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Sharma, Sanjeev
- A Survey on Paraphrase Detection and Generation Techniques
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Affiliations
1 Department of Computer Science and Applications, Sant Baba Bhag Singh University, Jalandhar, IN
2 Department of Computer Applications, DAV University, Jalandhar, IN
1 Department of Computer Science and Applications, Sant Baba Bhag Singh University, Jalandhar, IN
2 Department of Computer Applications, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 26 (2017), Pagination: 76-83Abstract
Whenever “the same thing,” need to be expressed using different ways or by various alternatives an automated paraphrase generation mechanism would be useful. One reason why paraphrase generation systems have been difficult to build is because paraphrases are hard to define. Although the strict interpretation of the term “paraphrase” is quite narrow because it requires exactly identical meaning, in linguistics literature paraphrases are most often characterized by an approximate equivalence of semantics across sentences or phrases. This paper presents a survey of paraphrase generation techniques for Indian and foreign languages.Keywords
Paraphrasing, Sentence Simplification, Sentence Fusion, Sentence Compression.References
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- COVID-19 Severity Analysis Using Improved Machine Learning Algorithm
Abstract Views :131 |
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Authors
Affiliations
1 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
1 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 11-19Abstract
The new pandemic produced by the COVID-19 virus has resulted in an overflow of medical treatment in clinical centers all over the world. The fast and exponential growth in the number of COVID-19-infected individuals has necessitated an effective and timely prediction of probable infections and their effects in order to reduce health-care quality overload. As a result, intelligent models are being developed and used to assist medical workers in making more accurate diagnoses concerning the health condition of COVID-19-infected individuals. The purpose of this research is to present an alternative algorithmic approach for predicting the health status of COVID-19 patients in Mexico. Different prediction models were assessed and compared, including Adaboost, gradient boosting machine, random forests, and light gradient boosting machine. Additionally, Grid search hyperparameter optimization is used to improve the algorithm's success rate. The optimal model feature analysis procedure is being carried out. The purpose of this study is to analyses features in terms of feature importance as indicated by SHapely adaptive exPlanations (SHAP) values in order to identify relevant predictive factors that can identify patients at high risk of mortality.Keywords
Machine Learning, COVID-19, Hyperparameter Tuning, SHAP Analysis.References
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- https://www.kaggle.com/marianarfranklin/mexico-covid19-clinical-data/
- COVID-19 Diagnosis Using Machine Learning
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Authors
Affiliations
1 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
1 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 107-113Abstract
Over 4 million individuals have already died as a result of the deadly contagious viral COVID-19 worldwide. The infection can seriously harm the lungs, increasing the chance of fatal health effects. The only way to lower the mortality rate due to this deadly illness and to halt its growth is through early detection. Deep learning has recently come to light as one of the most useful methods for computer aided diagnosis for helping clinicians make correct illness diagnoses. However, deep learning models require a lot of processing, so hardware with TPUs and GPUs is required to execute these models. To create machine learning models that can be used on mobile and peripheral devices, experts are currently working. In this context, the goal of this study is to create a concise Convolution Neural Network-based computer-aided diagnostic system that can be used on devices with limited processing capacity, such as mobile phones and iPads, to identify the presence of the Covid-19 virus in x-ray pictures. On the basis of various assessment parameters, the findings plainly show that the suggested model outperforms other transfer learning models such as Resnet50, Inception, and Xception. According to various evaluation parameters, the findings definitely show that the proposed model outperforms other transfer learning models like Resnet50, Inception, and Xception.Keywords
Deep Learning, CNN, COVID-19, Transfer Learning, Image Enhancement.References
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