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              <text>Cloud-enabled Diabetic Retinopathy Prediction System using optimized deep Belief Network Classifier</text>
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              <text>Classification; Cloud-based prediction and diagnosis; Diabetic Retinopathy; Optimized deep Belief Network; Stochastic neighbor embedding</text>
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              <text>Diabetic retinopathy disease is one of the notorious metabolic disorders happens due to increase of blood sugar level in human body. In computer vision, images are recognized as the indispensable tool for precise prediction and diagnosis of diabetic retinopathy. Therefore, the proposed research study considers the fundus images of various patients containing the diabetic disease. Basic idea behind this research is to introduce a stochastic neighbor embedding (SNE) feature extraction approach for the sake of dimensional reduction and unnecessary noise removal from the fundus images. After feature extraction, the proposed optimized deep belief network (O-DBN) classifier model is capable of measuring the image features into various classes that gives the severity levels of diabetic retinopathy disease. Moreover, the proposed cloud-enabled diabetic retinopathy prediction system using the SNE feature extraction and O-DBN classification model could outperform the existing online prediction systems in terms of sensitivity, specificity, F1-score, prediction time and accuracy.  2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</text>
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              <text>Rajavel R.; Sundaramoorthy B.; Gr K.; Ravichandran S.K.; Leelasankar K.</text>
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              <text>Journal of Ambient Intelligence and Humanized Computing, Vol-14, No. 10, pp. 14101-14109.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/s12652-022-04114-2" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s12652-022-04114-2&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133258401&amp;amp;doi=10.1007%2Fs12652-022-04114-2&amp;amp;partnerID=40&amp;amp;md5=21a1ce4436441cc09b2bb2875b16c2ce" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133258401&amp;amp;doi=10.1007%2fs12652-022-04114-2&amp;amp;partnerID=40&amp;amp;md5=21a1ce4436441cc09b2bb2875b16c2ce&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISSN: 18685137</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Rajavel R., Department of CSE, CHRIST University, Bangalore, India; Sundaramoorthy B., School of Physiotherapy, Aarupadai Veedu Medical College &amp;amp; Hospital, Pondicherry, India; Gr K., Department of CSE, Vel Tech Rangarajan Dr.Sagunthala R&amp;amp;D Institute of Science and Technology, Chennai, India; Ravichandran S.K., Department of CSE, CHRIST University, Bangalore, India; Leelasankar K., School of Computing, SRM Institute of Science &amp;amp; Technology, Kattankulathur, India</text>
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