2321-0850
2.47 [According Google C. Report] | SJIF : 5.263 | PIF : 4.128
| Sr. No. | Title and Author Name | Page No. | Download |
|---|---|---|---|
| 1 | Title : Automatic Detection of Melanoma and Non Melanoma Skin Cancer: Using Classification Framework of Neural Network Authors : Sultana Bano , Anugrah Srivastava Click Here For Abstract Abstract :In Automatic Detection of Melanoma and Non Melanoma skin Cancer, the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing and compare the result with two method Discrete wavelet transformation and lifting wavelet transformation. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 90.2% in LWT and 89.1% in DWT method. Auto-associative neural network is 81.2% in LWT method and 80.2% in DWT method .The image in database include dermoscopy photo and digital photo. |
1-5 | |
| 2 | Title : Magnify Lifeless Nodes in WSN Using Shortest Path ALGO for Reducing Energy Diversion Authors : Mohit Bakshi , Anugrah Srivastava Click Here For Abstract Abstract :— To magnify network of lifeless node in Wireless Sensor Networks (WSNs).The selection of path for data deportation are eclectic in such a form that the over all energy absorbed along the path is lessen. Wireless sensor network is a set of a large number of small devices which gain information from physical environment using sensor nodes. These nodes measure, store and send the information to other nodes in the network. To transmit data sensor nodes require battery power. So power boost is a major issue in wireless sensor network. To support high extensible and better data gathering, sensor nodes are often grouped into dislocate, non-flapping subsets called clusters. An efficient routing algorithm is required to utilize power of nodes. In this paper we aim to improve network lifetime using LEACH based protocol. We are using DBEA-LEACH (distance-based energy aware) additionally selects a cluster head not only based on distance, but also by examining residual energy of the node greater than the average residual energy level of nodes in the network. We propose the enhanced LEACH convention for first node die time upgrade. In this paper i am doing one more enhancement to use Dijkstra’s algorithm as routing algorithm to reduce power consumption. We are using Dijkstra‟s algorithm to reduce the power consumption and finding the shortest power consumed path between Source to Destination using minimum number no nodes. |
6-8 | |
| 3 | Title : Medical images Compression using convolutional neural network with LWT Authors : Surbhit Shukla , Anugrah Srivastava Click Here For Abstract Abstract :In compression of medical image using convolutional neural network trained with the back-propagation algorithm and lefted wavelet transformation is proposed to compress high quality medical images. It gives much better result as compared to feed-forward neural network . Medical image processing process is one of the most important section of research in medical applications in digital medical information. In this new approach , a three hidden layer convolutional network (CNN) is applied directly as the main compression algorithm to compress an MRI, X-ray, computer tomography images. After training with sufficient sample images, the compression process will be carried out on the target image. The coupling weights and activation values of each neuron in the hidden layer will be stored after training. Compression is achieved by using smaller number of hidden neurons as compare to the number of image pixels due to lesser information being stored. experimental results proves that the anticipated algorithm is superior to another algorithm in both lossy and lossless compression for all medical images tested Experimental results show that the CNN is able to achieve comparable compression performance to popular existing medical image compression schemes such as JPEG2000 and JPEG-LS. The Wavelet-SPIHT algorithm provides PSNR very important values for MRI and CT scan images. |
9-12 |
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