Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Annappa, B.Subject: search.filters.subject.Transfer Learning

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    Transfer Learning Based Model for Colon Cancer Prediction Using VGG16
    (Institute of Electrical and Electronics Engineers Inc., 2023) Koppad, S.; Annappa, B.; Acharjee, A.
    Colon cancer, or a colorectal cancer, is a malignant neoplasm that originates in the colon. It is one of the most prevalent forms of cancer globally, with significant impacts on morbidity and mortality rates. The essential task is to detect it and detect it at an initial phase for curing the patient precisely. The artificial intelligence plays important roles in the colon cancer prediction. The authors proposed various models on colon cancer prediction using ML and DL. The existing approaches are unable to achieve good accuracy for the colon cancer prediction. This research work suggests a transfer learning based framework for the colon cancer prediction. This framework is planned on the basis of VGG16 and CNN in colon cancer prediction. The proposed framework is implemented in python and results is analysed concerning accuracy, precision, recall. © 2023 IEEE.
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    Semantic Segmentation of Remotely Sensed Images using Multisource Data: An Experimental Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2024) Putty, A.; Annappa, B.; Prajwal, R.; Pariserum Perumal, S.P.
    Remotely sensed data obtained from diverse sensors provide rich information for a wide range of applications in remote sensing, such as land use and land cover mapping. Due to the availability of a large amount of data, advanced deep-learning techniques have been incorporated into this domain. However, these techniques require a significant amount of annotated data, which can be challenging to obtain for land-use and land-cover mapping. Multisource data fusion has become crucial in remotely sensed image analysis to overcome this challenge, providing significant benefits across various applications. This paper analyzes the fusion of multisource data tailored for land-use and land-cover mapping. The analysis showcases that incorporating the novel knowledge transfer approach from multisource data has helped to achieve a 1-6% improvement in mIoU for the Kaggle Aerial Image dataset. © 2024 IEEE.