Abha Thakral

Robustness Analysis of Buffer-Based Routing Algorithms in Wireless Mesh Network

Wireless Mesh Network arises as a promising innovation for providing quick and productive communication for which numerous algorithms have been proposed in networking infrastructure. For routing there are various performance parameters such as throughput, network congestion, resiliency, fairness, robustness, network jitter, delay, stability, optimality, simplicity, completeness, etc. Robustness provides the capability to deal with all the failures that come across during the connection in the network to increase the network performance. In this paper, we have shown and analyzed three algorithms namely robustness parameter Resilient multicasting, Resilient Opportunistic Mesh Routing for Wireless Mesh Network (ROMER) and Buffer-Based Routing (BBR) in Wireless Mesh Networks. We have shown that network performance of BBR is better than these approaches which is analyzed through various parameters such as network congestion, network throughput and resiliency.

“Space in space”: Cyber security capabilities in Indian context

With expanding IT infrastructure and exponential growth of internet and its ever-pervasive nature, world is now connected and communicating beyond the barriers of space and time. But as the technology empowers us, it also exposes us to new threats and cyber-crimes. These crimes do not recognize any boundaries and impinge upon nearly every cross-section of the society. Cyber security as a concept, as a need of the hour may have gained ground, but the gaps need to be bridged to face the challenges of ever-growing cyber-crimes.

Novel technique for prediction analysis using normalization for an improvement in K-means clustering

Clustering is the unsupervised classification of spatterns in a dataset. Clustering is widely used to discover distributed patterns and classify them as clusters. Clustering algorithms uses a similarity measure based on distance. In order to cluster data points, k-means uses Euclidean distance measure and central point choice. In the K-means clustering, data points will be stacked and a central point is chosen. From the central point chosen, Euclidean distance will be computed and on that basis clusters will be assigned to the data points. One of the drawbacks of K-means is that numbers of clusters has to be provided due to which some data points remains un-clustered. In this paper, we propose a clustering calculation through which number of clusters can be characterised naturally. The proposed technique will improve accuracy and decrease clustering time moreover cluster quality will also be improved through multiple iterations.

Predictive analysis of dengue and malaria

Massive volumes of information or data are being produced each and every day in health care like hospital, clinics, doctors offices etc. Examining these enormous and intricate data groups is crucial to succeed new discoveries. An emergent research field known as Data and Visual Analytics is pertained with creating information and developing insight from enormous, dynamic, equivocal and perhaps conflicting digital data for better understanding and efficient decision making. The visualization, human interaction, and data layout are centered on multi-scale representations, which will help it to access the information, the derived data and the inference procedures related with it at various levels of resolution. In this paper, we have studied the number of incidences in dengue and malaria in different years and according to it, the output is attained through the predictive analysis model techniques and according to this an inference is drawn.