• Nuclear Engineering and Technology
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• v.44 no.6
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• pp.663-672
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• 2012

As software based digital I&C (Instrumentation and Control) systems are used more prevalently in nuclear plants, enhancement of software dependability has become an important issue in the area of nuclear I&C systems. Critical attributes of software dependability are safety and reliability. These attributes are tightly related to software failures caused by faults. Software testing and V&V (Verification and Validation) activities are hence important for enhancing software dependability. If the risky modules of safety-critical software can be predicted, it will be possible to focus on testing and V&V activities more efficiently and effectively. It should also make it possible to better allocate resources for regulation activities. We propose a prediction technique to estimate risky software modules by adopting machine learning models based on software complexity metrics. An empirical study with various machine learning algorithms was executed for comparing the prediction performance. Experimental results show SVMs (Support Vector Machines) perform as well or better than the other methods.

• Journal of the military operations research society of Korea
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• v.22 no.2
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• pp.90-112
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• 1996

Research issues in software engineering in recent may be object oriented methodology and software quality. Since Halstead has proposed metric-software science in 1977, software quality area has been studied in steady but inactively until 1980s. As international standards such as ISO 9000-3, 9126 were enacted in 1990s early, interest in software quality is increased but many problems such as how to validate metric, measure quality or apply metric are remained. This paper proposes software quality metric methodology which software developer or project manager can use in measuring quality and validating metric during software development. The methodology is classified by several phases: establishment of quality requirement, identification of quality metric, data collection, metric implementation, metric validation. In order to show its applicability, test program, metrics and data are applied to each phase of the methodology. Consideration of this methodology as a methodology for software quality measurement similar to development methodology for software development is needed.

• Journal of Gastric Cancer
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• v.22 no.4
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• pp.395-407
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• 2022

Purpose: This study aimed to investigate the relationship between clinical and laboratory parameters and complication status to predict which patients can be safely discharged from the hospital on the third postoperative day (POD). Materials and Methods: Data from a prospectively maintained database of 2,110 consecutive patients with gastric adenocarcinoma who underwent curative surgery were reviewed. The third POD vital signs, laboratory data, and details of the course after surgery were collected. Patients with grade II or higher complications after the third POD were considered unsuitable for early discharge. The performance metrics were calculated for all algorithm parameters. The proposed algorithm was tested using a validation dataset of consecutive patients from the same center. Results: Of 1,438 patients in the study cohort, 142 (9.9%) were considered unsuitable for early discharge. C-reactive protein level, body temperature, pulse rate, and neutrophil count had good performance metrics and were determined to be independent prognostic factors. An algorithm consisting of these 4 parameters had a negative predictive value (NPV) of 95.9% (95% confidence interval [CI], 94.2-97.3), sensitivity of 80.3% (95% CI, 72.8-86.5), and specificity of 51.1% (95% CI, 48.3-53.8). Only 28 (1.9%) patients in the study cohort were classified as false negatives. In the validation dataset, the NPV was 93.7%, sensitivity was 66%, and 3.3% (17/512) of patients were classified as false negatives. Conclusions: Simple clinical and laboratory parameters obtained on the third POD can be used when making decisions regarding the safe early discharge of patients who underwent gastrectomy.

• Journal of Distribution Science
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• v.10 no.2
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• pp.43-52
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• 2012

As experiential education services are growing, the need for proper management is increasing. Considering that adequate measures are an essential factor for achieving success in managing something, it is important for managers to use a proper system of metrics to measure the performance of experiential education services. However, in spite of this need, little research has been done to develop a valid and reliable set of metrics for assessing the quality of experiential education services. The current study aims to develop a multi-item instrument for assessing the service quality of experiential education. The specific procedure is as follows. First, we generated a pool of possible metrics based on diverse literature on service quality. We elicited possiblemetric items not only from general service quality metrics such as SERVQUAL and SERVPERF but also from educational service quality metrics such as HEdPERF and PESPERF. Second, specialist teachers in the experiential education area screened the initial metrics to boost face validity. Third, we proceeded with multiple rounds of empirical validation of those metrics. Based on this processes, we refined the metrics to determine the final metrics to be used. Fourth, we examined predictive validity by checking the well-established positive relationship between each dimension of metrics and customer satisfaction. In sum, starting with the initial pool of scale items elicited from the previous literature and purifying them empirically through the surveying method, we developed a four-dimensional systemized scale to measure the superiority of experiential education and named it "Experiential Education PERFormance" (EEPERF). Our findings indicate that students (consumers) perceive the superiority of the experiential education (EE) service in the following four dimensions: EE-empathy, EE-reliability, EE-outcome, and EE-landscape. EE-empathy is a judgment in response to the question, "How empathetically does the experiential educational service provider interact with me?" Principal measures are "How well does the service provider understand my needs?," and "How well does the service provider listen to my voice?" Next, EE-reliability is a judgment in response to the question, "How reliably does the experiential educational service provider interact with me?" Major measures are "How reliable is the schedule here?," and "How credible is the service provider?" EE-outcome is a judgmentin response to the question, "What results could I get from this experiential educational service encounter?" Representative measures are "How good is the information that I will acquire form this service encounter?," and "How useful is this service encounter in helping me develop creativity?" Finally, EE-landscape is a judgment about the physical environment. Essential measures are "How convenient is the access to the service encounter?,"and "How well managed are the facilities?" We showed the reliability and validity of the system of metrics. All four dimensions influence customer satisfaction significantly. Practitioners may use the results in planning experiential educational service programs and evaluating each service encounter. The current study isexpected to act as a stepping-stone for future scale improvement. In this case, researchers may use the experience quality paradigm that has recently arisen.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.596-600
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• 2009

To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. They usually adopt logistic regression analysis, And, they state that the correctness of prediction is about 60${\sim}$70%, We performed a similar experiment with Eclipse 3.3 to check their prediction effectiveness, However, the result shows that correctness is about 40% which is much lower than the original results. We also found that univariate logistic regression analysis produces better results than multivariate logistic regression analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.943-947
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• 2009

To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. In order to apply defect prediction models, we need to determine a threshold value. Because we cannot know actually where defects are, it is difficult to determine threshold. Therefore, we performed a series of experiments to explore the issue of determining a threshold. In the experiments, we applied defect prediction models to other systems different from the system used in building the prediction model. Specifically, we have applied three models - Olague model, Zhou model, and Gyimothy model - to four different systems. As a result, we found that the prediction capabilities varied considerably with a chosen threshold value. Therefore, we need to perform a study on the determination of an appropriate threshold value to improve the applicably of defect prediction models.

• Korean Journal of Biological Psychiatry
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• v.27 no.1
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• pp.18-26
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• 2020

Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• Archives of Plastic Surgery
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• v.44 no.3
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• pp.223-227
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• 2017

Background Carpal tunnel release is one of the most common surgical procedures performed by hand surgeons. The authors created a surgical simulation of open carpal tunnel release utilizing a mobile and rehearsal platform app. This study was performed in order to validate the simulator as an effective training platform for carpal tunnel release. Methods The simulator was evaluated using a number of metrics: construct validity (the ability to identify variability in skill levels), face validity (the perceived ability of the simulator to teach the intended material), content validity (that the simulator was an accurate representation of the intended operation), and acceptability validity (willingness of the desired user group to adopt this method of training). Novices and experts were recruited. Each group was tested, and all participants were assigned an objective score, which served as construct validation. A Likert-scale questionnaire was administered to gauge face, content, and acceptability validity. Results Twenty novices and 10 experts were recruited for this study. The objective performance scores from the expert group were significantly higher than those of the novice group, with surgeons scoring a median of 74% and medical students scoring a median of 45%. The questionnaire responses indicated face, content, and acceptability validation. Conclusions This mobile-based surgical simulation platform provides step-by-step instruction for a variety of surgical procedures. The findings of this study help to demonstrate its utility as a learning tool, as we confirmed construct, face, content, and acceptability validity for carpal tunnel release. This easy-to-use educational tool may help bring surgical education to a new- and highly mobile-level.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.2
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• pp.163-173
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• 2021

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose. Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-E_max, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women's age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use. Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.