Jagpreet Sidhu – författare

Scientific Study from the year 2017 in the subject Computer Science - General, grade: 1.00, University of the Punjab, language: English, abstract: This work presents the evaluation of grid for DV to MPEG4 video conversion with following objectives: Study and Implementation of the Grid Environment; Design and Implementation of DV to MPEG 4 conversion process on Grid Environment; Evaluation of performance of Grid for DV to MPEG 4 conversion with respect to time. The first objective intends to study the grid computing concepts, their types, their relationship with other computing technologies, the open source middleware available for its implementation etc. It also involves the analysis, design and implementation of a grid environment. The second objective intends to study the compute, storage and network intensive problem of DV to MPEG4 video conversion and to implement it on the grid environment designed in above step. The third objective intends to evaluate the performance of grid (designed in step 1) by executing the conversion process (as defined by step 2) with different parameters. We have developed the python script (appendix A) to assist us to find suitable resource on a grid system and then splitting a DV video job into some job slices to be remotely scheduled and processed at grid nodes. After conducting thirty runs for three experiments by taking videos of different lenghts on one to ten processing nodes it is clear that grid shows benefits over single/centralised systems but it doesn t show up lenear increase in performance by increasing the number of grid processing nodes.

Scientific Study from the year 2018 in the subject Computer Sciences - Artificial Intelligence, grade: 1, Post Graduate Government College, language: English, abstract: Every natural language contains a large number of words. These words can have different senses in different context; such words with multiple senses are known as sense tagged words. Word sense reflects the basic concept of the word and the words with several meanings cause ambiguity in the sentence, and the process that decides which of the denotation is accurate in the sentence among several meanings of the word is known as Word Sense Disambiguation. Human beings are good at understanding the meaning of the word by reading the sentence but the same task is difficult for a machine: to understand and accurately sense the correct meaning of the word. Machines can easily understand the set of rules and it is a difficult task to create such rules that can easily disambiguate the word in the context. This task is complicated because every natural language has their own set of rules such as grammatical rules, part-of-speech, antonomy, and synonym. Therefore, a machine is trained by special algorithm so that it can tag the word with its correct sense. If the correct sense of the word is determined, that correct sense is helpful in retrieving the basic concepts of the word. As such this is very difficult task for a machine to retrieve the basic definition of word. In this proposed work, K-Nearest Neighbor (KNN) approach is used to disambiguate the sense tagged words. The KNN is based on supervised learning method. The proposed technique evaluates the performance on Hindi sense tagged words and these are obtained from Hindi Wordnet. The results show the effectiveness of the proposed technique in sense tagged words.