Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Influence of welding process parameters on microstructure and mechanical properties of friction stir welded aluminium matrix composite(Trans Tech Publications Ltd ttp@transtec.ch, 2017) Prabhu B, S.; Shettigar, A.K.; Karthik, K.; Rao, S.S.; Herbert, M.In this study, the effect of process parameters on microstructure and mechanical properties of friction stir welded aluminium matrix composites(AMC) have been explored. The results indicated that the recrystallized grain size at the bottom of the weld region is smaller than that at the top region due to difference in the heat transfer at the weld region. The joint strength of AMCs depends on proper selection of process parameters like tool rotational speed and welding speed. If process parameter values are beyond the optimal value, the joint strength decreases due to formation of defects. Maximum tensile strength is obtained for rotational speed of 1000 rpm and welding speed of 80mm/min. © 2017 Trans Tech Publications, Switzerland.Item A Hybrid Feature Modeling Approach for Content-Based Medical Image Retrieval(Institute of Electrical and Electronics Engineers Inc., 2018) Karthik, K.; Kamath S․, S.With the proliferation of various imaging based diagnostic procedures in the healthcare field, patient-specific scan images constitute huge volumes of data that needs to be well-organized and managed for supporting clinical decision support applications. One such crucial application with a significant impact on point-of-care treatment quality is a Content Based Medical Image Retrieval (CBMIR) system that can assist doctors in disease diagnosis based on similar image retrieval. Medical images are multi-dimensional and often contain manifold information, due to which efficient techniques for optimal feature extraction from large-scale image collections are the need of the day. In this paper, an efficient CBMIR model is proposed that is built on multi-level feature sets extracted from medical images. Four different feature extraction techniques are used to optimally represent images in a multi-dimensional feature space, for facilitating classification using supervised machine learning algorithms and top-k similar image retrieval. Experimental validation of proposed model on the standard ImageCLEF 2009 dataset containing 12,560 X-ray images across 116 classes showed promising results in terms of classification accuracy of 85.91%. © 2018 IEEE.Item Automatic Quality Enhancement of Medical Diagnostic Scans with Deep Neural Image Super-Resolution Models(Institute of Electrical and Electronics Engineers Inc., 2020) Karthik, K.; Kamath S․, S.; Kamath, S.U.In modern healthcare, diagnostic imaging is an essential component for diagnosing ailments and delivering quality healthcare. Given the variety in medical scanning techniques, a recurring issue across different modalities is that the scan quality is often affected by artifacts introduced by hardware and software faults in the imaging equipment. Significant challenges in the 3D Imaging Techniques include low quality/low-resolution scan images or the addition of unwanted artifacts due to patient movement. Researchers have put forth solutions ranging from machine learning algorithms like Gradient Descent to more complex Deep CNN models for rectifying these faults. In this paper, we aim to benchmark deep learning models for improving the quality of diagnostic images, through Super-resolution, for enabling faster and easier detection of anomalies that may be missed otherwise. Super-resolution CNN and Deep CNN architectures were employed for up-sampling medical scans for enhancing their quality. The CNN models were trained to learn motion artifact characteristics that are a result of patient movement and negate its effects in the super-resolved output. We present comparative results of six super-resolution models on a standard dataset and metrics. During the experimental evaluation, it was observed that the ResNet SRCNN model outperformed all other models used for comparison by a large margin, with an improvement of 4.87 to 8.68% over the other state-of-the-art models. © 2020 IEEE.Item Analysis and Prediction of Fantasy Cricket Contest Winners Using Machine Learning Techniques(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Karthik, K.; S. Krishnan, G.S.; Shetty, S.; Bankapur, S.; Kolkar, R.; Ashwin, T.S.; Vanahalli, M.K.Cricket is one of the well-known sports across the world. The increasing interest of cricket in recent years resulted in different forms like T20, T10 from test and one day format. The craze of all these formats of cricket matches today has come into online fantasy cricket league games. Dream11 is one such app that is most popular in this context, along with many similar apps. Creating a dream team of 11 players from playing 11 of both teams involves skills, ideas and luck. Predicting a winner among all the joined contestants based on the previous historical data is a challenging task. In this paper, we used a feed-forward deep neural network (DNN) classifier for predicting the winning contestant for the top three positions in a fantasy league cricket contest. The performance of the DNN approach was compared against that of state-of-the-art machine learning approaches like k-nearest neighbours (KNN), logistic regression (LR), Naive Bayes (NB), random forest (RF), support vector machines (SVM) and in predicting the fantasy cricket contest winners. Among the methods used, DNN showed the best results for all three positions, showing its consistency in predicting the winners and outperforms the state-of-the-art machine learning classifiers by 13%, 8% and 9%, respectively, for first, second and third winning positions, respectively. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item COVIDDX: AI-based clinical decision support system for learning COVID-19 disease representations from multimodal patient data(SciTePress, 2021) Mayya, V.; Karthik, K.; Kamath S․, S.; Karadka, K.; Jeganathan, J.The COVID-19 pandemic has affected the world on a global scale, infecting nearly 68 million people across the world, with over 1.5 million fatalities as of December 2020. A cost-effective early-screening strategy is crucial to prevent new outbreaks and to curtail the rapid spread. Chest X-ray images have been widely used to diagnose various lung conditions such as pneumonia, emphysema, broken ribs and cancer. In this work, we explore the utility of chest X-ray images and available expert-written diagnosis reports, for training neural network models to learn disease representations for diagnosis of COVID-19. A manually curated dataset consisting of 450 chest X-rays of COVID-19 patients and 2,000 non-COVID cases, along with their diagnosis reports were collected from reputed online sources. Convolutional neural network models were trained on this multimodal dataset, for prediction of COVID-19 induced pneumonia. A comprehensive clinical decision support system powered by ensemble deep learning models (CADNN) is designed and deployed on the web. The system also provides a relevance feedback mechanism through which it learns multimodal COVID-19 representations for supporting clinical decisions. © © 2021 by SCITEPRESS – Science and Technology Publications, Lda. 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