{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T06:15:22Z","timestamp":1774160122537,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031440830","type":"print"},{"value":"9783031440847","type":"electronic"}],"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\/978-3-031-44084-7_2","type":"book-chapter","created":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T09:02:09Z","timestamp":1695459729000},"page":"13-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Detection of Chicken Disease Based on Day-Age Using Pre Trained Model of CNN"],"prefix":"10.1007","author":[{"given":"K.","family":"Sreenivasulu","sequence":"first","affiliation":[]},{"given":"H. Aini Sosan","family":"Khan","sequence":"additional","affiliation":[]},{"given":"K.","family":"Damini","sequence":"additional","affiliation":[]},{"given":"M.","family":"Akhila","sequence":"additional","affiliation":[]},{"given":"G.","family":"Bharathi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,24]]},"reference":[{"issue":"23","key":"2_CR1","doi-asserted-by":"publisher","first-page":"13396","DOI":"10.3390\/su132313396","volume":"13","author":"G Ahmed","year":"2021","unstructured":"Ahmed, G., Malick, R.A.S., Akhunzada, A., Zahid, S., Sagri, M.R., Gani, A.: An approach towards IoT-based predictive service for early detection of diseases in poultry chickens. Sustainability 13(23), 13396 (2021)","journal-title":"Sustainability"},{"key":"2_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106740","volume":"194","author":"K Cuan","year":"2022","unstructured":"Cuan, K., Zhang, T., Li, Z., Huang, J., Ding, Y., Fang, C.: Automatic newcastle disease detection using sound technology and deep learning method. Comput. Electron. Agric. 194, 106740 (2022)","journal-title":"Comput. Electron. Agric."},{"key":"2_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.110819","volume":"191","author":"S Neethirajan","year":"2022","unstructured":"Neethirajan, S.: ChickTrack\u2013a quantitative tracking tool for measuring chicken activity. Measurement 191, 110819 (2022)","journal-title":"Measurement"},{"key":"2_CR4","first-page":"1607","volume":"166","author":"AI Adebiyi","year":"2021","unstructured":"Adebiyi, A.I., Mcilwaine, K., Oluwayelu, D.O., Smyth, V.J.: Detection and characterization of chicken astrovirus associated with hatchery disease in commercial day-old turkeys in southwestern Nigeria. Adv. Virol. 166, 1607\u20131614 (2021)","journal-title":"Adv. Virol."},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Bakar, M.A.A., Ker, P.J., Tang, S.G., Lee, H.J., Zainal, B.S.: Classification of unhealthy chicken based on chromaticity of the comb. In: 2022 IEEE International Conference on Computing (ICOCO), pp. 1\u20135. IEEE (2022)","DOI":"10.1109\/ICOCO56118.2022.10031812"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Thavamani, S., Vijayakumar, J., Sruthi, K.: GLCM and K-means based chicken gender classification. In: 2021 Smart Technologies, Communication and Robotics (STCR), pp. 1\u20135. IEEE (2021)","DOI":"10.1109\/STCR51658.2021.9588864"},{"issue":"1","key":"2_CR7","first-page":"1","volume":"4","author":"DA Almashhadany","year":"2021","unstructured":"Almashhadany, D.A.: Detection of antimicrobial residues among chicken meat by simple, reliable, and highly specific techniques. SVU-Int. J. Vet. Sci. 4(1), 1\u20139 (2021)","journal-title":"SVU-Int. J. Vet. Sci."},{"issue":"1","key":"2_CR8","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1186\/s12951-022-01376-y","volume":"20","author":"K Gu","year":"2022","unstructured":"Gu, K.: Development of nanobody-horseradish peroxidase-based sandwich ELISA to detect Salmonella Enteritidis in milk and in vivo colonization in chicken. J. Nanobiotechnol. 20(1), 167 (2022)","journal-title":"J. Nanobiotechnol."},{"key":"2_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jviromet.2021.114065","volume":"291","author":"S Syahruni","year":"2021","unstructured":"Syahruni, S.: Development of lateral flow assay based on anti-IBDV IgY for the rapid detection of Gumboro disease in poultry. J. Virol. Methods 291, 114065 (2021)","journal-title":"J. Virol. Methods"},{"issue":"4","key":"2_CR10","doi-asserted-by":"publisher","first-page":"614","DOI":"10.54203\/scil.2021.wvj78","volume":"11","author":"MSI Basit","year":"2021","unstructured":"Basit, M.S.I., Mamun, M.A., Rahman, M.M., Noor, M.: Isolation and molecular detection of mycoplasma gallisepticum in commercial layer chickens in Sylhet, Bangladesh. World\u2019s Vet. J. 11(4), 614\u2013620 (2021)","journal-title":"World\u2019s Vet. J."},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"112689","DOI":"10.1016\/j.bios.2020.112689","volume":"171","author":"P Vizzini","year":"2021","unstructured":"Vizzini, P., et al.: Highly sensitive detection of campylobacter spp In chicken meat using a silica nanoparticle enhanced dot blot DNA biosensor. Biosens. Bioelectron. 171, 112689 (2021)","journal-title":"Biosens. Bioelectron."},{"issue":"11","key":"2_CR12","doi-asserted-by":"publisher","first-page":"4331","DOI":"10.47191\/ijcsrr\/V5-i11-31","volume":"5","author":"M Saikia","year":"2022","unstructured":"Saikia, M., Bhattacharjee, K., Sarmah, P.C., Deka, D.K.: Comparative evaluation of direct smear and culture methods for detection of trichomonas gallinae infection in pigeon and chicken of Assam. Int. J. Curr. Sci. Res. Rev. 5(11), 4331\u20134335 (2022)","journal-title":"Int. J. Curr. Sci. Res. Rev."},{"key":"2_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107501","volume":"204","author":"J Yang","year":"2023","unstructured":"Yang, J., Zhang, T., Fang, C., Zheng, H.: A defencing algorithm based on deep learning improves the detection accuracy of caged chickens. Comput. Electron. Agric. 204, 107501 (2023)","journal-title":"Comput. Electron. Agric."},{"key":"2_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2022.123807","volume":"253","author":"J Sun","year":"2023","unstructured":"Sun, J.: Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm. Talanta 253, 123807 (2023)","journal-title":"Talanta"},{"issue":"6","key":"2_CR15","doi-asserted-by":"publisher","first-page":"3002","DOI":"10.3390\/s23063002","volume":"23","author":"S Cakic","year":"2023","unstructured":"Cakic, S., Popovic, T., Krco, S., Nedic, D., Babic, D., Jovovic, I.: Developing edge AI computer vision for smart poultry farms using deep learning and HPC. Sensors 23(6), 3002 (2023)","journal-title":"Sensors"},{"issue":"3","key":"2_CR16","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3390\/cells12030379","volume":"12","author":"C Tao","year":"2023","unstructured":"Tao, C., Du, J., Wang, J., Hu, B., Zhang, Z.: Rapid identification of infectious pathogens at the single-cell level via combining hyperspectral microscopic images and deep learning. Cells 12(3), 379 (2023)","journal-title":"Cells"},{"key":"2_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107545","volume":"204","author":"S Subedi","year":"2023","unstructured":"Subedi, S., Bist, R., Yang, X., Chai, L.: Tracking pecking behaviors and damages of cage-free laying hens with machine vision technologies. Comput. Electron. Agric. 204, 107545 (2023)","journal-title":"Comput. Electron. Agric."},{"issue":"18","key":"2_CR18","doi-asserted-by":"publisher","first-page":"2425","DOI":"10.3390\/ani12182425","volume":"12","author":"Y Ren","year":"2022","unstructured":"Ren, Y., et al.: A high-performance day-age classification and detection model for chick based on attention encoder and convolutional neural network. Animals 12(18), 2425 (2022)","journal-title":"Animals"},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"issue":"27","key":"2_CR20","doi-asserted-by":"publisher","first-page":"e7310","DOI":"10.1002\/cpe.7310","volume":"34","author":"JR Dwaram","year":"2022","unstructured":"Dwaram, J.R., Madapuri, R.K.: Crop yield forecasting by long short\u2010term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India. Concurrency Comput.: Pract. Experience 34(27), e7310 (2022)","journal-title":"Concurrency Comput.: Pract. Experience"},{"key":"2_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/8777026","volume":"2022","author":"MM Venkata Chalapathi","year":"2022","unstructured":"Venkata Chalapathi, M.M., Rudra Kumar, M., Sharma, N., Shitharth, S.: Ensemble learning by high-dimensional acoustic features for emotion recognition from speech audio signal. Secur. Commun. Netw. 2022, 1\u201310 (2022). https:\/\/doi.org\/10.1155\/2022\/8777026","journal-title":"Secur. Commun. Netw."},{"key":"2_CR22","unstructured":"Inoue, H.: Data augmentation by pairing samples for images classification. arXiv 2018, arXiv:1801.02929"},{"key":"2_CR23","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/978-981-16-8484-5_10","volume-title":"Computational Intelligence in Machine Learning: Select Proceedings of ICCIML 2021","author":"M Rudra Kumar","year":"2022","unstructured":"Rudra Kumar, M., Pathak, R., Gunjan, V.K.: Diagnosis and medicine prediction for covid-19 using machine learning approach. In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds.) Computational Intelligence in Machine Learning: Select Proceedings of ICCIML 2021, pp. 123\u2013133. Springer Nature Singapore, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-8484-5_10"},{"key":"2_CR24","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-981-16-8484-5_1","volume-title":"Computational Intelligence in Machine Learning","author":"M Rudra Kumar","year":"2022","unstructured":"Rudra Kumar, M., Pathak, R., Gunjan, V.K.: Machine learning-based project resource allocation fitment analysis system (ML-PRAFS). In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds.) Computational Intelligence in Machine Learning. LNEE, vol. 834, pp. 1\u201314. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-8484-5_1"},{"key":"2_CR25","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, NV, USA"}],"container-title":["Lecture Notes in Computer Science","Mining Intelligence and Knowledge Exploration"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44084-7_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T20:39:19Z","timestamp":1730147959000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44084-7_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440830","9783031440847"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44084-7_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"24 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mining Intelligence and Knowledge Exploration","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kristiansand","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","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":"28 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mike2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mike.org.in\/2023\/","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":"87","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":"22","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":"16","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":"25% - 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":"3-4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}