{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T11:52:09Z","timestamp":1769773929317,"version":"3.49.0"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159830","type":"print"},{"value":"9783032159847","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-15984-7_26","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:34:37Z","timestamp":1769718877000},"page":"377-394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Proposing an Efficient CNN-Based Architecture for Image Processing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4066-5750","authenticated-orcid":false,"given":"Verner Rafael","family":"Ferreira","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3684-3814","authenticated-orcid":false,"given":"Anne Magaly","family":"de Paula Canuto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"26_CR1","unstructured":"Dolphin, R.: Overfitting in ML: understanding and avoiding the pitfalls. https:\/\/towardsdatascience.com\/overfitting-in-ml-avoiding-the-pitfalls-d5225b7118d. Accessed 28 Aug 2023"},{"key":"26_CR2","unstructured":"Geifman, A.: The correct way to measure inference time of deep neural networks (2020). https:\/\/towardsdatascience.com\/the-correct-wayto-measure-inference-time-of-deep-neural-networks-304a54e5187f"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"26_CR4","doi-asserted-by":"publisher","first-page":"6506","DOI":"10.3390\/app13116506","volume":"13","author":"L He","year":"2023","unstructured":"He, L., He, L., Peng, L.: CFormerFaceNet: efficient lightweight network merging a CNN and transformer for face recognition. Appl. Sci. 13(11), 6506 (2023)","journal-title":"Appl. Sci."},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Huang, G., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"26_CR7","unstructured":"Iandola, F.N., et al.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv preprint arXiv:1602.07360 (2016)"},{"key":"26_CR8","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning. PMLR (2015)"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Munir, M., Avery, W., Marculescu, R.: MobileViG: graph-based sparse attention for mobile vision applications. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPRW59228.2023.00215"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Sandler, M., et al.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"26_CR12","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning. PMLR (2019)"},{"key":"26_CR13","unstructured":"Tan, M., Le, Q.: EfficientNetV2: smaller models and faster training. In: International Conference on Machine Learning. PMLR (2021)"},{"key":"26_CR14","unstructured":"Teich, D.A., Teich, P.R.: PLASTER: a framework for deep learning performance. Technology Report TIRIAS Research (2018)"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Zheng, T., et al.: An efficient mobile model for insect image classification in the field pest management. Eng. Sci. Technol. Int. J. 39, 101335 (2023)","DOI":"10.1016\/j.jestch.2023.101335"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15984-7_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:34:42Z","timestamp":1769718882000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15984-7_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159830","9783032159847"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15984-7_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}