{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:56:04Z","timestamp":1777704964063,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Age-Related Macular Degeneration is a progressive, irreversible eye condition that causes vision loss and impairs quality of life. The lost potential of the optic nerve cannot be regained, but a patient with Age-Related Macular Degeneration must have early diagnosis and treatment in order to prevent visual loss. The diagnosis of Age-Related Macular Degeneration is based on visual field loss tests, a patient\u2019s medical history, intraocular pressure, and a physical fundus evaluation. Age-Related Macular Degeneration must be diagnosed early in order to avoid irreparable structural damage and vision loss. The objective of the proposed study is to develop a new optimization-driven strategy-based recurrent neural network using the Internet of Things for the identification of age-related macular degeneration. The Recurrent Neural Network (RNN) classifier is trained using the Particle Swarm Optimization (PSO) technique included into the RNN-IoMT. Initially, the input picture is sent through pre-processing in order to remove noise and artefacts. The generated preprocessed picture is simultaneously sent to optical disc detection and blood vessel detection. In addition, picture level characteristics are extracted from the image that has been preprocessed. Finally, the image-level, optic disc-level, and blood vessel-level features are retrieved and compiled into a feature vector. The acquired feature vector is fed into the RNN classifier, with the suggested PSO used to train the RNN for Age-Related Macular Degeneration detection via the Internet of Medical Things. The suggested PSO+RNN exhibits better performance with enhanced precision of 97.194%, sensitivity of 97.184%, and specificity of 97.2044%, respectively.<\/jats:p>","DOI":"10.3233\/jifs-233044","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T11:22:03Z","timestamp":1698405723000},"page":"11093-11105","source":"Crossref","is-referenced-by-count":2,"title":["Detection and diagnosis of age-related macular degeneration using recurrent neural network with cloud architecture and internet of things"],"prefix":"10.1177","volume":"45","author":[{"given":"Anna","family":"Alphy","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, SRM IST Delhi NCR Campus Ghaziabad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Rajamohamed","sequence":"additional","affiliation":[{"name":"Department of ECE, Indra Ganesan College of Engineering, Trichy, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayaraj","family":"Velusamy","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Vidhya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Ravi","sequence":"additional","affiliation":[{"name":"Department of ECE, Sona College of Technology, Salem, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arun Sekar","family":"Rajasekaran","sequence":"additional","affiliation":[{"name":"Department of ECE, SR University, Warangal, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-233044_ref1","doi-asserted-by":"crossref","first-page":"8747","DOI":"10.1038\/s41598-023-35759-4","article-title":"Three-month outcomes of faricimab loading therapy for wet age-related macular degeneration in Japan","volume":"13","author":"Mukai","year":"2023","journal-title":"Scientific Reports"},{"key":"10.3233\/JIFS-233044_ref2","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.2147\/OPTH.S409822","article-title":"Initial real-world experience with faricimab in treatment-resistant neovascular age-related macular degeneration","author":"Leung","year":"2023","journal-title":"Clinical Ophthalmology"},{"key":"10.3233\/JIFS-233044_ref3","doi-asserted-by":"crossref","unstructured":"Masayuki Hata , Hata Maki , Andriessen Elisabeth MMA , Juneau Rachel , Pilon Fr\u00e9d\u00e9rique , Crespo-Garcia Sergio , Diaz-Marin Roberto et al. 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