{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:53:40Z","timestamp":1777391620192,"version":"3.51.4"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000015","name":"Department of Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012483","name":"The University of Georgia Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012483","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,7]]},"DOI":"10.1109\/icaic57335.2023.10044121","type":"proceedings-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T18:25:00Z","timestamp":1677090300000},"page":"1-5","source":"Crossref","is-referenced-by-count":5,"title":["Long Short-Term Memory Networks for Monitoring Groundwater Contamination at the Hanford Site"],"prefix":"10.1109","author":[{"given":"Michael P.","family":"Murphy","sequence":"first","affiliation":[{"name":"University of Houston-Victoria,Department of Computer Science,Texas,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hirak","family":"Mazumdar","sequence":"additional","affiliation":[{"name":"University of Houston-Victoria,Department of Computer Science,Texas,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hardik A.","family":"Gohel","sequence":"additional","affiliation":[{"name":"University of Houston-Victoria,Department of Computer Science,Texas,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hilary P.","family":"Emerson","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory,Richland,Washington,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel I.","family":"Kaplan","sequence":"additional","affiliation":[{"name":"University of Georgia,Savannah River Ecology Lab,Aiken,SC,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.03.014"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5416722"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/w12041023"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MetroCon56047.2022.9971133"},{"key":"ref5","volume-title":"Interim Report: 100NR2 Apatite Treatability Test: Low Concentration Calcium Citrate-Phosphate Solution Injection for In Situ Strontium90 Immobilization","author":"Mark","year":"2008"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.22214\/ijraset.2021.37362"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306"},{"key":"ref8","volume-title":"Understanding LSTM - tutorial into Long Short-Term Memory Recurrent Neural Networks","author":"Staudemeyer","year":"2019"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/hydrology9070125"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.5194\/hess-26-1727-2022"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00516-9"},{"key":"ref12","volume":"20","author":"Wang","journal-title":"Changyong FENG Log-transformation and its implications for data analysis Biostatistics in psychiatry"},{"key":"ref13","volume-title":"The Effect of Class Distribution on Classifier Learning: An Empirical Study","author":"Weiss","year":"2001"},{"key":"ref14","volume-title":"Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020","author":"Turner","year":"2021"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/su141811598"},{"key":"ref16","article-title":"Performance Metrics (Error Measures) in Machine Learning Regression","author":"Botchkarev","journal-title":"Forecasting and Prognostics: Properties and Typology"},{"key":"ref17","article-title":"Exploration and Analysis of Legacy Data at the Hanford Site Using PHOENIX-19044","author":"Gorton","journal-title":"United States"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2021.109760"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2021.648071"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"}],"event":{"name":"2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC)","location":"Houston, TX, USA","start":{"date-parts":[[2023,2,7]]},"end":{"date-parts":[[2023,2,9]]}},"container-title":["2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10044118\/10044119\/10044121.pdf?arnumber=10044121","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T16:52:23Z","timestamp":1707843143000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10044121\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,7]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/icaic57335.2023.10044121","relation":{},"subject":[],"published":{"date-parts":[[2023,2,7]]}}}