{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:28:03Z","timestamp":1758274083858,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030923099"},{"type":"electronic","value":"9783030923105"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92310-5_37","type":"book-chapter","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T14:04:20Z","timestamp":1638799460000},"page":"315-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Neural Network Pruning Based on\u00a0Improved Constrained Particle Swarm Optimization"],"prefix":"10.1007","author":[{"given":"Jihene","family":"Tmamna","sequence":"first","affiliation":[]},{"given":"Emna Ben","family":"Ayed","sequence":"additional","affiliation":[]},{"given":"Mounir Ben","family":"Ayed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Tmamna, J., Ayed, E.B., Ayed, M.B.: Deep learning for internet of things in fog computing: survey and open issues. In: International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 1\u20136 (2020). https:\/\/doi.org\/10.1109\/ATSIP49331.2020.9231685","key":"37_CR1","DOI":"10.1109\/ATSIP49331.2020.9231685"},{"unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: International Conference on Learning Representations (ICLR) (2017)","key":"37_CR2"},{"doi-asserted-by":"crossref","unstructured":"Li, H., Ma, C., Xu, W., Liu, X.: Feature statistics guided efficient filter pruning. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 2619\u20132625 (2020)","key":"37_CR3","DOI":"10.24963\/ijcai.2020\/363"},{"unstructured":"Xiao, X., Wang, Z., Rajasekaran, S.: Autoprune: automatic network pruning by regularizing auxiliary parameters. In: Advances in Neural Information Processing Systems, pp. 13681\u201313691. Vancouver Canada (2019)","key":"37_CR4"},{"doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, C., Wang, X.: Convolutional neural network pruning with structural redundancy reduction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14913\u201314922 (2021)","key":"37_CR5","DOI":"10.1109\/CVPR46437.2021.01467"},{"unstructured":"Liu, Z., Sun, M., Zhou, T., Huang, G., Darrell, T.: Rethinking the value of network pruning. In: International Conference on Learning Representations (ICLR) (2019)","key":"37_CR6"},{"doi-asserted-by":"crossref","unstructured":"He, Y., Lin, J., Liu, Z., Wang, H., Li, L.J., Han, S.: Amc: automl for model compression and acceleration on mobile devices. In: Proceedings of the European Conference on Computer Vision, Munich, Germany, pp. 784\u2013800 (2018)","key":"37_CR7","DOI":"10.1007\/978-3-030-01234-2_48"},{"issue":"4","key":"37_CR8","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/JSTSP.2020.2975987","volume":"14","author":"A Jordao","year":"2020","unstructured":"Jordao, A., Lie, M., Schwartz, W.R.: Discriminative layer pruning for convolutional neural networks. IEEE J. Sel. Top. Sig. Process. 14(4), 828\u2013837 (2020)","journal-title":"IEEE J. Sel. Top. Sig. Process."},{"doi-asserted-by":"crossref","unstructured":"He, Y., Liu, P., Wang, Z., Hu, Z., Yang, Y.: Filter pruning via geometric median for deep convolutional neural networks acceleration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 4340\u20134349 (2019)","key":"37_CR9","DOI":"10.1109\/CVPR.2019.00447"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: MetaPruning: meta learning for automatic neural network channel pruning. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 3296\u20133305 (2019)","key":"37_CR10","DOI":"10.1109\/ICCV.2019.00339"},{"doi-asserted-by":"publisher","unstructured":"Elkerdawy, S., Elhoushi, M., Singh, A., Zhang, H., Ray, N.: One-shot layer-wise accuracy approximation for layer pruning. In: IEEE International Conference on Image Processing, pp. 2940\u20132944. IEEE, Abu Dhabi (2020). https:\/\/doi.org\/10.1109\/ICIP40778.2020.9191238","key":"37_CR11","DOI":"10.1109\/ICIP40778.2020.9191238"},{"doi-asserted-by":"publisher","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol. 4, pp. 1942\u20131948. IEEE, Perth (1995). https:\/\/doi.org\/10.1109\/ICNN.1995.488968","key":"37_CR12","DOI":"10.1109\/ICNN.1995.488968"},{"key":"37_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1007\/11881070_128","volume-title":"Advances in Natural Computation","author":"G Zhenyu","year":"2006","unstructured":"Zhenyu, G., Bo, C., Min, Y., Binggang, C.: Self-adaptive chaos differential evolution. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 972\u2013975. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11881070_128"},{"doi-asserted-by":"publisher","unstructured":"Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC 2006), vol. 1, pp. 695\u2013701. IEEE, Vienna (2005). https:\/\/doi.org\/10.1109\/CIMCA.2005.1631345","key":"37_CR14","DOI":"10.1109\/CIMCA.2005.1631345"},{"issue":"2\u20134","key":"37_CR15","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/S0045-7825(99)00389-8","volume":"186","author":"K Deb","year":"2000","unstructured":"Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2\u20134), 311\u2013338 (2000)","journal-title":"Comput. Methods Appl. Mech. Eng."}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92310-5_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T08:10:46Z","timestamp":1655799046000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92310-5_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030923099","9783030923105"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92310-5_37","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"2 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","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":"1093","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":"226","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":"177","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":"21% - 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":"2.57","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":"6","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":"Yes","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 the COVID-19 pandemic the conference was held online.","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)"}}]}}