Gene Ontology (GO) is an important bioinformatics scheme to unify the representation of gene and gene product attributes across all species. Measuring similarity or distance between GO terms is a key step for determining hidden relationship between genes. The notion of similarity between GO terms is a usual step in knowledge discovery related tasks. In literature various similarity measures between GO terms have been proposed. We have introduced a novel similarity measure scheme to improve three conventional similarity measures to reduce their limitations. The salient feature of the proposed GO Semantic Similarity (gSemSim) measure is its ability to show more realistic similarity between concepts in perspective of domain knowledge. A comparative result with other technique has also been presented that showing an improved contextual meaning of the proposed semantic similarity. This study is expected to assist the community of bio informaticians in the selection of better similarity measure required for correct annotations of genes in gene ontology.

IMS (IP Multimedia Subsystem) is designed to provide multiple-services and can be accessed from any access network. With the advent of new multimedia services; user requirement also changed and required delay tolerant services from the service providers. The new multimedia services demands the restrictions on network parameters and this can be achieved by analyzing the traffic flow from the core as well as access network capabilities. As access and core IMS network are independent of each other and does not exchange user SLA with each other. In this paper we proposed framework based on Session border controller (SBC) that proactively monitored the traffic statistics as well as interact with access network operator for getting user's SLA and reserve resources accordingly. We are also proposing alterations in SIP invite and re-invite message to incorporate requested information. These alterations will be useful in ensuring availability of user's handset capabilities to IMS core network.

In semantic computing, Match is an operator that takes as an input two graph-like structures; it can be database schemas or XML schemas and generates a mapping between the corresponding nodes of the two graphs. In semantic schema matching, we attempt to explore the mappings between the two schemas; based on their semantics by employing any semantic similarity measure. In this study, we have defined taxonomy of all possible semantic similarity measures; moreover we also proposed an approach that exploits semantic relations stored in the DBpedia dataset while utilizing a hybrid ranking system to dig out the similarity between nodes of the two graphs.

In last decade of computing, a growing popularity for semantic cache schemes has been observed. Numerous techniques have been proposed for improved performance of semantic cache. Semantic cache is aimed towards reducing the network traffic load with the ability to address some queries without contacting the server. Semantic cache overcomes the limitations of previous page-caching and tuple-cache techniques. A prime concern of semantic cache includes query processing (query response-time) and cache management. The efficiency of semantic cache can be improved by using semantic indexing sachems. Semantic indexing sachems are segment based and hierarchical semantic indexing scheme. Both these sachems can improve the efficiency of query processing and cache management. In this paper, we have performed the evaluation of index schemes for Semantic Cache through the experimental study. Evaluations of these schemes are available in literature but experimental study is not available. Through this experiment, we have highlighted which technique is most suited in what kind of scenario.

Classification of regional territories and countries related to endangered species has been investigated by data mining techniques and graphical modeling using an extensive data set of species. We developed the graphical models (hereafter referred to as ‘ESDI’) using cosine, jaccard similarity, K Mean clustering and cliques in graph modeling for a large number of countries. Environmental variables associated with species records were identified in context of their diversification to integration with our proposed prototype. We have shown that the problem of finding the most coherent clusters is reducible to finding maximum clique. Key findings include the urge to ameliorate communication about the loss and protection of endangered species and their concerned projects. The proposed framework is presented to serves a portal to knowledge discovery. We have concluded that the proposed framework model and its associated data mining similarity measures can be useful for investigating various scientific and management oriented questions related to protection of endangered species with emphasis on collaboration among regional countries. The rationale behind the proposed approach is that the countries which have been grouped into same clique inherit a lot of argues illustrating common reasons of their struggles towards ecological safety with minimization of perils for endangered species. The development and implementation of a regional approach based on this similar grouping address the actions that could offer significant benefits in achieving their goal for ecological policies. Other critical actions at this clique level include fortifying and elevating harmonization of legal frameworks with emphasis on prevention procedural issues; awareness realizations of endangered species issues and its priority. Such actions will eventually lead towards implementation of essential plans fulfilling co-operative expertise and common endeavors.