{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T20:01:40Z","timestamp":1776283300723,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11760-023-02741-6","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T08:02:14Z","timestamp":1695888134000},"page":"515-523","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Chronological pelican remora optimization-enabled deep learning for detection of autism spectrum disorder"],"prefix":"10.1007","volume":"18","author":[{"given":"Gopalsamy Venkadakrishnan","family":"Sriramakrishnan","sequence":"first","affiliation":[]},{"given":"Vaddadi Vasudha","family":"Rani","sequence":"additional","affiliation":[]},{"given":"Satish","family":"Thatavarti","sequence":"additional","affiliation":[]},{"given":"Balajee","family":"Maram","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"2741_CR1","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, L., Chi, L., Zhao, Z.: Autism screening using deep embedding representation. In International Conference on Computational Science, 160\u2013173 (2019)","DOI":"10.1007\/978-3-030-22741-8_12"},{"key":"2741_CR2","doi-asserted-by":"publisher","first-page":"153341","DOI":"10.1109\/ACCESS.2020.3016734","volume":"8","author":"F Ke","year":"2020","unstructured":"Ke, F., Choi, S., Kang, Y.H., Cheon, K.A., Lee, S.W.: Exploring the structural and strategic bases of autism spectrum disorders with deep learning. Ieee Access. 8, 153341\u2013215335 (2020)","journal-title":"Ieee Access."},{"issue":"10","key":"2741_CR3","doi-asserted-by":"publisher","first-page":"182","DOI":"10.3390\/children7100182","volume":"7","author":"H Sewani","year":"2020","unstructured":"Sewani, H., Kashef, R.: An auto encoder-based deep learning classifier for efficient diagnosis of autism. Children 7(10), 182 (2020)","journal-title":"Children"},{"issue":"10","key":"2741_CR4","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1177\/09544119211024778","volume":"235","author":"K Ganesh","year":"2021","unstructured":"Ganesh, K., Umapathy, S., Thanaraj Krishnan, P.: Deep learning techniques for automated detection of autism spectrum disorder based on thermal imaging. Proc. Inst. Mech. Eng. 235(10), 1113\u20131127 (2021)","journal-title":"Proc. Inst. Mech. Eng."},{"key":"2741_CR5","doi-asserted-by":"crossref","unstructured":"Niu, K., Guo, J., Pan, Y., Gao, X., Peng, X., Li, N., Li, H.: Multichannel deep attention neural networks for the classification of autism spectrum disorder using neuro imaging and personal characteristic data. Complexity (2020)","DOI":"10.1155\/2020\/1357853"},{"key":"2741_CR6","doi-asserted-by":"crossref","unstructured":"Maksoud, R., du Preez, S., Eaton-Fitch, N., Thapaliya, K., Barnden, L., Cabanas, H., Staines, D., Marshall-Gradisnik, S.: A systematic review of neurological impairments in myalgic encephalomyelitis\/chronic fatigue syndrome using neuro imaging techniques. 15(4), e0232475 (2020)","DOI":"10.1371\/journal.pone.0232475"},{"key":"2741_CR7","doi-asserted-by":"crossref","unstructured":"Eslami, T., Saeed, F.: Auto-ASD-network: a technique based on deep learning and support vector machines for diagnosing autism spectrum disorder using fMRI data. In: Proceedings of the 10th ACM International Conference on Bioinformatics, 646\u2013651 (2019)","DOI":"10.1145\/3307339.3343482"},{"issue":"5","key":"2741_CR8","doi-asserted-by":"publisher","first-page":"602","DOI":"10.3390\/brainsci12050602","volume":"12","author":"MJ Ayoub","year":"2022","unstructured":"Ayoub, M.J., Keegan, L., Tager-Flusberg, H., Gill, S.V.: Neuroimaging techniques as descriptive and diagnostic tools for infants at risk for autism spectrum disorder: a systematic review. Brain Sci. 12(5), 602 (2022)","journal-title":"Brain Sci."},{"key":"2741_CR9","doi-asserted-by":"crossref","unstructured":"Eslami, T., Raiker, J.S., Saeed, F.: Explainable and scalable machine learning algorithms for detection of autism spectrum disorder using fMRI data. In Neural Engineering Techniques for Autism Spectrum Disorder, pp 39\u201354 (2021)","DOI":"10.1016\/B978-0-12-822822-7.00004-1"},{"key":"2741_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104949","volume":"139","author":"M Khodatars","year":"2021","unstructured":"Khodatars, M., Shoeibi, A., Sadeghi, D., Ghaasemi, N., Jafari, M., Moridian, P., Khadem, A., Alizadehsani, R., Zare, A., Kong, Y., Khosravi, A.: Deep learning for neuro imaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput. Biol. Med. 139, 104949 (2021)","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"2741_CR11","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s40120-019-00153-8","volume":"8","author":"AAA Valliani","year":"2019","unstructured":"Valliani, A.A.A., Ranti, D., Oermann, E.K.: Deep learning and neurology: a systematic review. Neurol. Therapy 8(2), 351\u2013365 (2019)","journal-title":"Neurol. Therapy"},{"key":"2741_CR12","doi-asserted-by":"crossref","unstructured":"Heinsfeld, A.S., Franco, A.R., Craddock, R.C., Buchweitz, A., Meneguzzi, F.: Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage Cli. 17, 16\u201323 (2018)","DOI":"10.1016\/j.nicl.2017.08.017"},{"key":"2741_CR13","doi-asserted-by":"crossref","unstructured":"Sairam, K., Naren, J., Vithya, G., Srivathsan, S.: Computer aided system for autism spectrum disorder using deep learning methods. Int. J. Psychosoc. Rehabil. 23(1), (2019)","DOI":"10.37200\/IJPR\/V23I1\/PR190254"},{"issue":"1","key":"2741_CR14","first-page":"91","volume":"9","author":"NA Ali","year":"2020","unstructured":"Ali, N.A., Syafeeza, A.R., Jaafar, A.S., Alif, M.K.M.F., Ali, N.A.: Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm. IAES Int. J. Artif. Intell. 9(1), 91\u201399 (2020)","journal-title":"IAES Int. J. Artif. Intell."},{"key":"2741_CR15","doi-asserted-by":"crossref","unstructured":"Mayor-Torres, J.M., Ravanelli, M., Medina-De Villiers, S.E., Lerner, M.D., Riccardi, G.: Interpretable sincnet-based deep learning for emotion recognition from eeg brain activity. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 412\u2013415 (2021)","DOI":"10.1109\/EMBC46164.2021.9630427"},{"issue":"8","key":"2741_CR16","doi-asserted-by":"publisher","first-page":"3636","DOI":"10.3390\/app11083636","volume":"11","author":"FZ Subah","year":"2021","unstructured":"Subah, F.Z., Deb, K., Dhar, P.K., Koshiba, T.: A deep learning approach to predict autism spectrum disorder using multisite resting-state fMRI. Appl. Sci. 11(8), 3636 (2021)","journal-title":"Appl. Sci."},{"issue":"23","key":"2741_CR17","doi-asserted-by":"publisher","first-page":"6762","DOI":"10.3390\/s20236762","volume":"20","author":"JH Lee","year":"2020","unstructured":"Lee, J.H., Lee, G.W., Bong, G., Yoo, H.J., Kim, H.K.: Deep-learning-based detection of infants with autism spectrum disorder using auto-encoder feature representation. Sensors. 20(23), 6762 (2020)","journal-title":"Sensors."},{"issue":"1","key":"2741_CR18","volume":"1921","author":"AS Mohanty","year":"2021","unstructured":"Mohanty, A.S., Parida, P., Patra, K.C.: Identification of Autism Spectrum Disorder using Deep Neural Network. In Journal of Physics: Conference Series. 1921(1), 012006 (2021)","journal-title":"In Journal of Physics: Conference Series."},{"key":"2741_CR19","unstructured":"Xie, J., Wang, L., Webster, P., Yao, Y., Sun, J., Wang, S., Zhou, H.: A two-stream end-to-end deep learning network for recognizing atypical visual attention in autism spectrum disorder. arXiv preprint arXiv:1911.11393 (2019)"},{"key":"2741_CR20","doi-asserted-by":"crossref","unstructured":"Saranya, A., Anandan, R.,. \"FIGS-DEAF: an novel implementation of hybrid deep learning algorithm to predict autism spectrum disorders using facial fused gait features.. Distributed and Parallel Databases, pp 1\u201326 (2021)","DOI":"10.1007\/s10619-021-07361-y"},{"issue":"4","key":"2741_CR21","doi-asserted-by":"publisher","DOI":"10.2196\/24754","volume":"9","author":"H Wang","year":"2021","unstructured":"Wang, H., Avillach, P.: Diagnostic classification and prognostic prediction using common genetic variants in autism spectrum disorder: Genotype-based deep learning. JMIR Med. Inform. 9(4), e24754 (2021)","journal-title":"JMIR Med. Inform."},{"issue":"6","key":"2741_CR22","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3390\/jimaging6060047","volume":"6","author":"M Tang","year":"2020","unstructured":"Tang, M., Kumar, P., Chen, H., Shrivastava, A.: Deep multimodal learning for the diagnosis of autism spectrum disorder. J. Imag. 6(6), 47 (2020)","journal-title":"J. Imag."},{"issue":"3","key":"2741_CR23","doi-asserted-by":"publisher","first-page":"855","DOI":"10.3390\/s22030855","volume":"22","author":"P Trojovsky","year":"2022","unstructured":"Trojovsky, P., Dehghani, M.: Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors 22(3), 855 (2022)","journal-title":"Sensors"},{"key":"2741_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115665","volume":"185","author":"H Jia","year":"2021","unstructured":"Jia, H., Peng, X., Lang, C.: Remora optimization algorithm. Expert Syst. Appl. 185, 115665 (2021)","journal-title":"Expert Syst. Appl."},{"key":"2741_CR25","unstructured":"Acerta-abide dataset, https:\/\/github.com\/lsa-pucrs\/acerta-abide. Accessed on July 2022."},{"issue":"7","key":"2741_CR26","doi-asserted-by":"publisher","first-page":"2115","DOI":"10.1007\/s10773-019-04103-w","volume":"58","author":"S Jiang","year":"2019","unstructured":"Jiang, S., Zhou, R.G., Hu, W., Li, Y.: Improved quantum image median filtering in the spatial domain. Int. J. Theor. Phys. 58(7), 2115\u20132133 (2019)","journal-title":"Int. J. Theor. Phys."},{"issue":"6","key":"2741_CR27","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3390\/e19060242","volume":"19","author":"S Albelwi","year":"2017","unstructured":"Albelwi, S., Mahmood, A.: A framework for designing the architectures of deep convolutional neural networks. Entropy 19(6), 242 (2017)","journal-title":"Entropy"},{"key":"2741_CR28","doi-asserted-by":"crossref","unstructured":"Fernandis, J.R.: ALOA: Ant lion optimization algorithm-based deep learning for breast cancer classification. Multimedia Res. 4(1), (2021)","DOI":"10.46253\/j.mr.v4i1.a5"},{"issue":"3","key":"2741_CR29","first-page":"40","volume":"2","author":"SB Chandanapalli","year":"2019","unstructured":"Chandanapalli, S.B., Sreenivasa Reddy, E., Rajya Lakshmi, D.: Convolutional neural network for water quality prediction in WSN. J. Netw. Commun. Syst. 2(3), 40\u201347 (2019)","journal-title":"J. Netw. Commun. Syst."},{"key":"2741_CR30","doi-asserted-by":"crossref","unstructured":"Srinivas, K.: Prediction of e-learning efficiency by deep learning in E-khool online portal networks. Multimedia Res. 3(4) (2020)","DOI":"10.46253\/j.mr.v3i4.a2"},{"key":"2741_CR31","doi-asserted-by":"crossref","unstructured":"Rahman, Md.M., Usman, O.L., Muniyandi, R.C., Sahran, S., Mohamed, S., Razak, R.A.: A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder. Brain Sci. 10(12), (2020)","DOI":"10.3390\/brainsci10120949"},{"key":"2741_CR32","doi-asserted-by":"crossref","unstructured":"Usman, O.L., Muniyandi, R.C., Omar, K., Mohamad, M.: Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism. PLoS ONE. 16(2), (2021)","DOI":"10.1371\/journal.pone.0245579"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02741-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02741-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02741-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T15:43:03Z","timestamp":1706197383000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02741-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2741"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02741-6","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]},"assertion":[{"value":"16 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}