MCA, M.Tech

Raji Ramachandran currently serves as an Assistant Professor (Senior Grade) at the Department of Computer Science Applications at Amrita School of Engineering, Amritapuri. She has completed DOEACC Level B examination, deemed professionally equivalent to M. C. A. degree course by the Govt. of India. She has completed M. Tech. and currently pursuing her doctoral studies.


Publication Type: Conference Paper

Year of Publication Title


Raji Ramachandran, Nidhin, R., and Shogil, P. P., “Anomaly Detection in Role Administered Relational Databases - A Novel Method”, in International Conference on Computational Intelligence and Data Science, North Cap University, Guru gram, India, 2018.


Raji Ramachandran, Arya, P., and Jayanthy, P. G., “A Novel Method for Intrusion Detection in Relational Databases”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.[Abstract]

The importance of data security and confidentiality increases day by day, since for most companies and organizations data remains as the most important asset. Standard database security measures like access control mechanisms, authentication and encryption technologies are of little help when it comes to preventing data theft from insiders. By incorporating intrusion detection mechanisms, we can improve the security features of a Database Management System (DBMS). In this paper we propose a novel method for detecting intrusions in databases using data mining techniques like clustering and classification. Experiments show that our method outperforms other methods with higher accuracy and reduced false alarm rate.

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Raji Ramachandran, Nair, D. P., and Jasmi, J., “A Horizontal Fragmentation Method Based on Data Semantics”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India, 2016.[Abstract]

The need for distributed database systems increases day by day due to the need of running organizations from different locations. The efficiency and performance of distributed query processing depends on fragmentation and allocation method used. Usually, the fragmentation solutions are based on empirical data and analyzing query patterns. These methods can only be applied for the existing distributed databases and cannot be applied during initial database design. Also, most of the existing methods does not consider the semantics or relationships of the data being fragmented. Better fragments can be produced if data semantics are considered during fragmentation. Related data can be obtained from dataset using clustering technique. In this paper we propose a new horizontal fragmentation scheme based on clustering. Performance and time improvements are found while performing with clustered fragments.

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K. Ka George, Dr. Santhosh Kumar C., Panda, Ab, Raji Ramachandran, Das, K. Aa, and S. Veni, “Minimizing the false alarm probability of speaker verification systems for mimicked speech”, in 2015 International Conference on Computing and Network Communications, CoCoNet 2015, 2015, pp. 703-709.[Abstract]

Speaker verification (SV) systems need to be robust to mimicked voices of target speakers as non-target trials to make them usable in critical applications. However, the performance of SV systems for mimicked voice test conditions has not been extensively explored. © 2015 IEEE.

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Sandhya Harikumar and Raji Ramachandran, “Hybridized fragmentation of very large databases using clustering”, in 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2015, 2015.[Abstract]

Due to the ever growing needs of managing huge volume of data, together with the desire for consistent, scalable, reliable and efficient retrieval of information, an intelligent mechanism to design the storage structure for distributing the databases has become inevitable. The two critical facets of distributed databases are data fragmentation and allocation. Existing fragmentation techniques are based on the frequency and type of the queries as well as the statistics of the empirical data. However, very limited work is done to fragment the data based on the pattern of the tuples and the attributes responsible for such patterns. This paper presents a unique approach towards hybridized fragmentation, by applying subspace clustering algorithm, to come up with a set of fragments which partitions the data with respect to tuples as well as attributes. Projected clustering is the one that determines the clusters in the subspaces of high dimensional data. This concept leads to find the closely correlated attributes for different sets of instances thereby giving good hybridized fragments for distributed databases. Experimental results show that fragmenting the database based on clustering, results in reduced database access time as compared to the fragments chosen at design time using certain statistics. © 2015 IEEE.

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