Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks

Abstract: With an intention of improving healthcare performance, wearable technology products utilize several digital health sensors which are classically linked into sensor networks, including body-worn and ambient sensors. On the other hand, intracerebral hemorrhage (ICH) defines the injury of blood vessels in the brain regions, which is accountable for 10–15% of strokes. X-ray computed tomography (CT) scans are commonly employed to determine the position and size of the hemorrhages. Manual segmentation of the CT scans by planimetry using a radiologist is effective; however, it consumes more time. Therefore, this paper develops deep learning (DL)–based ICH diagnosis using GrabCut-based segmentation with synergic deep learning (SDL), named GC-SDL model. The proposed method make use of Gabor filtering for noise removal, thereby the image quality can be raised. In addition, GrabCut-based segmentation technique is applied to identify the diseased portions effectively in the image. To perform the feature extraction process, SDL model is utilized and finally, softmax (SM) layer is employed as a classifier. In order to investigate the performance of the GC-SDL model, an extensive set of experimentation takes place using a benchmark ICH dataset, and the results are examined under different evaluation metrics. The experimental outcome stated that the GC-SDL model has reached a higher sensitivity of 94.01%, specificity of 97.78%, precision of 95.79%, and accuracy of 95.73%.

Design of Microstrip Antennas Using Artificial Neural Networks

Abstract: A general design procedure is suggested for the micro strip antennas using artificial neural networks and this is demonstrated using the rectangular patch geometry. In the design procedure, synthesis and analysis are defined as forward side and reverse side of the problem.

Clustering Algorithm Combined with Hill Climbing for Classification of Remote Sensing Image

Abstract: Clustering is an unsupervised classification method widely used for classification of remote sensing images. As the spatial resolution of remote sensing images getting higher and higher, the complex structure is the simple objects becomes obvious, which makes the classification algorithm based on pixels being losing their advantages. In this paper, four different clustering algorithms such as K-means, Moving K-means, Fuzzy K-means and Fuzzy Moving K-means are used for classification of remote sensing images. In all the traditional clustering algorithms, number of clusters and initial centroids are randomly selected and often specified by the user. In this paper, a hill climbing algorithm for the histogram of the input image will generate the number of clusters and initial centroids required for clustering. It overcomes the shortage of random initialization in traditional clustering and achieves high computational speed by reducing the number of iterations. The experimental results show that Fuzzy Moving K-means has classified the remote sensing image more accurately than other three algorithms. DOI: http://dx.doi.org/10.11591/ijece.v4i6.6608