In today’s world where data plays the very important role, we have various sources of pre-data like online books, equation analysis, encyclopedia, common-sense reasoning, common-sense knowledge, etc. The increasing capacity of pre-training language models have given knowledge intensive natural language processing (KI-NLP) a new boost for advanced functionalities for establishing a stable, flexible, robust and efficient model. Though pre-trained models have its own drawback for handling the KI-NLP tasks, we are here to discuss the challenges faced in this field. A wide variety of pre-trained language models enhanced with external knowledge sources have been proposed and are in rapid development to meet this difficulty. In this research we have also discusses the challenges in NLP in terms of generation of knowledge intensive models. We have also defined some mathematical model and its framework dependability for pre-training different language in NLP. Finally, we have also discussed about variety of literature reviews based on we intend to describe the present progress of pre-trained language model-based knowledge-enhanced models (PLMKEs) in this work by deconstructing their three key elements: information sources, knowledge-intensive NLP tasks, and knowledge fusion methods.
Machine learning is becoming increasingly prevalent. However, in the discipline of Bioinformatics and Computational Biology, it is not a popular use case. Machine learning techniques are used in only a few technologies. The majority of the tools are built using deterministic techniques and algorithms. Deoxyribonucleic acid (DNA) is a biological macromolecule composed up of deoxyribonucleic acid. Its main function is to store data. Due to breakthroughs in sequencing technology, DNA sequence data is presently rising at an exponential pace, ushering the study of DNA sequences into the big data age. Machine learning is also a powerful tool for massive processing it learns on its own from large volumes of data. We've talked about machine learning techniques and how they can be used to improve genome sequencing accuracy. In our review we have also discussed about genome sequence for Mycobacterium Tuberculosis. Tuberculosis is because of the bacteria, Tuberculosis caused by Mycobacterium tuberculosis. TB is considered one of the leading the reasons for dying all over the world. MDR-TB is a form of germs that cause tuberculosis that is not susceptible to anti-TB medications such as isoniazid (INH) and rifampin (RMP).
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