Venugopal K R

Tejashwini P S, Thriveni J, Venugopal K R

Brain tumor is deliberated as one of the severe health complications which lead to decrease in life expectancy of the individuals and is also considered as a prominent cause of mortality worldwide. Therefore, timely detection and prediction of brain tumors can be helpful to prevent death rates due to brain tumors. Biomedical image analysis is a widely known solution to diagnose brain tumor. Although MRI is the current standard method for imaging tumors, its clinical usefulness is constrained by the requirement of manual segmentation which is time-consuming. Deep learning-based approaches have emerged as a promising solution to develop automated biomedical image exploration tools and the UNet architecture is commonly used for segmentation. However, the traditional UNet has limitations in terms of complexity, training, accuracy, and contextual information processing. As a result, the modified UNet architecture, which incorporates residual dense blocks, layered attention, and channel attention modules, in addition to stacked convolution, can effectively capture both coarse and fine feature information. The proposed SLCA UNet approach achieves good performance on the freely accessible Brain Tumor Segmentation (BraTS) dataset, with an average performance of 0.845, 0.845, 0.999, and 8.1 in terms of Dice, Sensitivity, Specificity, and Hausdorff95 for BraTS 2020 dataset, respectively.

Lata B T, Jansi P K R, Shaila K et al.

Secure Transmission of data packets in Wireless Sensor Networks is an important area of Research. There is a possibility of an attacker creating security holes in the network. Hence, network security and reliability can be achieved by discovering random multiple paths using multiple domains, and forwarding data packets from the source node to the destination node. We have designed, Multiple Domain Routing with Overlap of Nodes (MDRON) and Multiple Domain Routing Without Overlap of Nodes (MDRWON) algorithms, in which packets follow multiple optimized paths simultaneously. The Special node algorithm searches the node which has maximum power and these nodes are used for transferring the packet from one domain to another domain. Simulation results using MATLAB shows that performance is better than Purely Random Propagation (PRP) and Non Repetitive Random Propagation(NRRP) Algorithms.

Srikantaiah K C, Suraj M, Venugopal K R et al.

There is an explosive growth of information in the World Wide Web thus posing a challenge to Web users to extract essential knowledge from the Web. Search engines help us to narrow down the search in the form of Search Engine Result Pages (SERP). Web Content Mining is one of the techniques that help users to extract useful information from these SERPs. In this paper, we propose two similarity based mechanisms; WDES, to extract desired SERPs and store them in the local depository for offline browsing and WDICS, to integrate the requested contents and enable the user to perform the intended analysis and extract the desired information. Our experimental results show that WDES and WDICS outperform DEPTA [1] in terms of Precision and Recall.