YMCA University of Science and Technology

About: YMCA University of Science and Technology is a education organization based out in Faridabad, India. It is known for research contribution in the topics: Web page & Web crawler. The organization has 299 authors who have published 568 publications receiving 4547 citations.

SemCrawl: Framework for Crawling Ontology Annotated Web Documents for Intelligent Information Retrieval

Abstract: Web is considered as the largest information pool and search engine, a tool for extracting information from web, but due to unorganized structure of the web it is getting difficult to use search engine tool for finding relevant information from the web. Future search engine tools will not be based merely on keyword search, whereas they will be able to interpret the meaning of the web contents to produce relevant results. Design of such tools requires extracting information from the contents which supports logic and inferential capability. This paper discusses the conceptual differences between the traditional web and semantic web, specifying the need for crawling semantic web documents. In this paper a framework is proposed for crawling the ontologies/semantic web documents. The proposed framework is implemented and validated on different collection of web pages. This system has features of extracting heterogeneous documents from the web, filtering the ontology annotated web pages and extracting triples from them which supports better inferential capability.

Critical Condition Detection Using Lion Hunting Optimizer and SVM Classifier in a Healthcare WBAN

Abstract: A timely critical condition detection and early notification are two essential requirements in a healthcare wireless body area network for the correct treatment of patients. However, most of the systems have limited capabilities and so could not detect the exact condition in a precise time interval. In addition to these it needs a reduction in the false alert rate, as issuing alerts for the deviation in each incoming packet increases the false alert rate and these false alerts consume more network resources. In order to fulfill the above-mentioned requirements, a dynamic alert system has been designed in this regard to make it more efficient, also, a new kind of hybridization approach is being introduced to it with the additive support of a nature-inspired optimization strategy named Lion Hunting and a machine-learning technique called support vector machine. The simulation is done using a network simulator NS-2.35, and the proposed alerting system outperforms others.

A Novel and Efficient Algorithm for Deploying Mobile Sensors in Subsurface

Abstract: The demand for oil is growing steadily from emerging and developing economies while oil field discoveries continue to decline. Therefore the gap between demand and supply will increase with time. Subsurface Exploration deals with extracting valuable hydrocarbons from oil wells. Due to its hazardous nature, it’s one of the most difficult fields to carry experimentations on. Uncertainties associated with this field are result of various factors such as lack of information regarding location, size and spread of natural resource. In order to handle above listed factors, mobile wireless sensors seems to be a promising paradigm for increasing productivity and throughput by serving as intelligent investigators. At the time of this listing, none of the researchers have proposed the deployment of mobile wireless sensors in the oil fields. Therefore, this work contributes a unique strategy that could deploy mobile sensors in the subsurface so as to get real time information which otherwise is not possible. Moreover, the proposed algorithm provides efficient coverage and connectivity metric. Also, a mathematical model has been presented along with its comparison with other existing node placement strategies in other related fields and it is found that our algorithm provides better coverage and connectivity.

State-of-artsurvey of various web prefetching techniques

Abstract: Web access delay faced by the users is a big challenge in front of researchers. Various techniques have been proposed in the last decade that could help reduce this latency time. Primarily, it includes Caching and Web Prefetching. A web object is cacheable and of help to the user if it is not dynamic. Web Prefetching on the other hand pre-fetches and keeps the web objects to be accessed by the user in future in cache based on his interest pattern. But still there is a gap between the user request and the result provided. There is a lack in the relevancy of the results. Web prefetching can be improved if domain ontology could be used and semantics could be analyzed and matched. Semantic Web Prefetching is a technique that tries to analyze the semantics of the keywords provided by the user or the content of the web page. This paper is an effort to provide the state-of-the-art survey of various Semantic Web Prefetching Techniques.