Browsing by Author "Khattri, P."

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A prototype of an intelligent search engine using machine learning based training for learning to rank
(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2014) Rai, P.; Prabhumoye, S.; Khattri, P.; Sandhu, L.S.; Kamath Sâ€¤, S.
Learning to Rank is a concept that focuses on the application of supervised or semi-supervised machine learning techniques to develop a ranking model based on training data. In this paper, we present a learning based search engine that uses supervised machine learning techniques like selection based and review based algorithms to construct a ranking model. Information retrieval techniques are used to retrieve the relevant URLs by crawling the Web in a Breadth-First manner, which are then used as training data for the supervised and review based machine learning techniques to train the crawler. We used the Gradient Descent Algorithm to compare the two techniques and for result analysis. Â© Springer International Publishing Switzerland 2014.
Primary education for the specially-abled
(2013) Chandra, S.; Sagar, M.; Rout, P.; Khattri, P.; Domanal, S.; Ram Mohana Reddy, Guddeti
Technology can support the children who require special needs, who if given the proper training and opportunity, can compete on a basis of equality with their peers. This should be the basic philosophy of a programmer who designs programs, standards for programs, or evaluation of programs. Proper education for these children will lead to enhancing their capability to lead a dignified life and also help them to earn a square meal. Technology is needed to teach them and hence the necessity can clearly be seen for further research and development in this field. In addition to software being used as teaching tools at schools and at-home, the learning process should be more interesting so that child should feel engaged. A daily routine, not only a syllabus/homework, using technologies such as text to speech conversion and image morphology is needed to both help them understand concepts of classroom syllabus and motivate them to learn more at home as specially-abled children need to be given enough motivation, as well as time, to succeed. The system developed proves to be useful to specially-abled children to memorize rhymes, recognize common sounds (like that of animals) as well as develop haptic abilities using a game like interface. � 2013 IEEE.
Primary education for the specially-abled
(2013) Chandra, S.; Sagar, M.; Rout, P.; Khattri, P.; Domanal, S.G.; Guddeti, G.
Technology can support the children who require special needs, who if given the proper training and opportunity, can compete on a basis of equality with their peers. This should be the basic philosophy of a programmer who designs programs, standards for programs, or evaluation of programs. Proper education for these children will lead to enhancing their capability to lead a dignified life and also help them to earn a square meal. Technology is needed to teach them and hence the necessity can clearly be seen for further research and development in this field. In addition to software being used as teaching tools at schools and at-home, the learning process should be more interesting so that child should feel engaged. A daily routine, not only a syllabus/homework, using technologies such as text to speech conversion and image morphology is needed to both help them understand concepts of classroom syllabus and motivate them to learn more at home as specially-abled children need to be given enough motivation, as well as time, to succeed. The system developed proves to be useful to specially-abled children to memorize rhymes, recognize common sounds (like that of animals) as well as develop haptic abilities using a game like interface. Â© 2013 IEEE.
A prototype of an intelligent search engine using machine learning based training for learning to rank
(2014) Rai, P.; Prabhumoye, S.; Khattri, P.; Sandhu, L.R.S.; Sowmya, Kamath S.
Learning to Rank is a concept that focuses on the application of supervised or semi-supervised machine learning techniques to develop a ranking model based on training data. In this paper, we present a learning based search engine that uses supervised machine learning techniques like selection based and review based algorithms to construct a ranking model. Information retrieval techniques are used to retrieve the relevant URLs by crawling the Web in a Breadth-First manner, which are then used as training data for the supervised and review based machine learning techniques to train the crawler. We used the Gradient Descent Algorithm to compare the two techniques and for result analysis. � Springer International Publishing Switzerland 2014.