{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:52:04Z","timestamp":1743007924189,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031417733"},{"type":"electronic","value":"9783031417740"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-41774-0_14","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T03:25:20Z","timestamp":1695266720000},"page":"175-188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classifying Chicken-Made Food Images Using Enhanced MobilNetV2"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1416-7138","authenticated-orcid":false,"given":"Abdulaziz","family":"Anorboev","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4656-0479","authenticated-orcid":false,"given":"Javokhir","family":"Musaev","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5038-7038","authenticated-orcid":false,"given":"Sarvinoz","family":"Anorboeva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4541-2255","authenticated-orcid":false,"given":"Jeongkyu","family":"Hong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3247-2948","authenticated-orcid":false,"given":"Ngoc Thanh","family":"Nguyen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5319-7674","authenticated-orcid":false,"given":"Yeong-Seok","family":"Seo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7851-7323","authenticated-orcid":false,"given":"Dosam","family":"Hwang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"14_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/978-3-319-48746-5_18","volume-title":"Ubiquitous Computing and Ambient Intelligence","author":"P McAllister","year":"2016","unstructured":"McAllister, P., Zheng, H., Bond, R., Moorhead, A.: Towards personalised training of machine learning algorithms for food image classification using a smartphone camera. In: Garc\u00eda, C.R., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds.) UCAmI 2016. LNCS, vol. 10069, pp. 178\u2013190. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48746-5_18"},{"key":"14_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/978-3-319-70742-6_36","volume-title":"New Trends in Image Analysis and Processing \u2013 ICIAP 2017","author":"G Waltner","year":"2017","unstructured":"Waltner, G., et al.: Personalized dietary self-management using mobile vision-based assistance. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 385\u2013393. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70742-6_36"},{"key":"14_CR3","doi-asserted-by":"publisher","first-page":"128732","DOI":"10.1109\/ACCESS.2022.3227796","volume":"10","author":"M Chun","year":"2022","unstructured":"Chun, M., Jeong, H., Lee, H., Yoo, T., Jung, H.: Development of Korean food image classification model using public food image dataset and deep learning methods. IEEE Access 10, 128732\u2013128741 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3227796","journal-title":"IEEE Access"},{"key":"14_CR4","doi-asserted-by":"publisher","first-page":"9339","DOI":"10.1109\/ACCESS.2023.3239658","volume":"11","author":"J Musaev","year":"2023","unstructured":"Musaev, J., Anorboev, A., Nguyen, N.T., Hwang, D.: KeepNMax: keep N maximum of epoch-channel ensemble method for deep learning models. IEEE Access 11, 9339\u20139350 (2023)","journal-title":"IEEE Access"},{"key":"14_CR5","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/978-3-030-88113-9_23","volume-title":"Advances in Computational Collective Intelligence","author":"J Musaev","year":"2021","unstructured":"Musaev, J., Nguyen, N.T., Hwang, D.: EMaxPPE: epoch\u2019s maximum prediction probability ensemble method for deep learning classification models. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds.) ICCCI 2021. LNCS, vol. 1463, pp. 293\u2013303. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88113-9_23"},{"unstructured":"Haas, R.B.J.C.N., Taubin, R.M.G.: Veggievision: a produce recognition system. IBM TJ Watson Research Center, PO Box, vol. 704 (2012)","key":"14_CR6"},{"doi-asserted-by":"crossref","unstructured":"Singla, A., Yuan, L., Ebrahimi, T.: Food\/non-food image classification and food categorization using pre-trained Googlenet model. In: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, pp. 3\u201311. ACM (2016)","key":"14_CR7","DOI":"10.1145\/2986035.2986039"},{"doi-asserted-by":"publisher","unstructured":"Islam, K.T., Wijewickrema, S., Pervez, M., O\u2019Leary, S.: An exploration of deep transfer learning for food image classification. In: Digital Image Computing: Techniques and Applications (DICTA), Canberra, ACT, Australia 2018, pp. 1\u20135 (2018). https:\/\/doi.org\/10.1109\/DICTA.2018.8615812","key":"14_CR8","DOI":"10.1109\/DICTA.2018.8615812"},{"doi-asserted-by":"publisher","unstructured":"Beijbom, O., Joshi, N., Morris, D., Saponas, S., Khullar, S.: Menu-Match: restaurant-specific food logging from images. In: 2015 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 2015, pp. 844\u2013851 (2015). https:\/\/doi.org\/10.1109\/WACV.2015.117","key":"14_CR9","DOI":"10.1109\/WACV.2015.117"},{"doi-asserted-by":"publisher","unstructured":"Barbon, S., Costa Barbon, A.P.A.D., Mantovani, R.G., Barbin, D.F.: Machine learning applied to near-infrared spectra for chicken meat classification. J. Spectrosc. 2018, 12 (2018). Article ID 8949741. https:\/\/doi.org\/10.1155\/2018\/8949741","key":"14_CR10","DOI":"10.1155\/2018\/8949741"},{"key":"14_CR11","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.infrared.2018.11.036","volume":"96","author":"BC Geronimo","year":"2019","unstructured":"Geronimo, B.C., et al.: Computer vision system and near-infrared spectroscopy for identification and classification of chicken with wooden breast, and physicochemical and technological characterization. Infrared Phys. Technol. 96, 303\u2013310 (2019)","journal-title":"Infrared Phys. Technol."},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1007\/s12161-019-01682-6","volume":"13","author":"E Mirzaee-Ghaleh","year":"2020","unstructured":"Mirzaee-Ghaleh, E., Taheri-Garavand, A., Ayari, F., et al.: Identification of fresh-chilled and frozen-thawed chicken meat and estimation of their shelf life using an e-nose machine coupled fuzzy KNN. Food Anal. Methods 13, 678\u2013689 (2020). https:\/\/doi.org\/10.1007\/s12161-019-01682-6","journal-title":"Food Anal. Methods"},{"key":"14_CR13","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1177\/0003702818788878","volume":"72","author":"IMN Perez","year":"2018","unstructured":"Perez, I.M.N., Badar\u00f3, A.T., Barbon, S., Barbon, A.P.A., Pollonio, M.A.R., Barbin, D.F.: Classification of chicken parts using a portable near-infrared (NIR) spectrophotometer and machine learning. Appl. Spectrosc. 72, 1774\u20131780 (2018)","journal-title":"Appl. Spectrosc."},{"issue":"2","key":"14_CR14","doi-asserted-by":"publisher","first-page":"496","DOI":"10.3390\/agriculture13020496","volume":"13","author":"Y Xiong","year":"2023","unstructured":"Xiong, Y., et al.: Non-destructive detection of chicken freshness based on electronic nose technology and transfer learning. Agriculture 13(2), 496 (2023). https:\/\/doi.org\/10.3390\/agriculture13020496","journal-title":"Agriculture"},{"doi-asserted-by":"publisher","unstructured":"Huang, G., et al.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017).https:\/\/doi.org\/10.1109\/cvpr.2017.243","key":"14_CR15","DOI":"10.1109\/cvpr.2017.243"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2015)","key":"14_CR16","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","key":"14_CR17","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"6","key":"14_CR18","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"issue":"5\u20136","key":"14_CR19","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1080\/01969722.2017.1418724","volume":"49","author":"VC Tran","year":"2018","unstructured":"Tran, V.C., Hwang, D., Nguyen, N.T.: Hashtag recommendation approach based on content and user characteristics. Cybern. Syst. 49(5\u20136), 368\u2013383 (2018). https:\/\/doi.org\/10.1080\/01969722.2017.1418724","journal-title":"Cybern. Syst."},{"issue":"2","key":"14_CR20","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1504\/IJIIDS.2007.014949","volume":"1","author":"L Sliwko","year":"2007","unstructured":"Sliwko, L., Nguyen, N.T.: Using multi-agent systems and consensus methods for information retrieval in internet. Int. J. Intell. Inf. Database Systems 1(2), 181\u2013198 (2007). https:\/\/doi.org\/10.1504\/IJIIDS.2007.014949","journal-title":"Int. J. Intell. Inf. Database Systems"},{"unstructured":"Nguyen N.T: Metody wyboru consensusu i ich zastosowanie w rozwia\u0327zywaniu konflikt\u00f3w w systemach rozproszonych. Oficyna Wydawnicza Politechniki Wroc\u0142awskiej (2002)","key":"14_CR21"}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41774-0_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:31:19Z","timestamp":1695277879000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41774-0_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031417733","9783031417740"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41774-0_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Budapest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hungary","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":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"218","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":"59","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":"27% - 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":"3.01","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":"1.86","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}