乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T11:38:50Z","timestamp":1725449930912},"reference-count":30,"publisher":"Wiley","issue":"21","license":[{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2023,9,25]]},"abstract":"SUMMARY<\/jats:title>In this article, the detection of COVID\u201019 patient based on attention segmental recurrent neural network (ASRNN) with Archimedes optimization algorithm (AOA) using ultra\u2010low\u2010dose CT (ULDCT) images is proposed. Here, the ultra\u2010low\u2010dose CT images are gathered via real time dataset. The input images are preprocessed with the help of convolutional auto\u2010encoder to recover the ULDCT images quality by removing noises. The preprocessed images are given to generalized additive models with structured interactions (GAMI) for extracting the radiomic features. The radiomic features, such as morphologic, gray scale statistic, Haralick texture are extracted using GAMI\u2010Net. The ASRNN classifier, whose weight parameters optimized with Archimedes optimization algorithm enables COVID\u201019 ULDCT images classification as COVID\u201019 or normal. The proposed approach is activated in MATLAB platform. The proposed ASRNN\u2010AOA\u2010ULDCT attains accuracy 22.08%, 24.03%, 34.76%, 34.65%, 26.89%, 45.86%, and 32.14%; precision 23.34%, 26.45%, 34.98%, 27.06%, 35.87%, 34.44%, and 22.36% better than the existing methods, such as DenseNet\u2010HHO\u2010ULDCT, ELM\u2010DNN\u2010ULDCT, EDL\u2010ULDCT, ResNet 50\u2010ULDCT, SDL\u2010ULDCT, CNN\u2010ULDCT, and DRNN\u2010ULDCT, respectively.<\/jats:p>","DOI":"10.1002\/cpe.7705","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T10:11:25Z","timestamp":1683108685000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Detection of COVID<\/scp>\u201019 patient based on attention segmental recurrent neural network (ASRNN) Archimedes optimization algorithm using ultra\u2010low\u2010dose CT<\/scp> images"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-4078-1473","authenticated-orcid":false,"given":"G.","family":"Kannan","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Tamil Nadu Nagercoil India"}]},{"given":"Karunambiga","family":"K","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Karpagam Institute of Technology Tamil Nadu Coimbatore India"}]},{"given":"P. J.","family":"Sathish Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (CSE) Panimalar Engineering College Tamil Nadu Chennai India"}]},{"given":"Francis H.","family":"Shajin","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering Anna University Tamil Nadu India"}]}],"member":"311","published-online":{"date-parts":[[2023,5,3]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcv.2020.104371"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/22221751.2020.1744483"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-07715-1"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phrs.2020.104896"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mayocp.2020.05.013"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.25827"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajp.2020.102053"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s15010-020-01432-5"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110286"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11655-020-3192-6"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/jnm.3019"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00034-021-01850-2"},{"key":"e_1_2_8_14_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15567036.2021.1986606","article-title":"Diminishing energy consumption cost and optimal energy management of photovoltaic aided electric vehicle (PV\u2010EV) by GFO\u2010VITG approach","author":"Rajesh P","year":"2021","journal-title":"Energy Sources A"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218126622500931"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/09720529.2020.1784535"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejphar.2020.173381"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40745-020-00289-7"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12664-020-01075-2"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajp.2020.102111"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07225-6"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107698"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2021.01.004"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106885"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104306"},{"key":"e_1_2_8_26_1","doi-asserted-by":"crossref","unstructured":"ArabiH GholamiankhahF MostafapourS ShojaerazaviS GoushbolaghNA ZaidiH.Low\u2010dose Covid\u201019 CT imaging: noise\u2010to\u2010noise versus supervised deep learning\u2010based denoising. Proceedings of the 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS\/MIC);2021:1\u20102; IEEE.","DOI":"10.1109\/NSS\/MIC44867.2021.9875557"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10140-020-01886-y"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2017.e00393"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108192"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106548"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01893-z"}],"container-title":["Concurrency and Computation: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.7705","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T01:53:51Z","timestamp":1692842031000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpe.7705"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,3]]},"references-count":30,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2023,9,25]]}},"alternative-id":["10.1002\/cpe.7705"],"URL":"https:\/\/doi.org\/10.1002\/cpe.7705","archive":["Portico"],"relation":{},"ISSN":["1532-0626","1532-0634"],"issn-type":[{"value":"1532-0626","type":"print"},{"value":"1532-0634","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,3]]},"assertion":[{"value":"2021-11-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-14","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}