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Design and Development of Hybrid CNN Algorithm for ASD Using Data Mining Techniques


Affiliations
1 Department of Computer Science, Kovai Kalaimagal college of arts and science Coimbatore, Tamil Nadu, India
 

Autism is one of the most complex and different types of disorders, and it necessitates precise diagnosis based on characteristics including societal engagement, limited verbal communication, and repetitive behaviour.A timely and correct diagnosis of Autism Spectrum Disorder can ensure that you receive the appropriate medical treatment and control to help you recover. In this publication, Artificial Neural Networks are used to classify anxiety-related arousal in children with Autism Spectrum Dis-order(ASD), which is one of the most important fields of data mining research.The main objective of this study is to apply the Artificial Neural Network Algorithm (ANN), Convolutional Neural Network Algorithm called as (CNN), in ASD using the Adaptive Kalman Filter Gaussian Mixture Model (AKFGMD). In this paper we have developed a proposed hybrid algorithm which was implemented in ASD dataset and the result relevant to the proposed algorithm is better result when compared with other classifications method with respect to accuracy, sensitivity and specificity.

Keywords

Adaptive Kalman Filter Gaussian Mixture Model (AKFGMD), Artificial Neural Network Algorithm, Convolutional Neural Network Algorithm, Hybrid CNN, Autism Spectrum Disorder.
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  • . Thabtah, Fadi. (2018) Machine learning in autistic spectrum disorder behavioral research: A review and ways forward. Informatics for Health and Social Care: 1-20.

  • . Vaishali, R., and R. Sasikala. (2018) A machine learning based approach to classify Autism with optimum behaviour sets. International Journal of Engineering & Technology 7(4): 18.

  • . Y. Kong, J. Gao, Y. i. Xu, Y. Pan, J. Wang, J. Liu,(2019) Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier, Neuro computing, Volume 324,Pages 63-68.

  • . S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. Guerrero, B. Glocker, D. Rueckert,(2018) Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease, Medical Image Analysis, Volume 48, Pages 117-130.

  • . V. Tsiaras, P. G. Simos, R. Rezaie, B. R. Sheth, E. Garyfallidis, E. M. Castillo, A. C. Papanicolaou,(2011) Extracting biomarkers of autism from MEG resting-state functional connectivity networks, Computers in Biology and Medicine, Volume 41, Issue 12, Pages 1166- 1177.

  • . C. P. Amaral, M. A. Simões, S.a Mouga, J. Andrade, M. Castelo-Branco,(2017) A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study, Journal of Neuroscience Methods, Volume 290, Pages 105-115.

  • . A. Ishii-Takahashi, R. Takizawa, Y. Nishimura, Kawakubo, H. Kuwabara, J. Matsubayashi, K. Hamada, S. Okuhata, N. Yahata, T. Igarashi, S. Kawasaki, H. Yamasue, N. Kato, K. Kasai, Y. Kano,(2014)

  • . Prefrontal activation during inhibitory control measured by near-infrared spectroscopy for differentiating between autism spectrum disorders and attention deficit hyperactivity disorder in adults, NeuroImage: Clinical, Volume 4, Pages 53-63.

  • . C. L. Tsai, C. Y. Pan, C. H. Wang, Y. T. Tseng, K.W. Hsieh,(2011) An event-related potential and behavioral study of impaired inhibitory control in children with autism spectrum disorder, Research in Autism Spectrum Disorders, Volume 5, Issue 3, Pages 1092-1102.

  • . L. Q. Uddin, V. Menon, C. B. Young, S. Ryali, T. Chen, A. Khouzam, N. J. Minshew, A. Y. Hardan,(2011) Multivariate Searchlight Classification of Structural Magnetic Resonance Imaging in Children and Adolescents with Autism, Biological Psychiatry, Volume 70, Issue 9,Pages 833-841.

  • . M. Ahmadlou, H. Adeli, A. Adeli, (2012) Fuzzy Synchronization Likelihood-wavelet methodology for diagnosis of autism spectrum disorder, Journal of Neuroscience Methods, Volume 211, Issue 2, Pages 203-209.

  • . Y. Jiao, R. Chen, X. Ke, K. Chu, Z. Lu, E. H. Herskovits,(2010) Predictive models of autism spectrum disorder based on brain regional cortical thickness, NeuroImage, Volume 50, Issue 2, Pages 589-599.

  • . F. Thabtah, F. Kamalov, K. Rajab,(2018) A new computational intelligence approach to detect autistic features for autism screening, International Journal of Medical Informatics, Volume 117,Pages 112-124.

  • . L. Bozgeyikli, A. Raij, S. Katkoori and R. Alqasemi,(2018) A Survey on Virtual Reality for Individuals with Autism Spectrum Disorder: Design Considerations, in IEEE Transactions on Learning Technologies, vol. 11, no. 2, pp. 133- 151, 1.

  • . H. Zhao, A. R. Swanson, A. S. Weitlauf, Z. E. Warren and N. Sarkar,(2018) Hand-in-Hand: A Communication-Enhancement Collaborative Virtual Reality System for Promoting Social Interaction in Children With Autism Spectrum Disorders,in IEEE Transactions on Human- Machine Systems, vol. 48, no. 2, pp. 136-148.

  • . A. Kushki, A. Khan, J. Brian and E. Anagnostou,(2015) A Kalman Filtering Framework for Physiological Detection of Anxiety-Related Arousal in Children With Autism Spectrum Disorder, in IEEE Transactions on Biomedical Engineering, vol. 62, no. 3, pp. 990-1000.

  • . L. D. Olio, A. Ibeas, J. D. Ona, R. D. Ona,(2018) Public Transportation Quality of Service, Elsevier, Pages 155-179.

  • . Paul T. Shattuck, PhD; Mary Wagner, PhD; Sarah Narendorf, MSW; Paul Sterzing, MSSW;Melissa Hensley, MSW,(2011)Post–High School Service Use Among Young Adults With an Autism Spectrum Disorder. ARTICLE-2011.

  • . SumanRaj ,SarfarazMasood, Computer Engineering(2020) Analysis and Detection of Autism Spectrum Disorder Using Machine Learning Techniques.


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  • Design and Development of Hybrid CNN Algorithm for ASD Using Data Mining Techniques

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Authors

D. Umanandhini
Department of Computer Science, Kovai Kalaimagal college of arts and science Coimbatore, Tamil Nadu, India

Abstract


Autism is one of the most complex and different types of disorders, and it necessitates precise diagnosis based on characteristics including societal engagement, limited verbal communication, and repetitive behaviour.A timely and correct diagnosis of Autism Spectrum Disorder can ensure that you receive the appropriate medical treatment and control to help you recover. In this publication, Artificial Neural Networks are used to classify anxiety-related arousal in children with Autism Spectrum Dis-order(ASD), which is one of the most important fields of data mining research.The main objective of this study is to apply the Artificial Neural Network Algorithm (ANN), Convolutional Neural Network Algorithm called as (CNN), in ASD using the Adaptive Kalman Filter Gaussian Mixture Model (AKFGMD). In this paper we have developed a proposed hybrid algorithm which was implemented in ASD dataset and the result relevant to the proposed algorithm is better result when compared with other classifications method with respect to accuracy, sensitivity and specificity.

Keywords


Adaptive Kalman Filter Gaussian Mixture Model (AKFGMD), Artificial Neural Network Algorithm, Convolutional Neural Network Algorithm, Hybrid CNN, Autism Spectrum Disorder.

References