Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Protein secondary structural class prediction using effective feature modeling and machine learning techniques(Institute of Electrical and Electronics Engineers Inc., 2018) Bankapur, S.; Patil, N.Protein Secondary Structural Class (PSSC) prediction is an important step to find its further folds, tertiary structure and functions, which in turn have potential applications in drug discovery. Various computational methods have been developed to predict the PSSC, however, predicting PSSC on the basis of protein sequences is still a challenging task. In this study, we propose an effective approach to extract features using two techniques (i) SkipXGram bi-gram: in which skipped bi-gram features are extracted and (ii) Character embedded features: in which features are extracted using word embedding approach. The combined feature sets from the proposed feature modeling approach are explored using various machine learning classifiers. The best performing classifier (i.e. Random Forest) is benchmarked against state-of-the-art PSSC prediction models. The proposed model was assessed on two low sequence similarity benchmark datasets i.e. 25PDB and FC699. The performance analysis demonstrates that the proposed model consistently outperformed state-of-the-art models by a factor of 3% to 23% and 4% to 6% for 25PDB and FC699 datasets respectively. © 2018 IEEE.Item Survey of Leukemia Cancer Cell Detection Using Image Processing(Springer Science and Business Media Deutschland GmbH, 2022) Devi, T.G.; Patil, N.; Rai, S.; Philipose, C.S.Cancer is the development of abnormal cells that divide at an abnormal pace, uncontrollably. Cancerous cells have the ability to destroy other normal tissues and can spread throughout the body. Cancer cells can develop in various parts of the body. The paper focuses on leukemia which is a type of blood cancer. Blood cancer usually start in the bone marrow where the blood is produced in the body. The types of blood cancer are: Leukemia, Non-Hodgkin lymphoma, Hodgkin lymphoma, and Multiple myeloma. Leukemia is a type of blood cancer that originates in the bone marrow. Leukemia is seen when the body produces an abnormal amount of white blood cells that hinder the bone marrow from creating red blood cells and platelets. Several detection methods to identify the cancerous cells have been proposed. Identification of the cancer cells through cell image processing is very complex. The use of computer aided image processing allows the images to be viewed in 2D and 3D making it easier to identify the cancerous cells. The cells have to undergo segmentation and classification in order to identify the cancerous tumours. Several papers propose segmentation methods, classification methods and some propose both. The purpose of this survey is to review various papers that use either conventional methods or machine learning methods to detect the cells as cancerous and non-cancerous. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Machine Learning based COVID-19 Mortality Prediction using Common Patient Data(Institute of Electrical and Electronics Engineers Inc., 2022) Agrawal, S.; Patil, N.COVID-19 was declared a pandemic in 2020, and it caused havoc worldwide. The fact that it is unpredictable adds to its lethality. The world has already seen various COVID-19 infection waves, subsequent waves being even more deadly. Many patients are asymptomatic initially but suddenly develop breathing problems. More than four million people have died due to COVID-19. It is necessary to forecast a patient's likelihood of dying so that appropriate precautions can be implemented. In this study, a COVID-19 mortality prediction model which uses machine learning is proposed. Most of the current research work requires several patient features and lab test results to predict mortality. However, we suggest a simpler and more efficient technique that relies solely on X-rays and basic patient information such as age and gender. Several ensemble-based models were evaluated and compared using a variety of metrics, and the best method was able to achieve a classification accuracy of 92.6% and AUPRC of 0.95. © 2022 IEEE.Item Comparative Analysis of Intrusion Detection System using ML and DL Techniques(Springer Science and Business Media Deutschland GmbH, 2023) Sunil, C.K.; Reddy, S.; Kanber, S.G.; Vuddanti, V.R.; Patil, N.Intrusion detection system (IDS) protects the network from suspicious and harmful activities. It scans the network for harmful activity and any potential breaching. Even in the presence of the so many network intrusion APIs there are still problems in detecting the intrusion. These problems can be handled through the normalization of whole dataset, and ranking of feature on benchmark dataset before training the classification models. In this paper, used NSL-KDD dataset for the analysation of various features and test the efficiency of the various algorithms. For each value of k, then, trained each model separately and evaluated the feature selection approach with the algorithms. This work, make use of feature selection techniques like Information gain, SelectKBest, Pearson coefficient and Random forest. And also iterate over the number of features to pick the best values in order to train the dataset.The selected features then tested on different machine and deep learning approach. This work make use of stacked ensemble learning technique for classification. This stacked ensemble learner contains model which makes un-correlated error there by making the model more robust. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.