Browsing by Author "Gnanamurthy, R.K."

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Executable specification and prototyping of network protocols using UML and Java
(2009) Sekaran, K.C.; Gnanamurthy, R.K.
Network protocols are often implemented in software and / or hardware, and, it becomes essential to design and test them in an efficient manner. This paper explores a dual phase approach for developing network protocols: in the first phase protocols are modeled using UML (Unified Modeling Language) as the formalism, and, in the second phase, use of executable specification and prototyping of protocols based on Java is supported. The prototyping of a protocol is useful for further investigations such as verification ofprotocol properties, test case generation etc. Once the second phase provides a satisfied result, the developers can go ahead in developing and deploying the protocol in the real environment. Yet another objective in this work is to design executable constructs in Java to specify protocols and prototyping them. The protocols designed using this approach ensures sustenance of the models already developed. Illustration of using executable constructs in Java to specify and prototyping ofprotocols, and comparison with native implementations is presented in this paper. �2009 IEEE.
Executable specification and prototyping of network protocols using UML and Java
(2009) Chandra Sekaran, K.C.; Gnanamurthy, R.K.
Network protocols are often implemented in software and / or hardware, and, it becomes essential to design and test them in an efficient manner. This paper explores a dual phase approach for developing network protocols: in the first phase protocols are modeled using UML (Unified Modeling Language) as the formalism, and, in the second phase, use of executable specification and prototyping of protocols based on Java is supported. The prototyping of a protocol is useful for further investigations such as verification ofprotocol properties, test case generation etc. Once the second phase provides a satisfied result, the developers can go ahead in developing and deploying the protocol in the real environment. Yet another objective in this work is to design executable constructs in Java to specify protocols and prototyping them. The protocols designed using this approach ensures sustenance of the models already developed. Illustration of using executable constructs in Java to specify and prototyping ofprotocols, and comparison with native implementations is presented in this paper. Â©2009 IEEE.
Influence of news on individual confidence bias in stock markets
(2016) Mukund, Y.R.; Naresh, V.; Patil, S.; Chandrasekaran, K.; Vijaya, Kumar, V.; Gnanamurthy, R.K.
The Phenomenon of stock markets is a complex one and is something which, has attracted researchers and statisticians for a long time. Complex statistics have long dominated this field where the prediction models are usually stochastic. The advent of machine learning gave us a new way of looking at the problem. Much work has been done in analyzing the stock market to predict the stock index of a particular or-ganization. However, most of the work done is based on the previous stock data and other statistical parameters. Our work, uses data such as the online news articles about a particular company and aims to help a trader conclude the market sentiment towards that company through sentiment analysis. The online raw data is obtained through crawling and is indexed, weighted and subject to sentiment analysis to output the final sentiment of the market. It is found that the Naive-Bayesian Classifier is the more suitable op-tion among the Decision Tree and Random Forests for the task of sentiment analysis. The Final Sentiment Factor ar-rived at, is found to reect the real time market sentiment quite accurately. It is also shown that the sentiment factor can be used as an input to a more complex analysis model. This new model, performs better than the existing models.
Influence of news on individual confidence bias in stock markets
(Association for Computing Machinery acmhelp@acm.org, 2016) Mukund, Y.R.; Naresh, V.; Patil, S.; Chandrasekaran, K.; Vijaya, V.; Gnanamurthy, R.K.
The Phenomenon of stock markets is a complex one and is something which, has attracted researchers and statisticians for a long time. Complex statistics have long dominated this field where the prediction models are usually stochastic. The advent of machine learning gave us a new way of looking at the problem. Much work has been done in analyzing the stock market to predict the stock index of a particular or-ganization. However, most of the work done is based on the previous stock data and other statistical parameters. Our work, uses data such as the online news articles about a particular company and aims to help a trader conclude the market sentiment towards that company through sentiment analysis. The online raw data is obtained through crawling and is indexed, weighted and subject to sentiment analysis to output the final sentiment of the market. It is found that the Naive-Bayesian Classifier is the more suitable op-tion among the Decision Tree and Random Forests for the task of sentiment analysis. The Final Sentiment Factor ar-rived at, is found to reect the real time market sentiment quite accurately. It is also shown that the sentiment factor can be used as an input to a more complex analysis model. This new model, performs better than the existing models.