{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T11:23:23Z","timestamp":1762082603206,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031227912"},{"type":"electronic","value":"9783031227929"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-22792-9_14","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T01:22:43Z","timestamp":1672536163000},"page":"175-189","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Exploration of\u00a0Autism Spectrum Disorder Classification from\u00a0Structural and\u00a0Functional MRI Images"],"prefix":"10.1007","author":[{"given":"Jovan","family":"Krajevski","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9363-1527","authenticated-orcid":false,"given":"Ilinka","family":"Ivanoska","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7350-9583","authenticated-orcid":false,"given":"Kire","family":"Trivodaliev","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3373-8637","authenticated-orcid":false,"given":"Slobodan","family":"Kalajdziski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3411-2399","authenticated-orcid":false,"given":"Sonja","family":"Gievska","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"14_CR1","unstructured":"Abadi, M., et al.: $$\\{$$TensorFlow$$\\}$$: a system for $$\\{$$Large-Scale$$\\}$$ machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265\u2013283 (2016)"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/j.neuroimage.2016.10.045","volume":"147","author":"A Abraham","year":"2017","unstructured":"Abraham, A., et al.: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. Neuroimage 147, 736\u2013745 (2017)","journal-title":"Neuroimage"},{"key":"14_CR3","doi-asserted-by":"publisher","first-page":"654315","DOI":"10.3389\/fncom.2021.654315","volume":"15","author":"F Almuqhim","year":"2021","unstructured":"Almuqhim, F., Saeed, F.: ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data. Front. Comput. Neurosci. 15, 654315 (2021)","journal-title":"Front. Comput. Neurosci."},{"key":"14_CR4","unstructured":"Artifact detection tools ART (2019). http:\/\/www.nitrc.org\/projects\/artifact_detect. Accessed 30 12 2019"},{"issue":"365","key":"14_CR5","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants, B.B., Tustison, N., Song, G., et al.: Advanced normalization tools (ANTS). Insight J 2(365), 1\u201335 (2009)","journal-title":"Insight J"},{"key":"14_CR6","unstructured":"Batista, G., Silva, D.F., et al.: How k-nearest neighbor parameters affect its performance. In: Argentine Symposium on Artificial Intelligence, pp. 1\u201312. Citeseer (2009)"},{"key":"14_CR7","unstructured":"Bengs, M., Gessert, N., Schlaefer, A.: 4D Spatio-temporal deep learning with 4D fMRI data for autism spectrum disorder classification. arXiv preprint arXiv:2004.10165 (2020)"},{"key":"14_CR8","doi-asserted-by":"publisher","unstructured":"Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010. Physica-Verlag HD, pp. 177\u2013186. Springer (2010). https:\/\/doi.org\/10.1007\/978-3-7908-2604-3_16","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"14_CR9","unstructured":"Chollet, F., et al.: Keras: The python deep learning library. Astrophy. Source Code Libr., pp. ascl-1806 (2018)"},{"issue":"1","key":"14_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.10","volume":"4","author":"A Di Martino","year":"2017","unstructured":"Di Martino, A., et al.: Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci. data 4(1), 1\u201315 (2017)","journal-title":"Sci. data"},{"issue":"6","key":"14_CR11","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A DI Martino","year":"2014","unstructured":"DI Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659\u2013667 (2014)","journal-title":"Mol. Psychiatry"},{"key":"14_CR12","volume-title":"Essentials of Neuroimaging for Clinical Practice","author":"DD Dougherty","year":"2008","unstructured":"Dougherty, D.D., Rauch, S.L., Rosenbaum, J.F.: Essentials of Neuroimaging for Clinical Practice. American Psychiatric Pub, Washington (2008)"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"El Gazzar, A., Cerliani, L., van Wingen, G., Thomas, R.M.: Simple 1-D convolutional networks for resting-state fMRI based classification in autism. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852002"},{"key":"14_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-3-030-32695-1_11","volume-title":"OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging","author":"A El-Gazzar","year":"2019","unstructured":"El-Gazzar, A., Quaak, M., Cerliani, L., Bloem, P., van Wingen, G., Mani Thomas, R.: A hybrid 3DCNN and 3DC-LSTM based model for 4D spatio-temporal fMRI data: an ABIDE autism classification study. In: Zhou, L., et al. (eds.) OR 2.0\/MLCN -2019. LNCS, vol. 11796, pp. 95\u2013102. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32695-1_11"},{"key":"14_CR15","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871\u20131874 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"14_CR16","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367\u2013378 (2002)","journal-title":"Comput. Stat. Data Anal."},{"issue":"1","key":"14_CR17","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3\u201342 (2006). https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Mach. Learn."},{"issue":"2","key":"14_CR18","first-page":"105","volume":"4","author":"AE Hoerl","year":"1975","unstructured":"Hoerl, A.E., Kannard, R.W., Baldwin, K.F.: Ridge regression: some simulations. Commun. Stat. Theory Methods 4(2), 105\u2013123 (1975)","journal-title":"Commun. Stat. Theory Methods"},{"key":"14_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression","author":"DW Hosmer Jr","year":"2013","unstructured":"Hosmer, D.W., Jr., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, vol. 398. Wiley, Hoboken (2013)"},{"key":"14_CR20","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"issue":"2","key":"14_CR21","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","volume":"62","author":"M Jenkinson","year":"2012","unstructured":"Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62(2), 782\u2013790 (2012)","journal-title":"Neuroimage"},{"key":"14_CR22","doi-asserted-by":"publisher","first-page":"104096","DOI":"10.1016\/j.compbiomed.2020.104096","volume":"127","author":"H Jiang","year":"2020","unstructured":"Jiang, H., Cao, P., Xu, M., Yang, J., Zaiane, O.: Hi-GCN: a hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Comput. Biol. Med. 127, 104096 (2020)","journal-title":"Comput. Biol. Med."},{"key":"14_CR23","doi-asserted-by":"publisher","first-page":"104949","DOI":"10.1016\/j.compbiomed.2021.104949","volume":"139","author":"M Khodatars","year":"2021","unstructured":"Khodatars, M., et al.: Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput. Biol. Med. 139, 104949 (2021)","journal-title":"Comput. Biol. Med."},{"key":"14_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-00889-5_16","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"M Khosla","year":"2018","unstructured":"Khosla, M., Jamison, K., Kuceyeski, A., Sabuncu, M.R.: 3D convolutional neural networks for classification of functional connectomes. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 137\u2013145. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_16"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Kim, B.H., Ye, J.C.: Understanding graph isomorphism network for rs-fMRI functional connectivity analysis. Front. Neurosci., 630 (2020)","DOI":"10.3389\/fnins.2020.00630"},{"key":"14_CR26","unstructured":"Kingma, D.P., Ba, J.: ADAM: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"14_CR27","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jneumeth.2016.03.001","volume":"264","author":"X Li","year":"2016","unstructured":"Li, X., Morgan, P.S., Ashburner, J., Smith, J., Rorden, C.: The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J. Neurosci. Methods 264, 47\u201356 (2016)","journal-title":"J. Neurosci. Methods"},{"key":"14_CR28","doi-asserted-by":"publisher","first-page":"102233","DOI":"10.1016\/j.media.2021.102233","volume":"74","author":"X Li","year":"2021","unstructured":"Li, X., et al.: BrainGNN: interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)","journal-title":"Med. Image Anal."},{"issue":"3","key":"14_CR29","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A., Wiener, M., et al.: Classification and regression by randomForest. R News 2(3), 18\u201322 (2002)","journal-title":"R News"},{"key":"14_CR30","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"14_CR31","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1007\/978-981-16-7167-8_77","volume-title":"Innovative Data Communication Technologies and Application","author":"A Qayyum","year":"2022","unstructured":"Qayyum, A., et al.: An efficient 1DCNN-LSTM deep learning model for assessment and classification of fMRI-based autism spectrum disorder. In: Raj, J.S., Kamel, K., Lafata, P. (eds.) Innovative Data Communication Technologies and Application, vol. 96, pp. 1039\u20131048. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-7167-8_77"},{"key":"14_CR32","doi-asserted-by":"publisher","first-page":"108506","DOI":"10.1016\/j.jneumeth.2019.108506","volume":"335","author":"A Riaz","year":"2020","unstructured":"Riaz, A., Asad, M., Alonso, E., Slabaugh, G.: DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI. J. Neurosci. Methods 335, 108506 (2020)","journal-title":"J. Neurosci. Methods"},{"issue":"3","key":"14_CR33","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/0920-5489(94)90017-5","volume":"16","author":"M Riedmiller","year":"1994","unstructured":"Riedmiller, M.: Advanced supervised learning in multi-layer perceptrons-from backpropagation to adaptive learning algorithms. Comput. Stan. Interfaces 16(3), 265\u2013278 (1994)","journal-title":"Comput. Stan. Interfaces"},{"key":"14_CR34","unstructured":"Rish, I., et al.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp. 41\u201346 (2001)"},{"issue":"3","key":"14_CR35","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/21.97458","volume":"21","author":"SR Safavian","year":"1991","unstructured":"Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660\u2013674 (1991)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"14_CR36","doi-asserted-by":"publisher","first-page":"155584","DOI":"10.1109\/ACCESS.2019.2949577","volume":"7","author":"S Sarraf","year":"2019","unstructured":"Sarraf, S., Desouza, D.D., Anderson, J.A., Saverino, C.: MCADNNeT: recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models. IEEE Access 7, 155584\u2013155600 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"14_CR37","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s00247-021-05042-7","volume":"52","author":"SD Serai","year":"2021","unstructured":"Serai, S.D.: Basics of magnetic resonance imaging and quantitative parameters T1, T2, T2*, T1rho and diffusion-weighted imaging. Pediatr. Radiol. 52(2), 217\u2013227 (2021). https:\/\/doi.org\/10.1007\/s00247-021-05042-7","journal-title":"Pediatr. Radiol."},{"key":"14_CR38","unstructured":"Smith, S.M.: Bet: brain extraction tool. FMRIB TR00SMS2b, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Department of Clinical Neurology, Oxford University, John Radcliffe Hospital, Headington, UK (2000)"},{"key":"14_CR39","unstructured":"Statistical parametric mapping SPM12 (2018). https:\/\/www.fil.ion.ucl.ac.uk\/spm\/software\/spm12\/. Accessed 30 12 2019"},{"key":"14_CR40","doi-asserted-by":"publisher","first-page":"247","DOI":"10.31887\/DCNS.2013.15.3\/osporns","volume":"15","author":"O Sporns","year":"2022","unstructured":"Sporns, O.: Structure and function of complex brain networks. Dialogues Clin. Neurosci. 15, 247\u2013262 (2022)","journal-title":"Dialogues Clin. Neurosci."},{"issue":"1","key":"14_CR41","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"14_CR42","doi-asserted-by":"crossref","unstructured":"Tahmassebi, A., Gandomi, A.H., McCann, I., Schulte, M.H., Goudriaan, A.E., Meyer-Baese, A.: Deep learning in medical imaging: fMRI big data analysis via convolutional neural networks. In: Proceedings of the Practice and Experience on Advanced Research Computing, pp. 1\u20134 (2018)","DOI":"10.1145\/3219104.3229250"},{"key":"14_CR43","doi-asserted-by":"publisher","first-page":"440","DOI":"10.3389\/fpsyt.2020.00440","volume":"11","author":"RM Thomas","year":"2020","unstructured":"Thomas, R.M., Gallo, S., Cerliani, L., Zhutovsky, P., El-Gazzar, A., Van Wingen, G.: Classifying autism spectrum disorder using the temporal statistics of resting-state functional MRI data with 3D convolutional neural networks. Front. Psych. 11, 440 (2020)","journal-title":"Front. Psych."},{"key":"14_CR44","doi-asserted-by":"publisher","first-page":"116137","DOI":"10.1016\/j.neuroimage.2019.116137","volume":"202","author":"JD Tournier","year":"2019","unstructured":"Tournier, J.D., et al.: Mrtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202, 116137 (2019)","journal-title":"Neuroimage"},{"key":"14_CR45","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.neubiorev.2017.01.002","volume":"74","author":"S Vieira","year":"2017","unstructured":"Vieira, S., Pinaya, W.H., Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci. Biobehav. Rev. 74, 58\u201375 (2017)","journal-title":"Neurosci. Biobehav. Rev."},{"key":"14_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/b95439","volume-title":"Support Vector Machines: Theory and Applications","author":"L Wang","year":"2005","unstructured":"Wang, L.: Support Vector Machines: Theory and Applications, vol. 177. Springer Science & Business Media, Berlin (2005)"},{"key":"14_CR47","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3389\/fninf.2018.00023","volume":"12","author":"D Wen","year":"2018","unstructured":"Wen, D., Wei, Z., Zhou, Y., Li, G., Zhang, X., Han, W.: Deep learning methods to process fMRI data and their application in the diagnosis of cognitive impairment: a brief overview and our opinion. Front. Neuroinform. 12, 23 (2018)","journal-title":"Front. Neuroinform."},{"key":"14_CR48","doi-asserted-by":"publisher","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","volume":"63","author":"J Wen","year":"2020","unstructured":"Wen, J., et al.: Convolutional neural networks for classification of Alzheimer\u2019s disease: Overview and reproducible evaluation. Med. Image Anal. 63, 101694 (2020)","journal-title":"Med. Image Anal."},{"key":"14_CR49","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1089\/brain.2012.0073","volume":"2","author":"S Whitfield-Gabrieli","year":"2012","unstructured":"Whitfield-Gabrieli, S., Nieto-Castanon, A.: CONN: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125\u201341 (2012). https:\/\/doi.org\/10.1089\/brain.2012.0073","journal-title":"Brain Connect."},{"issue":"1","key":"14_CR50","doi-asserted-by":"publisher","first-page":"S173","DOI":"10.1016\/j.neuroimage.2008.10.055","volume":"45","author":"MW Woolrich","year":"2009","unstructured":"Woolrich, M.W., et al.: Bayesian analysis of neuroimaging data in FSL. Neuroimage 45(1), S173\u2013S186 (2009)","journal-title":"Neuroimage"},{"key":"14_CR51","first-page":"100290","volume":"8","author":"X Yang","year":"2022","unstructured":"Yang, X., Zhang, N., Schrader, P.: A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. Mach. Learn. Appl. 8, 100290 (2022)","journal-title":"Mach. Learn. Appl."},{"key":"14_CR52","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.neucom.2020.05.113","volume":"469","author":"W Yin","year":"2022","unstructured":"Yin, W., Li, L., Wu, F.X.: Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing 469, 332\u2013345 (2022)","journal-title":"Neurocomputing"}],"container-title":["Communications in Computer and Information Science","ICT Innovations 2022. Reshaping the Future Towards a New Normal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22792-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:07:29Z","timestamp":1672538849000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22792-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031227912","9783031227929"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22792-9_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICT Innovations","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on ICT Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Skopje","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"North Macedonia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ictinnovations2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ictinnovations.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.5135","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.2037","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2 extended abstracts are in the preface","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}