The growing popularity of Internet Distributed System has drawn enormous attention in business and research communities for handling large number of client requests. These requests are managed by a... Show More

The growing popularity of Internet Distributed System has drawn enormous attention in business and research communities for handling large number of client requests. These requests are managed by a set of servers. However, the requests may not be equally distributed due to their random nature of arrivals. The optimal assignment of the requests to the servers is a well-known NP-hard problem. Therefore, many algorithms have been proposed to address this problem. However, these algorithms suffer from an excessive number of comparisons. In this paper, a Swapping-based Intra- and inter-Server (SIS) load balancing with padding algorithm is proposed for its solution. The algorithm undergoes a three-phase process to balance the loads among the servers. The proposed algorithm is compared with a client-server load balancing algorithm and the performance is measured in terms of the number of load comparisons and load factor. The simulation outcomes show the efficacy of the proposed algorithm.

Content Based Image Retrieval (CBIR) is an emerging research area in computer vision, in which, we yield similar images as per the query content. For the implementation of CBIR system, feature... Show More

Content Based Image Retrieval (CBIR) is an emerging research area in computer vision, in which, we yield similar images as per the query content. For the implementation of CBIR system, feature extraction plays a vital role, where colour feature is quite remarkable. But, due to unevenly colored or achromatic surfaces, the role of texture is also important. In this paper, an efficient and fast CBIR system is proposed, which is based on a fusion of computationally light weighted colour and texture features; chromaticity moment, colour percentile, and local binary pattern (LBP). Using these features with multiclass classifier, the authors propose a supervised query image classification and retrieval model, which filters all irrelevant class images. Basically, this model categorizes and recovers the class of a query image based on its visual content, and this successful classification of image significantly enhances the performance and searching time of retrieval system. Descriptive experimental analysis on benchmark databases confirms the effectiveness of proposed retrieval framework.

This paper introduces a new approach of clustering of text documents based on a set of words using graph mining techniques. The proposed approach clusters (groups) those text documents having... Show More

This paper introduces a new approach of clustering of text documents based on a set of words using graph mining techniques. The proposed approach clusters (groups) those text documents having searched successfully for the given set of words from a set of given text documents. The document-word relation can be represented as a bi-partite graph. All the clustering of text documents is represented as sub-graphs. Further, the paper proposes an algorithm for clustering of text documents for a given set of words. It is an automated system and requires minimal human interaction for the clustering of text documents. The algorithm has been implemented using C++ programming language and observed satisfactory results.

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative... Show More

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.

Knowledge extraction is very much possible from the community graph using graph mining techniques. The authors have studied the related definitions of graph partition in terms of both mathematical... Show More

Knowledge extraction is very much possible from the community graph using graph mining techniques. The authors have studied the related definitions of graph partition in terms of both mathematical as well as computational aspects. To derive knowledge from a particular sub-community graph of a large community graph, the authors start partitioning the large community graph into smaller sub-community graphs. Thus, the knowledge extraction from the sub-community graph becomes easier and faster. The proposed approach of partition is done by detection of edges among the community members of dissimilar community. By studying existing techniques followed by different researchers, the authors propose a new and simple algorithm for partitioning the community graph into sub-community graphs using graph mining techniques. Finally, the authors have considered a benchmark dataset as example which verifies the strength and easiness of the proposed algorithm.

In order to manage the vast information available on web, crawler plays a significant role. The working of crawler should be optimized to get maximum and unique information from the World Wide Web.... Show More

In order to manage the vast information available on web, crawler plays a significant role. The working of crawler should be optimized to get maximum and unique information from the World Wide Web. In this paper, architecture of migrating crawler is proposed which is based on URL ordering, URL scheduling and document redundancy elimination mechanism. The proposed ordering technique is based on URL structure, which plays a crucial role in utilizing the web efficiently. Scheduling ensures that URLs should go to optimum agent for downloading. To ensure this, characteristics of both agents and URLs are taken into consideration for scheduling. Duplicate documents are also removed to make the database unique. To reduce matching time, document matching is made on the basis of their Meta information only. The agents of proposed migrating crawler work more efficiently than traditional single crawler by providing ordering and scheduling of URLs.