2020

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https://escholar.umt.edu.pk/handle/123456789/4422

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    A Computationally Intelligent System for Prediction of Protein Function using Pattern Recognition
    (UMT,Lahore, 2020) AHMAD HASSAN BUTT
    Pattern recognition systems are emerging in a multitude of computer applications. They have been part of many computationally intelligent systems like optical character recognition systems, biometric verification systems, weather forecasting, decision support systems, etc. Computationally intelligent systems are also considered significant components in the toolkit of a biologist. Such systems are essentially required by the majority of the modern research projects in the biological sciences. Most of these projects use computationally intelligent systems for either DNA or protein sequence analysis. Protein molecules are composed of a large sequence of Amino Acids. With the rapid discovery of new protein sequences in past decades, functional identification of the hypothetical or uncharacterized protein sequence or its primary structure is confronted as a challenging task in computational biology and proteomics. To explore the problems associated with protein function prediction, some computational techniques were proposed in the past, but are still not effective in terms of efficiency and accuracy. Based on pattern recognition feature extractions and machine learning classification algorithms, the outcome of the current research study has developed a computationally intelligent system that will be an effective practical approach in predicting protein functional attributes. The observed results, obtained from the proposed system in predicting protein functions, have shown better performance outcomes in terms of accuracy as compared to the existing state-of-art systems. Finally, we conclude that the proposed system will be effective and useful in problems relating to Bioinformatics, medicinal biology and drug discovery. This system will enhance the experimental research dynamics into exceptionally interpreting and analyzing the biological data for quick progress in areas of vaccine discovery, drug interactions, disease predictions and most importantly biological datasets characterizations.
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    BRIDGING THE COMMUNICATION BARRIER FOR THE DEAF IN PAKISTAN USING INFORMATION TECHNOLOGY
    (UMT, Lahore, 2020-04) NABEEL SABIR KHAN
    The deaf community in the world uses a gesture-based language, generally known as sign language. Every country has a different sign language; for instance, USA has American Sign Language (ASL) and UK has British Sign Language (BSL). The deaf community in Pakistan uses Pakistan Sign Language (PSL), which like other natural languages, has a vocabulary, sentence structure, and word order. Majority of the hearing community is not aware of PSL due to which there exists a huge communication gap between the two groups. Similarly, deaf persons are unable to read text written in English and Urdu. Hence, the provision of an effective translation model can support the cognitive capability of the deaf community to interpret natural language materials available on the Internet and in other useful resources. This research involves exploiting natural language processing (NLP) techniques to support the deaf community by proposing a novel machine translation model that translates English sentences into equivalent Pakistan Sign Language (PSL). Though a large number of machine translation systems have been successfully implemented for natural to natural language translations, natural to sign language machine translation is a relatively new area of research. State-of-the-art works in natural to sign language translation are mostly domain specific and suffer from low accuracy scores. Major reasons are specialized language structures for sign languages, and lack of annotated corpora to facilitate development of more generalizable machine translation systems. To this end, a grammar-based machine translation model is proposed to translate sentences written in English language into equivalent PSL sentences. To the best of our knowledge, this is a first effort to translate any natural language to PSL using core NLP techniques. The proposed approach involves a structured process to investigate the linguistic structure of PSL and formulate the grammatical structure of PSL sentences. These rules are then formalized into a context-free grammar, which, in turn, can be efficiently implemented as a parsing module for translation and validation of target PSL sentences. The whole concept is implemented as a software system, comprising the NLP pipeline and an external service to render the avatar-based video of translated words, in order to compensate the cognitive hearing deficit of deaf people. The accuracy of the proposed translation model has been evaluated manually and automatically. Quantitative results reveal a very promising Bilingual Evaluation Understudy (BLEU) score of 0.78. Subjective evaluations demonstrate that the system can compensate for the cognitive hearing deficit of end users through the system output expressed as a readily interpretable avatar. Comparative analysis shows that our proposed system works well for simple sentences but struggles to translate compound and compound complex sentences correctly, which warrants future ongoing research.