was divided into multiple training (56 to 61 compounds) and test (17 to 12 compounds) sets using a chemical diversity sampling method developed in this group. Highly predictive models characterized by the leave-one out cross-validated $R^2$ ($q^2$) values greater than 0.8 for the training sets and $R^2$ values greater than 0.7 for the test sets have been obtained. The robustness of models was confirmed by the Y-randomization test: all models built using training sets with randomly shuffled activities were characterized by low $q^2{\leq}0.26$ and $R^2{\leq}0.22$ for training and test sets, respectively. Twelve best models (with the highest values of both $q^2$ and $R^2$) predicted the activities of the external validation set of seven compounds with $R^2$ ranging from 0.71 to 0.93.

• Korean Journal of Clinical Laboratory Science
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• v.38 no.2
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• pp.117-124
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• 2006

The analytical measurement range (AMR) is the range of analyte values that a method can directly measure on a specimen without any dilution, concentration, or other pretreatment not part of the usual assay process. The linearity of the AMR is its ability to obtain test results which are directly proportional to the concentration of analyte in the sample from the upper and lower limit of the AMR. The AMR validation is the process of confirming that the assay system will correctly recover the concentration or activity of the analyte over the AMR. The test specimen must have analyte values which, at a minimum, are near the low, midpoint, and high values of the AMR. The AMR must be revalidated at least every six months, at changes in major system components, and when a complete change in reagents for a procesure is introduced; unless the laboratory can demonstrate that changing the reagent lot number does not affect the range used to report patient test results. The AMR linearity was total protein (0-16.6), albumin (0-8.1), total bilirubin (0-18.1), alkaline phosphatase (0-1244.3), aspartate aminotransferase (0-1527.9), alanine aminotransferase (0-1107.9), gamma glutamyl transpeptidase (0-1527.7), creatine kinase (0-1666.6), lactate dehydrogenase (0-1342), high density lipoprotein cholesterol (0.3-154.3), sodium (35.4-309), creatinine (0-19.2), blood urea nitrogen (0.5-206.2), uric acid (0-23.9), total cholesterol (-0.3-510), triglycerides (0.7-539.6), glucose (0-672.7), amylase (0-1595.3), calcium (0-23.9), inorganic phosphorus (0.03-17.0), potassium (0.1-116.5), chloride (3.3-278.7). We are sure that materials for the AMR affect the evaluation of the upper limit of the AMR in the process system.

• Analytical Science and Technology
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• v.24 no.6
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• pp.533-543
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• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.

• Analytical Science and Technology
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• v.37 no.2
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• pp.114-122
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• 2024

A transmission Raman spectroscopy-based quantitative model, which can analyze the content of a drug product containing naproxen sodium as its active pharmaceutical ingredient (API), was developed. Compared with the existing analytical method, i.e., high-performance liquid chromatography (HPLC), Raman spectroscopy exhibits high test efficiency owing to its shorter sample pre-treatment and measurement time. Raman spectroscopy is environmentally friendly since samples can be tested rapidly via a nondestructive method without sample preparation using solvent. Through this analysis method, rapid on-site analysis was possible and it could prevent the production of defective tablets with potency problems. The developed method was applied to the assays of the naproxen sodium of coated tablets that were manufactured in commercial scale and the content of naproxen sodium was accurately predicted by Raman spectroscopy and compared with the reference analytical method such as HPLC. The method validation of the new approach was also performed. Further, the specificity, linearity, accuracy, precision, and robustness tests were conducted, and all the results were within the criteria. The standard error of cross-validation and standard error of prediction values were determined as 0.949 % and 0.724 %, respectively.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.299-307
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• 2022

In this paper, we propose a pre-processing method that converts to Spider Chart image data for classification of gripping movement using EMG (electromyography) sensors and Convolution Neural Networks (CNN) deep learning. First, raw data for six hand gestures are extracted from five test subjects using an 8-channel armband and converted into Spider Chart data of octagonal shapes, which are divided into several sliding windows and are learned. In classifying six hand gestures, the classification performance is compared with the proposed pre-processing method and the existing methods. Deep learning was performed on the dataset by dividing 70% of the total into training, 15% as testing, and 15% as validation. For system performance evaluation, five cross-validations were applied by dividing 80% of the entire dataset by training and 20% by testing. The proposed method generates 97% and 94.54% in cross-validation and general tests, respectively, using the Spider Chart preprocessing, which was better results than the conventional methods.

• The Journal of Korean Physical Therapy
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• v.27 no.4
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• pp.214-220
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• 2015

Purpose: The purpose of this study was to investigate the availability of software for rehabilitation with the Kinect sensor by presenting an efficient algorithm based on machine learning when classifying the motion data of the PNF pattern if the subjects were wearing a patient gown. Methods: The motion data of the PNF pattern for upper extremities were collected by Kinect sensor. The data were obtained from 8 normal university students without the limitation of upper extremities. The subjects, wearing a T-shirt, performed the PNF patterns, D1 and D2 flexion, extensions, 30 times; the same protocol was repeated while wearing a patient gown to compare the classification performance of algorithms. For comparison of performance, we chose four algorithms, Naive Bayes Classifier, C4.5, Multilayer Perceptron, and Hidden Markov Model. The motion data for wearing a T-shirt were used for the training set, and 10 fold cross-validation test was performed. The motion data for wearing a gown were used for the test set. Results: The results showed that all of the algorithms performed well with 10 fold cross-validation test. However, when classifying the data with a hospital gown, Hidden Markov model (HMM) was the best algorithm for classifying the motion of PNF. Conclusion: We showed that HMM is the most efficient algorithm that could handle the sequence data related to time. Thus, we suggested that the algorithm which considered the sequence of motion, such as HMM, would be selected when developing software for rehabilitation which required determining the correctness of the motion.

• Journal of Korean Clinical Nursing Research
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• v.18 no.2
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• pp.307-316
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• 2012

Purpose: The purpose of this study was to develop and test psychometric properties the Korean version of the End-Stage Renal Disease Adherence Questionnaire (KESRD-AQ). The ESRD-AQ, a 46-item, multidimensional, self-administrated questionnaire which was developed to assess treatment adherence to hemodialysis (HD) attendance, medications, fluid restrictions, and diet prescription among patients on maintenance hemodialysis, has been validated. Methods: The KESRD-AQ was developed by performing both translation and backtranslation. The content validity and test- retest reliability of the KESRD-AQ were evaluated by establishing item-level content validity index (I-CVI) and intra-class correlation coefficients (ICC), respectively. Construct validity was assessed by adopting a known-group analysis comparing adheres and non-adherers using Mann-Whitney U Test. Results: 41 Korean-American patients with ESRD on HD from 3 outpatient dialysis centers in California participated in the study. The KESRD-AQ showed excellent content validity (average I-CVI=.96) and test-retest reliability (ICC=.917, p=.004). The construct validity indicated that the KESRD-AQ distinguished adheres and non-adheres (p=.02~.047). Conclusion: The KESRD-AQ is a valid and reliable instrument to measure treatment adherence.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.