{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:10:50Z","timestamp":1772118650893,"version":"3.50.1"},"reference-count":15,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"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":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Time series data generated by IoT devices are called uncertain data, and streaming them is difficult since they have uncertain values and flow. The dataset is time series and big data since it is increasing continuously. An enormous amount of data are analyzed to predict the abnormalities in the climate or the environment. At the same time, the monitoring and generating process should be continuous because discontinuity in data generation will not provide accurate conditions. Earlier works focused on the device status detection process and intimate, where the process cannot continue where the faulty device is located. This article carries out the entire process in two phases: (i) Investigate the status of the IoT devices to replace or rectify the issues that occur in the devices and continue the same process at the exact location. (ii) Pre\u2010process the data using exploratory data analytics and analyze and predict the IoT\u2010Bigdata using a robust machine learning algorithm, Long\u2010Short\u2010Term\u2010Memory. The proposed machine learning algorithms are implemented in Python with environmental datasets to predict abnormalities. The experimental results help verify the algorithms' efficiency and compare them with similar methods to evaluate the performance. From the comparison, it is identified that the proposed algorithms outperform the others.<\/jats:p>","DOI":"10.1002\/itl2.623","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T22:04:22Z","timestamp":1733954662000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analyzing Uncertain Time Series Temperature Data for Forecasting and Streaming Using\n                    <scp>EDA<\/scp>\n                    \u2010\n                    <scp>LSTM<\/scp>\n                    Model"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6419-789X","authenticated-orcid":false,"given":"Gara Jaya","family":"Raju","sequence":"first","affiliation":[{"name":"Department of CSE JNTUK  Kakinada India"}]},{"given":"G. Samuel Vara Prasada","family":"Raju","sequence":"additional","affiliation":[{"name":"Andhra University  Visakhapatnam India"}]}],"member":"311","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12010035"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-6684-6361-1.ch012"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICATIECE56365.2022.10047380"},{"key":"e_1_2_7_5_1","first-page":"341","volume-title":"Artificial Intelligence in Forecasting","author":"Kumar S. 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R.","year":"2017","journal-title":"International Journal of Electronics and Communication Engineering and Technology"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.31154\/cogito.v10i1.708.692-705"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.17485\/ijst\/2017\/v10i30\/115514"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ATSIP62566.2024.10639027"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.02.041"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3070344"}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.623","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T18:26:33Z","timestamp":1761071193000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.623"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,11]]},"references-count":15,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1002\/itl2.623"],"URL":"https:\/\/doi.org\/10.1002\/itl2.623","archive":["Portico"],"relation":{"has-review":[{"id-type":"doi","id":"10.1002\/ITL2.623\/v2\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.623\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.623\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.623\/v2\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.623\/v2\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.623\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.623\/v1\/decision1","asserted-by":"object"}]},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"value":"2476-1508","type":"print"},{"value":"2476-1508","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,11]]},"assertion":[{"value":"2024-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e623"}}