Book Chapters
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28507
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Item An Effective Diabetic Retinopathy Detection Using Hybrid Convolutional Neural Network Models(Springer Science and Business Media Deutschland GmbH, 2023) Kumar, N.; Ahmed, R.; Venkatesh, B.H.; Anand Kumar, M.Loss of vision in the present era of the developing world is mainly caused by diabetic retinopathy. More than 103 million people are believed to be affected. It is estimated that around 40 million beings have diabetes in the United States, and according to the World Health Organization (WHO), 347 million people are living with the disease globally. Diabetic retinopathy (DR) is a long-term diabetes-related eye condition. Roughly, 45–50% of the American citizens suffering from diabetes undergo some unique stages that can be categorized. When DR is diagnosed on a timely basis, the possibility of it extending to the course of vision impairment can be delayed and stopped, though this is not entirely true and a very daunting task because it seldom reveals any symptom before it escalates to a stage of no return to effectively treat it. The paper uses convolutional neural network models to achieve an effective classification for diabetic detection of retinal fundus images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Categorizing Relations via Semi-supervised Learning Using a Hybrid Tolerance Rough Sets and Genetic Algorithm Approach(Springer Science and Business Media Deutschland GmbH, 2022) Agrawal, S.; Ahmed, R.; Anand Kumar, M.; Ramanna, S.In the last few decades, we have seen a tremendous increase in the amount of data available on the web. There have been significant advances in constructing knowledge bases consisting of relations from the text data. These relations are words in the text often represented as pairs (Noun, Context), for example (Disease, Symptom), which can be classified into some predefined category to give us some useful information. Categorization of relations using tolerance-rough set based semi-supervised learning algorithm (TPL) have been successfully demonstrated in several works. However, an unexplored problem is the automatic selection of hyper parameters of the TPL algorithm. This paper proposes a genetic algorithm-based approach (TPL-GA) for optimizing the hyper-parameters that are fundamental to the TPL algorithm. The proposed approach was tested on two standard datasets drawn from different domains representing two different languages: English and Hindi text. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.