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

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....

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Vydáno v:	Multimedia tools and applications Ročník 83; číslo 13; s. 37523 - 37548
Hlavní autoři:	Vidyadhari, Ch, Karrothu, Aravind, Manickavasagam, Prabhakar, Anjali Devi, S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.04.2024 Springer Nature B.V
Témata:	Algorithms Autism Classification Computer Communication Networks Computer Science Data Structures and Information Theory Deep learning Fractional calculus Image acquisition Image filters Machine learning Multimedia Information Systems Neural networks Optimization Social factors Special Purpose and Application-Based Systems Social Optimization Algorithm Fractional Calculus Autism Spectrum Disorder Gaussian filter Driving Training-Based Optimization
ISSN:	1573-7721, 1380-7501, 1573-7721
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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.
AbstractList	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. 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.
Author	Vidyadhari, Ch Anjali Devi, S. Karrothu, Aravind Manickavasagam, Prabhakar
Author_xml	– sequence: 1 givenname: Ch surname: Vidyadhari fullname: Vidyadhari, Ch email: chalasanividyadhari@gmail.com organization: Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology – sequence: 2 givenname: Aravind surname: Karrothu fullname: Karrothu, Aravind organization: Department of Information Technology, GMR Institute of Technology – sequence: 3 givenname: Prabhakar surname: Manickavasagam fullname: Manickavasagam, Prabhakar organization: School of Computer Science & Engineering, REVA University – sequence: 4 givenname: S. surname: Anjali Devi fullname: Anjali Devi, S. organization: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation
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Cites_doi	10.1016/S0140-6736(18)31129-2 10.1016/j.patcog.2008.06.010 10.1007/s10619-021-07361-y 10.18280/ts.380332 10.1016/j.compbiomed.2021.104949 10.1088/1757-899X/105/1/012022 10.1016/j.matcom.2022.06.007 10.33969/AIS.2020.21001 10.3390/app11083636 10.1089/cmb.2020.0252 10.3390/s20236762 10.1186/s13637-017-0057-1 10.1016/j.jneumeth.2019.108538 10.11113/jurnalteknologi.v83.16389 10.1016/j.cie.2021.107250 10.3390/computation8030074 10.1109/ISDA.2012.6416547 10.1038/s41598-022-14225-7 10.1016/j.irbm.2021.06.003 10.1109/EMBC.2013.6610336 10.1109/ICME.2019.00055 10.23919/INDIACom49435.2020.9083712 10.1109/CISP-BMEI.2017.8301965 10.1155/2014/396529
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SubjectTerms	Algorithms Autism Classification Computer Communication Networks Computer Science Data Structures and Information Theory Deep learning Fractional calculus Image acquisition Image filters Machine learning Multimedia Information Systems Neural networks Optimization Social factors Special Purpose and Application-Based Systems
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Title	Autism Spectrum Disorder Detection Using Fractional Social Driving Training-Based Optimization Enabled Deep Learning
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