{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T22:19:22Z","timestamp":1771539562225,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,2,18]],"date-time":"2020-02-18T00:00:00Z","timestamp":1581984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,2,18]]},"DOI":"10.1145\/3384544.3384552","type":"proceedings-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T03:58:24Z","timestamp":1588564704000},"page":"31-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["ADHD Identification using Convolutional Neural Network with Seed-based Approach for fMRI Data"],"prefix":"10.1145","author":[{"given":"Gangani","family":"Ariyarathne","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka"}]},{"given":"Senuri","family":"De Silva","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka"}]},{"given":"Sanuwani","family":"Dayarathna","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka"}]},{"given":"Dulani","family":"Meedeniya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka"}]},{"given":"Sampath","family":"Jayarathne","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Old Dominion University, Virginia, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,4,17]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association. 2013. Diagnostic and statistical manual of mental disorders (DSM-5\u00ae). American Psychiatric Pub.","DOI":"10.1176\/appi.books.9780890425596"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2004.10.020"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3991\/ijoe.v15i13.10744"},{"key":"e_1_3_2_1_4_1","unstructured":"Rubasinghe I. D. and Meedeniya D. A. 2020. A Review of Supportive Computational Approaches for Neurological Disorder Identification. in Interdisciplinary Approaches to Altering Neurodevelopmental Disorders Wadhera T. Kakkar D. Ed. IGI Gloabal Ch.16."},{"key":"e_1_3_2_1_5_1","volume-title":"proceedings of International Conference on Intelligent Computing, 225--232","author":"Kuang D.","unstructured":"Kuang, D., Guo, X., An, X., Zhao, Y., and He, L. 2014. Discrimination of ADHD Based on fMRI Data with Deep Belief Network. In proceedings of International Conference on Intelligent Computing, 225--232."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MERCon.2019.8818865"},{"key":"e_1_3_2_1_7_1","first-page":"133","article-title":"Overview of functional magnetic resonance imaging","volume":"22","author":"Glover G. H.","year":"2011","unstructured":"Glover, G. H. 2011. Overview of functional magnetic resonance imaging. Neurosurgery Clinics.22, 2 (2011), 133--139.","journal-title":"Neurosurgery Clinics."},{"key":"e_1_3_2_1_8_1","volume-title":"Front. Neuroinform. 12","author":"Wen D.","year":"2018","unstructured":"Wen, D., Wei, Z., Zhou, Y., Li, G., Zhang, X., and Han, W. 2018. Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment. Front. Neuroinform. 12, (2018), 23."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Zou L. Zheng J. Miao C. Mckeown M. J. and Wang Z. J. 2017. 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI. IEEE Access 5 (2017) 23626--23636.","DOI":"10.1109\/ACCESS.2017.2762703"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1037\/abn0000013"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22818"},{"key":"e_1_3_2_1_12_1","volume-title":"proceedings of International Conference Information, Cybernetics Computer Society Systems.","author":"Miao B.","year":"2017","unstructured":"Miao, B., and Zhang, Y. 2017. A feature selection method for classification of ADHD. In proceedings of International Conference Information, Cybernetics Computer Society Systems. (2017), 21--25."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpsycho.2013.01.008"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Peng X. Lin P. Zhang T. and Wang J. 2013. Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS One 8 11 (2013).","DOI":"10.1371\/journal.pone.0079476"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/1753-4631-4-S1-S1"},{"key":"e_1_3_2_1_16_1","volume-title":"proceedings of SPIE - The International Society for Optical Engineering","author":"Solmaz B.","year":"2012","unstructured":"Solmaz, B., Dey, S., Rao, A. R. and Shah, M. 2012. ADHD classification using bag of words approach on network features. In proceedings of SPIE - The International Society for Optical Engineering, February (2012), 83144T."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Hao A. J. He B. L. and Yin C. H. 2015. \"Discrimination of ADHD children based on Deep Bayesian Network \" 2015 In proceedings of IET International Conference Biomedical Image Signal Processing (ICBISP 2015) 1--6.","DOI":"10.1049\/cp.2015.0764"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2379621"},{"key":"e_1_3_2_1_19_1","first-page":"305","volume-title":"Ch. 13","author":"Rubasinghe I. D.","unstructured":"Rubasinghe, I. D. and Meedeniya, D. A. 2020. Automated Neuroscience Decision Support Framework. In Deep Learning Techniques for Biomedical and Health Informatics. Agarwal, B. et al. Ed. Academic Press, Ch. 13, pp. 305--326."},{"key":"e_1_3_2_1_20_1","volume-title":"International Workshop on Pattern Recognition in Neuroimaging, 1--4.","author":"Tabas A.","unstructured":"Tabas, A., Balaguer-Ballester, E. and Igual, L. 2014. Spatial discriminant ICA for RS-fMRI characterisation. In International Workshop on Pattern Recognition in Neuroimaging, 1--4."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Hull J. V. Dokovna L. B. Jacokes Z. J. Torgerson C. M. Irimia A. and Van Horn J. D. 2017. Resting-state functional connectivity in autism spectrum disorders: a review. Front. psychiatry 7 (2017) 205.","DOI":"10.3389\/fpsyt.2016.00205"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Bellec P. Chu C. Chouinard-Decorte F. Benhajali Y. Margulies D. S. and Craddock R. C. 2017. The neuro bureau ADHD-200 preprocessed repository. Neuroimage 144 (2017) 275--286.","DOI":"10.1016\/j.neuroimage.2016.06.034"},{"key":"e_1_3_2_1_23_1","first-page":"1","article-title":"The Emerging Neurobiology of Attention Deficit Hyperactivity Disorder: The Key Role of the Prefrontal Association Cortex","volume":"154","author":"Arnsten A. F. T.","year":"2010","unstructured":"Arnsten, A. F. T. 2010. The Emerging Neurobiology of Attention Deficit Hyperactivity Disorder: The Key Role of the Prefrontal Association Cortex. The Journal of. Pediatric. 154, 5 (2010), 1--20.","journal-title":"Pediatric."}],"event":{"name":"ICSCA 2020: 2020 9th International Conference on Software and Computer Applications","location":"Langkawi Malaysia","acronym":"ICSCA 2020"},"container-title":["Proceedings of the 2020 9th International Conference on Software and Computer Applications"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3384544.3384552","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3384544.3384552","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:20:06Z","timestamp":1755840006000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3384544.3384552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,18]]},"references-count":23,"alternative-id":["10.1145\/3384544.3384552","10.1145\/3384544"],"URL":"https:\/\/doi.org\/10.1145\/3384544.3384552","relation":{},"subject":[],"published":{"date-parts":[[2020,2,18]]},"assertion":[{"value":"2020-04-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}