Qualification: 
Ph.D, M.Tech, BE
r_prasannakumar@blr.amrita.edu

Dr. R. Prasanna Kumar serves as a Assistant Professor - Selection Grade in the Department of Computer Science and Engineeing, School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru. He has 18 years of experience in teaching. His areas of interest includes Data Analytics, Machine Learning, Theory of Computation, Compiler Design and Python Programming.

Dr. R. Prasanna Kumar has guided 7 M.E projects and is Doctoral Committee Member for 2 Ph.D. Research Scholars in Anna University, Chennai. He has delivered many guest Lectures and acted as resource person in Faculty development programs. He has also acted as program committee member and session chair in national and international conferences.

Education

  • Ph. D.
    Anna University, Chennai
  • M. Tech. in Computer Science and Engineering
    Dr. MGR Educational and Research Institute, Chennai.
  • B. E. in Computer Science and Engineering
    Sri Venkateswara College of Engineering Sriperumbudur, University of Madras, Chennai

Certifications

  • Cisco Networking Academy Certification on “Introduction to Cyber Security course".
  • ORACLE academy certification on “Database Programming with PL/SQL”.
  • Cisco Networking Academy Certification on MODULE – I in CCNA.

Publications

Publication Type: Journal Article

Year of Publication Title

2020

T. .Vignesh and R. Prasanna Kumar, “Land Cover Mapping for LISS IV Multispectral Satellite Images Using Self Attention Convolutional Neural Network”, International Journal of Advanced Science and Technology, vol. 29, pp. 231 - 239, 2020.[Abstract]


Deep learning techniques is the one of the emerging fields in machine learning and it has a various application such as object classification in remote sensing images, medical image analysis, recognition of various patterns, object identification, handwriting generation etc. Deep learning techniques are using supervised and unsupervised approaches to learn multiple levels illustrations of spatial, spectral and textural features of hierarchical architecture for the roles of land cover monitoring including land use and land cover classification and change detection. In this research work, we analyzed the performance of self-attention convolutional neural network (SACNN) based on the application of land cover mapping. Experiment result proved that, classification accuracy of SACNN is higher than traditional CNN and FCM algorithm.

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2020

R. P. Chiney and R. Prasanna Kumar, “Extractive summarization approach for news articles based on selective features”, International Journal of Advanced Science and Technology, vol. 29, pp. 8215-8224, 2020.

2017

R. Prasanna Kumar, “A Survey on Real-Time Automated Gridlock Control System”, International Journal of Innovative Research in Engineering & Management (IJIREM) , vol. 4, no. 1, 2017.

2017

R. Prasanna Kumar, “Hazardous Gas Detection and Alerting Using Sensor ”, International Journal of Innovative Research in Engineering & Management (IJIREM) , vol. 4, no. 1, 2017.

2015

T. Ravi and R. Prasanna Kumar, “Data Perturbation Techniques for Privacy Preservation in Association Rule Mining”, Australian Journal of Basic and Applied Sciences, vol. 9, no. 20, pp. 220-227, 2015.[Abstract]


In recent, data mining is becoming a popular analysis tool to extract knowledge from collection of large amount of data. The protection of the confidentiality of sensitive information in a database becomes a critical issue when releasing data to outside parties. Association analysis is a powerful and popular tool for discovering relationships hidden in large data sets. These process increases the legal responsibility of the parties. So, it is severe to reliably protect their data due to legal and customer concerns. In this paper, a review of the state-of-the-art methods of data perturbation techniques for privacy preservation is presented.

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2014

T. Ravi, R. Prasanna Kumar, and Napa, K. Kumar, “A non synthetic data perturbation technique for privacy preservation in association rule mining”, International Journal of Applied Engineering Research, vol. 9, no. 24, pp. 24311-24320, 2014.[Abstract]


For specific business problems, organizations share data and outsource. Preserving privacy of private data holds a vital role in business analytics. Consulting firms often handle sensitive third party data as part of client projects. By sharing their data, organizations face great risks while most of this sharing takes place with little furtiveness. These process increases the legal responsibility of the parties. So, it is severe to reliably protect their data due to legal and customer concerns. In this paper, a review of the state-of-theart methods for privacy preservation is presented. A novel perturbation technique using non synthetic additive perturbation technique for association rule mining is proposed in this paper. The above technique minimizes information loss that is common in synthetic perturbed data.

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2011

R. Prasanna Kumar, “Quality of Service Based Trust Management System A Review”, International Journal of Engineering Science and Technology , vol. 3, 2011.

2010

R. Prasanna Kumar, “Record Matching over Query Results using Fuzzy Ontological Document Clustering”, 2010.

Publication Type: Conference Paper

Year of Publication Title

2012

G. B. Mohan, R. Prasanna Kumar, and Ravi, T., “Coalescing Clustering and Classification”, in IET Chennai 3rd International on Sustainable Energy and Intelligent Systems (SEISCON 2012), Tiruchengode, India, 2012.[Abstract]


In Data Mining Clustering and Classification are two important techniques. In this paper we make use of large database (Diabetes dataset containing) to perform an integration of clustering and classification technique. We compared the results of simple classification technique (J48 classifier) with the results of integration of clustering (X-Means) and classification (J48) techniques based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis) a data mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate even when the dataset contains missing values.

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2010

R. Prasanna Kumar and Ravi, T., “FFT based data perturbation method in WSN routing”, in 2010 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, India, 2010.[Abstract]


Wireless Sensor Network (WSN) is an emerging technology that shows great promise for various futuristic applications both for mass public and military. The sensing technology combined with processing power and wireless communication makes it lucrative for being exploited in abundance in future. The inclusion of wireless communication technology also incurs various types of security threats. Although the content of sensor messages describing “events of interest” may be encrypted to provide confidentiality, the context surrounding these events may also be sensitive and therefore should be protected from eavesdroppers. An adversary armed with knowledge of the network deployment, routing algorithms, and the base-station (data sink) location can infer the temporal patterns of interesting events by merely monitoring the arrival of packets at the sink, thereby allowing the adversary to remotely track the spatio-temporal evolution of a sensed event. One of the most notable challenges threatening the successful deployment of sensor systems is privacy. Although many privacy-related issues can be addressed by security mechanisms, one sensor network privacy issue that cannot be adequately addressed by network security is source-location privacy.

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2010

G. Kalpana, R. Prasanna Kumar, and Ravi, T., “Classifier based duplicate record elimination for query results from web databases”, in Trendz in Information Sciences Computing(TISC2010), Chennai, India, 2010.[Abstract]


Record matching is an essential step in duplicate detection as it identifies records representing same real-world entity. Supervised record matching methods require users to provide training data and therefore cannot be applied for web databases where query results are generated on-the-fly. To overcome the problem, a new record matching method named Unsupervised Duplicate Elimination (UDE) is proposed for identifying and eliminating duplicates among records in dynamic query results. The idea of this paper is to adjust the weights of record fields in calculating similarities among records. Three classifiers namely weight component similarity summing classifier, support vector machine classifier and one class support vector machine classifier are iteratively employed with UDE where the first classifier utilizes the weights set to match records from different data sources. With the matched records as positive dataset and non duplicate records as negative set, the second classifier identifies new duplicates. Then, one-class support vector machine classifier is employed for further detecting the duplicates. The iteration stops when no duplicates can be identified. Thus, this paper takes advantage of dissimilarity among records from web databases and solves the online duplicate detection problem.

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