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 : Separation Based Advanced Energy Efficient Cluster Head Selection Techniques for WSN Authors : Mohit Bakshi , Anugrah Srivastava Click Here For Abstract Abstract :To maximize network lifetime in Wireless Sensor Networks (WSNs) the paths for data transfer are selected in such a way that the total energy consumed along the path is minimized. To support high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non-overlapping subsets called clusters.In this paper we aim to improve network lifetime byusing LEACH based protocol by considering residualenergy and distance of nodes in WSN. We adoptdynamic clustering with dynamic selection of clusterheads in first round and static clustering with dynamicselection of cluster heads from second round. One morereason for network to die early is unbalance clustersize, to handle that number of nodes in the cluster isfixed to a predefined value. We propose two new distance-based clustering routing protocols, which we call DBLEACH and DBEA-LEACH. The first approach (distance-based) selects a cluster head node by considering geometric distance between the candidate nodes to the base station. To further improve DB-LEACH, 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 |
1-6 | |
| 2 | Title : Skin Cancer Detection using Classification Framework of Neural Network Authors : Sultana Bano , Anugrah Srivastava Click Here For Abstract Abstract :Skin cancers are the most standard form of cancers found in humans. The unending bring of this cancer up in the around the world, the high restorative cost and demise rate have organized the early analysis of cancer. The different parts in finding of skin cancer include: a naturally skin cancer classification framework is created and the relationship of skin cancer picture utilizing diverse kind of neural network are contemplated with different sorts of preprocessing. The dataset images are feed into the system, and across different image processing procedure to enhance the image properties. Statistical region merging (SRM) algorithm is based on region growing and merging. At that point the typical skin is expelled from the skin influenced district lastly cancer cell is left in the picture. Helpful data can be removed from these pictures and go to the classification framework for preparing and testing. Two neural networks are used as classifier, Back-propagation neural network (BNN) and Auto-associative neural network (AANN). Recognition accuracy of the 3- layers back-propagation neural network classifier is 91% and auto-associative neural network is 82.6% in the image database that include dermoscopy photo and digital photo. The analysis of work is totally based on MATLAB software. |
7-10 |
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