rish321/Leraning-To-Advertise
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Studied the literature available on translations and built a tokenizer for various languages. Performed various tests on translation using Moses.
“Automated Robot Motion”, to show the robotic motion on shortest path with Obstacles on the way.
To model mart-management software, where everything was automated, from customer to inventory maintenance. A new concept of automatic product delivery for elite customer is also introduced in this. Various models and class diagram in the process were made. Software was developed and maintained using WAMP server.
To develop the game of scrabble in Java with complete functionalities and options for single player or multiplayer playing. The Computer should play intelligently with the goal of winning with the maximum number of points. Having a small initial vocabulary, The computer player should keep on learning with its experience of playing consecutive games.
Build a fabric that can coordinate the provisioning of compute resources by negotiating with a set of Hypervisors running across physical servers in the datacenter.
Hindi and Urdu are very similar languages with different scripts, so there is a need to have a transliteration system between these native languages.
Until now, only incidence matrix representation existed for a hypergraph, we have worked on the adjacency list representation for the same.
PurposeNet is a semantic knowledgebase of artifacts, developed with purpose as the underlying principle of design. We aim at extracting different semantic relations (descriptive properties) from the Wikipedia for artifacts and then develop ontologies automatically using the web ontology language (OWL). To devise methods to populate the list of artifacts which are the basic unit of this system and to improvise the PurposeNet architecture including various new concepts into it.
Provide a unified framework for analytics using various framework like hadoop, R, MPI etc.(Analytics Engine). User should not worry about choosing the framework. Automatic selection of framework and parameters based on algorithm, input size etc. is essential.
Designed a scalable and efficient search engine using the wikipedia data. The search engine took less than a sec to search even the longest queries(tested upto 10 words per query) . It supports field queries (for 5 fields- title, infobox, outlinks, category and content) and the index size was less than 1/4 of the data size. Build my own indexing mechanism not using nutch or lucene to index the wikipedia data. Added Features: This search engine involved creation of secondary and tertiary indices as well. The end result was a robust search engine parsing almost every query.
Public repository.