Diabetic Retinopathy that is DR which is a eye disease that affect retina and further later at severe stage it lead to vision loss. Early detection of DR is helpful to improve the screening of patient to prevent further damage. Retinal micro-aneurysms, haemorrhages, exudates and cotton wool spots are kind of major abnormality to find the Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). The main objective of our proposed work is to detect retinal micro aneurysms and exudates for automatic screening of DR using Support Vector Machine (SVM) and KNN classifier. To develop this proposed system, a detection of red and bright lesions in digital fundus photographs is needed. Micro-aneurysms are the first clinical sign of DR and it appear small red dots on retinal fundus images. To detect retinal micro-aneurysms, retinal fundus images are taken from Messidor, DB-reet dataset. After pre-processing, morphological operations are performed to find micro-aneurysms and then features are get extracted such as GLCM and Structural features for classification. In order to classify the normal and DR images, different classes must be represented using relevant and significant features. SVM gives better performance over KNN classifier.
Summary
Internet of Underwater Things (IoUT) comprises various resource‐constrained sensor nodes; therefore, the routing followed in acoustic medium underwater should be energy efficient to preserve their energies. Due to large area covered in the scenarios of underwater, the multi‐hop communication leads to energy hole problem. Therefore, Energy hole mitigation through Optimized Cluster Head (CH) selection and Strategic Routing (EOCSR) in IoUT is proposed in this paper. The proposed work not only optimizes CH selection using Tunicate Swarm Algorithm but also incorporates strategic routing to address energy hole problem. The simulation results show that EOCSR improves stability and lifetime of network by 16.8% and 17.7%, as compared with recently proposed Moth Flame Optimization‐based routing method.
Oceanographic data gathering, pollution monitoring, offshore exploration, catastrophe avoidance, aided navigation, and tactical surveillance are all expected to benefit from the Internet of Underwater Things (IoUT). However, the viability of various applications in underwater scenarios is possible only if the routing among the sensor nodes employed is strategically optimized. This paper proposes Strategic Cooperative Routing for IoUT (SC2R) that employs a cooperative node for data collection from each Cluster-Head (CH). CH selection is done through the energy and distance parameters. The cooperative nodes are positioned on water surface, other nodes being placed in the underwater terrain. This cooperative routing helps in the data collection for the time-critical scenario as it avoids multi-hop communication among the sensor nodes underwater. Due to decreased number of hops of communication, the delay in data transmission is reduced. The simulation results illustrate the efficacy of the proposed routing technique in comparison to competitive algorithms. The proposed protocol outperforms state of art routing protocols in terms of Network Lifetime and End to End Delay (EED).
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