{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:48:43Z","timestamp":1772556523916,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,7,24]]},"DOI":"10.1145\/3688574.3688592","type":"proceedings-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T12:21:55Z","timestamp":1726230115000},"page":"126-131","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Comparative Landslide Hazard Detection Analysis Based on ResNet and CNN"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6561-1859","authenticated-orcid":false,"given":"Xuegang","family":"Zhang","sequence":"first","affiliation":[{"name":"Qinghai Minzu University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5064-0719","authenticated-orcid":false,"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"National Demonstration Center for Experimental Communication Engineering Education, Qinghai Minzu University?Key Laboratory of Communication Engineering, Qinghai Minzu University, Qinghai Minzu University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2860-4259","authenticated-orcid":false,"given":"Yubo","family":"Wang","sequence":"additional","affiliation":[{"name":"Qinghai Minzu University, China"}]}],"member":"320","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3123501"},{"key":"e_1_3_2_1_2_1","volume-title":"Deep learning-based landslide susceptibility mapping[J]. Scientific reports","author":"Azarafza M","year":"2021","unstructured":"Azarafza M, Azarafza M, Akg\u00fcn H, Deep learning-based landslide susceptibility mapping[J]. Scientific reports, 2021, 11(1): 24112."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2829819"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2023.109492"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6659083"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab503"},{"issue":"2","key":"e_1_3_2_1_8_1","first-page":"6","article-title":"Convolutional neural network and its architectures[J]","volume":"12","author":"Shyam R","year":"2021","unstructured":"Shyam R. Convolutional neural network and its architectures[J]. Journal of Computer Technology & Applications, 2021, 12(2): 6-14.","journal-title":"Journal of Computer Technology & Applications"},{"key":"e_1_3_2_1_9_1","volume-title":"Understanding and improving layer normalization[J]. Advances in neural information processing systems","author":"Xu J","year":"2019","unstructured":"Xu J, Sun X, Zhang Z, Understanding and improving layer normalization[J]. Advances in neural information processing systems, 2019, 32."},{"key":"e_1_3_2_1_10_1","volume-title":"Transferable normalization: Towards improving transferability of deep neural networks[J]. Advances in neural information processing systems","author":"Wang X","year":"2019","unstructured":"Wang X, Jin Y, Long M, Transferable normalization: Towards improving transferability of deep neural networks[J]. Advances in neural information processing systems, 2019, 32."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10269-x"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2966319"},{"key":"e_1_3_2_1_13_1","first-page":"21056","article-title":"Deep residual learning in spiking neural networks[J]","volume":"34","author":"Fang W","year":"2021","unstructured":"Fang W, Yu Z, Chen Y, Deep residual learning in spiking neural networks[J]. Advances in Neural Information Processing Systems, 2021, 34: 21056-21069.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3117975"},{"key":"e_1_3_2_1_15_1","volume-title":"Magnetic resonance image diagnosis of femoral head necrosis based on ResNet18 network[J]. Computer methods and programs in biomedicine","author":"Liu Y","year":"2021","unstructured":"Liu Y, She G, Chen S. Magnetic resonance image diagnosis of femoral head necrosis based on ResNet18 network[J]. Computer methods and programs in biomedicine, 2021, 208: 106254."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11709-021-0797-6"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40948-020-00154-0"},{"key":"e_1_3_2_1_18_1","volume-title":"Dropout vs. batch normalization: an empirical study of their impact to deep learning[J]. Multimedia tools and applications","author":"Garbin C","year":"2020","unstructured":"Garbin C, Zhu X, Marques O. Dropout vs. batch normalization: an empirical study of their impact to deep learning[J]. Multimedia tools and applications, 2020, 79(19): 12777-12815."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105205"},{"key":"e_1_3_2_1_20_1","first-page":"1293","article-title":"CNN based traffic sign classification using Adam optimizer[C]\/\/2019 international conference on intelligent computing and control systems (ICCS)","volume":"2019","author":"Mehta S","unstructured":"Mehta S, Paunwala C, Vaidya B. CNN based traffic sign classification using Adam optimizer[C]\/\/2019 international conference on intelligent computing and control systems (ICCS). IEEE, 2019: 1293-1298.","journal-title":"IEEE"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.091"}],"event":{"name":"BDE 2024: 2024 6th International Conference on Big Data Engineering","location":"Xining China","acronym":"BDE 2024"},"container-title":["Proceedings of the 2024 6th International Conference on Big Data Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688574.3688592","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3688574.3688592","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T02:14:04Z","timestamp":1755915244000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688574.3688592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,24]]},"references-count":21,"alternative-id":["10.1145\/3688574.3688592","10.1145\/3688574"],"URL":"https:\/\/doi.org\/10.1145\/3688574.3688592","relation":{},"subject":[],"published":{"date-parts":[[2024,7,24]]},"assertion":[{"value":"2024-09-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}