Skip to main content

RETRACTED CHAPTER: A Machine Learning Approach to Predict and Classify the Levels of Autism Spectrum Disorder

  • Conference paper
  • First Online:
Cognitive Informatics and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1317))

Abstract

Data mining is used to extricate data from the large data arrangements. Data mining is defined as a method used to extract usable information from any broader coarse knowledge structure. It infers breaking down the designs of information in huge clumps of information using at least one programming. Information mining, similar to science and research, has applications in many fields. Information mining includes a successful assortment of information and warehousing just like handling a PC.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Change history

  • 23 February 2024

    A correction has been published.

References

  1. Lakshmi Praba, N., Nancy, V., Vigneshwari, S.: Mobile based privacy protected location based services with three layer security. Int. J. Appl. Eng. Res. 10(4), 10101–10108 (2015)

    Google Scholar 

  2. Pravin, A., Jacob, T.P., Nagarajan, G.: Robust technique for data security in multicloud storage using dynamic slicing with hybrid cryptographic technique. J. Ambient Intell. Human. Comput. 1–8 (2019)

    Google Scholar 

  3. Nagarajan, G., Minu, R.I., Devi, A.J.: Optimal nonparametric Bayesian model-based multimodal BoVW creation using multilayer pLSA. Circuits Syst. Signal Process. 39(2), 1123–1132 (2020)

    Article  Google Scholar 

  4. Bhoi, A.K., Mallick, P.K., Liu, C.M., Balas, V.E (eds.): Bio-inspired Neurocomputing, Springer (2021)

    Google Scholar 

  5. Indira, K., Christal Joy, E.: Prevention of spammers and promoters in video social networks using SVM-KNN. Int. J. Eng. Technol. 6, 2024–2030 (2014)

    Google Scholar 

  6. Nirmalrani, V., Sakthivel, P.: Framework for providing access to web data bases using budget aware role based access control. J. Theoret. Appl. Inform. Technol. 76(3) (2015)

    Google Scholar 

  7. Jacob, T.P., Pravin, A., Nagarajan, G.: Efficient spectrum sensing framework for cognitive networks. Concurr. Comput. Practice Exp. e5187

    Google Scholar 

  8. Kumar, S.M., Lakshmanan, L.: A Situation emergency building navigation disaster system using wireless sensor networks. In: 2018 International Conference on Communication and Signal Processing (ICCSP), pp. 0378–0382. IEEE (2018)

    Google Scholar 

  9. Prince Mary, S., Lakshmi, S.V., Anuhya, S.: Color detection and sorting using internet of things machine. J. Comput. Theor. Nanosci. 16(8), 3276–3280 (2019)

    Article  Google Scholar 

  10. Kanimozhi, V., Jacob, P.: UNSW-NB15 dataset feature selection and network intrusion detection using deep learning

    Google Scholar 

  11. Mallick, P.K., Balas, V.E., Bhoi, A.K., Zobaa, A.F. (eds.): Cognitive Informatics and Soft Computing: Proceeding of CISC 2017, vol. 768. Springer (2018)

    Google Scholar 

  12. Shaarephi, M.A., Shaarephi, A., Pedram, M.M.: The synthesis of rs-fMRI and sMRI results among young children who utilize the deep perception network to discriminate against autism spectrum disorders. J. Tell. Tell. Image 31, 895–903

    Google Scholar 

  13. Jinila, Y.B., Komathy, K.: Distributed and secured dynamic pseudo id generation for privacy preservation in vehicular ad hoc networks. J. Theoret. Appl. Inform. Technol. 66(1) (2014)

    Google Scholar 

  14. Subhashini, R., Akila, G.: Valence arousal similarity based recommendation services. In: 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015], pp. 1–4. IEEE (2015)

    Google Scholar 

  15. Selvi, M., Joe Prathap, P.M.: Analysis & classification of secure data aggregation in wireless sensor networks. Int. J. Eng. Adv. Technol. 8(4), 1404–1407 (2019)

    Google Scholar 

  16. Pua, E.P.K., Bowden, S.C., Seal, M.L.: Autism spectrum disorders: neuroimaging findings from systematic reviews. Res. Autism Spectr. Disorders 34, 28–33 (2017)

    Article  Google Scholar 

  17. Anderson, J.S., Nielsen, J.A., Froehlich, A.L., DuBray, M.B., Druzgal, T.J., Cariello, A.N., Cooperrider, B., Caitlin, Z., Fletcher, R.P., Alexander, A., Bigler, E., Lange, N., Lainhart, J.: Functional connectivity magnetic resonance imaging classification of autism. Brain 134, 3742–3754 (2011)

    Article  Google Scholar 

  18. Murdaugh, D.L., Shinkareva, S.V., Deshpande, H.R., Wang, J., Pennick, M.R., Kana, R.K.: Differential deactivation during mentalizing and classification of autism based on default mode network connectivity. PLoS ONE 7(11), Art. no. e50064 (2012)

    Google Scholar 

  19. Plitt, M., Barnes, K.A., Martin, A.: ‘Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards.’ NeuroImageClin. 7, 359–366 (2015)

    Google Scholar 

  20. Uddin, L.Q., Supekar, K., Lynch, C.J., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., Menon, V.: Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiat. 70, 869–879 (2013)

    Article  Google Scholar 

  21. Bi, X.-A., Wang, Y., Shu, Q., Sun, Q., Xu, Q.: Classification of autism spectrum disorder using random support vector machine cluster. Frontiers Genet. 9, 18 (2018)

    Article  Google Scholar 

  22. Wee, C.-Y., Wang, L., Shi, F., Yap, P.-T., Shen, D.: Diagnosis of autism spectrum disorders using regional and interregional morphological features. Hum. Brain Mapping 35, 3414–3430 (2014)

    Article  Google Scholar 

  23. Wang, L., Wee, C.-Y., Tang, X., Yap, P.-T., Shen, D.: Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder. Brain Imag. Behav. 10, 33–40 (2016)

    Article  Google Scholar 

  24. Xiao, X., Fang, H., Wu, J., Xiao, C., Xiao, T., Qian, L., Liang, F., Xiao, Z., Chu, K.K., Ke, X.: Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder. Autism Res. 10, 620–630 (2017)

    Article  Google Scholar 

  25. Chaddad, A., Desrosiers, C., Hassan, L., Tanougast, C.: Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neurosci. 18, 52 (2017)

    Article  Google Scholar 

  26. Aghdam, M.A., Sharifi, A., Pedram, M.M.: Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J. Digit. Imag. 31(6), 895–903 (2018)

    Article  Google Scholar 

  27. Adams, C., Lockton, E., Freed, J., Gaile, J., Earl, G., McBean, K., Green, J., Vail, A., Law, J.: The social communication intervention project: a randomized controlled trial of the effectiveness of speech and language therapy for school-age children who have pragmatic and social communication problems with or without autism spectrum disorder. Int. J. Language Commun. Disorders 47(3), 233–244 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Anandhi, T., Srihari, A., Eswar, G., Ajitha, P., Sivasangari, A., Gomathi, R.M. (2021). RETRACTED CHAPTER: A Machine Learning Approach to Predict and Classify the Levels of Autism Spectrum Disorder. In: Mallick, P.K., Bhoi, A.K., Marques, G., Hugo C. de Albuquerque, V. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1317. Springer, Singapore. https://doi.org/10.1007/978-981-16-1056-1_79

Download citation

Publish with us

Policies and ethics