http://hdl.handle.net/123456789/2631 2013-05-18T21:43:21Z 2013-05-18T21:43:21Z Princy Sugathan S http://hdl.handle.net/123456789/3626 2011-06-23T16:17:10Z 2011-06-22T18:30:00Z

Title: DNA COMPUTING Authors: Princy Sugathan S Abstract: In this paper I intend to present the computing technology that has a great future - DNA COMPUTING. DNA computing can be viewed as a manifestation of an emerging new area of science made possible by our rapidly developing ability to control the molecular world. DNA computing is in it’s infancy and it’s implications are only beginning to be explored. The paper begins with a brief description of DNA and its structure. An introduction to DNA COMPUTING and its origin has been given. Adleman experiment has been discussed, which gives solution to the “ HAMILTONIAN PATH PROBLEM” by the application of DNA COMPUTING. The salient features of DNA Computer (one that uses dna computing as its basic method of problem solving) have been mentioned. An insight into the advantages, disadvantages, applications and limitations of dna-computing has been made. Finally, the paper discusses the various stages in its path of development at present and the expectations in the near future.

2011-06-22T18:30:00Z Devika Rani. S http://hdl.handle.net/123456789/3625 2011-06-23T16:15:09Z 2011-06-22T18:30:00Z

Title: Sequence Data Mining Authors: Devika Rani. S Abstract: The concept of sequence Data Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in the year 1995. The problem was first introduced in the context of market analysis. It aimed to retrieve frequent patterns in the sequences of products purchased by customers through time ordered transactions. Later on it’s application was extended to complex applications like telecommunication, network detection, DNA research, etc. Several algorithms were proposed. The very first was Apriori algorithm, which was put forward by the founders itself. Later more scalable algorithms for complex applications were developed. Eg: GSP, Spade, PrefixSpan, etc. The area underwent considerable advancements since it’s introduction in a short span. Due to the exponential shoot in the amount of data generated in the world every second, it’s application on different domains gains more importance.

2011-06-22T18:30:00Z Suja.S http://hdl.handle.net/123456789/3624 2011-06-22T17:11:59Z 2011-06-21T18:30:00Z

Title: Ontology Based Information Retrieval Authors: Suja.S Abstract: An ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. With regard to converting words to meaning the key issue is to identify appropriate concepts that both describe and identify documents, as well as language employed in user requests.An ontology-based information retrieval process, in which the retrieval system is conceptually interpret the meaning of the query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user’s information need and to deliver the relevant content

2011-06-21T18:30:00Z Vani K http://hdl.handle.net/123456789/3623 2011-06-22T17:10:00Z 2011-06-21T18:30:00Z

Title: Example Based Machine Translation Authors: Vani K Abstract: EBMT was most notably attributed to Nagao and his famous translation by Analogy paper in 1984. EBMT retrieves similar examples (pairs of source phrases, sentences , or texts and their translations) from a database examples, adapting the examples to translate new input. Based on the intuition that humans make use of previously seen translation examples to translate unseen input. The EBMT system consists of two databases: An example database and a thesaurus and also three translation modules: analysis, example-based transfer, and generation. Translation performed using database of examples extracted from corpora. There are four stages of work in EBMT, namely, example acquisition, example base management, example application and target sentence synthesis.The underlying principle for EBMT is as simple as this: remember everything translated in the past and use everything available to facilitate the translation of the next utterance. We know computers are the most fantastic machines to memorize such things as text pairs and their frequencies, and we thus have reason to believe that EBMT represents the MT approach with the greatest potential. It opens a wide area of research opportunities.

2011-06-21T18:30:00Z