{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T12:38:07Z","timestamp":1776688687004,"version":"3.51.2"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032184795","type":"print"},{"value":"9783032184801","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\/978-3-032-18480-1_10","type":"book-chapter","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T11:45:03Z","timestamp":1776685503000},"page":"93-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Machine Learning Topologies at\u00a0Home with\u00a0Tiny Constraints for\u00a0Presence Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7599-8033","authenticated-orcid":false,"given":"Simone","family":"Tognocchi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6582-4623","authenticated-orcid":false,"given":"Alessandro","family":"Tomasoni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8301-826X","authenticated-orcid":false,"given":"Mohammadreza B.","family":"Mohajer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniele","family":"Lo Iacono","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1585-2313","authenticated-orcid":false,"given":"Danilo Pietro","family":"Pau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"key":"10_CR1","unstructured":"Andrew, G., et al.: Efficient convolutional neural networks for mobile vision applications. Mobilenets (2017)"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Han, Z., Hong, D., Gao, L., Roy, S.K., Zhang, B., Chanussot, J.: Reinforcement learning for neural architecture search in hyperspectral unmixing. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","DOI":"10.1109\/LGRS.2022.3199583"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Gupta, V.K., Lalwani, S.K., Bhati, G.S., Prakash, S., Sunny: Bayesian optimization based neural architecture search for classification of gases\/odors mixtures. IEEE Sens. J. 24(5), 7119\u20137125 (2024)","DOI":"10.1109\/JSEN.2024.3349862"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Pan, C., Yao, X.: Neural architecture search based on evolutionary algorithms with fitness approximation. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2021)","DOI":"10.1109\/IJCNN52387.2021.9533986"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Tan, M., et al.: MnasNet: platform-aware neural architecture search for mobile (2019)","DOI":"10.1109\/CVPR.2019.00293"},{"key":"10_CR6","unstructured":"Lin, J., Chen, W.-M., Lin, Y., Cohn, J., Gan, C., Han, S.: McuNet: tiny deep learning on IoT devices (2020)"},{"key":"10_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117876","volume":"207","author":"R Perego","year":"2022","unstructured":"Perego, R., Candelieri, A., Archetti, F., Pau, D.: AutoTinyML for microcontrollers: dealing with black-box deployability. Expert Syst. Appl. 207, 117876 (2022)","journal-title":"Expert Syst. Appl."},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Garavagno, A.M., Leonardis, D., Frisoli, A.: ColabNAS: obtaining lightweight task-specific convolutional neural networks following Occam\u2019s razor. Future Gener. Comput. Syst. 152, 152\u2013159 (2024)","DOI":"10.1016\/j.future.2023.11.003"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Sadhwani, J., Sabarimalai Manikandan, M.: Non-collaborative human presence detection using channel state information of Wi-Fi signal and long-short term memory neural network. In: 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1\u20136 (2021)","DOI":"10.1109\/ECAI52376.2021.9515148"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Zou, H., Zhou, Y., Yang, J., Jiang, H., Xie, L., Spanos, C.J.: DeepSense: device-free human activity recognition via autoencoder long-term recurrent convolutional network. In: Proceedings of IEEE International Conference on Communications (ICC) (2018)","DOI":"10.1109\/ICC.2018.8422895"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Muaaz, M., Chelli, A., Abdelgawwad, A.A., Mallofr\u00e9, A.C., P\u00f6tzold, M.: WiWeHAR: multimodal human activity recognition using Wi-Fi and wearable sensing modalities. IEEE Access 8, 164453\u2013164470 (2020)","DOI":"10.1109\/ACCESS.2020.3022287"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Meneghello, F., Garlisi, D., Fabbro, N.D., Tinnirello, I., Rossi, M.: SHARP: environment and person independent activity recognition with commodity IEEE 802.11 access points. IEEE Trans. Mob. Comput. 22(10), 6160\u20136175 (2023)","DOI":"10.1109\/TMC.2022.3185681"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Pau, D., Pisani, A., Candelieri, A.: Towards full forward on-tiny-device learning: a guided search for a randomly initialized neural network. Algorithms 17(1) (2024)","DOI":"10.3390\/a17010022"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Qiao, Y., Haocheng, X., Zhang, Y., Huang, S.: MicroNAS: zero-shot neural architecture search for MCUs (2024)","DOI":"10.23919\/DATE58400.2024.10546710"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Gao, J., Liu, Z., Wang, Y., Ji, W.: RaNAS: resource-aware neural architecture search for edge computing. ACM Trans. Archit. Code Optim. 22(1) (2025)","DOI":"10.1145\/3703353"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Yang, L., et al.: Co-exploration of neural architectures and heterogeneous ASIC accelerator designs targeting multiple tasks. In: 2020 57th ACM\/IEEE Design Automation Conference (DAC), pp. 1\u20136 (2020)","DOI":"10.1109\/DAC18072.2020.9218676"}],"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\/978-3-032-18480-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T11:45:13Z","timestamp":1776685513000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-18480-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032184795","9783032184801"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-18480-1_10","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":"21 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"}}]}}