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Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering model for text document using term frequency–inverse document frequency, which is considered as feature sets. Here, the initial centroid selection is much concentrated which can automatically cluster the text using weighted similarity measure in the proposed clustering process. In fact, the weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents, which is used to minimize weighted similarity among the documents. An advanced model for clustering is proposed by the hybrid optimization algorithm, which is the combination of the Jaya Algorithm (JA) and Grey Wolf Algorithm (GWO), and so the proposed algorithm is termed as JA-based GWO. Finally, the performance of the proposed model is verified through a comparative analysis with the state-of-the-art models. The performance analysis exhibits that the proposed model is 96.56% better than genetic algorithm, 99.46% better than particle swarm optimization, 97.09% superior to Dragonfly algorithm, and 96.21% better than JA for the similarity index. Therefore, the proposed model has confirmed its efficiency through valuable analysis.

Semantic service search engine is designed for DE, to provide semantic search support. Digital ecosystem (DE) is comprised of heterogeneous and distributed species which can play the dual role of service provider and service requester. DE needs to provide a reliable and trustworthy link between service providers and service requesters. So, semantic service search engine is a conceptual framework of service-ontology-based system which delivers semantic search support to DE. This paper is intended to measure the performance of the semantic service search engine based-on Support Vector Machine (SVM). The performance is evaluated through six performance indicators of information retrieval (IR). They are Precision, Recall, Mean average precision, Harmonic mean, Fallout rate and Mean Reciprocal Rank. The conclusion to this evaluation and future works are provided in the final section. General TermsQuality of service (QoS) evaluation, semantic service search engine, support vector machine, information retrieval, performance indicators.

Many researchers have addressed the need of a dynamic proven model of web crawler that will address the need of several dynamic commerce, research and ecommerce establishments over the web that majorly runs with the help of a search engine. The entire web architecture is changing from a traditional to a semantic. And on the other hand the web crawlers. The web crawler of today is vulnerable to omit several tons of pages without searching and also is incapable of capturing the hidden pages. There are several research problems of information retrieval, far from optimization such as supporting user to analyze the problem to determine information needs. The paper makes an analytical survey of several proven web crawlers capable of searching hidden pages. It also addresses the prospects and constraints of the methods and the ways to further enhance.