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Articles by A. Punitha
Total Records ( 2 ) for A. Punitha
  A. Punitha and J. Martin Leo Manickam
  Vehicular Ad-hoc Network (VANET) is a network where vehicles within the network can communicate with each other using the help of the road side equipment. There are wide applications using VANET and hence has emerged as an important technology in the automobile industry. Since, VANET is very helpful in providing the real time traffic information to the vehicle users, offering notification related to the post crash, street hassle handling and traffic vigilance ability, it is widely used and in good demand. But, VANETs are prone to several threats due to its security related challenges like reduced tolerance for error, very high mobility, etc and high rate of attacks like eavesdropping, impersonating, session hijacking on the vehicular network. Hence, safety is a high priority in VANET. In this study, we develop a group authentication mechanism to securely form a group of vehicle that can communicate with each other efficiently.
  A. Punitha and T. Santhanam
  One of the major challenges in medical domain is the extraction of intelligible knowledge from medical diagnosis data. It is quite common among the researching community to apply Principal Component Analysis (PCA) for the extraction of prominent features and to use feature correlation method for redundant features removal. This paper discusses a three-phase approach selection technique to extract features for further usage in clinical practice for better understanding and prevention of superfluous medical events. In the first phase PCA is employed to extract the relevant features followed by the elimination of redundant features using the class correlation and feature correlation technique in phase two and in the final phase Learning Vector Quantization (LVQ) network is utilized for classification. The proposed method is validated upon Wisconsin Breast Cancer Database (WBCD), which is a very well known dataset obtained from the UCI machine-learning repository. The abridged feature set and classification accuracy are found to be satisfactory.
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