• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• 재활치료과학
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• 제12권4호
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• pp.23-37
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• 2023

목적 : 본 연구에서는 국제기능장애 건강분류: 아동 청소년 버전(International Classification of Functioning, Disability and Health-Children and Youth version, ICF-CY) 프레임워크를 활용하여 개별화교육계획(individualized education plan, IEP) 협력적 팀 중재에 대해 체계적으로 고찰하고자 한다. 이를 통해 국내에서 IEP 협력적 팀 중재 속 작업치료사의 전문적인 영역을 마련하는 데 근거를 만들고, 개별화교육계획에서 협력적 팀 접근 중재의 목표를 통해 전문가로서 작업치료사의 역할에 대한 기반을 마련하고자 한다. 연구방법 : EBSCOhost, ProQuest, Web of Science를 통하여 2013년 1월부터 2023년 2월까지의 국외 논문을 검색하였다. 국외 검색어에는 "Special education", "Individualized education plan (IEP)", "IEP process", "IEP implementation", "Occupational therapy"를 사용하였다. 2차 분류를 통해 최종 10편의 연구를 분석하였다. 결과 : 분석 대상 연구의 근거 수준은 무작위 실험설계 연구가 가장 많았고, 중재 대상은 자폐성 장애가 가장 많았으며, 중재 방법은 환경 개선이 가장 많이 적용되었다. ICF-CY를 이용한 IEP 협력적 팀 중재의 목표 분석을 통해 활동에 관하여 5편, 참여와 관련하여 4편, 신체 구조 및 신체 기능과 관련하여 1편인 것으로 확인되었다. 결론 : IEP에서 협력적 팀 접근 중재 속 작업치료사의 역할은 중재의 목표에서 중요한 역할임을 확인할 수 있었다. 이를 기반으로 국내의 IEP에서 협력적 팀 접근의 전문가 중 하나로 작업치료사의 전문성을 설명할 수 있는 근거가 될 것으로 보인다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권2호
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• pp.159-165
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• 2002

This paper proposes a GA and Gradient Descent Method-based method for choosing an appropriate set of fuzzy rules for classification problems. The aim of the proposed method is to fond a minimum set of fuzzy rules that can correctly classify all training patterns. The number of inference rules and the shapes of the membership functions in the antecedent part of the fuzzy rules are determined by the genetic algorithms. The real numbers in the consequent parts of the fuzzy rules are obtained through the use of the descent method. A fitness function is used to maximize the number of correctly classified patterns, and to minimize the number of fuzzy rules. A solution obtained by the genetic algorithm is a set of fuzzy rules, and its fitness is determined by the two objectives, in a combinatorial optimization problem. In order to demonstrate the effectiveness of the proposed method, computer simulation results are shown.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.223-228
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• 2021

As a consequence of sudden outbreak of COVID-19 pandemic worldwide, educational institutes around the globe are forced to switch from traditional learning systems to e-learning systems. This has led to a variety of technology-driven pedagogies in e-teaching as well as e-learning. In order to take the best advantage, an appropriate understanding of the cognitive capability is of prime importance. This paper presents an intelligent cognitive maturity recognition system for confidence-based e-learning. We gather the data from actual test environment by involving a number of students and academicians to act as experts. Then a Genetic Programming based simulation and modeling is applied to generate a generalized classifier in the form of a mathematical expression. The simulation is derived towards an optimal space by carefully designed fitness function and assigning a range to each of the class labels. Experimental results validate that the proposed method yields comparative and superior results which makes it feasible to be used in real world scenarios.

• International Journal of Advanced Culture Technology
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• 제12권2호
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• pp.90-97
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• 2024

Shoulder impingement syndrome (SIS) is a common musculoskeletal condition characterized by pain and functional limitation due to the impingement of subacromial structures. This comprehensive review elucidates the complex nature of SIS, covering its pathophysiology, diagnostic methodologies, treatment options, and preventive measures. Through an exhaustive examination of current literature and clinical practices, the review highlights the importance of a multifaceted approach to SIS management. Physical therapy plays a pivotal role, focusing on exercises to strengthen shoulder musculature, enhance scapular stability, and improve range of motion. The review also discusses the strategic use of medications such as NSAIDs and corticosteroid injections, emphasizing their effectiveness in pain and inflammation management. Additionally, it advocates for structured rehabilitation programs post-treatment to restore function and prevent recurrence, recommending preventive strategies like ergonomic adjustments, targeted exercises, and proper technique training. This paper underscores the need for personalized and evidence-based treatment strategies, integrating physical therapy and pharmacological management when necessary.

• 한국전문물리치료학회지
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• 제23권1호
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• pp.55-64
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• 2016

Background: The International Classification of Functioning, Disability, and Health-core set (ICF-core set) for children and youth (CY) with cerebral palsy (CP) provides a useful conceptual framework and a guide for health care planning and measuring the changes brought by interventions across a multitude of dimensions from body functions to personal activities, social participation, and environmental factors for them. Objects: This single case study was reported to illustrate the use of a goal directed approach in applying the ICF-core set for CY with CP from a physical therapist perspective. Methods: An eleven year old boy with spastic CP, Gross Motor Function Classification System (GMFCS) level V, and his mother participated in an evaluation of his functioning state. The intervention goal was set through an interview using the ICF-core set, Canadian Occupational Performance Measure (COPM) and Goal Attainment Scale (GAS). Physical therapy was carried out on an outpatient basis using a goal directed approach for 30 min, 1 time/week during 12 weeks and the boy's gross motor function was assessed using the Gross Motor Function Measure (GMFM)-66 version (item set 2) before and after the intervention. Results: As measured by the boy's mother, the COPM score showed a meaningful clinical change (performance=mean 3.5, satisfaction=mean 2.5) and the T-score of GAS changed 34.4 after the goal directed approach. The GMFM-66 (item set 2) score changed from 31.8 to 38.7 and evaluation using the ICF-core set displayed improvement in 6 items of activity level between before and after the intervention. Conclusion: The ICF-core set for CY with CP is useful for understanding the overall functioning of CY with this condition and provides an opportunity to share and integrate information and opinions from different disciplines. We consider it as a useful tool in the universal language for the therapy and education of CY with CP.

• International Journal of Contents
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• 제8권4호
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• pp.30-35
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• 2012

Error surfaces provide us with very important information for training of feed-forward neural networks (FNNs). In this paper, we draw the contour plots of various error or objective functions for training of FNNs. Firstly, when applying FNNs to classifications, the weakness of mean-squared error is explained with the viewpoint of error contour plot. And the classification figure of merit, mean log-square error, cross-entropy error, and n-th order extension of cross-entropy error objective functions are considered for the contour plots. Also, the recently proposed target node method is explained with the viewpoint of contour plot. Based on the contour plots, we can explain characteristics of various error or objective functions when training of FNNs proceeds.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.

• International Journal of Contents
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• 제10권1호
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• pp.23-28
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• 2014

When developing a classifier using various objective functions, it is important to compare the performances of the classifiers. Although there are statistical analyses of objective functions for classifiers, simulation results can provide us with direct comparison results and in this case, a comparison criterion is considerably critical. A Receiver Operating Characteristics (ROC) graph is a simulation technique for comparing classifiers and selecting a better one based on a performance. In this paper, we adopt the ROC graph to compare classifiers trained by mean-squared error, cross-entropy error, classification figure of merit, and the n-th order extension of cross-entropy error functions. After the training of feed-forward neural networks using the CEDAR database, the ROC graphs are plotted to help us identify which objective function is better.

• International Journal of Contents
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• 제7권3호
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• pp.1-5
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• 2011

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.