Diabetic Retinopathy is one of the common eye diseases due to the complication of diabetes mellitus. Cotton wool spots, rough exudates, haemorrhages and microaneurysms are the symptoms of the diabetic retinopathy due to the fluid leakage that is caused by the high blood glucose level disorder. Early treatment to prevent a permanent blindness is important as it could save the diabetic retinopathy vision. Hence, in this study, we proposed to employ an automated detection method to diagnose the diabetic retinopathy. The dataset was obtained from the Kaggle Database and been divided for training, testing and validation purposes. Furthermore, Transfer Learning models, namely VGG19 were employed to extract the features before being processed by Machine Learning classifiers which are SVM, kNN and RF to classify the diabetic retinopathy. VGG19-SVM pipeline produced the best accuracy in training, testing and validation processes, achieving 99, 99 and 96 percents respectively.
This paper presents the analysis on geometrical parameters of the power-optimized coil based on Faradays principle by maximizing the coverage of magnetic flux linkage by the coil using a cylindrical permanent magnet of 6 mm diameter and 6 mm height. Faradays law states that induced voltage is the rate of change of flux linkage, meaning more winding induces more voltage. However it will increase also the resistance of the coil because the length of copper wire will also increase, which will reduce the generated power and power-density by the harvester according to Joules and Ohms laws. Simulation is used to virtually wind the inner and outer geometrical parameters of the coil using the given boundaries and the dimensions with highest output power are determined. The proposed form of the coil is cap-like shape which covers top half of the magnet where the amount of surrounding magnetic flux linkage is maximal. The result showed the induced power could be improved up to 60% using this method compared to usage of conventional ring-shaped coils.
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