Performance and Analysis of a U-Net Model for Automated Skin Lesion Segmentation

A greater proportion of people are affected by skin cancer, particularly melanoma, which has a higher tendency to metastasize. For Dermatologist, Visual inspections are most challenging & complex task for melanoma detection. To solve this problem, dermoscopic images are analyzed and segmented. Due to the sensitivity involved in surgical operations, existing techniques are unable to achieve higher accuracy. As a result, computer-aided systems are essential to detect & segment dermoscopic images.     In this paper, for segmentation 5000 skin images were taken from the HAM10000 dataset. Prior to segmentation, preprocessing is done by resizing images. A novel U Net structure is a fully convolutional network is presented & implemented using up-sampling and down-sampling technique with Rectified Linear Units (ReLU) for activation functions. The outcomes of proposed methodology shows performance improvement for skin-lesion segmentation with 94.7 % pixel accuracy & 89.2 % dice coefficient compared with existing KNN & SVM techniques

A Survey on Anonymous On-Demand Routing Protocols for MANETs

At present Mobile ad hoc networks (MANET) is used in many real time applications and hence such networks are vulnerable to different kinds of security threats. MANET networks suffered more from security attacks due to use of free wireless communication frequency spectrum and dynamic topology. Therefore it becomes very tough to provide security to MANET under different adversarial environments like battlefields. For MANET, anonymous communications are vital under the adversarial environments, in which the identification of nodes as well as routes is replaced by pseudonyms or random numbers for the purpose of protection. There are many protocols presented for anonymous communication security for MANET, which hide node identities and routes from exterior observers in order to provide anonymity protection. This paper presents review of various anonymous on demand routing protocols

Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes

© 2017 Wong et al.; Published by Cold Spring Harbor Laboratory Press. Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted noncoding RNAs to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes