• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.112-127
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• 2002

A useful pattern is a pattern that contributes much to learning. For a classification problem those patterns near the class boundary surfaces carry more information to the classifier. For a regression problem the ones near the estimated surface carry more information. In both cases, the usefulness is defined only for those patterns either without error or with negligible error. Using only the useful patterns gives several benefits. First, computational complexity in memory and time for learning is decreased. Second, overfitting is avoided even when the learner is over-sized. Third, learning results in more stable learners. In this paper, we propose a pattern 'utility index' that measures the utility of an individual pattern. The utility index is based on the bias and variance of a pattern trained by a network ensemble. In classification, the pattern with a low bias and a high variance gets a high score. In regression, on the other hand, the one with a low bias and a low variance gets a high score. Based on the distribution of the utility index, the original training set is divided into a high-score group and a low-score group. Only the high-score group is then used for training. The proposed method is tested on synthetic and real-world benchmark datasets. The proposed approach gives a better or at least similar performance.

• Journal of Oriental Neuropsychiatry
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• v.29 no.2
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• pp.79-97
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• 2018

Objectives: The purpose of this systematic review was to investigate the clinical effects of neurofeedback training on reducing anxiety. Methods: Eight databases were used to extract clinical reports on neurofeedback intervention for anxiety reduction published until 2016. We analyzed the characteristics of selected studies and evaluated biases using the Risk of Bias (RoB) assessment. Results: A total of 22 clinical trials were extracted for the analysis. The risk of bias in most studies was high or unclear. The Chinese Classification of Mental Disorders-3 (CCMD-3) was the most frequently used diagnostic criteria, the Hamilton Rating Scale for Anxiety (HAMA) was the most frequently used assessment tool, and the alpha wave activity increase, sensorimotor rhythm (SMR), and theta wave training were the most frequently used intervention methods. All papers showed a statistically significant decrease of anxiety symptoms; however, significant adverse events were not reported. Conclusions: Neurofeedback intervention might be beneficial for reducing anxiety. However, the quality of the studies used in the analysis was low, and the heterogeneity of the population and interventions was revealed. Therefore, more scientifically designed clinical studies regarding neurofeedback training are required.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.71-77
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• 2018

The purpose of this study was to investigate the effectiveness of elastic band resistance training for muscle strength among community-dwelling older adults. The systematic review and meta-analysis was conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Data were pooled using fixed effect models. Sit to stand, arm curl, and grip strength were analyzed for main effects. Heterogeneity between studies was assessed using the I2 statistics and publication bias was evaluated by funnel plots. Twelves studies were included representing 611 participants. Elastic band resistance training was effective for lower (d=3.89, 95% CI: 3.03, 4.75) and upper extremity muscle strength (d=4.08, 95% CI: 2.94, 5.23). Heterogeneity was moderate and no significant publication bias was detected. Based on these findings, there is clear evidence that elastic band resistance training has significant positive effects on muscle strength among community-dwelling older adults. Further study will be needed to perform subgroup analysis using number of sessions and exercise intensity as predictors.

• Korean Journal of Exercise Nutrition
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• v.23 no.1
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• pp.36-47
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• 2019

[Purpose] We conducted a meta-analysis to evaluate the effects of Taekwondo training on the physical fitness factors in Korean elementary students comprehensively and quantitatively. [Methods] We classified research studies published until November 2018 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and selected a total of 17 research items; a meta-analysis of these items was then conducted. We used the Comprehensive Meta-Analysis 3.0 and Review Manager 5.3 to analyze the mean effect size, study quality, and publication bias. [Results] Taekwondo training improved the cardiopulmonary endurance, muscle endurance, and power of the elementary students, but was not practical or less useful on other physical fitness factors. The meta-regression analysis of the cardiopulmonary endurance and power items showed that the effect size was large when the sample size was small. Therefore, it is necessary to consider the sample size in interpreting the effect size for these two items. Further, during correction of the publication bias for the power items, the improvement effect by Taekwondo training was eliminated. [Conclusion] Taekwondo training is helpful for improving the cardiopulmonary endurance and muscle endurance of Korean elementary students but is not useful for other physical fitness factors.

• Physical Therapy Rehabilitation Science
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• v.10 no.2
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• pp.185-196
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• 2021

Objective: Falling is one of main accident to facilitate the physical injuries in order adults. The purpose of the systematic review was to determine the effects of postural balance training whether the recovery of falls in elderly with normal physical function or not throughout summing the selected studies quantitatively. Design: A systematic review Methods: MEDLINE and other four databases were searched up to April 20, 2021 and randomized controlled trials (RCTs) evaluating postural balance approaches on fall risks in elderly. The researched studies excluded the double studies, titles and abstract, and finally full-reported study. The selected RCTs studies were extracted characteristics of the studies and summary of results based on PICOS-SD (population, intervention, comparison, outcomes, and setting- study design) model to synthesize the papers qualitatively. Results: The review involved 22 RCT reports with 4,847 community older adults aged 65 years or over. Nineteen of the selected RCT studies reported dual or multimodal exercises show the beneficial effect for older adults compared to one-type treatment or no intervention. All of selected showed low risk in the selection, attrition, and reporting bias. However, detection bias showed low risk at 75% records of the involved RCTs and performance bias was low risk at only three records. Conclusions: The results of the systematic review propose that a standardized therapeutic approach and the intensity are needed for improving risk of falls in older adults.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.1-14
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• 2024

While text-to-image models have made remarkable progress in image synthesis, certain models, particularly generative diffusion models, have exhibited a noticeable bias to- wards generating images related to the culture of some developing countries. This paper introduces an empirical investigation aimed at mitigating the bias of image generative model. We achieve this by incorporating symbols representing Saudi culture into a stable diffusion model using the Dreambooth technique. CLIP score metric is used to assess the outcomes in this study. This paper also explores the impact of varying parameters for instance the quantity of training images and the learning rate. The findings reveal a substantial reduction in bias-related concerns and propose an innovative metric for evaluating cultural relevance.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.46-53
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• 2019

When a partitioned structure is derived from a data set using a clustering algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. Many algorithms in machine learning fields try to achieve optimized result from available training and test data. Optimization is determined by an evaluation function which has also a tendency toward a certain goal. It is inevitable to have a tendency in the evaluation function both for efficiency and for consistency in the result. But its preference for a specific goal in the evaluation function may sometimes lead to unfavorable consequences in the final result of the clustering. To overcome this bias problems, the first clustering process proceeds to construct an initial partition. The initial partition is expected to imply the possible range in the number of final clusters. We apply the data centric sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm that reduces bias effect resulting from how data is fed into the algorithm. Experiment results have been presented to show that the algorithm helps minimize the order bias effects. We have also shown that the current evaluation measure used for the clustering algorithm is biased toward favoring a smaller number of clusters and a larger size of clusters as a result.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.315-319
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• 2007

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.73-81
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• 2015

The paper proposes a prototype selection method and evaluates the generalization performance of standard algorithms and prototype based classification learning. The proposed prototype classifier defines multidimensional spheres with variable radii within class areas and generates a small set of training data. The nearest-neighbor classifier uses the new training set for predicting the class of test data. By decomposing bias and variance of the mean expected error value, we compare the generalization errors of k-nearest neighbor, Bayesian classifier, prototype selection using fixed radius and the proposed prototype selection method. In experiments, the bias-variance changing trends of the proposed prototype classifier are similar to those of nearest neighbor classifiers with all training data and the prototype selection rates are under 27.0% on average.