References
- Achenbach, T. and Rescorla, L. (2000). Manual for the ASEBA Preschool Forms & Profiles, University of Vermount, Burlington, VA.
- Al-Jarrah, O.Y., Yoo, P.D., Muhaidat, S., Karagiannidis, G.K. and Taha, K. (2015). Efficient machine learning for big data: A review, Big Data Research2(3): 87–93.
- Amaral, D.G., Schumann, C.M. and Nordahl, C.W. (2008). Neuroanatomy of autism, Trends in Neurosciences31(3): 137–145.
- Ashwin, C., Hietanen, J.K. and Baron-Cohen, S. (2015). Atypical integration of social cues for orienting to gaze direction in adults with autism, Molecular Autism6(1): 5–14.
- Baron-Cohen, S., Jolliffe, T., Mortimore, C. and Robertson, M. (1997). Another advanced test of theory of mind: Evidence from very high functioning adults with autism or Asperger syndrome, Journal of Child Psychology and Psychiatry38(7): 813–822.
- Bernier, R., Mao, A. and Yen, J. (2011). Diagnosing autism spectrum disorders in primary care, Practitioner255(1745): 27–30.
- Chitategmark, M. (2016). Social attention allocation in ASD: A review and meta-analysis of eye-tracking studies, Review Journal of Autism & Developmental Disorders3(3): 209–223.
- Christensen, D.L., Baio, J., Braun, K.V.N., Bilder, D., Charles, J., Constantino, J.N., Daniels, J., Durkin, M.S., Fitzgerald, R.T. and Kurziusspencer, M. (2016). Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and developmental disabilities monitoring network, 11 sites, United States, 2012, Morbidity and Mortality Weekly Report—Surveillance Summaries65(3): 1–23.
- Constantino, J.N., Kennon-Mcgill, S., Weichselbaum, C., Marrus, N. and Jones, W. (2017). Infant viewing of social scenes is under genetic control and is atypical in autism, Nature547(7663): 340–344.
- Drimalla, H., Landwehr, N., Baskow, I., Behnia, B. and Scheffer, T. (2018). Detecting autism by analyzing a simulated social interaction, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Berlin, Germany, pp. 193–208.
- Durkin, M., Maenner, M.J., Meaney, F.J., Levy, S.E. and DiGuiseppi, C. (2010). Socioeconomic inequality in the prevalence of autism spectrum disorder: Evidence from a US cross-sectional study, PLoS ONE5(7): e11551.
- Eack, S.M., Mazefsky, C.A. and Minshew, N.J. (2015). Misinterpretation of facial expressions of emotion in verbal adults with autism spectrum disorder, Autism19(3): 308–315.
- Gan, Y.L., Chen, J.Y. and Xu, L.H. (2019). Facial expression recognition boosted by soft label with a diverse ensemble, Pattern Recognition Letters125(4): 105–112.
- Greene, D.J., Colich, N., Iacoboni, M., Zaidel, E., Bookheimer, S.Y. and Dapretto, M. (2011). Atypical neural networks for social orienting in autism spectrum disorders, Neuroimage56(1): 354–362.
- Halim, A., Ford, G., Eric, G. and Wall Dennis, P. (2018). Machine learning approach for early detection of autism by combining questionnaire and home video identification, Journal of the American Medical Informatics Association25(8): 1000–1007.
- Hubert, B., Wicker, B., Moore, D.G., Monfardini, E., Duverger, H., Da Fonseca, D. and Deruelle, C. (2007). Brief report: Recognition of emotional and non-emotional biological motion in individuals with autistic spectrum disorders, Journal of Autism and Developmental Disorders37(7): 1386–1392.
- Jaiswal, S., Valstar, M.F., Gillott, A. and Daley, D. (2017). Automatic detection of ADHD and ASD from expressive behaviour in RGBD data, IEEE International Conference on Automatic Face & Gesture Recognition, Washington, DC, USA, pp. 762–769.
- Jiang, M., Sunday, M., Francis and Srishyla, D. (2019). Classifying individuals with ASD through facial emotion recognition and eye-tracking, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany, pp. 6063–6068.
- Kantavat, P., Kijsirikul, B., Songsiri, P., Fukui, K.-I. and Numao, M. (2018). Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation, International Journal of Applied Mathematics & Computer Science28(4): 705–717, DOI: 10.2478/amcs-2018-0054.
- Kerrianne, E., Morrison, A.E. and Pinkham, S.K. (2019). Psychometric evaluation of social cognitive measures for adults with autism, Autism Research12(5): 766–778.
- Liu, W., Li, M. and Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework, Autism Research9(8): 888–898.
- Manfredonia, J., Bangerter, A., Manyakov, N.V., Ness, S., Lewin, D., Skalkin, A., Boice, M., Goodwin, M. S., Dawson, G. and Hendren, R. (2018). Automatic recognition of posed facial expression of emotion in individuals with autism spectrum disorder, Journal of Autism and Developmental Disorders27(10): 1–15.
- Müller and Frith, U. (2005). Autism-explaining the enigma, Kindheit Und Entwicklung14(4): 257.
- Parkhi, O., Vedaldi, A. and Zisserman, A. (2015). Deep face recognition, British Machine Vision Conference, Swansea, UK, p. 6.
- Poria, S., Cambria, E., Bajpai, R. and Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion, Information Fusion37(2): 98–125.
- Remington, A., Swettenham, J., Campbell, R. and Coleman, M. (2009). Selective attention and perceptual load in autism spectrum disorder, Psychological Science20(11): 1388–1393.
- Rozga, A., Mumaw, M., King, T. and Robins, D.L. (2009). Lack of emotion-specific facial mimicry responses among high-functioning individuals with an autism spectrum disorder (poster), International Meeting for Autism Research, Chicago, IL, USA, pp. S43–S44.
- Rundo, L., Militello, C., Russo, G., Garufi, A., Vitabile, S. and Gilardi, M. (2017a). Automated prostate gland segmentation based on an unsupervised fuzzy c-means clustering technique using multispectral T1w and T2w MR imaging, Information8(2): 1–28.
- Rundo, L., Stefano, A., Militello, C., Russo, G., Sabini, M.G. and Arrigo, C. (2017b). A fully automatic approach for multimodal PET and MR image segmentation in Gamma Knife treatment planning, Computer Methods & Programs in Biomedicine144(3): 77–96.
- Samad, M.D., Diawara, N., Bobzien, J.L., Harrington, J.W., Witherow, M.A. and Iftekharuddin, K.M. (2018). A feasibility study of autism behavioral markers in spontaneous facial, visual, and hand movement response data, IEEE Transactions on Neural Systems & Rehabilitation EngineeringPP(99): 1–1.
- Sasson, N.J. (2006). The development of face processing in autism, Journal of Autism & Developmental Disorders36(3): 381–394.
- Sasson, N.J., Elison, J.T., Turner-Brown, L.M., Dichter, G.S. and Bodfish, J.W. (2011a). Brief report: Circumscribed attention in young children with autism, Journal of Autism & Developmental Disorders41(2): 242–247.
- Sasson, N.J., Pinkham, A.E., Carpenter, K.L. and Belger, A. (2011b). The benefit of directly comparing autism and schizophrenia for revealing mechanisms of social cognitive impairment, Journal of Neurodevelopmental Disorders3(2): 87–100.
- Sasson, N., Tsuchiya, N., Hurley, R., Couture, S.M., Penn, D.L., Adolphs, R. and Piven, J. (2007). Orienting to social stimuli differentiates social cognitive impairment in autism and schizophrenia, Neuropsychologia45(11): 2580–2588.
- Serra, A., Galdi, P. and Tagliaferri, R. (2018). Machine learning for bioinformatics and neuroimaging, Wiley Interdisciplinary Reviews: Data Mining & Knowledge Discovery8(5): e1248.
- Shaddy, D.J. (2006). Visual scanning and pupillary responses in young children with autism spectrum disorder, Journal of Clinical & Experimental Neuropsychology28(7): 1238–1256.
- Tariq, Q., Daniels, J. and Schwartz, J.N. (2018). Mobile detection of autism through machine learning on home video: A development and prospective validation study, PLoS Medicine15(11): e1002705.
- Traynor, J.M., Gough, A., Duku, E., Shore, D.I. and Hall, G.B.C. (2019). Eye tracking effort expenditure and autonomic arousal to social and circumscribed interest stimuli in autism spectrum disorder, Journal of Autism and Developmental Disorders49(1): 1988–2002.
- Trevisan, D.A., Hoskyn, M. and Birmingham, E. (2018). Facial expression production in autism: A meta-analysis, Autism Research11(2): 1586–1601.
- Wang, G.S., Chen, J.Y. and Zhang, K. (2018). The perception of emotional facial expressions by children with autism using hybrid multiple factorial design and eye-tracking, Chinese Science Bulletin63(31): 3204–3216, (in Chinese).
- Wang, Y. and Chen, W. (2010). Broken mirror theory of autism, Advances in Psychological Science18(2): 297–305.
- Xu, L., Fu, H.Y., Goodarzi, M., Cai, C.B., Yin, Q.B., Wu, Y., Tang, B.C. and She, Y. B. (2018). Stochastic cross validation, Chemometrics & Intelligent Laboratory Systems175(4): 74–81.
- Xu, M., Shen, J. and Yu, H.Y. (2017). A review on data-driven healthcare decision-making support, Industrial Engineering and Management21(1): 1–13.
- Yi, H., Song, X.F., Jiang, B., Liu, Y.F. and Zhou, Z.H. (2013). Fault diagnosis based on self-tuning support vector machine in sample unbalance condition, Transactions of Beijing Institute of Technology33(4): 394–398.
- Yi, L., Feng, C., Quinn, P.C., Ding, H., Li, J., Liu, Y. and Lee, K. (2014). Do individuals with and without autism spectrum disorder scan faces differently? A new multi-method look at an existing controversy, Autism Research7(1): 72–83.
- Zhao, S., Uono, S., Yoshimura, S., Kubota, Y. and Toichi, M. (2017). Atypical gaze cueing pattern in a complex environment in individuals with ASD, Journal of Autism Developmental Disorders47(7): 1978–1986.
- Zhong, S.S., Li, X. and Zhang, Y.J. (2019). Fault diagnosis of civil aero-engine driven by unbalanced samples based on DBN, Journal of Aerospace Power34(3): 708–716.
- Zunino, A., Morerio, P. and Cavallo, A. (2018). Video gesture analysis for autism spectrum disorder detection, 24th International Conference on Pattern Recognition (ICPR), Beijing, China, pp. 3421–3426.
- Zwaigenbaum, L., Bryson, S., Lord, C., Rogers, S., Carter, A., Carver, L., Chawarska, K., Constantino, J., Dawson, G. and Dobkins, K. (2009). Clinical assessment and management of toddlers with suspected autism spectrum disorder: Insights from studies of high-risk infants, Pediatrics123(5): 1383–1391.