{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T01:51:09Z","timestamp":1742953869499,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031213847"},{"type":"electronic","value":"9783031213854"}],"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\/978-3-031-21385-4_22","type":"book-chapter","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T05:03:52Z","timestamp":1670907832000},"page":"252-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Efficient Detection of Brain Stroke Using Machine Learning Robust Classification"],"prefix":"10.1007","author":[{"given":"Shaik Abdul","family":"Nabi","sequence":"first","affiliation":[]},{"given":"Revathi","family":"Durgam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Ieracitano, C., Mammone, N., Hussain, A., Morabito, F.C.: A novel multi-modal machine learning based approach for automatic classification of eeg recordings in dementia.\u00a0Elsevier, p, 1\u201335 (2019)","DOI":"10.1016\/j.neunet.2019.12.006"},{"key":"22_CR2","doi-asserted-by":"publisher","unstructured":"Pradeepa, S., Manjula, K.R., Vimal, S., Khan, M.S., Chilamkurti, N., Luhach, A.K.: DRFS: detecting risk factor of stroke disease from social media using machine learning techniques. Neural Process. Lett. (2020). https:\/\/doi.org\/10.1007\/s11063-020-10279-8","DOI":"10.1007\/s11063-020-10279-8"},{"key":"22_CR3","unstructured":"Lu, C.-F., et al.: Machine learning-based radiomics for molecular subtyping of gliomas. Clin. Cancer Res. 1\u201334 (2018)"},{"issue":"4","key":"22_CR4","doi-asserted-by":"publisher","first-page":"178","DOI":"10.3390\/diagnostics9040178","volume":"9","author":"W Chang","year":"2019","unstructured":"Chang, W., et al.: A machine-learning-based prediction method for hypertension outcomes based on medical data. Diagnostics 9(4), 178 (2019)","journal-title":"Diagnostics"},{"issue":"9","key":"22_CR5","doi-asserted-by":"publisher","first-page":"2632","DOI":"10.1007\/s00415-020-09859-4","volume":"267","author":"HC Kniep","year":"2020","unstructured":"Kniep, H.C., Sporns, P.B., Broocks, G., Kemmling, A., Nawabi, J., Rusche, T., Fiehler, J., Hanning, U.: Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans. J. Neurol. 267(9), 2632\u20132641 (2020). https:\/\/doi.org\/10.1007\/s00415-020-09859-4","journal-title":"J. Neurol."},{"issue":"1","key":"22_CR6","first-page":"1","volume":"40","author":"A Subudhi","year":"2019","unstructured":"Subudhi, A., Dash, M., Sabut, S.: Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. Biocybernetics Biomed. Eng. 40(1), 1\u201313 (2019)","journal-title":"Biocybernetics Biomed. Eng."},{"key":"22_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-50478-0_1","volume-title":"Machine Learning for Health Informatics","author":"A Holzinger","year":"2016","unstructured":"Holzinger, A.: Machine learning for health informatics. In: Holzinger, A. (ed.) Machine Learning for Health Informatics. LNCS (LNAI), vol. 9605, pp. 1\u201324. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50478-0_1"},{"issue":"4","key":"22_CR8","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449\u2013459 (2017). https:\/\/doi.org\/10.1007\/s10278-017-9983-4","journal-title":"J. Digit. Imaging"},{"issue":"10","key":"22_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jstrokecerebrovasdis.2020.105162","volume":"29","author":"MS Sirsat","year":"2020","unstructured":"Sirsat, M.S., Ferm\u00e9, E., C\u00e2mara, J.: Machine learning for brain stroke: a review. J. Stroke Cerebrovasc. Dis. 29(10), 1\u201317 (2020)","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"179457","DOI":"10.1109\/ACCESS.2020.3026350","volume":"8","author":"VB Shim","year":"2020","unstructured":"Shim, V.B., et al.: Rapid prediction of brain injury pattern in mTBI by combining FE analysis with a machine-learning based approach. IEEE Access 8, 179457\u2013179465 (2020)","journal-title":"IEEE Access"},{"issue":"3","key":"22_CR11","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1016\/j.bbe.2021.05.013","volume":"41","author":"SH Kassania","year":"2021","unstructured":"Kassania, S.H., Kassanib, P.H., Wesolowskic, M.J., Schneidera, K.A., Detersa, R.: Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach. Biocybernetics Biomed. Eng. 41(3), 867\u2013879 (2021)","journal-title":"Biocybernetics Biomed. Eng."},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1016\/j.nicl.2016.09.018","volume":"12","author":"Y Chen","year":"2016","unstructured":"Chen, Y., et al.: Automated quantification of cerebral edema following hemispheric infarction: application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs. NeuroImage: Clin. 12, 673\u2013680 (2016)","journal-title":"NeuroImage: Clin."},{"key":"22_CR13","doi-asserted-by":"publisher","unstructured":"Amin, J., Sharif, M., Raza, M. Yasmin, M.: Detection of brain tumor based on features fusion and machine learning. J. Ambient Intell. Humanized Comput. 117 (2018). https:\/\/doi.org\/10.1007\/s12652-018-1092-9","DOI":"10.1007\/s12652-018-1092-9"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Newaz, A.I., Sikder, A.K., Rahman, M.A., Uluagac, A.S.: HealthGuard: a machine learning-based security framework for smart healthcare systems. In: IEEE 2019 6th International Conference on Social Networks Analysis, Management and Security (SNAMS), Granada, Spain, 22-25 October 2019, pp. 389\u2013396 (2019)","DOI":"10.1109\/SNAMS.2019.8931716"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Khosla, A., Cao, Y., Lin, C.C.-Y., Chiu, H.-K., Hu, J., Lee, H.: An integrated machine learning approach to stroke prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201810, 25\u201328 July 2010, pp. 1\u20139 (2010)","DOI":"10.1145\/1835804.1835830"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Kohlschein, C., Schmitt, M., Schuller, B., Jeschke, S., Werner, C.J.: A machine learning based system for the automatic evaluation of aphasia speech. In: IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), 1215 October 2017, pp. 1\u20136 (2017)","DOI":"10.1109\/HealthCom.2017.8210766"},{"key":"22_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/6632956","volume":"2020","author":"H Chen","year":"2020","unstructured":"Chen, H., Khan, S., Kou, B., Nazir, S., Liu, W., Hussain, A.: A smart machine learning model for the detection of brain hemorrhage diagnosis based internet of things in smart cities. Complexity 2020, 1\u201310 (2020)","journal-title":"Complexity"},{"issue":"7","key":"22_CR18","doi-asserted-by":"publisher","first-page":"914","DOI":"10.3390\/healthcare9070914","volume":"9","author":"A Ramachandran","year":"2021","unstructured":"Ramachandran, A., Karuppiah, A.: A survey on recent advances in machine learning based sleep apnea detection systems. Healthcare 9(7), 914 (2021)","journal-title":"Healthcare"},{"key":"22_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/01616412.2019.1609159","volume":"41","author":"H Saber","year":"2019","unstructured":"Saber, H., Somai, M., Rajah, G.B., Scalzo, F., Liebeskind, D.S.: Predictive analytics and machine learning in stroke and neurovascular medicine. Neurol. Res. 41, 1\u201310 (2019)","journal-title":"Neurol. Res."},{"key":"22_CR20","doi-asserted-by":"publisher","unstructured":"Abdul, S., Rasool, S., Premchand, P.: Detection and extraction of videos using decision trees. Int. J. Adv. Comput. Sci. Appl. 2(12) (2011).https:\/\/doi.org\/10.14569\/IJACSA.2011.021222","DOI":"10.14569\/IJACSA.2011.021222"},{"key":"22_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2018.10.016","volume":"103","author":"A Subudhi","year":"2018","unstructured":"Subudhi, A., Acharya, U.R., Dash, M., Jena, S., Sabut, S.: Automated approach for detection of ischemic stroke using delaunay triangulation in brain MRI images. Comput. Biol. Med. 103, 1\u201335 (2018)","journal-title":"Comput. Biol. Med."},{"key":"22_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMI.2019.2901445","volume":"38","author":"KC Ho","year":"2019","unstructured":"Ho, K.C., Speier, W., Zhang, H., Scalzo, F., El-Saden, S., Arnold, C.W.: A machine learning approach for classifying ischemic stroke onset time from imaging. IEEE Trans. Med. Imaging 38, 1 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"22_CR23","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1186\/s12889-020-09766-3","volume":"20","author":"J Ji","year":"2020","unstructured":"Ji, J., Hu, L., Liu, B., et al.: Identifying and assessing the impact of key neighborhood-level determinants on geographic variation in stroke: a machine learning and multilevel modeling approach. BMC Public Health 20, 1666 (2020). https:\/\/doi.org\/10.1186\/s12889-020-09766-3","journal-title":"BMC Public Health"},{"key":"22_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1161\/STROKEAHA.120.029305","volume":"51","author":"H Kamel","year":"2020","unstructured":"Kamel, H., et al.: Machine learning prediction of stroke mechanism in embolic strokes of undetermined source. Stroke 51, 1\u20138 (2020)","journal-title":"Stroke"},{"issue":"7","key":"22_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/math8071115","volume":"8","author":"J Yu","year":"2020","unstructured":"Yu, J., Park, S., Lee, H., Pyo, C.-S., Lee, Y.S.: An elderly health monitoring system using machine learning and in-depth analysis techniques on the NIH stroke scale. Mathematics 8(7), 1\u201316 (2020)","journal-title":"Mathematics"},{"issue":"6","key":"22_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0234908","volume":"15","author":"CJ Ong","year":"2020","unstructured":"Ong, C.J.: Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports. PLoS ONE 15(6), 1\u201316 (2020)","journal-title":"PLoS ONE"},{"key":"22_CR27","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1007\/978-981-16-5301-8_60","volume-title":"Soft Computing for Security Applications","author":"R Durgam","year":"2022","unstructured":"Durgam, R., Devarakonda, N., Nayyar, A., Eluri, R.: Improved genetic algorithm using machine learning approaches to feature modelled for microarray gene data. In: Ranganathan, G., Fernando, X., Shi, F., El Allioui, Y. (eds.) Soft Computing for Security Applications. AISC, vol. 1397, pp. 859\u2013872. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-5301-8_60"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Nabi, S.A., Laxmi, K.R.:Prediction accuracy model aiming to improve prediction accuracy in congenital heart anomaly detection using hybrid feature selection with modified particle swarm optimization approach. In: Journal of Physics: Conference Series,\u00a0Vol. 1998,\u00a03rd International Conference on Smart and Intelligent Learning for Information Optimization (CONSILIO 2021), Hyderabad, India, 9\u201310 July 2021","DOI":"10.1088\/1742-6596\/1998\/1\/012011"},{"issue":"3","key":"22_CR29","first-page":"22","volume":"9","author":"A Kumar","year":"2019","unstructured":"Kumar, A.: Design of secure image fusion technique using cloud for privacy-preserving and copyright protection. Int. J. Cloud Appl. Comput. (IJCAC) 9(3), 22\u201336 (2019)","journal-title":"Int. J. Cloud Appl. Comput. (IJCAC)"},{"issue":"1","key":"22_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13638-020-01826-x","volume":"2020","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Zhang, Z.J., Lyu, H.: Object detection in real time based on improved single shot multi-box detector algorithm. EURASIP J. Wirel. Commun. Netw. 2020(1), 1\u201318 (2020). https:\/\/doi.org\/10.1186\/s13638-020-01826-x","journal-title":"EURASIP J. Wirel. Commun. Netw."}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21385-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T15:27:03Z","timestamp":1691594823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21385-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031213847","9783031213854"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21385-4_22","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":"14 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hyderabad","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaids2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icaids.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":"195","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":"43","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":"0","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":"22% - 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":"4","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":"4","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)"}}]}}