PURNA CHANDRA SETHI
@rdwu.ac.in
ASSISTANT PROFESSOR, DEPARTMENT OF COMPUTER SCIENCE
RAMA DEVI WOMEN'S UNIVERSITY
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computational Theory and Mathematics, Information Systems, Computer Networks and Communications
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Purna Chandra Sethi, Neelima Sahu, and Prafulla Kumar Behera
Springer Science and Business Media LLC
Nowadays security is main issue during transmission of data. Among many cryptographic methods, ECC is the public key asymmetric cryptosystem which provides faster computation over smaller size in comparison to other asymmetric key cryptosystems. In this paper, we have proposed a group security algorithm using the ECC cryptography algorithm. The group security is applied to ECC in terms of m-gram selection called ECC m-gram selection. Due to the group security implementation in terms of common grams, processing speed will be faster in comparison to individual item security. We have also made the comparison study between the traditional ECC algorithm with the proposed group security algorithm using generalized frequent-common gram selection for depicting lesser time requirements to achieve better security for the whole process.
Aradhana Sahoo, Pratyasha Mohanty, and Purna Chandra Sethi
Springer Nature Singapore
Purna Chandra Sethi and Prafulla Kumar Behera
IOP Publishing
Abstract The COVID-19 pandemic has resulted in a dramatic change in our day to day life. It affected not only the normal working of many organizations but also the traditional classroom teaching and learning methodologies. Since everyone has to maintain social distancing to follow COVID-19 guidelines, work from home is being preferred as the best alternative as a preventive measure from spreading the pandemic. In its severe impact, schools, colleges, and universities were shut down, pushing nearly 1.2 billion students out of the classroom. As a result, the education system has to suddenly adapt to a distinctive online-based e-learning approach over digital platforms. Research tells that online learning motivated more towards the retention of online resources with less cost in terms of money and time. But, it has also brought many challenges along the way. In this research work, we focus on some of the major challenges such as information security and network bandwidth problem during online teaching. The related security measures being adopted in our research work to secure personal information during any online teaching and learning process. We also focus on some basic learning models for provisioning effective online-based teaching and learning.
Ishwar K. Sethi and Anil K. Jain
Elsevier
Purna Chandra Sethi and Prafulla Kumar Behera
IEEE
In the present scenario, most of the computing operations are performed over the Internet. Many companies provide their services using Internet, so networking services becomes more important these days. Hence, there is high demand for secured management of information, along with faster processing of operations. Due to increased demand for network services, there is a need to increase the performance of these services. The gradual increase in the amount of important information increases the packet payloads. EHMA is a faster searching algorithm that reduces the searching time significantly. In the original paper [1], EHMA is implemented in two tiers, but this paper considers the implementation of EHMA in three tiers. It follows incremental clustering algorithm for grouping clusters according to their impact factors. This three tier implementation of EHMA improves the security of the information as it uses SHA-256 for security.
Purna Chandra Sethi and Chinmay Dash
IEEE
High impact event represents the information which are frequently used. The frequently used information is maintained in different clusters such that it can be accessed quickly without involving much searching time. Clustering methods are one of the key steps that lead to the transformation of data to knowledge. Clustering algorithms aims at partitioning an initial set of objects into disjoint groups (clusters) such that objects in the same subset are more similar to each other than objects in different groups. In this paper we present a generalization of the k-Windows clustering algorithm in metric spaces by following a selective Repeat ARQ protocol having fixed window size for accurate information transmission. The original algorithm was designed to work on data with numerical values. The proposed generalization does not assume anything about the nature of the data, but only considers the distance function over the data set. The efficiency of the proposed approach is demonstrated on msnbc data sets. Genetic algorithm approach is used to detect and predict high-impact events in different areas such as automotive manufacturing, networking for data transmission, etc. While the high-impact events occurs infrequently, they are quite costly, means they have high-impact on the system key performance indicator. This approach is based on mining these types of events and its impact on the total process execution. The classified data are clustered for future implementation which have similar feature. Due to the clustering concept the clustered data can be used for various applications, which makes it robust. The parameters are optimized for best solution. This approach is tested on high impact events that occurs in networking, during transmission and it was found to be robust, highly accurate and with less probability of fault, for prediction of future occurrences of such events.