R
Rajesh Kumar Dhanaraj
Researcher at Galgotias University
Publications - 43
Citations - 507
Rajesh Kumar Dhanaraj is an academic researcher from Galgotias University. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 2, co-authored 12 publications receiving 13 citations.
Papers
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Journal ArticleDOI
A Heuristic Angular Clustering Framework for Secured Statistical Data Aggregation in Sensor Networks.
Lalitha Krishnasamy,Rajesh Kumar Dhanaraj,D. Ganesh Gopal,Thippa Reddy Gadekallu,Mohamed K. Aboudaif,Emad Abouel Nasr +5 more
TL;DR: Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments.
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Black Hole and Sink Hole Attack Detection in Wireless Body Area Networks
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Design of pseudo-random number generator from turbulence padded chaotic map
Sathya Krishnamoorthi,Premalatha Jayapaul,Rajesh Kumar Dhanaraj,Vani Rajasekar,Balamurugan Balusamy,SK Hafizul Islam +5 more
TL;DR: In this paper, a Pseudo-random number generator (PRNG) with this architecture is devised to generate random bit sequences from secret keys, which are tested with NIST SP 800-22 statistical test suite and were shown to have good randomness.
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Random Forest Bagging and X-Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data
Rajesh Kumar Dhanaraj,Vinothsaravanan Ramakrishnan,M. Poongodi,Lalitha Krishnasamy,Mounir Hamdi,Ketan Kotecha,V. Vijayakumar +6 more
TL;DR: In this article, a random forest bagging X-means SQL Query Clustering (RFBXSQLQC) technique is proposed to enhance the antipattern detection accuracy.
Journal ArticleDOI
Bootstrap aggregative mean shift clustering for big data anti-pattern detection analytics in 5G/6G communication networks
Vinothsaravanan Ramakrishnan,Palanisamy Chenniappan,Rajesh Kumar Dhanaraj,Ching-Hsien Hsu,Ching-Hsien Hsu,Ching-Hsien Hsu,Yingyuan Xiao,Fadi Al-Turjman +7 more
TL;DR: The results show that the BAMSQLQC technique can increase the accuracy and reduce the time complexity of anti-patterns discovery for effective big data analytics in 5G networks compared to existing techniques.