Kamaljeet Kaur ¹, Parminder Singh ¹

Affiliations
1 Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana - 141006, Punjab, IN

Abstract

Machine Transliteration is becoming an important research area in Machine Translation, which ensures phonology of target language word. This paper describes the transliteration system that has been developed for multi script text. The proposed system is capable of transliterating English and Hindi text into equivalent Punjabi text using a rule based transliteration approach. Transliteration is more phonetic process because transliteration is more closely related with utterance. Thus there is need to perform phonetic analysis in addition to orthography for forming rules. The accuracy of English-Punjabi and Hindi-Punjabi transliteration is found to be 94% and 98% respectively.

Keywords

Grapheme, Orthography, Phonetic, Translation, Transliteration, Transliteration Rules

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Jeevan Bala ¹, Avinash Kaur ¹, Parminder Singh ¹

Affiliations
1 Department of computer Science Engineering, Lovely Professional University, Phagwara - 144411, Punjab, IN

Abstract

Objectives: To design a new framework to efficiently parallelize the steps of VLASPD algorithm using a hybridized apriori and fp-growth on GPU; to implement the existing and proposed framework in CUDA;to improve the performance factors like computational time, memory and CPU utilization.Methods/Statistical Analysis: This paper proposes the acceleration of Protein-Protein Interactions (PPIs) prediction on Graphics Processing Units(GPUs). A GPU can provide more processing cores and computational power in the same cost as a CPU.Findings: The frequently occurring patterns in the protein sequences can be used for PPIs prediction.The moving of the approaches from fixed length to variable length lead to computational complexity but also is found to be advantageous.Applications/Improvements:Sequence biology is since being researched by various computer engineers, the GPUs can be employed for predicting various sequence interactions like DNA-Proteins, etc. Since the GPU runs the parallel code efficiently, the methodology can be further improved if efficiently parallelized.

Keywords

Parallel VLASPD, Protein Interaction Prediction, Protein Sequences, Variable Length Patterns.

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