{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:51:58Z","timestamp":1742968318283,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030199449"},{"type":"electronic","value":"9783030199456"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-19945-6_16","type":"book-chapter","created":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T11:53:20Z","timestamp":1557489200000},"page":"238-246","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards a Better Compromise Between Shallow and Deep CNN for Binary Classification Problems of Unstructured Data"],"prefix":"10.1007","author":[{"given":"Khadoudja","family":"Ghanem","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,10]]},"reference":[{"key":"16_CR1","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. arXiv:1409.0575 (2014). ImageNet https:\/\/github.com\/itf\/imagenet-download"},{"key":"16_CR2","volume-title":"Deep Learning with Python","author":"F Chollet","year":"2018","unstructured":"Chollet, F.: Deep Learning with Python. Manning Publications, USA (2018)"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"van de Wolfshaar, J., Karaaba, M.F., Wiering, M.A.: Deep convolutional neural networks and support vector machines for gender recognition. In: IEEE Symposium Series on Computational Intelligence (2015)","DOI":"10.1109\/SSCI.2015.37"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Ozbulak, G., Aytar, Y., Ekenel, H.K.: How transferable are CNN-based features for age and gender classification? In: IEEE International Conference on Biometrics Special Interest Group (BIOSIG), pp. 1\u20136 (2016)","DOI":"10.1109\/BIOSIG.2016.7736925"},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.patcog.2017.06.028","volume":"72","author":"Pau Rodr\u00edguez","year":"2017","unstructured":"Rodriguez, P., Cucurull, G., Gonfaus, J.M., Roca, F.X,. Gonzalez, J.: Age and gender recognition in the wild with deep attention. Pattern Recogn. (2017). https:\/\/doi.org\/10.1016\/j.patcog.2017.06.028","journal-title":"Pattern Recognition"},{"key":"16_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patcog.2017.06.031","volume":"72","author":"Grigory Antipov","year":"2017","unstructured":"Antipov, G., Baccouche, M., Berrani, S.A., Dugelay, J.-L.: Effective training of convolutional neural networks for face-based gender and age prediction. Pattern Recogn. (2017). https:\/\/doi.org\/10.1016\/j.patcog.2017.06.031","journal-title":"Pattern Recognition"},{"key":"16_CR7","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ArXiv https:\/\/arxiv.org\/abs\/1409.1556 (2014)"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR9","doi-asserted-by":"publisher","unstructured":"Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. Comput. Vis. Pattern Recognit. 511\u2013518 (2001). https:\/\/doi.org\/10.1109\/CVPR.2001.990517","DOI":"10.1109\/CVPR.2001.990517"},{"key":"16_CR10","unstructured":"Keras. https:\/\/keras.io\/"},{"key":"16_CR11","unstructured":"http:\/\/www.face-rec.org\/databases\/"},{"issue":"8","key":"16_CR12","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1080\/0144929X.2011.624639","volume":"32","author":"K Ghanem","year":"2013","unstructured":"Ghanem, K., Caplier, A.: Towards a full emotional system. Behav. Inf. Technol. J. 32(8), 783\u2013799 (2013)","journal-title":"Behav. Inf. Technol. J."}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Networking"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-19945-6_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:30:38Z","timestamp":1709818238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-19945-6_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030199449","9783030199456"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-19945-6_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Networking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Paris","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mln2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.adda-association.org\/mln\/Home.html","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":"48","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":"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":"46% - 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","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)"}}]}}