{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T09:47:13Z","timestamp":1769766433134,"version":"3.49.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031530845","type":"print"},{"value":"9783031530852","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53085-2_8","type":"book-chapter","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T18:02:40Z","timestamp":1706551360000},"page":"82-96","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning Algorithms"],"prefix":"10.1007","author":[{"given":"Amit","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neha","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahul","family":"Chauhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akhilendra","family":"Khare","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhineet","family":"Anand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manish","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.vph.2016.11.008","volume":"89","author":"A Chakraborty","year":"2016","unstructured":"Chakraborty, A., de Wit, N.M., van der Flier, W.M., de Vries, H.E.: The blood brain barrier in Alzheimer\u2019s disease. Vasc. Pharmacol. 89, 12\u201318 (2016)","journal-title":"Vasc. Pharmacol."},{"issue":"3","key":"8_CR2","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1177\/0891988706291081","volume":"19","author":"JC Breitner","year":"2006","unstructured":"Breitner, J.C.: Dementia\u2014epidemiological considerations, nomenclature, and a tacit consensus definition. J. Geriatr. Psychiatry Neurol. 19(3), 129\u2013136 (2006)","journal-title":"J. Geriatr. Psychiatry Neurol."},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1007\/s11682-015-9468-3","volume":"10","author":"A Kiraly","year":"2016","unstructured":"Kiraly, A., Szabo, N., Toth, E., et al.: Male brain ages faster: the age and gender dependence of subcortical volumes. Brain Imaging Behav. 10, 901\u2013910 (2016)","journal-title":"Brain Imaging Behav."},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/978-3-540-79982-5_14","volume":"5128","author":"L Mesrob","year":"2008","unstructured":"Mesrob, L., Magnin, B., Colliot, O., et al.: Identification of atrophy patterns in alzheimer\u2019s disease based on SVM feature selection and anatomical parcellation. Med. Imaging Augmented Reality 5128, 124\u2013132 (2008)","journal-title":"Med. Imaging Augmented Reality"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Nusinovici, S., et al.: Logistic regression was as good as machine learning for predicting major chronic diseases. J. Clin. Epidemiol. 122, 56\u201369 (2020)","DOI":"10.1016\/j.jclinepi.2020.03.002"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Maliha, S.K., Ema, R.R., Ghosh, S.K., Ahmed, H., Mollick, M.R.J., Islam, T.: Cancer disease prediction using naive bayes, K-nearest neighbor and J48 algorithm. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1\u20137. IEEE, July 2019","DOI":"10.1109\/ICCCNT45670.2019.8944686"},{"key":"8_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ceh.2020.11.002","volume":"4","author":"M Desai","year":"2021","unstructured":"Desai, M., Shah, M.: An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and convolutional neural network (CNN). Clin. eHealth 4, 1\u201311 (2021)","journal-title":"Clin. eHealth"},{"key":"8_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1400-8","volume":"43","author":"A Murugan","year":"2019","unstructured":"Murugan, A., Nair, S.A.H., Kumar, K.S.: Detection of skin cancer using SVM, random forest and kNN classifiers. J. Med. Syst. 43, 1\u20139 (2019)","journal-title":"J. Med. Syst."},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.procs.2017.09.088","volume":"115","author":"KS Biju","year":"2017","unstructured":"Biju, K.S., Alfa, S.S., Lal, K., Antony, A., Akhil, M.K.: Alzheimer\u2019s detection based on segmentation of MRI image. Procedia Comput. Sci. 115, 474\u2013481 (2017)","journal-title":"Procedia Comput. Sci."},{"issue":"10","key":"8_CR10","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1016\/S1474-4422(15)00093-9","volume":"14","author":"S Teipel","year":"2015","unstructured":"Teipel, S., et al.: Multimodal imaging in Alzheimer\u2019s disease: validity and usefulness for early detection. Lancet Neurol. 14(10), 1037\u20131053 (2015)","journal-title":"Lancet Neurol."},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Jouffe, L.: Fuzzy inference system learning by reinforcement methods. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 28(3), 338\u2013355 (1998)","DOI":"10.1109\/5326.704563"},{"issue":"1","key":"8_CR12","first-page":"65","volume":"3","author":"G Katti","year":"2011","unstructured":"Katti, G., Ara, S.A., Shireen, A.: Magnetic resonance imaging (MRI)\u2013a review. Int. J. Dent. Clin. 3(1), 65\u201370 (2011)","journal-title":"Int. J. Dent. Clin."},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Kukreja, V., Dhiman, P.: A Deep Neural Network based disease detection scheme for citrus fruits. In: 2020 International Conference on Smart Electronics and Communication (ICOSEC), pp. 97\u2013101. IEEE, September 2020","DOI":"10.1109\/ICOSEC49089.2020.9215359"},{"issue":"3","key":"8_CR14","doi-asserted-by":"publisher","first-page":"495","DOI":"10.3390\/electronics11030495","volume":"11","author":"P Dhiman","year":"2022","unstructured":"Dhiman, P., et al.: A novel deep learning model for detection of severity level of the disease in citrus fruits. Electronics 11(3), 495 (2022)","journal-title":"Electronics"},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"Panwar, A., Yadav, R., Mishra, K., Gupta, S.: Deep learning techniques for the real time detection of Covid19 and pneumonia using chest radiographs. In: Proceedings of 19th IEEE International Conference on Smart Technologies, EUROCON 2021, pp. 250\u2013253 (2021). https:\/\/doi.org\/10.1109\/EUROCON52738.2021.9535604","DOI":"10.1109\/EUROCON52738.2021.9535604"},{"issue":"4","key":"8_CR16","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s00530-020-00694-1","volume":"27","author":"C Bhatt","year":"2021","unstructured":"Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K.U., Kumar, A.: The state of the art of deep learning models in medical science and their challenges. Multimed. Syst. 27(4), 599\u2013613 (2021). https:\/\/doi.org\/10.1007\/s00530-020-00694-1","journal-title":"Multimed. Syst."},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Sharma, N., Chakraborty, C., Kumar, R.: Optimized multimedia data through computationally intelligent algorithms. Multimedia Syst. 1\u201317 (2022)","DOI":"10.1007\/s00530-022-00918-6"}],"container-title":["Communications in Computer and Information Science","Recent Trends in Image Processing and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53085-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T08:03:01Z","timestamp":1712304181000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53085-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031530845","9783031530852"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53085-2_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"30 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RTIP2R","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Recent Trends in Image Processing and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Derby","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rtip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rtip2r-conference.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT, Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"216","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":"62","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":"29% - 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":"2.39","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":"2.79","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)"}}]}}