Automatic screening of diabetic retinopathy (DR) is a well-identified area of research in the domain of computer vision. It is challenging due to structural complexity and a marginal contrast difference between the retinal vessels and the background of the fundus image. As bright lesions are prominent in the green channel, we applied contrast-limited adaptive histogram equalization (CLAHE) on the green channel for image enhancement. This work proposes a novel diabetic retinopathy screening technique using an asymmetric deep learning feature. The asymmetric deep learning features are extracted using U-Net for segmentation of the optic disc and blood vessels. Then a convolutional neural network (CNN) with a support vector machine (SVM) is used for the DR lesions classification. The lesions are classified into four classes, i.e., normal, microaneurysms, hemorrhages, and exudates. The proposed method is tested with two publicly available retinal image datasets, i.e., APTOS and MESSIDOR. The accuracy achieved for non-diabetic retinopathy detection is 98.6% and 91.9% for the APTOS and MESSIDOR datasets, respectively. The accuracies of exudate detection for these two datasets are 96.9% and 98.3%, respectively. The accuracy of the DR screening system is improved due to the precise retinal image segmentation.

This paper deals with the design of a multi-slot hole-coupled microstrip antenna on a substrate of 2 mm thickness that gives multi-frequency (wideband) characteristics. The Method of Moments (MoM)-based IE3D software was used to simulate the results for return loss, VSWR, the Smith chart, and the radiation patterns. A tunnel-based artificial neural network (ANN) was also developed to calculate the radiation patterns of the antenna. The radiation patterns were measured experimentally at 10.5 GHz and 12 GHz. The experimental results were in good agreement with the simulated results from IE3D and those of the artificial neural network. A new method of using a genetic algorithm (GA) in an artificial neural network is also discussed. This new method was used to calculate the resonant frequency of a single-shorting-post microstrip antenna. The resonant frequency calculated using the genetic-algorithm-coupled artificial neural network was compared with the analytical and experimental results. The results obtained were in very good agreement with the experimental results.

In this paper, the genetic algorithm (GA) has been applied to calculate the optimized length and width of rectangular microstrip antennas. The inputs to the problem are the desired resonant frequency, dielectric constant, and thickness of the substrate; the outputs are the optimized length and width. The antennas considered are electrically thin. Method of moments (MoM)‐based IE3D software from Zealand Inc., USA, and experimental results are used to validate the GA‐based code. The results are in good agreement. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 431–433, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10940

The newly proposed weighted k nearest neighbour is known as standard deviation K nearest neighbour(SDKNN) classifier technique. It is based on the principle of standard deviation. Standard deviation measures spreading of attribute about mean. Spreading of attribute plays a significant role to improve the classification accuracy of a dataset. Most of our distance calculation method between two points is determined by using euclidean distance process for finding nearest neighbour. Our proposed technique is based on a new distance calculation formula to find nearest neighbour in KNN. We apply here standard deviations of attributes as power for calculating distance between train dataset and test dataset. Distance calculation between two points in k nearest neighbour classifier is modified according to the standard deviation of attribute. In this paper, standard deviation of attributes are used. In first attempt, we have used standard deviation of attributes as power for calculating K Nearest Neighbour to improve classification accuracy and in second attempt, based on mean of standard deviation attributes, distance in K Nearest Neighbour is processed to further improve the classification accuracy. Our concept is implemented on Pima Indian Diabetes Dataset (PIDD). The analysis on Pima Indian Diabetes Dataset (PIDD) is carried out by splitting dataset in to 90% training data and 10% testing data. We have found that, in our proposed technique, average classification accuracy gives result 83.2%, a great improvement as compared to other conventional technique.