Faculty Publications
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Item Deep Learning for COVID-19(Springer Science and Business Media Deutschland GmbH, 2022) Bs, B.S.; Manoj Kumar, M.V.; Thomas, L.; Ajay Kumar, M.A.; Wu, D.; Annappa, B.; Hebbar, A.; Vishnu Srinivasa Murthy, Y.V.S.Ever since the outbreak in Wuhan, China, a variant of Coronavirus named “COVID 19” has taken human lives in millions all around the world. The detection of the infection is quite tedious since it takes 3–14 days for the symptoms to surface in patients. Early detection of the infection and prohibiting it would limit the spread to only to Local Transmission. Deep learning techniques can be used to gain insights on the early detection of infection on the medical image data such as Computed Tomography (CT images), Magnetic resonance Imaging (MRI images), and X-Ray images collected from the infected patients provided by the Medical institution or from the publicly available databases. The same techniques can be applied to do the analysis of infection rates and do predictions for the coming days. A wide range of open-source pre-trained models that are trained for general classification or segmentation is available for the proposed study. Using these models with the concept of transfer learning, obtained resultant models when applied to the medical image datasets would draw much more insights into the COVID-19 detection and prediction process. Innumerable works have been done by researchers all over the world on the publicly available COVID-19 datasets and were successful in deriving good results. Visualizing the results and presenting the summarized data of prediction in a cleaner, unambiguous way to the doctors would also facilitate the early detection and prevention of COVID-19 Infection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item YARS-IDS: A Novel IDS for Multi-Class Classification(Institute of Electrical and Electronics Engineers Inc., 2023) Madwanna, Y.; Annappa, B.; Rashmi Adyapady, R.; Sneha, H.R.An Intrusion Detection System (IDS) is a defence system that provides safety and security against different threats and attacks, acting as a wall of defence against attackers. As internet usage increases, IDSs are becoming an essential part of day-to-day life. Various Machine Learning (ML) and Deep Learning (DL) based IDS are available, and the domain of IDS is still evolving and growing. Here this paper proposes two DL-based IDSs, first is a combination of LuNet and Bidirectional LSTM (Bi-LSTM) and other is a combination of Temporal Convolutional Network (TCN), CNN and Bi-LSTM. Such IDS must be fed with an efficient number of samples to keep them updated and accurate. The first model has been trained and tested against two benchmark datasets, NSL-KDD and UNSW-NB15. The second model has been trained and tested against the NSL-KDD dataset. To overcome the insufficient number of samples, the models have used a technique called Synthetic Minority Oversampling Technique (SMOTE). These models provided better experimental outcomes than traditional ML-based approaches and many DL approaches. They have better results in classification accuracy and, detection rate. The classification accuracy of the first model for UNSW-NB15 and NSL-KDD is 82.19% and 98.87% respectively. The classification accuracy of the second model for NSL-KDD is 98.8%. © 2023 IEEE.Item 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.