Skip to main content
SpringerLink
Log in

Computational Intelligence in Detection and Support of Autism Spectrum Disorder

  • Chapter
  • First Online:
Artificial Intelligence in Healthcare

Part of the book series: Brain Informatics and Health ((BIH))

Abstract

Autism Spectrum Disorder (ASD) refers to a spectrum of conditions characterised mainly by impairments in social interaction, speech and nonverbal communication, and restricted—repetitive behaviour. The lack of physical testing, done primarily via behaviour analysis, makes ASD diagnosis more difficult. The emergence of Computational Intelligence techniques has resulted in the development of a variety of fast and early ASD diagnosis methods based on multiple input modalities. The premise of computational intelligence (CI) and its efficiency in detecting and monitoring ASD has been examined in this chapter, which has recently advanced. Two types of studies have been discussed in this article. Several aspects of ASD screening, including questionnaires, eye scan paths, movement tracking, behavioural analysis from video, brain scans, and more, have been discussed using machine learning and deep learning. Secondly, ASD detection and monitoring applications have been studied extensively in the past year, with significant advances. Finally, a discussion has been made on the challenges faced in ASD detection and management with future research scopes.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Association AP, et al (2013) Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Publisher

    Google Scholar 

  2. Faras H, Al Ateeqi N, Tidmarsh L (2010) Autism spectrum disorders. Ann Saudi Med 30(4):295–300

    Article  Google Scholar 

  3. Dawson G, Rogers S, Munson J, Smith M, Winter J, Greenson J et al (2010) Randomized, controlled trial of an intervention for toddlers with autism: the Early Start Denver model. Pediatrics 125(1):e17-23

    Article  Google Scholar 

  4. Cidav Z, Munson J, Estes A, Dawson G, Rogers S, Mandell D (2017) Cost offset associated with Early Start Denver Model for children with autism. J Am Acad Child Adolesc Psychiatry 56(9):777–83

    Article  Google Scholar 

  5. Berlin LJ, Brooks-Gunn J, McCarton C, McCormick MC (1998) The effectiveness of early intervention: examining risk factors and pathways to enhanced development. Prev Med 27(2):238–45

    Article  Google Scholar 

  6. Organization WH et al (2012) World health statistics: a snapshot of global health. In: World health statistics: a snapshot of global health

    Google Scholar 

  7. Hamilton S (2006) Screening for developmental delay: reliable, easy-to-use tools: win-win solutions for children at risk and busy practitioners. J Fam Pract 55(5):415–23

    Google Scholar 

  8. Barton ML, Dumont-Mathieu T, Fein D (2012) Screening young children for autism spectrum disorders in primary practice. J Autism Dev Disord 42(6):1165–74

    Article  Google Scholar 

  9. Mukherjee SB, Aneja S, Krishnamurthy V, Srinivasan R (2014) Incorporating developmental screening and surveillance of young children in office practice. Indian Pediatr 51(8):627–35

    Article  Google Scholar 

  10. Robins DL, Casagrande K, Barton M, Chen CMA, Dumont-Mathieu T, Fein D (2014) Validation of the modified checklist for autism in toddlers, revised with follow-up (M-CHAT-R/F). Pediatrics 133(1):37–45

    Article  Google Scholar 

  11. Berument SK, Rutter M, Lord C, Pickles A, Bailey A (1999) Autism screening questionnaire: diagnostic validity. Br J Psychiatry 175(5):444–51

    Article  Google Scholar 

  12. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC et al (2000) The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord 30(3):205–23

    Article  Google Scholar 

  13. Lord C, Rutter M, Le Couteur A (1994) Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 24(5):659–85

    Article  Google Scholar 

  14. Schopler E, Reichler RJ, DeVellis RF, Daly K (1980) Toward objective classification of childhood autism: Childhood Autism Rating Scale (CARS). J Autism Develop Disord

    Google Scholar 

  15. Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley

    Google Scholar 

  16. Kruse R, Borgelt C, Braune C, Mostaghim S, Steinbrecher M, Klawonn F et al (2011) Computational intelligence. Springer

    Google Scholar 

  17. Fabietti M et al (2020) Artifact detection in chronically recorded local field potentials using long-short term memory neural network. In: Proceedings of the AICT 2020 (2020), p 1–6

    Google Scholar 

  18. Al Nahian MJ, Ghosh T, et al (2020) Towards artificial intelligence driven emotion aware fall monitoring framework suitable for elderly people with neurological disorder. In: Proceedings of brain information (2020), pp 275–286

    Google Scholar 

  19. Fabietti M, Mahmud M, Lotfi A (2021) Anomaly detection in invasively recorded neuronal signals using deep neural network: effect of sampling frequency. In: Proceedings of the AII (2021), pp 79–91

    Google Scholar 

  20. Al Nahian MJ et al (2021) Towards an accelerometer-based elderly fall detection system using cross-disciplinary time series features. IEEE Access 9:39413–31

    Article  Google Scholar 

  21. Fabietti M, Mahmud M, Lotfi A (2022) Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning. Brain Inform 9(1):1–17

    Article  Google Scholar 

  22. Lalotra GS, Kumar V, Bhatt A, Chen T, Mahmud M (2022) iReTADS: an intelligent real-time anomaly detection system for cloud communications using temporal data summarization and neural network. Secur Commun Netw 2022:9149164

    Article  Google Scholar 

  23. Mahmud M et al (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 29(6):2063–79

    Article  MathSciNet  Google Scholar 

  24. Biswas M, Kaiser MS, Mahmud M, Al Mamun S, Hossain MS, Rahman MA (2021) An XAI based autism detection: the context behind the detection. In: Mahmud M, Kaiser MS, Vassanelli S, Dai Q, Zhong N (eds) Proceedings of the brain informatics, LNAI, vol 12960. Springer, pp 448–459

    Google Scholar 

  25. Mahmud M, Kaiser MS, McGinnity TM, Hussain A (2021) Deep learning in mining biological data. Cogn Comput 13(1):1–33

    Article  Google Scholar 

  26. Deepa B et al (2022) Pattern descriptors orientation and MAP firefly algorithm based brain pathology classification using hybridized machine learning algorithm. IEEE Access 10:3848–63

    Article  Google Scholar 

  27. Mammoottil MJ, Kulangara LJ, Cherian AS, Mohandas P, Hasikin K, Mahmud M (2022) Detection of breast cancer from five-view thermal images using convolutional neural networks. J Healthc Eng 2022:4295221

    Article  Google Scholar 

  28. Kumar I et al (2022) Dense tissue pattern characterization using deep neural network. Cogn Comput 1–24. [ePub ahead of print]

    Google Scholar 

  29. Paul A, et al (2022) Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays. Neural Comput Appl 1–15

    Google Scholar 

  30. Prakash N et al (2021) Deep transfer learning COVID-19 detection and infection localization with superpixel based segmentation. Sustain Cities Soc 75:103252

    Article  Google Scholar 

  31. Ghosh T et al (2021) Artificial intelligence and internet of things in screening and management of autism spectrum disorder. Sustain Cities Soc 74:103189

    Article  Google Scholar 

  32. Watkins J, Fabietti M, Mahmud M (2020) Sense: a student performance quantifier using sentiment analysis. In: Proceedings of the IJCNN, pp 1–6

    Google Scholar 

  33. Satu M, et al (2020) Towards improved detection of cognitive performance using bidirectional multilayer long-short term memory neural network. In: Proceedings of the brain information, pp 297–306

    Google Scholar 

  34. Faria TH et al (2021) Smart city technologies for next generation healthcare. In: Data-driven mining, learning and analytics for secured smart cities, pp 253–274

    Google Scholar 

  35. Ghosh T et al (2021) An attention-based mood controlling framework for social media users. In: Proceedings of the brain information, pp 245–256

    Google Scholar 

  36. Biswas M, Tania MH, Kaiser MS et al (2021) ACCU3RATE: a mobile health application rating scale based on user reviews. PLoS ONE 16(12):e0258050

    Article  Google Scholar 

  37. Nawar A, Toma NT, Al Mamun S et al (2021) Cross-content recommendation between movie and book using machine learning. In: Proceedings of the AICT, pp 1–6

    Google Scholar 

  38. Ghosh T et al (2021) A hybrid deep learning model to predict the impact of COVID-19 on mental health form social media big data. Preprints 2021;2021(2021060654)

    Google Scholar 

  39. Satu MS et al (2021) TClustVID: a novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets. Knowl-Based Syst 226:107126

    Article  Google Scholar 

  40. Al Banna MH et al (2021) Attention-based bi-directional long-short term memory network for earthquake prediction. IEEE Access 9:56589–603

    Article  Google Scholar 

  41. Mahmud M, Kaiser MS, McGinnity TM, Hussain A (2021) Deep learning in mining biological data. Cogn Comput 13(1):1–33

    Article  Google Scholar 

  42. Hájek P (2013) Metamathematics of fuzzy logic, vol 4. Springer Science & Business Media

    Google Scholar 

  43. Bäck T, Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation. Release 97(1):B1

    Google Scholar 

  44. Hosseinzadeh M, Koohpayehzadeh J, Bali AO, Rad FA, Souri A, Mazaherinezhad A et al (2021) A review on diagnostic autism spectrum disorder approaches based on the internet of things and machine learning. J Supercomput 77(3):2590–608

    Article  Google Scholar 

  45. Sumi AI, Zohora M, Mahjabeen M, Faria TJ, Mahmud M, Kaiser MS, et al (2018) Fassert: a fuzzy assistive system for children with autism using internet of things. In: Wang S, Yamamoto V, Su J, Yang Y, Jones E, Iasemidis L et al (eds) Brain informatics. LNAI, vol 11309. Springer, pp. 403–412

    Google Scholar 

  46. Al Banna MH, Ghosh T, Taher KA, Kaiser MS, Mahmud M (2020) A monitoring system for patients of autism spectrum disorder using artificial intelligence. In: Mahmud M, Vassanelli S, Kaiser MS, Zhong N (eds) Proceedings of the brain informatics. LNAI, vol 12241. Springer, pp 251–262

    Google Scholar 

  47. Akter T, Ali MH, Satu MS, Khan MI, Mahmud M (2021) Towards autism subtype detection through identification of discriminatory factors using machine learning. In: Mahmud M, Kaiser MS, Vassanelli S, Dai Q, Zhong N (eds) Brain informatics. LNAI, vol 12960. Springer, pp 401–410

    Google Scholar 

  48. Ghosh T, Banna MHA, Rahman MS, Kaiser MS, Mahmud M, Hosen ASMS et al (2021) Artificial intelligence and internet of things in screening and management of autism spectrum disorder. Sustain Urban Areas 74:103189

    Google Scholar 

  49. Ahmed S, Hossain M, Nur SB, Shamim Kaiser M, Mahmud M et al (2022) Toward machine learning-based psychological assessment of autism spectrum disorders in school and community. In: Proceedings of trends in electronics and health informatics. Springer, pp 139–149

    Google Scholar 

  50. Thabtah FF (2017) Autistic spectrum disorder screening data for children data set. UCI Mach Learn Repos

    Google Scholar 

  51. Eraslan S, Yesilada Y, Yaneva V, Harper S (2020) Autism detection based on eye movement sequences on the web: a scanpath trend analysis approach. Zenodo. https://doi.org/10.5281/zenodo.3668740

  52. Carette R, Elbattah M, Dequen G, Guérin JL, Cilia F (2018) Visualization of eye-tracking patterns in autism spectrum disorder: method and dataset. In: 2018 thirteenth international conference on digital information management (ICDIM). IEEE, pp 248–253

    Google Scholar 

  53. Carette R, Cilia F, Dequen G, Bosche J, Guerin JL, Vandromme L (2017) Automatic autism spectrum disorder detection thanks to eye-tracking and neural network-based approach. In: International conference on IoT technologies for healthcare. Springer, pp 75–81

    Google Scholar 

  54. Elbattah M, Carette R, Dequen G, Guérin JL, Cilia F, Learning clusters in autism spectrum disorder: image-based clustering of eye-tracking scanpaths with deep autoencoder. In: (2019) 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 1417–1420

    Google Scholar 

  55. Carette R, Elbattah M, Cilia F, Dequen G, Guérin JL, Bosche J (2019) Learning to predict autism spectrum disorder based on the visual patterns of eye-tracking scanpaths. In: HEALTHINF, pp 103–112

    Google Scholar 

  56. Tao Y, Shyu ML (2019) SP-ASDNet: CNN-LSTM based ASD classification model using observer scanpaths. In: (2019) IEEE international conference on multimedia and expo workshops (ICMEW). IEEE, pp 641–646

    Google Scholar 

  57. Chita-Tegmark M (2016) Social attention in ASD: a review and meta-analysis of eye-tracking studies. Res Dev Disabil 48:79–93

    Article  Google Scholar 

  58. Thabtah F (2017) Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment. In: Proceedings of the 1st international conference on medical and health informatics 2017, pp 1–6

    Google Scholar 

  59. Duan H, Zhai G, Min X, Che Z, Fang Y, Yang X et al (2019) A dataset of eye movements for the children with autism spectrum disorder. Zenodo. https://doi.org/10.5281/zenodo.2647418

  60. Zunino A, Morerio P, Cavallo A, Ansuini C, Podda J, Battaglia F et al (2018) Video gesture analysis for autism spectrum disorder detection. In: International conference on pattern recognition (ICPR)

    Google Scholar 

  61. Goel N, Grover B, Gupta D, Khanna A, Sharma M et al (2020) Modified grasshopper optimization algorithm for detection of autism spectrum disorder. Phys Commun 41:101115

    Article  Google Scholar 

  62. Pratama TG, Hartanto R, Setiawan NA (2019) Machine learning algorithm for improving performance on 3 AQ-screening classification. Commun Sci Technol 4(2):44–9

    Article  Google Scholar 

  63. Thabtah F, Peebles D (2020) A new machine learning model based on induction of rules for autism detection. Health Inform J 26(1):264–86

    Article  Google Scholar 

  64. Küpper C, Stroth S, Wolff N, Hauck F, Kliewer N, Schad-Hansjosten T et al (2020) Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning. Sci Rep 10(1):1–11

    Article  Google Scholar 

  65. Levy S, Duda M, Haber N, Wall DP (2017) Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism. Molecul Autism 8(1):1–17

    Google Scholar 

  66. Oh SL, Jahmunah V, Arunkumar N, Abdulhay EW, Gururajan R, Adib N et al (2021) A novel automated autism spectrum disorder detection system. Complex Intell Syst 7(5):2399–413

    Article  Google Scholar 

  67. Baygin M, Dogan S, Tuncer T, Barua PD, Faust O, Arunkumar N et al (2021) Automated ASD detection using hybrid deep lightweight features extracted from EEG signals. Comput Biol Med 134:104548

    Article  Google Scholar 

  68. Khodatars M, Shoeibi A, Sadeghi D, Ghaasemi N, Jafari M, Moridian P et al (2021) Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput Biol Med 139:104949

    Article  Google Scholar 

  69. Parvathi M et al (2021) Early detection support mechanism in ASD using ML classifier. Turkish J Comput Math Educ (TURCOMAT) 12(10):4543–9

    Google Scholar 

  70. Jagota V, Bhatia V, Vives L, Prasad AB (2021) ML-PASD: predict autism spectrum disorder by machine learning approach. In: Artificial intelligence for accurate analysis and detection of autism spectrum disorder. IGI Global, pp 82–93

    Google Scholar 

  71. Mishra M, Pati UC (2021) Autism detection using surface and volumetric morphometric feature of sMRI with Machine learning approach. In: International conference on advanced network technologies and intelligent computing. Springer, pp 625–33

    Google Scholar 

  72. Raj S, Masood S (2020) Analysis and detection of autism spectrum disorder using machine learning techniques. Proc Comput Sci 167:994–1004

    Article  Google Scholar 

  73. Thabtah F, Peebles D (2020) A new machine learning model based on induction of rules for autism detection. Health Inform J 26(1):264–86

    Article  Google Scholar 

  74. Omar KS, Mondal P, Khan NS, Rizvi MRK, Islam MN (2019) A machine learning approach to predict autism spectrum disorder. In: 2019 international conference on electrical, computer and communication engineering (ECCE). IEEE, pp 1–6

    Google Scholar 

  75. Hossain MD, Kabir MA, Anwar A, Islam MZ (2021) Detecting autism spectrum disorder using machine learning techniques. Health Inform Sci Syst 9(1):1–13

    Google Scholar 

  76. Zheng ZK, Staubitz JE, Weitlauf AS, Staubitz J, Pollack M, Shibley L et al (2021) A predictive multimodal framework to alert caregivers of problem behaviors for children with ASD (PreMAC). Sensors 21(2):370

    Article  Google Scholar 

  77. Van Steensel FJ, Bögels SM, Perrin S (2011) Anxiety disorders in children and adolescents with autistic spectrum disorders: a meta-analysis. Clin Child Fam Psychol Rev 14(3):302–17

    Article  Google Scholar 

  78. Jansen L, Gispen-de Wied CC, Wiegant VM, Westenberg HG, Lahuis BE, Van Engeland H (2006) Autonomic and neuroendocrine responses to a psychosocial stressor in adults with autistic spectrum disorder. J Autism Dev Disord 36(7):891–9

    Article  Google Scholar 

  79. Vinkers CH, Penning R, Hellhammer J, Verster JC, Klaessens JH, Olivier B et al (2013) The effect of stress on core and peripheral body temperature in humans. Stress 16(5):520–30

    Article  Google Scholar 

  80. Viqueira Villarejo M, García Zapirain B. Méndez Zorrilla A (2012) A stress sensor based on galvanic skin response (GSR) controlled by ZigBee. Sensors (Basel) 12(5):6075–6101

    Google Scholar 

  81. Cabibihan JJ, Javed H, Aldosari M, Frazier TW, Elbashir H (2016) Sensing technologies for autism spectrum disorder screening and intervention. Sensors 17(1):46

    Article  Google Scholar 

  82. Tang TY (2016) Helping neuro-typical individuals to “Read” the emotion of children with autism spectrum disorder: an internet-of-things approach. In: Proceedings of the the 15th international conference on interaction design and children, pp 666–671

    Google Scholar 

  83. Notenboom T (2017) Using technology to recognise emotions in autistic people [B.S. thesis]. University of Twente

    Google Scholar 

  84. Northrup CM, Lantz J, Hamlin T (2016) Wearable stress sensors for children with autism spectrum disorder with in situ alerts to caregivers via a mobile phone. Iproceedings 2(1):e6119

    Article  Google Scholar 

  85. (haftungsbeschraenkt) AU. LetMeTalk (2014). https://apps.apple.com/us/app/letmetalk/id919990138

  86. Coughdrop. https://www.coughdrop.com/

  87. Nival. ABA cards (2020). https://apps.apple.com/us/app/aba-cards/id1507765578

  88. Autism T (2015) Social story creator educators. https://apps.apple.com/us/app/social-story-creator-educators/id998334331

  89. Rethink ed. https://www.rethinked.com/edu/

  90. Mozolic-Staunton B, Donelly M, Yoxall J, Barbaro J (2020) Early detection for better outcomes: universal developmental surveillance for autism across health and early childhood education settings. Res Autism Spectrum Disord 71:101496

    Article  Google Scholar 

  91. Language therapy for children with autism (mita) - apps on Google Play. Google. https://play.google.com/store/apps/details?id=com.imagiration.mita

  92. Jade - apps on Google Play. Google. https://play.google.com/store/apps/details?id=com.jadeautism.jadeautism&hl=en&gl=US

Download references

Acknowledgements

MM is supported by the AI-TOP (2020-1-UK01-KA201-079167) and DIVERSASIA (618615-EPP-1-2020-1-UKEPPKA2-CBHEJP) projects funded by the European Commission under the Erasmus+ programme.

Author information

Authors and Affiliations

  1. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    Sabbir Ahmed, Silvia Binte Nur, Md. Farhad Hossain & M Shamim Kaiser

  2. Applied Intelligence and Informatics Lab, Wazed Miah Science Research Centre (WMSRC), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M Shamim Kaiser

  3. Department of Computer Science, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  4. Computing and Informatics Research Centre, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  5. Medical Technologies Innovation Facility, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  6. Department of Computer Science, University of Huddersfield, Huddersfield, HD1 3DH, UK

    Tianhua Chen

Authors
  1. Sabbir Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Silvia Binte Nur
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Md. Farhad Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M Shamim Kaiser
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mufti Mahmud
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tianhua Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to M Shamim Kaiser or Mufti Mahmud .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Huddersfield, Huddersfield, UK

    Tianhua Chen

  2. Department of Computer Science, University of Huddersfield, Holmfirth, UK

    Jenny Carter

  3. Department of Computer Science, Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  4. Computing, Engineering and Media, De Montfort University, Leicester, UK

    Arjab Singh Khuman

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ahmed, S., Nur, S.B., Farhad Hossain, M., Kaiser, M.S., Mahmud, M., Chen, T. (2022). Computational Intelligence in Detection and Support of Autism Spectrum Disorder. In: Chen, T., Carter, J., Mahmud, M., Khuman, A.S. (eds) Artificial Intelligence in Healthcare. Brain Informatics and Health. Springer, Singapore. https://doi.org/10.1007/978-981-19-5272-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-5272-2_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5271-5

  • Online ISBN: 978-981-19-5272-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation