• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.208-220
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• 2016

Purpose: Fusel oil is a potent volatile aroma compound found in many alcoholic beverages. At low concentrations, it makes an essential contribution to the flavor and aroma of fermented alcoholic beverages, while at high concentrations, it induced an off-flavor and is thought to cause undesirable side effects. In this work, we introduce Fourier transform near-infrared (FT-NIR) spectroscopy as a rapid and nondestructive technique for the quantitative determination of fusel oil in the Korean alcoholic beverage "soju". Methods: FT-NIR transmittance spectra in the 1000-2500 nm region were collected for 120 soju samples with fusel oil concentrations ranging from 0 to 1400 ppm. The calibration and validation data sets were designed using data from 75 and 45 samples, respectively. The net analyte signal (NAS) was used as a preprocessing method before the application of the partial least-square regression (PLSR) and principal component regression (PCR) methods for predicting fusel oil concentration. A novel variable selection method was adopted to determine the most informative spectral variables to minimize the effect of nonmodeled interferences. Finally, the efficiency of the developed technique was evaluated with two different validation sets. Results: The results revealed that the NAS-PLSR model with selected variables ($R^2_{\upsilon}=0.95$, RMSEV = 100ppm) did not outperform the NAS-PCR model (($R^2_{\upsilon}=0.97$, RMSEV = 7 8.9ppm). In addition, the NAS-PCR shows a better recovery for validation set 2 and a lower relative error for validation set 3 than the NAS-PLSR model. Conclusion: The experimental results indicate that the proposed technique could be an alternative to conventional methods for the quantitative determination of fusel oil in alcoholic beverages and has the potential for use in in-line process control.

• The Plant Pathology Journal
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• v.32 no.2
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• pp.123-135
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• 2016

U.S. cotton production is suffering from the yield loss caused by the reniform nematode (RN), Rotylenchulus reniformis. Management of this devastating pest is of utmost importance because, no upland cotton cultivar exhibits adequate resistance to RN. Nine populations of RN from distinct regions in Alabama and one population from Mississippi were studied and thirteen morphometric features were measured on 20 male and 20 female nematodes from each population. Highly correlated variables (positive) in female and male RN morphometric parameters were observed for body length (L) and distance of vulva from the lip region (V) (r = 0.7) and tail length (TL) and c' (r = 0.8), respectively. The first and second principal components for the female and male populations showed distinct clustering into three groups. These results show pattern of sub-groups within the RN populations in Alabama. A one-way ANOVA on female and male RN populations showed significant differences ($p{\leq}0.05$) among the variables. Multiple sequence alignment (MSA) of 18S rRNA sequences (421) showed lengths of 653 bp. Sites within the aligned sequences were conserved (53%), parsimony-informative (17%), singletons (28%), and indels (2%), respectively. Neighbor-Joining analysis showed intra and inter-nematodal variations within the populations as clone sequences from different nematodes irrespective of the sex of nematode isolate clustered together. Morphologically, the three groups (I, II and III) could not be distinctly associated with the molecular data from the 18S rRNA sequences. The three groups may be identified as being non-geographically contiguous.

• Clinics in Shoulder and Elbow
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• v.22 no.4
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• pp.173-182
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• 2019

Background: Patient reported outcome measures assess clinical progress from the patient's perspective. This study explored the relationship between shoulder outcome measures (The Disability of the Arm, Shoulder and Hand [DASH], American Shoulder and Elbow Surgeons Standard Shoulder Assessment score [ASES], and Constant score) by comparing the best possible scores obtained in an asymptomatic population compared to overall perception of health, as measured by the SF-36 outcome measure. Methods: Volunteers (age range, 20-69 years) with asymptomatic shoulders and no history of shoulder pain, injury, surgery, imaging, or pathology (bilaterally) were included. The DASH and ASES measures were completed by 111 volunteers (72 female, 39 male), of which 92 completed the Constant score (56 female, 36 male). The SF-36 was completed by all volunteers (level of evidence: IV case series). Results: The mean (${\bar{x}}$) score for ASES measure on the right shoulder was higher for the left-hand dominant side (${\bar{x}}=100.00$ vs. 95.02, p-value<0.001); no other significant differences. Better SF-36 scores were associated with better DASH scores. Our prediction models suggest that perception of overall health affects the DASH scores. Sex affected all three shoulder measures scores. Conclusions: Comparing scores of shoulder outcome measures to the highest possible score is not the most informative way to interpret patient progress. Variables such as health status, sex, and hand dominance need to be considered. Furthermore, it is possible to use these variables to predict scores of outcome measures, which facilitates the healthcare provider to deliver individualized care to their patients.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• The Korean Journal of Food And Nutrition
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• v.27 no.6
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• pp.1182-1192
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• 2014

The object of this study was to employ effective marketing methods using SNS by determining how food-related SNS usage characteristics have influence on dine-out motivation and restaurant satisfaction and how this affects people's quality of lie. Survey respondents were men and women who have had used some kinds of food-related SNS. The survey included general characteristics of respondents, food-related SNS usage characteristics, dine-out motivation, restaurant satisfaction, and food-related quality of life. Food-related SNS usage characteristics were divided into convenience, effective time-spending, and informative; Dine-out motivation was sorted into entertainment motivation and social motivation by factor analysis. Analysis of the connections between the variables by AMOS showed that among food-related SNS usage characteristics, convenience did not have a significant influence on either entertainment or social motivation. Informative had a positive effect on entertainment motivation (p<0.05), but not on social motivation. On the other hand, effective time-spending through food-related SNS had an impact on both entertainment (p<0.001) and social (p<0.05) motivation. Moreover, the effect of dine-out motivation on restaurant satisfaction showed that entertainment motivation (p<0.05) and social motivation (p<0.01) both have significant influences on restaurant satisfaction. Also, restaurant satisfaction turned out to affect quality of life (p<0.05). As a result of this study, the usage of food-related SNS did not directly influence customers' restaurant satisfaction and quality of life; (p<0.05). As a result of this study, the usage of food-related SNS did not directly influence customers' restaurant satisfaction and quality of life; however, it had an impact on dine-out motivation and gain pleasure of dining out and help improve the quality of life in the long run; thus, it is believed that marketing strategies thorough SNS by restaurant industry are required.

• Korean Journal of Veterinary Research
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• v.45 no.4
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• pp.545-552
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• 2005

The major protocol features of the rodent uterotrophic assay have been evaluated using a range of reference chemicals. The protocol variables considered include the selection of the test species and route of chemical administration, the age of the test animals, the maintenance diet used, and the specificity of the assay for estrogens. The rodents were ovariectomized under general anesthesia via bilateral flank incisions and randomly assigned to groups of 5 animals. Chemicals were DEHP, DBP, BPA and NP, were injected sc once daily with combinations of chemicals treatments for 3 days. In the results, the reported estrogenic chemicals DEHP and DBP were both negative in the single dose treatments. But, in the combinations of chemicals treatments, DEHP and DBP increased in bud number of mammary gland. Treatment of ovariectomized mice with combinations of other chemicals resulted in uterine and vaginal hyperplasia. The additive estrogenic effects were seen with the combinations of $17{\beta}$-Bestradiol and DBP treatment. the competitive estrogenic effects were seen with the combinations of $17{\beta}$-Bestradiol and nonylphenol, $17{\beta}$-Bestradiol and bisphenol-A treatments. These results offers a sysmatic and mechanistically informative approach to assessing estrogenicity. it provides a useful profile of activity using a reasonable amount of resources and is compatible with the study of individual chemicals as well as the investigation of interactions among combinations of chemicals. The results described illustrate the intrinsic complexity of evaluating chemicals for estrogenic activities and conform the need for rigorous attention to experimental design and criteria for assessing estrogenic activity.

• Restorative Dentistry and Endodontics
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• v.39 no.3
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• pp.164-171
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• 2014

Objectives: Designing in situ models for caries research is a demanding procedure, as both clinical and laboratory parameters need to be incorporated in a single study. This study aimed to construct an informative guideline for planning in situ models relevant to preexisting caries studies. Materials and Methods: An electronic literature search of the PubMed database was performed. A total 191 of full articles written in English were included and data were extracted from materials and methods. Multiple variables were analyzed in relation to the publication types, participant characteristics, specimen and appliance factors, and other conditions. Frequencies and percentages were displayed to summarize the data and the Pearson's chi-square test was used to assess a statistical significance (p < 0.05). Results: There were many parameters commonly included in the majority of in situ models such as inclusion criteria, sample sizes, sample allocation methods, tooth types, intraoral appliance types, sterilization methods, study periods, outcome measures, experimental interventions, etc. Interrelationships existed between the main research topics and some parameters (outcome measures and sample allocation methods) among the evaluated articles. Conclusions: It will be possible to establish standardized in situ protocols according to the research topics. Furthermore, data collaboration from comparable studies would be enhanced by homogeneous study designs.

• Journal of Korean Academy of Nursing
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• v.43 no.1
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• pp.1-10
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• 2013

Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.2
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• pp.183-188
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• 2022

It is a difficult task to estimate parameters in even a simple stock assessment model such as a surplus production model, using only data about temporal catch-per-unit-effort (CPUE) (or survey index) and fishery yields. Such difficulty is exacerbated when time-varying parameters are treated as random effects (aka state variables). To overcome the difficulty, previous studies incorporated somewhat subjective assumptions (e.g., B₁=K) or informative priors of parameters. A key is how to build an objective joint prior of parameters, reducing subjectivity. Given the limited data on temporal CPUEs and fishery yields from 1999-2020 for common squid Todarodes pacificus, we built a joint prior of only two parameters, intrinsic growth rate (r) and carrying capacity (K), based on the resilience level of the population (Froese et al., 2017), and used a Bayesian state-space production assessment model. We used template model builder (TMB), a R package for implementing the assessment model, and estimating all parameters in the model. The predicted annual biomass was in the range of 0.76×10⁶ to 4.06×10⁶ MT, the estimated MSY was 0.13×10⁶ MT, the estimated r was 0.24, and the estimated K was 2.10×10⁶ MT.