Consultation Management Model based on Behavior Classification of Special-Needs Students
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초록
Unlike behaviors that are generally known, information regarding unspecific behaviors is insufficient. For an education or guidance regarding the unspecific behaviors, collection and management of data regarding the unspecific behaviors of special-needs students are needed. In this paper, a consultation management model based on behavior classification of special-needs students using machine learning is proposed. It collects data by photographing the behavior of special students in real time, analyzes the behavior pattern, composes a data set, and trains it in the suggestion system. It is possible to improve the accuracy by comparing the behavior of special students photographed later into the suggestion system and analyzing the results by comparing it with the existing data again. The test has been performed by arbitrarily applying unspecific behaviors that are not stored in the database, and the forecast model has accurately classified and grouped the input data. Also, it has been verified that it is possible to accurately distinguish and classify the behaviors through the feature data of the behaviors even if there are some errors in the input process.
Abstract
Unlike behaviors that are generally known, information regarding unspecific behaviors is insufficient. For an education or guidance regarding the unspecific behaviors, collection and management of data regarding the unspecific behaviors of special-needs students are needed. In this paper, a consultation management model based on behavior classification of special-needs students using machine learning is proposed. It collects data by photographing the behavior of special students in real time, analyzes the behavior pattern, composes a data set, and trains it in the suggestion system. It is possible to improve the accuracy by comparing the behavior of special students photographed later into the suggestion system and analyzing the results by comparing it with the existing data again. The test has been performed by arbitrarily applying unspecific behaviors that are not stored in the database, and the forecast model has accurately classified and grouped the input data. Also, it has been verified that it is possible to accurately distinguish and classify the behaviors through the feature data of the behaviors even if there are some errors in the input process.
참고문헌 (15)
- Zigmond, N. P, "Handbook of special education", pp.160~172 (2017)
- Anderson, N. A., "Preservice teachers' observations of cooperating teachers and peers while participating in an early field experience", Teacher Education Quarterly, 32(4), pp.97~117 (2005)
- 김정민, "개별차원의 긍정적 행동지원이 중증 자폐성 장애중학생의 특수학급에서의 수업참여행동에 미치는 영향", 특수교육학연구, 49(2), pp.45~67 (2014), 한국특수교육학회[DOI]
- Pierangelo, R., "Teaching students with autism spectrum" (2008), Corwin Press
- 정여진, "딥러닝 프레임워크의 비교: 티아노, 텐서플로, CNTK를 중심으로", 지능정보연구, 23(2), pp.1~17 (2017), 한국지능정보시스템학회
- Abadi, M., "TensorFlow: learning functions at scale", Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming, pp.1~1 (2016)
- Vishnu, A., "Distributed tensorflow with MPI", arXiv preprint arXiv:1603.02339., ~ (2016)
- Ladislav Rampasek, "TensorFlow: Biology’s Gateway to Deep Learning?", Cell Systems, 2(1), pp.12~14 (2016), Elsevier BV[DOI]
- Yann LeCun, "Deep learning", Nature, 521(7553), pp.436~444 (2015), Springer Science and Business Media LLC[DOI]
- Albawi, S., "Understanding of a convolutional neural network", 2017 International Conference on Engineering and Technology (ICET), pp.1~6 (2017)
- Bluche, T., "Feature extraction with convolutional neural networks for handwritten word recognition", 2013 12th International Conference on Document Analysis and Recognition, pp.285~289 (2013), IEEE
- 설승환, "기두부와 단 분리 시 조각의 식별을 위한 합성곱 신경망 구조 설계", 한국정보기술학회논문지, 16(6), pp.81~89 (2018), 한국정보기술학회[DOI]
- Alex Krizhevsky, "ImageNet classification with deep convolutional neural networks", Communications of the ACM, 60(6), pp.84~90 (2017), Association for Computing Machinery (ACM)[DOI]
- Lin, M., "Network in network", arXiv preprint arXiv:1312.4400., ~ (2013)
- Szegedy, C, "Going deeper with convolutions", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1~9 (2015)