Akhilesh K. Sharma

An Efficient Approach for Inverted Index Pruning Based on Document Relevance

Information Retrieval deals with retrieving documents from a large collection that matches the information need of a user. Efficient retrieval is based on the proper storage of the inverted index. There have been many techniques for reducing the size of the inverted index. Static index pruning is one such technique, which is used to reduce the index size. This paper investigates a static index pruning approach which is useful to reduce the index size. The proposed approach prunes the entire document from the index based on its importance and relevance of top-k results. The elimination takes place on the basis of the score of the individual document. Experiments have been conducted on the FIRE text collection. Based on the results, it was found that for specific collections, the proposed model gives better precision values for the retrieval of top 30 and above documents.

An Analytical approach based on self organized maps (SOM) in Indian classical music raga clustering

This paper is mainly focusing the aspects regarding the Self organized maps in the recognition of the Ragas and the strategy behind the Digital signal processing for the raga recognition and clustering of the same. Paper mainly describes all the features extraction mechanism for the SOM input and we devised an algorithm for creating the clusters of the raga based on their PCP (pitch class profiles) and the onsets detected. Our strategy is very promising that its providing better clusters of the raga patterns and the raga segments are very much clearly be distinguished from the other ragas, as we compared them with the formation of the key attributes. The Indian classical music history is very old and the need for identifying the music based on the raga onsets is very much helping for the music professionals and domestic users for identifying and detecting the raga types and using the same without the help or availability of any experts nearby. Thus our strategy is very promising in nature for the novice users and practitioners as well as home users. The very early learners would also find it very interesting and supportive after due course of frequent uses of it. At last we provided the future possibilities for the enhancements.

Analytical Approach on Indian Classical Raga Measures by Feature Extraction with EM and Naive Bayes

analysis is the main task in the musical information retrieval (MIR) systems. In this paper an analytical study based on these MIR techniques has been carried out to perform analysis of the Indian classical music and Indian ragas. The ragas are further classified into various thaats and their pitch class profiles and statistical measures. This paper demonstrates the strategy by which the various raga can be categorized using these statistical measures. The choices of algorithm used are the EM algorithm and the Naive bayes algorithm. Indian classical music is very popular because of the musical styles and the emotions it can reveal. Thus MIR (musical information retrieval) and its musical analysis is a very good choice for the researchers who have both knowledge of music and computer background. This paper includes the Matlab programming environment and toolbox for the effective result simulations. The EM and naive bayes algorithm have been utilized and the open source platform has been used for the rest of the work. Keywordsalgorithm, naive bayes, Indian classical music, music information retrieval, classification, clustering.

A New Approach for Compression on Textual Data

Data compression algorithms are used to reduce the redundancy, storage requirement and efficiently reduce communication costs. Data Encryption is used to protect our data from unauthorized users. Due to the unprecedented explosion in the amount of digital data transmitted via the Internet, it has become necessary to develop a compression algorithm that can effectively use available network bandwidth and also taking into account the security aspects of the compressed data transmitting over Internet. This paper presents an encoding technique that offers high compression ratios. An intelligent and reversible transformation technique is applied to source text to improve the capability of algorithms to compress the transmitted data. The results prove that the proposed method performs better than many other popular techniques with respected to compression ratio and the speed of compression and requires some additional processing on the server/nodes.

Rigorous Design of Moving Sequencer Atomic Broadcast in Distributed Systems

This article investigates a new mechanism to design moving sequencer based atomic broadcast in distributed systems. There are two very crucial observations have been drawn from [1], (i) fixed sequencer based atomic broadcast mechanisms are build upon either unicast broadcast (UB) or broadcast broadcast (BB) or unicast unicast broadcast (UUB) variant and (ii) moving sequencer based atomic broadcast can also be designed with the help of any of three variants (UB, BB or UUB). The various mechanisms given for moving sequencer based atomic broadcast like RMP [2], DTP [3] and Pin Wheel [4] are based on broadcast broadcast (BB) variant of fixed sequencer atomic broadcast. While mechanism proposed in [5] is based on unicast broadcast (UB) variant of fixed sequencer atomic broadcast. This work proposes a mechanism that relies on unicast unicast broadcast (UUB) variant of fixed sequencer atomic broadcast in order to build moving sequencer atomic broadcast. This work does not give any comparison with other mechanisms but it presents a new way to design moving sequencer atomic broadcast. B formal method [6] has been used to design different models for atomic broadcast and ProB [7] model animator and checker tool has been used to verify it.

On Using Chi Square Based Term Scoring for Static Index Pruning

In this study, a novel technique for static index pruning based on document relevance with chi square scoring is presented. The term presents in the document are score using the chi-square statistical method. It takes into account the terms occurrences and the expected frequency in the document. The expected frequency is estimated by the terms entropy and the dispersion value of the document. During the computation of document score, the associated terms are averaged by their chi-square based score. The performance of the proposed algorithm is tested on the FIRE 2010 dataset. Experimental results show that the proposed approach increases the Precision for pruning level 60 and above for the top-30 documents retrieval.

Phrase Term Static Index Pruning Based on the Term Cohesiveness

This paper proposes a static index pruning method for phrase queries which is based on the cohesive similarity between terms. The co-occurrence between terms is model by terms cohesiveness within document. The less relevant terms gets pruned away while assuring that there is no change in the top-k results. The proposed method creates an effective pruned index. This method also considers the term proximity based on the term frequency and the terms informative ness. The experiments were conducted on a number of different standard text collections, and analysis of the results shows promising results and is comparable with the existing static pruning method. It is an advantage of the proposed approach that it can be applies to standard inverted index for phrase queries also.