DG Vaishnav College

Chennai, India

DG Vaishnav College

Chennai, India

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Santhanam T.,Dg Vaishnav College | Subhajini A.C.,Noorul Islam University
Journal of Computer Science | Year: 2011

Problem statement: Accurate weather forecasting plays a vital role for planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting. Approach: A neural network model has been developed for weather forecasting, based on various factors obtained from meteorological experts. This study evaluates the performance of Radial Basis Function (RBF) with Back Propagation (BPN) neural network. The back propagation neural network and radial basis function neural network were used to test the performance in order to investigate effective forecasting technique. Results: The prediction accuracy of RBF was 88.49%. Conclusion: The results indicate that proposed radial basis function neural network is better than back propagation neural network. © 2011 Science Publications.


Santhanam T.,Dg Vaishnav College
Proceedings of the 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, PRIME 2013 | Year: 2013

The latest trend in authenticating users is by using the potentiality of biometrics. Keystroke dynamics is a behavioural biometrics which captures the typing rhythms of users and then authenticates them based on the dynamics captured. In this paper, a detailed study on the evolution of keystroke dynamics as a measure of authentication is carried out. This paper gives an insight from the infancy stage to the current work done on this domain which can be used by researchers working on this topic. © 2013 IEEE.


Vijayaraghavan K.,Prathyusha Institute of Technology and Management | Nalini S.P.K.,Dg Vaishnav College
Biotechnology Journal | Year: 2010

This article recapitulates the scientific advancement towards the greener synthesis of silver nanoparticles. Applications of noble metals have increased throughout human civilization, and the uses for nano-sized particles are even more remarkable. "Green" nanoparticle synthesis has been achieved using environmentally acceptable solvent systems and eco-friendly reducing and capping agents. Numerous microorganisms and plant extracts have been applied to synthesize inorganic nanostructures either intracellularly or extracellularly. The use of nanoparticles derived from noble metals has spread to many areas including jewelery, medical fields, electronics, water treatment and sport utilities, thus improving the longevity and comfort in human life. The application of nanoparticles as delivery vehicles for bactericidal agents represents a new paradigm in the design of antibacterial therapeutics. Orientation, size and physical properties of nanoparticles influences the performance and reproducibility of a potential device, thus making the synthesis and assembly of shape- and size-controlled nanocrystals an essential component for any practical application. This need has motivated researchers to explore different synthesis protocols. © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Premalatha K.,Dg Vaishnav College | Raghavan P.S.,Madras Christian College | Viswanathan B.,Indian Institute of Technology Madras
Applied Catalysis A: General | Year: 2012

The spinel type chromites MCr 2O 4 (M = Co, Ni and Cu) were synthesized by the co-precipitation method and have been characterized by a number of analytical tools. The efficacy of the synthesized system as catalysts for the oxidation of benzyl alcohol by molecular oxygen has been studied wherein it has been observed that benzaldehyde is formed with 100% selectivity. The catalytic activity of the chromites follows the order Co > Ni > Cu. The effect of the reaction parameters like the amount of the catalyst and reaction time was studied and it has been observed that the surface area and chemical hardness of the catalysts play a significant role. © 2012 Elsevier B.V. All rights reserved.


Muralidharan R.,Dg Vaishnav College | Narasimhan D.,Madras Christian College
Journal of Applied Pharmaceutical Science | Year: 2012

This study is a documentation of medicinal plants used for gastro intestinal problem by villagers around Gingee hills of Villupuram District. A total of 28 Dicot plants belong to 24 families are used to cure gastrointestinal problem. Isolation of active principles and anti-microbial activity should be studied on these medicinally plants. Emphasis also made for proper documentation and conservation of these medicinal plants.


Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. Cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-Means and Fuzzy C-Means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in Fuzzy C-Means algorithm than the k-Means. © 2014 Elsevier B.V.


Velmurugan T.,Dg Vaishnav College | Santhanam T.,Dg Vaishnav College
Information Technology Journal | Year: 2011

Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging in different groups are dissimilar. Clustering is an unsupervised learning technique. Data clustering is the subject of active research in several fields such as statistics, pattern recognition and machine learning. From a practical perspective clustering plays an outstanding role in data mining applications in many domains. The main advantage of clustering is that interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. Clustering algorithms can be applied in many areas, for instance marketing, biology, libraries, insurance, city-planning, earthquake studies and www document classification. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms. A variety of algorithms have recently emerged that meet these requirements and were successfully applied to real-life data mining problems. They are subject of this survey. Also, this survey explores the behavior of some of the partition based clustering algorithms and their basic approaches with experimental results. © 2011 Asian Network for Scientific Information.


Santhanam T.,DG Vaishnav College | Sundaram S.,DG Vaishnav College
Journal of Computer Science | Year: 2010

Problem statement: This study used data mining modeling techniques to examine the blood donor classification. The availability of blood in blood banks is a critical and important aspect in a healthcare system. Blood banks (in the developing countries context) are typically based on a healthy person voluntarily donating blood and is used for transfusions or made into medications. The ability to identify regular blood donors will enable blood banks and voluntary organizations to plan systematically for organizing blood donation camps in an effective manner. Approach: Identify the blood donation behavior using the classification algorithms of data mining. The analysis had been carried out using a standard blood transfusion dataset and using the CART decision tree algorithm implemented in Weka. Results: Numerical experimental results on the UCI ML blood transfusion data with the enhancements helped to identify donor classification. Conclusion: The CART derived model along with the extended definition for identifying regular voluntary donors provided a good classification accuracy based model. © 2010 Science Publications.


Velmurugan T.,DG Vaishnav College | Santhanam T.,DG Vaishnav College
Journal of Computer Science | Year: 2010

Problem statement: Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging to different groups are dissimilar. Clustering is an unsupervised learning technique. The main advantage of clustering is that interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. Clustering algorithms can be applied in many domains. Approach: In this research, the most representative algorithms K-Means and K-Medoids were examined and analyzed based on their basic approach. The best algorithm in each category was found out based on their performance. The input data points are generated by two ways, one by using normal distribution and another by applying uniform distribution. Results: The randomly distributed data points were taken as input to these algorithms and clusters are found out for each algorithm. The algorithms were implemented using JAVA language and the performance was analyzed based on their clustering quality. The execution time for the algorithms in each category was compared for different runs. The accuracy of the algorithm was investigated during different execution of the program on the input data points. Conclusion: The average time taken by K-Means algorithm is greater than the time taken by K-Medoids algorithm for both the case of normal and uniform distributions. The results proved to be satisfactory. © 2010 Science Publications.


Velmurugan T.,Dg Vaishnav College | Santhanam T.,Dg Vaishnav College
European Journal of Scientific Research | Year: 2010

Data mining approach and its technology used to extract the unknown pattern from the large set of data for the business and real time applications. The unlabeled data from the large data set can be classified in an unsupervised manner using clustering and classification algorithms. Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. The result of the clustering process and its domain application efficiency are determined through the algorithms. This research work deals with two of the most delegated clustering algorithms namely centroid based K-Means and representative object based Fuzzy C-Means. These two algorithms are implemented and the performance is analyzed based on their clustering result quality. The behavior of both the algorithms depends on the number of data points as well as on the number of clusters. The input data points are generated by two ways, one by using normal distribution and another by applying uniform distribution (by Box-Muller formula). The performance of the algorithm is investigated during different execution of the program on the input data points. The execution time for each algorithm is also analyzed and the results are compared with one another. © EuroJournals Publishing, Inc. 2010.

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