{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:30:40Z","timestamp":1742959840977,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030630829"},{"type":"electronic","value":"9783030630836"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-63083-6_15","type":"book-chapter","created":{"date-parts":[[2020,11,20]],"date-time":"2020-11-20T16:18:09Z","timestamp":1605889089000},"page":"195-205","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning Based Hyperspectral Images Analysis for Shrimp Contaminated Detection"],"prefix":"10.1007","author":[{"given":"Minh-Hieu","family":"Nguyen","sequence":"first","affiliation":[]},{"given":"Xuan-Huyen","family":"Nguyen-Thi","sequence":"additional","affiliation":[]},{"given":"Cong-Nguyen","family":"Pham","sequence":"additional","affiliation":[]},{"given":"Ngoc C.","family":"L\u00ea","sequence":"additional","affiliation":[]},{"given":"Huy-Dung","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,21]]},"reference":[{"key":"15_CR1","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Schelkanova, I., Pandya, A., Muhaseen, A., Saiko, G., Douplik, A.: 13 - early optical diagnosis of pressure ulcers. In: Igor, M. (ed.) Biophotonics for Medical Applications, pp. 347\u2013375. Woodhead Publishing (2015)","DOI":"10.1016\/B978-0-85709-662-3.00013-0"},{"key":"15_CR3","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/B978-0-12-802838-4.00016-9","volume-title":"Imaging in Dermatology","author":"F Vasefi","year":"2016","unstructured":"Vasefi, F., MacKinnon, N., Farkas, D.L.: Chapter 16 - hyperspectral and multispectral imaging in dermatology. In: Hamblin, M.R., Avci, P., Gupta, G.K. (eds.) Imaging in Dermatology, pp. 187\u2013201. Academic Press, Boston (2016)"},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1007\/s12161-017-1050-8","volume":"11","author":"X Yu","year":"2018","unstructured":"Yu, X., Tang, L., Wu, X., Lu, H.: Nondestructive freshness discriminating of shrimp using visible\/near-infrared hyperspectral imaging technique and deep learning algorithm. J. Food Anal. Methods 11, 768\u2013780 (2018)","journal-title":"J. Food Anal. Methods"},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"63","DOI":"10.3390\/jimaging4050063","volume":"4","author":"M Al-Sarayreh","year":"2018","unstructured":"Al-Sarayreh, M., Reis, M., Yan, W., Klette, R.: Detection of red-meat adulteration by deep spectral-spatial features in hyperspectral images. J. Imaging 4, 63 (2018)","journal-title":"J. Imaging"},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"Li, X., Li, R., Wang, M., Liu, Y., Zhang, B., Zhou, J.C.: Hyperspectral imaging and their applications in the nondestructive quality assessment of fruits and vegetables (2017). https:\/\/doi.org\/10.5772\/intechopen.72250","DOI":"10.5772\/intechopen.72250"},{"key":"15_CR7","unstructured":"Specim: Specim FX10 - user guide 1.0. Specim imaging Oy Ltd"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Li, Y., Zhang, H., Xue, X., Jiang, Y., Shen, Q.: Deep learning for remote sensing image classification: a survey. In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, p. e1264 (May 2018). https:\/\/doi.org\/10.1002\/widm.1264","DOI":"10.1002\/widm.1264"},{"key":"15_CR9","series-title":"Intelligent Systems Reference Library","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/978-3-030-32606-7_3","volume-title":"Deep Learning in Healthcare","author":"W Wang","year":"2020","unstructured":"Wang, W., et al.: Medical image classification using deep learning. In: Chen, Y.-W., Jain, L.C. (eds.) Deep Learning in Healthcare. ISRL, vol. 171, pp. 33\u201351. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-32606-7_3"},{"key":"15_CR10","unstructured":"Lu, Y.: Food image recognition by using convolutional neural networks (CNNs) (December 2016)"},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"e13041","DOI":"10.1111\/jfpe.13041","volume":"42","author":"N Thanasarn","year":"2019","unstructured":"Thanasarn, N., Chaiprapat, S., Waiyakan, K., Thongkaew, K.: Automated discrimination of deveined shrimps based on grayscale image parameters. J. Food Process Eng. 42, e13041 (2019). https:\/\/doi.org\/10.1111\/jfpe.13041","journal-title":"J. Food Process Eng."},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Sural, S., Qian, G., Pramanik, S.: Segmentation and histogram generation using the HSV color space for image retrieval. In: Proceedings of International Conference on Image Processing, vol. 2, pp. II-589 (February 2002). https:\/\/doi.org\/10.1109\/ICIP.2002.1040019","DOI":"10.1109\/ICIP.2002.1040019"},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","volume":"77","author":"B Russell","year":"2008","unstructured":"Russell, B., Torralba, A., Murphy, K., Freeman, W.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77, 157\u2013173 (2008). https:\/\/doi.org\/10.1007\/s11263-007-0090-8","journal-title":"Int. J. Comput. Vis."},{"issue":"6789","key":"15_CR14","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1038\/35016072","volume":"405","author":"R Hahnloser","year":"2000","unstructured":"Hahnloser, R., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., Seung, H.S.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789), 947\u2013951 (2000)","journal-title":"Nature"},{"key":"15_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/3-540-59497-3_175","volume-title":"From Natural to Artificial Neural Computation","author":"J Han","year":"1995","unstructured":"Han, J., Moraga, C.: The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Mira, J., Sandoval, F. (eds.) IWANN 1995. LNCS, vol. 930, pp. 195\u2013201. Springer, Heidelberg (1995). https:\/\/doi.org\/10.1007\/3-540-59497-3_175"},{"key":"15_CR16","unstructured":"Kingma, P., Lei Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980v9 (2014)"},{"key":"15_CR17","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Industrial Networks and Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63083-6_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T21:21:16Z","timestamp":1619299276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-63083-6_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030630829","9783030630836"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63083-6_15","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"21 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"INISCOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Industrial Networks and Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","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":"iniscom2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iniscom.eai-conferences.org\/2020\/","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":"Confy","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","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":"26","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":"44% - 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":"3.2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID-19 pandemic the conference was held virtually.","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)"}}]}}