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Chronological pelican remora optimization-enabled deep learning for detection of autism spectrum disorder

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Abstract

The foremost goal of this investigation is to construct a productive paradigm for ASD detection wielding devised Chronological Pelican Remora Optimization Algorithm (CPROA). Initially, median filtering is used to eliminate distortions during the pre-processing stage, and the selected portion is extricated using ROI extraction. After that, the nub-region is extracted based on functional connectivity using the proposed Pelican Remora Optimization (PRO), which is the combination of Pelican Optimization Algorithm (POA) and Remora Optimization Algorithm (ROA). The final step is to classify ASD into normal and abnormal conditions by exploiting Deep Convolutional Neural Network, where the classifier is trained by using CPROA. The newly introduced CPROA is obtained by the amalgamation of chronological principle with POA and ROA. The designed model resulted in high performance with high accuracy of 0.952, recall of 0.958, and F1-score of 0.963.

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Data availability

The data underlying this article are available in Acerta-abide dataset, “https://github.com/lsa-pucrs/acerta-abide”.

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Acknowledgements

I would like to convey my sincere gratitude to the co-authors of this publication for their insightful advice and support throughout the conception and planning of this research project.

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There was no specific funding for this study.

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Authors and Affiliations

  1. Computer Science and Engineering, Sandip Universit, Nashik, Maharashtra, 422213, India

    Gopalsamy Venkadakrishnan Sriramakrishnan

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

    Vaddadi Vasudha Rani

  3. Department of Internet of Things, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India

    Satish Thatavarti

  4. Department of Computer Science and Engineering, Chitkara University, Gharuan, Mohali, Punjab, 140055, India

    Balajee Maram

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  1. Gopalsamy Venkadakrishnan Sriramakrishnan
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  4. Balajee Maram
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All authors have made substantial contributions to the conception and design, revising the manuscript, and the final approval of the version to be published. Additionally, all writers committed to take responsibility for every component of the work, ensuring that any concerns about the accuracy or integrity of any part of the work would be duly examined and addressed.

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Correspondence to Gopalsamy Venkadakrishnan Sriramakrishnan.

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Sriramakrishnan, G.V., Rani, V.V., Thatavarti, S. et al. Chronological pelican remora optimization-enabled deep learning for detection of autism spectrum disorder. SIViP 18, 515–523 (2024). https://doi.org/10.1007/s11760-023-02741-6

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  • DOI: https://doi.org/10.1007/s11760-023-02741-6

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