Mayank Singh

Work place: Krishna Engineering College, Ghaziabad, India



Research Interests: Computational Science and Engineering, Software Construction, Software Development Process, Software Engineering, Computer Networks, Data Mining, Data Structures and Algorithms


Dr. Mayank Singh has completed his M. E in software engineering from Thapar University and PhD from Uttarakhand Technical University. His Research area includes Software Engineering, Software Testing, Wireless Sensor Networks and Data Mining. Presently He is working as Associate Professor in Krishna Engineering College, Ghaziabad. He is associated with CSI, IE (I), IEEE Computer Society India and ACM.

Author Articles
Ontology Based Information Retrieval in Semantic Web: A Survey

By Vishal Jain Mayank Singh

DOI:, Pub. Date: 8 Sep. 2013

In present age of computers, there are various resources for gathering information related to given query like Radio Stations, Television, Internet and many more. Among them, Internet is considered as major factor for obtaining any information about a given domain. When a user wants to find some information, he/she enters a query and results are produced via hyperlinks linked to various documents available on web. But the information that is retrieved to us may or may not be relevant. This irrelevance is caused due to huge collection of documents available on web. Traditional search engines are based on keyword based searching that is unable to transform raw data into knowledgeable representation data. It is a cumbersome task to extract relevant information from large collection of web documents. These shortcomings have led to the concept of Semantic Web (SW) and Ontology into existence. Semantic Web (SW) is a well defined portal that helps in extracting relevant information using many Information Retrieval (IR) techniques. Current Information Retrieval (IR) techniques are not so advanced that they can be able to exploit semantic knowledge within documents and give precise result. The terms, Information Retrieval (IR), Semantic Web (SW) and Ontology are used differently but they are interconnected with each other. Information Retrieval (IR) technology and Web based Indexing contributes to existence of Semantic Web. Use of Ontology also contributes in building new generation of web- Semantic Web. With the help of ontologies, we can make content of web as it will be markup with the help of Semantic Web documents (SWD’s). Ontology is considered as backbone of Software system. It improves understanding between concepts used in Semantic Web (SW). So, there is need to build an ontology that uses well defined methodology and process of developing ontology is called Ontology Development.

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Ontology Development and Query Retrieval using Protégé Tool

By Vishal Jain Mayank Singh

DOI:, Pub. Date: 8 Aug. 2013

This paper highlights the explicit description about concept of ontology which is concerned with the development and methodology involved in building ontology. The concept of ontologies has contributed to the development of Semantic Web where Semantic Web is an extension of the current World Wide Web in which information is given in a well-defined meaning that translates the given unstructured data into knowledgeable representation data thus enabling computers and people to work in cooperation. Thus, we can say that Semantic Web is information in machine understandable form. It is also called as Global Information Mesh (GIM). Semantic Web technology can be used to deal with challenges including traditional search engines and retrieval techniques within given organizations or for e-commerce applications whose initial focus is on professional users. Ontology represents information in a manner so that this information can also be used by machines not only for displaying, but also for automating, integrating, and reusing the same information across various applications which may include Artificial Intelligence, Information Retrieval (IR) and many more. Ontology is defined as a collection of set of concepts, their definitions and the relationships among them represented in a hierarchical manner that is termed as Taxonomy. There are various tools available for developing ontologies like Hozo, DOML, and AltovaSemantic Works etc. We have used protégé which is one of the most widely used ontology development editor that defines ontology concepts (classes), properties, taxonomies, various restrictions and class instances. It also supports several ontology representation languages, including OWL. There are various versions of protégé available like WebProtege 2.0 beta, Protégé 3.4.8, Protégé 4.1 etc. In this paper, we have illustrated ontology development using protégé 3.1 by giving an example of Computer Science Department of University System. It may be useful for future researchers in making ontology on protégé version 3.1.

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Clustering Based on Node Density in Heterogeneous Under-Water Sensor Network

By Sharad Saxena Shailendra Mishra Mayank Singh

DOI:, Pub. Date: 8 Jun. 2013

An underwater sensor network comprise of sensors and vehicles to perform numerous tasks. In underwater ad-hoc sensor network acoustic signals are transmitted through multi-hop sequence so as to save sensors’ energy and to achieve longer life time. Re-charging batteries of deep water deployed sensors is practically not feasible. Clustering is the best strategy to achieve efficient multi-hopping, where cluster head is made responsible to collect local data and forward it to the sink. Cluster-head selection is the challenging job in a cluster, as it loses its energy in transmitting its own data and aggregated data, as compared to other sensors. In this paper we have proposed an Under Water Density Based Clustered Sensor Network (UWDBCSN) scheme using heterogeneous sensors. The scheme utilizes two types of sensors: one having high energy capacity, working as cluster head, having small quantity and other are ordinary sensors in huge quantity. Further cluster-head selection is based on node degree i.e. the density of the sensors in a region. The proposed scheme is found to be more energy efficient helps in extending the life time of underwater sensor networks.

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