Mobile QR Code QR CODE

※ The user interface design of www.ieiespc.org has been recently revised and updated. Please contact inter@theieie.org for any inquiries regarding paper submission.

Journal Search

  1. Home
  2. Archive
  3. 2024-06 (Vol.13 No.03)
  4. 10.5573/IEIESPC.2024.13.3.236
XML PDF INFO REF

REFERENCES

1 
``Prevalence of autism spectrum disorder among children aged 8 years autism and developmental disabilities monitoring network, 11 sites,'' United States, 2010. Morbidity and Mortality Weekly Report. Surveillance Summaries, 2014.DOI
2 
C. Lord, M. Rutter, A. Le Couteur, ``Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders,'' Journal of Autism and Developmental Disorders, vol. 24, no. 5, pp 659-685, 1994.DOI
3 
``ABIDE. Autism brain imaging data exchange'', 2013.URL
4 
J. Ashburner, K. J. Friston. ``Voxel-based morphometry - the methods,'' NeuroImage, vol. 11, no. 6, pp. 805-821, 2000.DOI
5 
N. Boddaert, N. Chabane, H. Gervais, C. Good, M. Bourgeois, M. Plumet, ``Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study,'' Neuroimage, vol. 23, no. 1, pp. 364-369, 2004.DOI
6 
L. Bonilha, F. Cendes, C. Rorden, M. Eckert, P. Dalgalarrondo, L. M. Li, C. E. Steiner, ``Gray and white matter imbalance - Typical structural abnormality underlying classic autism?,'' Brain and Development, Vol. 30, no. 6, 2008.DOI
7 
D. C. Rojas, E. Peterson, E. Winterrowd, M. L. Reite, S.J. Rogers, J. R. Tregellas. ``Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiatry,'' vol. 6, no. 1, pp 56, 2006.DOI
8 
K. L. Hyde, F. Samson, A. C. Evans, L. Mottron. ``Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry,'' Human Brain Mapping, vol. 31, no. 4, pp. 556-566, 2010.DOI
9 
N. Hadjikhani., R. M. Joseph, J. Snyder, H. Tager-Flusberg, ``Anatomical differences in the mirror neuron system and social cognition network in autism. Cerebral Cortex,'' vol. 16, no. 9, pp. 1276-1282, 2006.DOI
10 
S. Calderoni, A. Retico, L. Biagi, R. Tancredi, F. Muratori, M. Tosetti. ``Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses,'' Neuroimage, vol. 59, no. 2, pp. 1013-1022, 2014.DOI
11 
C. Ecker, V. Rocha-Rego, P. Johnston, J. Mourao-Miranda, A Marquand, E. M Daly. '' Investigating the predictive value of whole-brain structural mr scans in autism: a pattern classification approach,'' NeuroImage, vol. 40, pp. 44-56., 2010.DOI
12 
M. C. Lai, M. V. Lombardo, J. Suckling, A. N. Ruigrok, B. Chakrabarti, C. Ecker, ``Biological sex affects the neurobiology of autism,'' Brain, vol. 136(9), 2799-2815, 2013.DOI
13 
S. Vigneshwaran, B. S. Mahanand, S. Suresh, R. Savitha, ``Autism spectrum disorder detection using projection based learning meta-cognitive RBF network,'' In Proceedings of the 2013 international joint conference on neural networks (IJCNN), pp. 1-8, 2013.DOI
14 
S. Vigneshwaran, S. Suresh, B. S. Mahanand, N. Sundararajan, ``ASD detection in males using MRI- an age-group based study,'' In Proceedings of the 2015 international joint conference on neural networks (IJCNN), 2015.DOI
15 
S. Vigneshwaran, S. Suresh, N. Sundararajan, B. S. Mahanand, ``Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network'', Expert Systems with Applications, vol. 42, no. 22, pp. 8775-8790, 2015.DOI
16 
G.-B. Huang, Q. Y. Zhu, and C. K. Siew, ``Extreme learning machine: theory and applications'', Neurocomputing, vol. 70, no. 1-3, pp. 985-990, 2006.DOI
17 
S. Suresh, S. N. Omkar, V. Mani, T. N. G. Prakash, ``Lift coefficient prediction at high angle of attack using recurrent neural network'', Aerospace Science and Technology, vol. 7, pp. 595-602, 2003.DOI
18 
T. M. Epalle, Y. Song, Z. Liu, H. Lu, ``Multi-atlas classification of autism spectrum disorder with hinge loss trained deep architectures: ABIDE I results'', Applied Soft Computing, 2021.DOI
19 
T. Eslami, V. Mirjalili, A. Fong, A. R. Laird, F. Seed, ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data. Frontiers in Neuroinformatics, vol. 13, pp. 488-571, 2019.DOI