Browsing by Author "Bhuvan, B.M."

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Algorithmic approach for strategic cell tower placement
(IEEE Computer Society help@computer.org, 2015) Kashyap, R.; Bhuvan, B.M.; Chamarti, S.; Bhat, T.; Jothish, M.; Annappa, B.
The increasing number of cell phone users and the usage of cell phones in remote areas have demanded the network service providers to increase their coverage and extend it to all places. Cost of placing a cell tower depends on the height and location, and as it can be very expensive, they have to be placed strategically to minimize the cost. The research aims to find a simple implementable algorithm which effectively determines the strategic positions of the cell towers. Given a satellite image and population density, and obtaining topographical information from GIS (Geographic Information Systems), potential tower locations can be determined. Applying the proposed three stage algorithm, out of many potential tower locations only the indispensible and optimal locations can be chosen. In addition, this algorithm helps to find out the optimal height of the tower at a chosen potential tower location. Hence, the proposal will provide cost-effective way for tower placement specifying their optimal position and height to cover any area and population. Â© 2014 IEEE.
Evaluation of Machine Learning Frameworks on Bank Marketing and Higgs Datasets
(Institute of Electrical and Electronics Engineers Inc., 2015) Bhuvan, B.M.; Jain, S.; Rao, V.D.; Patil, N.; Raghavendra, G.S.
Big data is an emerging field with different datasets of various sizes are being analyzed for potential applications. In parallel, many frameworks are being introduced where these datasets can be fed into machine learning algorithms. Though some experiments have been done to compare different machine learning algorithms on different data, these experiments have not been tested out on different platforms. Our research aims to compare two selected machine learning algorithms on data sets of different sizes deployed on different platforms like Weka, Scikit-Learn and Apache Spark. They are evaluated based on Training time, Accuracy and Root mean squared error. This comparison helps us to decide what platform is best suited to work while applying computationally expensive selected machine learning algorithms on a particular size of data. Experiments suggested that Scikit-Learn would be optimal on data which can fit into memory. While working with huge, data Apache Spark would be optimal as it performs parallel computations by distributing the data over a cluster. Hence this study concludes that spark platform which has growing support for parallel implementation of machine learning algorithms could be optimal to analyze big data. Â© 2015 IEEE.
Semantic sentiment analysis using context specific grammar
(Institute of Electrical and Electronics Engineers Inc., 2015) Bhuvan, B.M.; Rao, V.D.; Jain, S.; Ashwin, T.S.; Guddeti, G.
The increasing number of e-commerce and social networking sites are producing large amount of data pertaining to reviews of a product, restaurant etc. A keen observation reveals that the text data gathered from any social review site are specific to a context and are subjective in nature promoting varied perceptions of sentiments. The novel idea is to define context specific grammar as semantics for a particular domain. Our research aims to develop a scalable model where features obtained from matching semantic patterns are used to predict the sentiment polarity of movie reviews and also provide a sentiment score for each review. The proposed model is intended to be flexible so that it could be applied to any domain by redefining the semantics specific to that domain. There are many other models which give accuracies greater than 80% using various methods. A study suggests that 70% accurate program is as good as humans as they have varied perceptions of sentiment about a movie review as it is a subjective summary of a movie. Our model might give lesser accuracy but it uses a cognitive approach trying to catch these varied perceptions by learning from a combination of positive and negative grammars. Analyzing results from various experiments we find that Logistic Regression with SGD on Apache Spark performs better with accuracy of 64.12% while being highly scalable. High dependency on the grammars is a limitation of the model. Improvements can be done by defining different quality and quantity of grammars. Â© 2015 IEEE.