{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:44:09Z","timestamp":1769820249743,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11872166"],"award-info":[{"award-number":["11872166"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11502063"],"award-info":[{"award-number":["11502063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021KJZD18"],"award-info":[{"award-number":["2021KJZD18"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hefei Normal University","award":["11872166"],"award-info":[{"award-number":["11872166"]}]},{"name":"Hefei Normal University","award":["11502063"],"award-info":[{"award-number":["11502063"]}]},{"name":"Hefei Normal University","award":["2021KJZD18"],"award-info":[{"award-number":["2021KJZD18"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Activity recognition methods often include some hyper-parameters based on experience, which greatly affects their effectiveness in activity recognition. However, the existing hyper-parameter optimization algorithms are mostly for continuous hyper-parameters, and rarely for the optimization of integer hyper-parameters and mixed hyper-parameters. To solve the problem, this paper improved the traditional cuckoo algorithm. The improved algorithm can optimize not only continuous hyper-parameters, but also integer hyper-parameters and mixed hyper-parameters. This paper validated the proposed method with the hyper-parameters in Least Squares Support Vector Machine (LS-SVM) and Long-Short-Term Memory (LSTM), and compared the activity recognition effects before and after optimization on the smart home activity recognition data set. The results show that the improved cuckoo algorithm can effectively improve the performance of the model in activity recognition.<\/jats:p>","DOI":"10.3390\/e24060845","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T01:43:27Z","timestamp":1655775807000},"page":"845","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Research on Hyper-Parameter Optimization of Activity Recognition Algorithm Based on Improved Cuckoo Search"],"prefix":"10.3390","volume":"24","author":[{"given":"Yu","family":"Tong","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Hefei Normal University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Hefei University of Technology, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107561","DOI":"10.1016\/j.patcog.2020.107561","article-title":"Sensor-based and vision-based human activity recognition: A comprehensive survey","volume":"108","author":"Dang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.procs.2019.08.100","article-title":"Human activity recognition: A survey","volume":"155","author":"Jobanputra","year":"2019","journal-title":"Procedia Comput. 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