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With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research proposes a novel algorithm that implements the Support Vector Machine over a multi-class dataset and is efficient in a distributed environment (here, Hadoop). The idea is to divide the dataset into half recursively and thus compute the optimal Support Vector Machine for this half during the training phase, much like a divide and conquer approach. While testing, this structure has been effectively exploited to significantly reduce the prediction time. Our algorithm has shown better computation time during the prediction phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the dataset grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms., Comment: 8 Pages

Data mining algorithms are originally designed by assuming the data is available at one centralized site.These algorithms also assume that the whole data is fit into main memory while running the algorithm. But in today's scenario the data has to be handled is distributed even geographically. Bringing the data into a centralized site is a bottleneck in terms of the bandwidth when compared with the size of the data. In this paper for multiclass SVM we propose an algorithm which builds a global SVM model by merging the local SVMs using a distributed approach(DSVM). And the global SVM will be communicated to each site and made it available for further classification. The experimental analysis has shown promising results with better accuracy when compared with both the centralized and ensemble method. The time complexity is also reduced drastically because of the parallel construction of local SVMs. The experiments are conducted by considering the data sets of size 100s to hundred of 100s which also addresses the issue of scalability., Comment: Presente in the WCI, Kochi, India, 2015

Support Vector Machines (SVMs) were primarily designed for 2-class classification. But they have been extended for N-class classification also based on the requirement of multiclasses in the practical applications. Although N-class classification using SVM has considerable research attention, getting minimum number of classifiers at the time of training and testing is still a continuing research. We propose a new algorithm CBTS-SVM (Centroid based Binary Tree Structured SVM) which addresses this issue. In this we build a binary tree of SVM models based on the similarity of the class labels by finding their distance from the corresponding centroids at the root level. The experimental results demonstrates the comparable accuracy for CBTS with OVO with reasonable gamma and cost values. On the other hand when CBTS is compared with OVA, it gives the better accuracy with reduced training time and testing time. Furthermore CBTS is also scalable as it is able to handle the large data sets., Comment: Presented in ICACCI, Kochi, India, 2015

Malwares are big threat to digital world and evolving with high complexity. It can penetrate networks, steal confidential information from computers, bring down servers and can cripple infrastructures etc. To combat the threat/attacks from the malwares, anti- malwares have been developed. The existing anti-malwares are mostly based on the assumption that the malware structure does not changes appreciably. But the recent advancement in second generation malwares can create variants and hence posed a challenge to anti-malwares developers. To combat the threat/attacks from the second generation malwares with low false alarm we present our survey on malwares and its detection techniques., Comment: 5 Pages

Exponential growth of the web increased the importance of web document classification and data mining. To get the exact information, in the form of knowing what classes a web document belongs to, is expensive. Automatic classification of web document is of great use to search engines which provides this information at a low cost. In this paper, we propose an approach for classifying the web document using the frequent item word sets generated by the Frequent Pattern (FP) Growth which is an association analysis technique of data mining. These set of associated words act as feature set. The final classification obtained after Na\"ive Bayes classifier used on the feature set. For the experimental work, we use Gensim package, as it is simple and robust. Results show that our approach can be effectively classifying the web document., Comment: 9 Pages

The dynamic web has increased exponentially over the past few years with more than thousands of documents related to a subject available to the user now. Most of the web documents are unstructured and not in an organized manner and hence user facing more difficult to find relevant documents. A more useful and efficient mechanism is combining clustering with ranking, where clustering can group the similar documents in one place and ranking can be applied to each cluster for viewing the top documents at the beginning.. Besides the particular clustering algorithm, the different term weighting functions applied to the selected features to represent web document is a main aspect in clustering task. Keeping this approach in mind, here we proposed a new mechanism called Tf-Idf based Apriori for clustering the web documents. We then rank the documents in each cluster using Tf-Idf and similarity factor of documents based on the user query. This approach will helps the user to get all his relevant documents in one place and can restrict his search to some top documents of his choice. For experimental purpose, we have taken the Classic3 and Classic4 datasets of Cornell University having more than 10,000 documents and use gensim toolkit to carry out our work. We have compared our approach with traditional apriori algorithm and found that our approach is giving better results for higher minimum support. Our ranking mechanism is also giving a good F-measure of 78%., Comment: 5 Pages

The size of web has increased exponentially over the past few years with thousands of documents related to a subject available to the user. With this much amount of information available, it is not possible to take the full advantage of the World Wide Web without having a proper framework to search through the available data. This requisite organization can be done in many ways. In this paper we introduce a combine approach to cluster the web pages which first finds the frequent sets and then clusters the documents. These frequent sets are generated by using Frequent Pattern growth technique. Then by applying Fuzzy C- Means algorithm on it, we found clusters having documents which are highly related and have similar features. We used Gensim package to implement our approach because of its simplicity and robust nature. We have compared our results with the combine approach of (Frequent Pattern growth, K-means) and (Frequent Pattern growth, Cosine_Similarity). Experimental results show that our approach is more efficient then the above two combine approach and can handles more efficiently the serious limitation of traditional Fuzzy C-Means algorithm, which is sensitiveto initial centroid and the number of clusters to be formed., Comment: 11 Pages, 2 figures

Search engine returns thousands of web pages for a single user query, in which most of them are not relevant. In this context, effective information retrieval from the expanding web is a challenging task, in particular, if the query is ambiguous. The major question arises here is that how to get the relevant pages for an ambiguous query. We propose an approach for the effective result of an ambiguous query by forming community vector based on association concept of data minning using vector space model and the freedictionary. We develop clusters by computing the similarity between community vectors and document vectors formed from the extracted web pages by the search engine. We use Gensim package to implement the algorithm because of its simplicity and robust nature. Analysis shows that our approach is an effective way to form clusters for an ambiguous query., Comment: 11 Pages, 1 figure

We investigate the independent points in the sky require to search the periodic gravitational wave, assuming the noise power spectral density to be flat. We have made an analysis with different initial azimuth of the Earth for a week data set. The analysis shows significant difference in the independent points in the sky for the search. We numerically obtain an approximate relation to make trade-off between computational cost and sensitivities. We also discuss the feasibility of the coherent search in small frequency band in reference to advanced LIGO., Comment: 10 Pages, 2 figures

We study the problem of all sky search in reference to continuous gravitational wave (CGW) whose wave-form are known in advance. We employ the concept of Fitting Factor and study the variation in the bank of search templates with different Earth azimuth at t=0. We found that the number of search templates varies significantly. Hence, accordingly, the computational demand for the search may be reduced up to two orders by time shifting the data., Comment: 12 Pages, 3 figures

We investigate the matching of continuous gravitational wave (CGW) signals in an all sky search with reference to Earth based laser interferometric detectors. We consider the source location as the parameters of the signal manifold and templates corresponding to different source locations. It has been found that the matching of signals from locations in the sky that differ in their co-latitude and longitude by $\pi$ radians decreases with source frequency. We have also made an analysis with the other parameters affecting the symmetries. We observe that it may not be relevant to take care of the symmetries in the sky locations for the search of CGW from the output of LIGO-I, GEO600 and TAMA detectors., Comment: 16 pages, 7 figures, 3 Tables, To appear in Int. J. Mod. Phys. D

This thesis is devoted to the investigations of gravitational wave (GW) data analysis from a continuous source e.g. a pulsar, a binary star system. The first Chapter is an introduction to gravitational wave and second Chapter is on the data analysis concept for the detection of GW. In third Chapter we developed the Fourier Transform (FT) of a continuous gravitational wave (CGW) for ground based laser interferometric detectors for the data set of one day observation time incorporating the effects arising due to rotational as well as orbital motion of the earth. The transform is applicable for arbitrary location of detector and source. In Chapter four we have generalized the FT for the data set for (i) one year observation time and (ii) arbitrary observation time. As an application of the transform we considered spin down and N-component signal analysis. In fifth Chapter we have made an analysis of the number of templates required for matched filter analysis as applicable to these sources. We have employed the concept of {\it Fitting Factor (FF)}; treating the source location as the parameters of the signal manifold and have studied the matching of the signal with templates corresponding to different source locations. We have investigated the variation of {\it FF} with source location and have noticed a symmetry in template parameters, $\theta_T$ and $\phi_T$. It has been found that the two different template values in source location, each in $\theta_T$ and $\phi_T$, have same {\it FF}. We have also computed the number of templates required assuming the noise power spectral density $S_n(f)$ to be flat. It is observed that higher {\it FF} requires exponentially increasing large number of templates. Appendix contains the source codes developed for the computation., Comment: Ph.D Thesis, based on gr-qc/0111050, 0207119 and 0209009 (All three papers to be apppear in MNRAS), 179 Pages and 39 figures

We have studied the problem of all sky search in reference to continuous gravitational wave particularly for such sources whose wave-form are known in advance. We have made an analysis of the number of templates required for matched filter analysis as applicable to these sources. We have employed the concept of {\it fitting factor} {\it (FF)}; treating the source location as the parameters of the signal manifold and have studied the matching of the signal with templates corresponding to different source locations. We have investigated the variation of FF with source location and have noticed a symmetry in template parameters, $\theta_T$ and $\phi_T$. It has been found that the two different template values in source location, each in $\theta_T$ and $\phi_T$, have same {\it FF}. We have also computed the number of templates required assuming the noise power spectral density $S_n(f)$ to be flat. It is observed that higher {\it FF} requires exponentially increasing large number of templates., Comment: Accepted in MNRAS, 14 pages, 5 figures

In this paper we obtain the Fourier Transform of a continuous gravitational wave. We have analysed the data set for (i) one year observation time and (ii) arbitrary observation time, for arbitrary location of detector and source taking into account the effects arising due to rotational as well as orbital motion of the earth. As an application of the transform we considered spin down and N-component signal analysis., Comment: Accepted in MNRAS, 14 pages, 4 figures

We present the Fourier Transform of a continuous gravitational wave. We have analysed the data set for one day observation time and our analysis is applicable for arbitrary location of detector and source. We have taken into account the effects arising due to rotational as well as orbital motions of the earth., Comment: Accepted in MNRAS, 22 pages, 9 figures

We present the Fourier Transform of continuous gravitational wave for arbitrary location of detector and source and for any duration of observation time in which both rotational motion of earth about its spin axis and orbital motion around sun has been taken into account. We also give the method to account the spin down of continuous gravitational wave., Comment: 3 pages