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Autism Spectrum Disorder Detection Using Fractional Social Driving Training-Based Optimization Enabled Deep Learning

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Abstract

Autism Spectrum Disorder (ASD) is neurodevelopment-based impact on interactive communication and social skills. Diagnosing ASD is one of serious issues that start manifesting at low ages, and is difficult to diagnose at early stages. Autism is characterized by both environmental and genetic factors. Lack of communication issues, social interaction, and limited interest behaviors are possible individuality of autism noticed in children, along other symptoms. This paper aims at ASD detection by Deep Quantum Neural Network (DQNN), wherein this network is trained by proposed Fractional Social Driving Training-Based Optimization (FSDTBO). The initial stage of this processing starts with acquisition of image from dataset, and further pre-processing is carried out using Gaussian filter, and this filtered image is suspended for Regions of Interest (ROI) extraction. Also, extraction of nub region is done by proposed Social Driving Training-Based Optimization (SDTBO), from which classification process is done by considering extracted features too. Here, proposed FSDTBO is integration process among Fractional Calculus (FC) and SDTBO, wherein SDTBO is collaboration between Social Optimization Algorithm (SOA) and Driving Training-Based Optimization (DTBO). Moreover, classification performance of ASD is found based on three metrics, like accuracy, specificity, and sensitivity with superior values of 0.90, 0.94, and 0.96.

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Data availability

In case of benchmark data: Acerta-ABIDE dataset taken from, "https://github.com/lsa-pucrs/acerta-abide", accessed on December 2022.

References

  1. Subah FZ, Deb K, Dhar PK, Koshiba T (2021) A deep learning approach to predict autism spectrum disorder using multisite resting-state fMRI. Appl Sci 11(8):3636

    Article  Google Scholar 

  2. Ali NA, Syafeeza AR, Jaafar AS, Alif MKMF, Ali NA (2020) Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm. IAES Int J Artif Intell 9(1):91–99

    Google Scholar 

  3. Xie J, Wang L, Webster P, Yao Y, Sun J, Wang S. Zhou H (2019) A two-stream end-to-end deep learning network for recognizing atypical visual attention in autism spectrum disorder, arXiv preprint arXiv:1911.11393

  4. Saranya A, Anandan R (2022) FIGS-DEAF: an novel implementation of hybrid deep learning algorithm to predict autism spectrum disorders using facial fused gait features. Distrib Parallel Databases 40(4):753–778

    Article  Google Scholar 

  5. Yin W, Mostafa S, Wu FX (2021) Diagnosis of autism spectrum disorder based on functional brain networks with deep learning. J Comput Biol 28(2):146–165

    Article  Google Scholar 

  6. Xu L, Liu Y, Yu J, Li X, Yu X, Cheng H, Li J (2020) Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy. J Neurosci Methods h331:108538

    Article  Google Scholar 

  7. Mohanty AS, Parida P (1921) Patra KC (2021) Identification of autism spectrum disorder usingdeep neural network. J Phys: Conf Ser 1:012006

    Google Scholar 

  8. Lee JH, Lee GW, Bong G, Yoo HJ, Kim HK (2020) Deep-learning-based detection of infants with autism spectrum disorder using auto-encoder feature representation. Sensors 20(23):6762

    Article  Google Scholar 

  9. Lord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J (2018) Autism spectrum disorder. The lancet 392(10146):508–520

    Article  Google Scholar 

  10. Yuan J, Holtz C, Smith T, Luo J (2016) Autism spectrum disorder detection from semi-structured and unstructured medical data. EURASIP J Bioinf Syst Biol 2017(1):1–9

    Article  Google Scholar 

  11. Erkan U, Thanh DN (2019) Autism spectrum disorder detection with machine learning methods. Curr Psychiatr Res Rev Formerly Curr Psychiatr Rev 15(4):297–308

    Google Scholar 

  12. Khodatars M, Shoeibi A, Sadeghi D, Ghaasemi N, Jafari M, Moridian P, Khadem A, Alizadehsani R, Zare A, Kong Y, Khosravi A (2021) Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput Biol Med 139:104949

    Article  Google Scholar 

  13. Tian Y, Min X, Zhai G, Gao Z (2019) Video-based early asd detection via temporal pyramid networks. In proceedings of 2019 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp 272–277

  14. Husna RNS, Syafeeza AR, Hamid NA, Wong YC, Raihan RA (2021) Functional magnetic resonance imaging for autism spectrum disorder detection using deep learning. Jurnal Teknologi 83(3):45–52

    Article  Google Scholar 

  15. Radhakrishnan M, Ramamurthy K, Choudhury KK, Won D, Manoharan TA (2021) Performance analysis of deep learning models for detection of autism spectrum disorder from eeg signals. Traitement du Signal 38(3):853–863

    Article  Google Scholar 

  16. Acerta-abide dataset is taken from, https://github.com/lsa-pucrs/acerta-abide. Accessed on Dec 2022

  17. Kumar A, Sodhi SS (2020) Comparative analysis of gaussian filter, median filter and denoise autoenocoder. In proceedings of 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom), pp 45–51

  18. Kim B, Kehtarnavaz N, LeBoulluec P, Liu H, Peng Y, Euhus D (2013) Automation of ROI extraction in hyperspectral breast images. In proceedings of 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 3658–3661

  19. Bhaladhare PR, Jinwala, DC (2014) A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm. Advances in Computer Engineering 1–12

  20. Dehghani M, Trojovská E, Trojovský P (2022) Driving training-based optimization: A new human-based metaheuristic algorithm for solving optimization problems, scientific reports, pp 1–21

  21. Karimi N, Khandani K (2022) Social optimization algorithm with application to economic dispatch problem. Int Trans Electr Energy Systems 30(11):e12593

    Google Scholar 

  22. Lakshmiprabha NS, Majumder S (2012) Face recognition system invariant to plastic surgery. In proceedings of 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), pp 258–263

  23. Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local gabor binary pattern histogram sequence (lgbphs): A novel non-statistical model for face representation and recognition. In Tenth IEEE International Conference on Computer Vision (ICCV'05), 1, pp 786–791

  24. Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi N.Z (2021) A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images. IRBM 43(4):290–299

  25. Hemanth DJ (2020) EEG signal based modified Kohonen neural networks for classification of human mental emotions. J Artif Intell Systems 2:1–13

    Article  Google Scholar 

  26. Kerstin B, Dmytro B, Terry F, Tobias O, Robert S, Ramona W (2019) Efficient learning for deep quantum neural networks. Nature 1–24

  27. Liu GH, Yang JY (2008) Image retrieval based on the texton co-occurrence matrix. Pattern Recogn 41(12):3521–3527

    Article  Google Scholar 

  28. Liu G, Zhang S, Xie Z (2017) A novel infrared and visible face fusion recognition method based on non-subsampled contourlet transform. In proceedings of 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE pp 1–6

  29. Minarno AE, Munarko Y, Kurniawardhani A, Bimantoro F (2016) Classification of texture using multi texton histogram and probabilistic neural network. In IOP Conf Series: Mater Sci Eng, IOP Publ 105(1):012022

    Article  Google Scholar 

  30. Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-Qaness MA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

    Article  Google Scholar 

  31. Pan JS, Zhang LG, Wang RB, Snášel V, Chu SC (2022) Gannet optimization algorithm: A new metaheuristic algorithm for solving engineering optimization problems. Math Comput Simul 202:343–373

    Article  MathSciNet  Google Scholar 

  32. Mirri S, Delnevo G, Roccetti M (2020) Is a COVID-19 second wave possible in Emilia-Romagna (Italy)? Forecasting a future outbreak with particulate pollution and machine learning. Computation 8(3):1–25

    Article  Google Scholar 

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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Authors and Affiliations

  1. Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Hyderabad, India

    Ch Vidyadhari

  2. Department of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh, 532127, India

    Aravind Karrothu

  3. School of Computer Science & Engineering, REVA University, Bangalore, 560064, India

    Prabhakar Manickavasagam

  4. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

    S. Anjali Devi

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  1. Ch Vidyadhari
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  2. Aravind Karrothu
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  4. S. Anjali Devi
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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Ch Vidyadhari.

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Vidyadhari, C., Karrothu, A., Manickavasagam, P. et al. Autism Spectrum Disorder Detection Using Fractional Social Driving Training-Based Optimization Enabled Deep Learning. Multimed Tools Appl 83, 37523–37548 (2024). https://doi.org/10.1007/s11042-023-16784-x

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