{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:33:04Z","timestamp":1743114784252,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031585340"},{"type":"electronic","value":"9783031585357"}],"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\/978-3-031-58535-7_11","type":"book-chapter","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T17:01:50Z","timestamp":1719939710000},"page":"128-138","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fusion of\u00a0Handcrafted Features and\u00a0Deep Features to\u00a0Detect COVID-19"],"prefix":"10.1007","author":[{"given":"Koushik","family":"Gunda","sequence":"first","affiliation":[]},{"given":"Soumendu","family":"Chakraborty","sequence":"additional","affiliation":[]},{"given":"Dubravko","family":"Culibrk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"issue":"12","key":"11_CR1","doi-asserted-by":"publisher","first-page":"246","DOI":"10.3390\/fi11120246","volume":"11","author":"S Albahli","year":"2019","unstructured":"Albahli, S.: A deep ensemble learning method for effort-aware just-in-time defect prediction. Future Internet 11(12), 246 (2019)","journal-title":"Future Internet"},{"issue":"4","key":"11_CR2","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1002\/jmv.26699","volume":"93","author":"R Alizadehsani","year":"2021","unstructured":"Alizadehsani, R., et al.: Risk factors prediction, clinical outcomes, and mortality in Covid-19 patients. J. Med. Virol. 93(4), 2307\u20132320 (2021)","journal-title":"J. Med. Virol."},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/BF03167769","volume":"5","author":"JM Boone","year":"1992","unstructured":"Boone, J.M., Seshagiri, S., Steiner, R.M.: Recognition of chest radiograph orientation for picture archiving and communications systems display using neural networks. J. Digit. Imaging 5, 190\u2013193 (1992)","journal-title":"J. Digit. Imaging"},{"key":"11_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.coastaleng.2019.103593","volume":"155","author":"D Buscombe","year":"2020","unstructured":"Buscombe, D., Carini, R.J., Harrison, S.R., Chickadel, C.C., Warrick, J.A.: Optical wave gauging using deep neural networks. Coast. Eng. 155, 103593 (2020)","journal-title":"Coast. Eng."},{"key":"11_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105339","volume":"172","author":"S Cao","year":"2020","unstructured":"Cao, S., Zhao, D., Liu, X., Sun, Y.: Real-time robust detector for underwater live crabs based on deep learning. Comput. Electron. Agric. 172, 105339 (2020)","journal-title":"Comput. Electron. Agric."},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Chakraborty, C., Abougreen, A.: Intelligent Internet of Things and advanced machine learning techniques for Covid-19. EAI Endorsed Trans. Pervasive Health Technol. 7(26) (2021)","DOI":"10.4108\/eai.28-1-2021.168505"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.patrec.2019.11.025","volume":"129","author":"ND Cilia","year":"2020","unstructured":"Cilia, N.D., De Stefano, C., Fontanella, F., Marrocco, C., Molinara, M., Di Freca, A.S.: An end-to-end deep learning system for medieval writer identification. Pattern Recogn. Lett. 129, 137\u2013143 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"11_CR9","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.neucom.2020.04.110","volume":"403","author":"Y Ding","year":"2020","unstructured":"Ding, Y., Zhu, Y., Feng, J., Zhang, P., Cheng, Z.: Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing 403, 348\u2013359 (2020)","journal-title":"Neurocomputing"},{"key":"11_CR10","unstructured":"Guan, W., et al.: Clinical characteristics of coronavirus disease 2019, pp. 1708\u20131720 (2020)"},{"key":"11_CR11","unstructured":"Howard, A., Zhmoginov, A., Chen, L.C., Sandler, M., Zhu, M.: Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation (2018)"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"11_CR13","unstructured":"Jian, S., Kaiming, H., Shaoqing, R., Xiangyu, Z.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 770\u2013778 (2016)"},{"issue":"11","key":"11_CR14","doi-asserted-by":"publisher","first-page":"3922","DOI":"10.3390\/s21113922","volume":"21","author":"S Lal","year":"2021","unstructured":"Lal, S., et al.: Adversarial attack and defence through adversarial training and feature fusion for diabetic retinopathy recognition. Sensors 21(11), 3922 (2021)","journal-title":"Sensors"},{"issue":"18","key":"11_CR15","doi-asserted-by":"publisher","first-page":"6189","DOI":"10.3390\/s21186189","volume":"21","author":"R Mahum","year":"2021","unstructured":"Mahum, R., et al.: A novel hybrid approach based on deep CNN features to detect knee osteoarthritis. Sensors 21(18), 6189 (2021)","journal-title":"Sensors"},{"issue":"1","key":"11_CR16","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.32604\/cmc.2022.018621","volume":"70","author":"K Manzoor","year":"2022","unstructured":"Manzoor, K., et al.: A lightweight approach for skin lesion detection through optimal features fusion. Comput. Mater. Continua 70(1), 1617\u20131630 (2022)","journal-title":"Comput. Mater. Continua"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"10737","DOI":"10.1007\/s00521-020-04870-2","volume":"33","author":"T Meraj","year":"2021","unstructured":"Meraj, T., et al.: Lung nodules detection using semantic segmentation and classification with optimal features. Neural Comput. Appl. 33, 10737\u201310750 (2021)","journal-title":"Neural Comput. Appl."},{"issue":"17","key":"11_CR18","doi-asserted-by":"publisher","first-page":"3722","DOI":"10.3390\/s19173722","volume":"19","author":"N Nasrullah","year":"2019","unstructured":"Nasrullah, N., Sang, J., Alam, M.S., Mateen, M., Cai, B., Hu, H.: Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors 19(17), 3722 (2019)","journal-title":"Sensors"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Orioli, L., Hermans, M.P., Thissen, J.P., Maiter, D., Vandeleene, B., Yombi, J.C.: Covid-19 in diabetic patients: related risks and specifics of management. In: Annales d\u2019endocrinologie, vol.\u00a081, pp. 101\u2013109. Elsevier (2020)","DOI":"10.1016\/j.ando.2020.05.001"},{"key":"11_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103545","volume":"116","author":"AG Pacheco","year":"2020","unstructured":"Pacheco, A.G., Krohling, R.A.: The impact of patient clinical information on automated skin cancer detection. Comput. Biol. Med. 116, 103545 (2020)","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"11_CR21","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1007\/s00530-021-00826-1","volume":"28","author":"V Ravi","year":"2022","unstructured":"Ravi, V., Narasimhan, H., Chakraborty, C., Pham, T.D.: Deep learning-based meta-classifier approach for Covid-19 classification using CT scan and chest X-ray images. Multimed. Syst. 28(4), 1401\u20131415 (2022)","journal-title":"Multimed. Syst."},{"key":"11_CR22","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"11_CR23","unstructured":"Somasundaram, K., Genish, T., et\u00a0al.: An atlas based approach to segment the hippocampus from MRI of human head scans for the diagnosis of Alzheimers disease. Int. J. Comput. Intell. Inform. 5(1) (2015)"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"1","key":"11_CR25","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s13089-009-0003-x","volume":"1","author":"J Yang","year":"2009","unstructured":"Yang, J., Zhang, M., Liu, Z., Ba, L., Gan, J., Xu, S.: Detection of lung atelectasis\/consolidation by ultrasound in multiple trauma patients with mechanical ventilation. Critical Ultrasound J. 1(1), 13\u201316 (2009)","journal-title":"Critical Ultrasound J."},{"key":"11_CR26","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.biosystemseng.2019.01.003","volume":"179","author":"X Zhuang","year":"2019","unstructured":"Zhuang, X., Zhang, T.: Detection of sick broilers by digital image processing and deep learning. Biosys. Eng. 179, 106\u2013116 (2019)","journal-title":"Biosys. Eng."}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58535-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T08:58:14Z","timestamp":1732352294000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58535-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031585340","9783031585357"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58535-7_11","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jammu","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iitjammu.ac.in\/cvip2023\/","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":"Online CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"461","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":"140","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":"30% - 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","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}