{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T18:20:00Z","timestamp":1776363600252,"version":"3.51.2"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032184764","type":"print"},{"value":"9783032184771","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\/978-3-032-18477-1_57","type":"book-chapter","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T17:10:27Z","timestamp":1776359427000},"page":"530-541","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight Hybrid Convolutional Kolmogorov\u2013Arnold Network for Efficient Image Classification on CIFAR-10"],"prefix":"10.1007","author":[{"given":"Himanshu","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0635-0808","authenticated-orcid":false,"given":"Sachchida Nand","family":"Chaurasia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"57_CR1","unstructured":"Azam, B., Akhtar, N.: Suitability of KANs for computer vision: a preliminary investigation. arXiv preprint arXiv:2406.09087 (2024)"},{"key":"57_CR2","unstructured":"Blealtan, A.: Efficient Kolmogorov\u2013Arnold networks. GitHub Repository (2024). https:\/\/github.com\/Blealtan\/efficient-kan"},{"key":"57_CR3","unstructured":"Bodner, A.D., et\u00a0al.: Convolutional Kolmogorov\u2013Arnold networks. arXiv preprint arXiv:2406.13155 (2024)"},{"key":"57_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126966","volume":"274","author":"H Cai","year":"2025","unstructured":"Cai, H., et al.: Hierarchical multi-task circuit modeling for PVT robustness via KAN-CNN integration. Expert Syst. Appl. 274, 126966 (2025)","journal-title":"Expert Syst. Appl."},{"key":"57_CR5","unstructured":"Canziani, A., Paszke, A., Culurciello, E.: An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678 (2016)"},{"issue":"2","key":"57_CR6","first-page":"314","volume":"19","author":"T Chen","year":"2019","unstructured":"Chen, T., Yu, S., Wang, Z., Pan, L., Zhang, H., Yang, Q.: DeepWear: adaptive local offloading for on-wearable deep learning. IEEE Trans. Mob. Comput. 19(2), 314\u2013330 (2019)","journal-title":"IEEE Trans. Mob. Comput."},{"key":"57_CR7","unstructured":"Cheon, M.: Demonstrating the efficacy of Kolmogorov\u2013Arnold networks in vision tasks. arXiv preprint arXiv:2406.14916 (2024)"},{"key":"57_CR8","unstructured":"Drokin, I.: Kolmogorov-Arnold convolutions: Design principles and empirical studies. arXiv preprint arXiv:2407.01092 (2024)"},{"key":"57_CR9","unstructured":"Goyal, M., Goyal, R., Lall, B.: Learning activation functions: a new paradigm of understanding neural networks. arXiv preprint arXiv:1906.09529 (2020)"},{"key":"57_CR10","doi-asserted-by":"crossref","unstructured":"Han, D., Kim, J., Kim, J.: Deep pyramidal residual networks (2017). https:\/\/arxiv.org\/abs\/1610.02915","DOI":"10.1109\/CVPR.2017.668"},{"issue":"3","key":"57_CR11","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.240999","volume":"12","author":"LF Herbozo Contreras","year":"2025","unstructured":"Herbozo Contreras, L.F., Cui, J., Yu, L., Huang, Z., Nikpour, A., Kavehei, O.: KAN-EEG: towards replacing backbone-MLP for an effective seizure detection system. Roy. Soc. Open Sci. 12(3), 240999 (2025)","journal-title":"Roy. Soc. Open Sci."},{"key":"57_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107358","volume":"103","author":"C Jiang","year":"2025","unstructured":"Jiang, C., Li, Y., Luo, H., Zhang, C., Du, H.: KANsnet: Kolmogorov-Arnold networks and multi slice partition channel priority attention in convolutional neural network for lung nodule detection. Biomed. Sig. Process. Control 103, 107358 (2025)","journal-title":"Biomed. Sig. Process. Control"},{"key":"57_CR13","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: U-KAN makes strong backbone for medical image segmentation and generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a039, pp. 4652\u20134660 (2025)","DOI":"10.1609\/aaai.v39i5.32491"},{"key":"57_CR14","unstructured":"Liu, H., Lample, G., Li, X., Manning, C.: Kolmogorov\u2013Arnold networks. arXiv preprint arXiv:2403.11204 (2024)"},{"key":"57_CR15","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Pattern recognition method for detecting partial discharge in oil-paper insulation equipment using optical FP sensor array based on KAN-CNN algorithm. J. Lightwave Technol. (2025)","DOI":"10.1109\/JLT.2025.3552628"},{"key":"57_CR16","unstructured":"Ramachandran, P., Zoph, B., Le, Q.V.: Learning activation functions: a new perspective. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"57_CR17","unstructured":"Tan, M., Le, Q.: Efficientnetv2: smaller models and faster training. In: Proceedings of the 38th International Conference on Machine Learning (2021)"},{"key":"57_CR18","doi-asserted-by":"crossref","unstructured":"Yang, Z., Zhang, J., Luo, X., Lu, Z., Shen, L.: MedKAN: an advanced Kolmogorov-Arnold network for medical image classification. arXiv preprint arXiv:2502.18416 (2025)","DOI":"10.1109\/BIBM66473.2025.11356561"},{"key":"57_CR19","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.neucom.2020.11.068","volume":"429","author":"M Zhu","year":"2021","unstructured":"Zhu, M., Min, W., Wang, Q., Zou, S., Chen, X.: PFLU and FPFLU: two novel non-monotonic activation functions in convolutional neural networks. Neurocomputing 429, 110\u2013117 (2021). https:\/\/doi.org\/10.1016\/j.neucom.2020.11.068","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-18477-1_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T17:10:34Z","timestamp":1776359434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-18477-1_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032184764","9783032184771"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-18477-1_57","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":"17 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PReMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition and Machine Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"11 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"premi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/premi25-git-dev-ashirbad97s-projects.vercel.app\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}