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Found 6 papers, 0 papers with code
Unveiling the potential of large language models in generating semantic and cross-language clones
Similarly, if someone possesses a code snippet in a particular programming language but seeks equivalent functionality in a different language, a semantic cross-language code clone generation approach could provide valuable assistance. In this study, using SemanticCloneBench as a vehicle, we evaluated how well the GPT-3 model could help generate semantic and cross-language clone variants for a given fragment. We have comprised a diverse set of code fragments and assessed GPT-3s performance in generating code variants. Through extensive experimentation and analysis, where 9 judges spent 158 hours to validate, we investigate the model's ability to produce accurate and semantically correct variants.
Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach
The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment.
Automatic Polyp Segmentation Using Convolutional Neural Networks
The risk of developing colorectal cancer could be reduced by early diagnosis of polyps during a colonoscopy.
Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks
Breast cancer is one of the leading causes of death across the world in women.
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification
Automatic detection of leukemic B-lymphoblast cancer in microscopic images is very challenging due to the complicated nature of histopathological structures.
Breast Cancer Diagnosis with Transfer Learning and Global Pooling
Breast cancer is one of the most common causes of cancer-related death in women worldwide.
