{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:54:12Z","timestamp":1769993652442,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research at King Khalid University","award":["RGP2\/29\/44"],"award-info":[{"award-number":["RGP2\/29\/44"]}]},{"name":"Scientific Research at King Khalid University","award":["PNURSP2023R114"],"award-info":[{"award-number":["PNURSP2023R114"]}]},{"name":"Scientific Research at King Khalid University","award":["RSPD2023R521"],"award-info":[{"award-number":["RSPD2023R521"]}]},{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["RGP2\/29\/44"],"award-info":[{"award-number":["RGP2\/29\/44"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2023R114"],"award-info":[{"award-number":["PNURSP2023R114"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["RSPD2023R521"],"award-info":[{"award-number":["RSPD2023R521"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]},{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RGP2\/29\/44"],"award-info":[{"award-number":["RGP2\/29\/44"]}]},{"name":"King Saud University, Riyadh, Saudi Arabia","award":["PNURSP2023R114"],"award-info":[{"award-number":["PNURSP2023R114"]}]},{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSPD2023R521"],"award-info":[{"award-number":["RSPD2023R521"]}]},{"name":"Future University","award":["RGP2\/29\/44"],"award-info":[{"award-number":["RGP2\/29\/44"]}]},{"name":"Future University","award":["PNURSP2023R114"],"award-info":[{"award-number":["PNURSP2023R114"]}]},{"name":"Future University","award":["RSPD2023R521"],"award-info":[{"award-number":["RSPD2023R521"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment of IoT devices for arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, and early recognition of abnormal heart rhythms. However, it is challenging to diagnose and manually classify arrhythmia as the manual diagnosis of ECG signals is a time-consuming process. Therefore, the current article presents the automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) approach in the IoT platform. The proposed AAC-FFAHDL system exploits the hyperparameter-tuned DL model for ECG signal analysis, thereby diagnosing arrhythmia. In order to accomplish this, the AAC-FFAHDL technique initially performs data pre-processing to scale the input signals into a uniform format. Further, the AAC-FFAHDL technique uses the HDL approach for detection and classification of arrhythmia. In order to improve the classification and detection performance of the HDL approach, the AAC-FFAHDL technique involves an FFA-based hyperparameter tuning process. The proposed AAC-FFAHDL approach was validated through simulation using the benchmark ECG database. The comparative experimental analysis outcomes confirmed that the AAC-FFAHDL system achieves promising performance compared with other models under different evaluation measures.<\/jats:p>","DOI":"10.3390\/s23198265","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T08:29:12Z","timestamp":1696580952000},"page":"8265","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5026-7227","authenticated-orcid":false,"given":"Ahmed S.","family":"Almasoud","sequence":"first","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4103-2434","authenticated-orcid":false,"given":"Hanan Abdullah","family":"Mengash","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Majdy M.","family":"Eltahir","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Riyadh 12372, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nabil Sharaf","family":"Almalki","sequence":"additional","affiliation":[{"name":"Department of Special Education, College of Education, King Saud University, Riyadh 12372, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mrim M.","family":"Alnfiai","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1066-8261","authenticated-orcid":false,"given":"Ahmed S.","family":"Salama","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e13298","DOI":"10.1111\/exsy.13298","article-title":"IoT based arrhythmia classification using the enhanced hunt optimization-based deep learning","volume":"40","author":"Kumar","year":"2023","journal-title":"Expert Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kodati, S., Reddy, K.P., Ravi, G., and Sreekanth, N. 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