Ch. Satyananda Reddy

A Hybrid Method for Query based Automatic Summarization System

Automatic text summarization is one of the research goals of Natural Language Processing which relieves humans from studying each and every line in a text document to understand the underlying concepts in it. Automatic text summarization is aimed to create a brief outline of a given text covering the important points in the text. Automatic text summarization can be generic or query specific. This paper is focused on Query specific text summarization where a summary of the given text is constructed based on the given query. Query specific text summarization is based on the calculation of the relationship between sentences in the text document and the query given. Several statistical techniques and linguistic techniques have been developed to find the relationship between the given query and the sentences in the document. These methods when used alone could not give desired accuracy in the results. In this paper a sentence scoring method is defined based on existing sentence scoring methods. It attempts to combine the individual results of these methods to give a better assessment of the relationship between the sentences.

Trust Based Participant Driven Privacy Control in Participatory Sensing

Widespread use of sensors and multisensory personal devices generate a lot of personal information. Sharing this information with others could help in various ways. However, this information may be misused when shared with all. Sharing of information between trusted parties overcomes this problem. This paper describes a model to share information based on interactions and opinions to build trust among peers. It also considers institutional and other controls, which influence the behaviour of the peers. The trust and control build confidence. The computed confidence bespeaks whether to reveal information or not thereby increasing trusted cooperation among peers.

Extractive Text Summarization Using Lexical Association and Graph Based Text Analysis

Keyword extraction is an important phase in automatic text summarization process because it directly affects the relevance of the system generated summary. There are many procedures for extracting keywords, but all of these aim to find the words that directly represent the topic of the document. Identifying lexical association between terms is one of the existing techniques proposed for determining the topic of the document. In this paper, we have made use of lexical association and graph based ranking techniques for retrieving keywords from a source text and subsequently to assign them a relative weight. The individual weights of the extracted keywords are used to rank the sentences in the source text. Our summarization system is tested with DUC 2002 dataset and is found to be effective when compared to the existing context based summarization systems.

Backup route establishment for QoS routing protocol in MANET

In this paper, we propose to design a backup route establishment for QoS routing protocol. In this protocol, the potential failures of network and node are identified and a backup routing is initiated. For detection of failures, a path evaluation function is determined based on the metrics energy drain rate and interference, congestion status are measured. The primary path satisfying the QoS parameters nodes static resource capacity, dynamic resourcme availability, neighborhood quality, and link quality is established. Once failure is detected, back up routes are established and transmission is redirected on these back up routes. Simulation results show that the proposed protocol has reduced recovery delay and improved throughput.

Extractive Summarization Technique Based on Fuzzy Membership Calculation Using Rough Sets

Selection of important sentences from a document is an important task in Automatic text summarization systems. When these sentences are presented without any modification in their syntax and semantics, they form extractive summary of the given text document. Extractive summarization techniques based on statistical methods like word frequency, indicator phrases and word co-occurrence are language independent. Recent studies have shown that word co-occurrence information can improve the quality of extractive summaries. This paper presents a statistical technique to identify important sentences of a text document using word co-occurrence information. The proposed technique adopts the rough set approach of Reduct and Core calculation for a given data. Fuzzy core concept is used, when the Core is empty or whenever the Core has to be enhanced. The proposed technique is tested with DUC 2002 data Sets and has given good results.

Exponential Genetic Algorithm-Based Stable and Load-Aware QoS Routing Protocol for MANET

As the actual links in mobile ad hoc networks (MANETs) are dynamic, the feasible path generated may not exist during the transmission of multimedia data. There is a need for generating multiple paths for guaranteed transmission. The multi-path finding can be achieved through a genetic-based algorithm that faces the major challenge in quality-of-service (QoS)-aware routing. To enhance the QoS communication over the MANETs, an exponential genetic algorithm (GA)-based stable and load-aware QoS routing protocol (SLAQR) is proposed in this paper. This paper chiefly focuses on the enhancement of the GA-based routing algorithm by including exponential function in the searching process and modifying the fitness function, which incorporates QoS metrics like the node’s static resource capacity, dynamic resource availability, neighborhood quality, and link quality. The originality of the proposed protocol comes from the fact that it introduces multiple parameters into the route quality computation and integrates the exponential function into the GA. For experimental validation, the simulation of the proposed method is done and the results are compared with existing protocols such as GAQR (GA-Based QoS Routing), QMRB-AODV (QoS Mobile Routing Backbone Over Ad Hoc On-Demand Distance Vector Routing), and EISGA (Ensemble of Immigrant Strategies with Genetic Algorithm). From the outcome, we conclude that the delivery ratio, throughput, and delay of our proposed SLAQR approach have improved the GAQR approach by 58%, 56%, and 97%, respectively.