Faculty Publications
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Publications by NITK Faculty
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Item An improved system blind identification method based on second-order cyclostationary statistics and the properties of group delay, has been proposed. This is achieved by applying a correction to the estimated phase (by the spectral correlation density of the system output) for the poles, in the group delay domain. The results indicate a significant improvement in system blind identification, in terms of root mean square error. Depending upon the signal-to-noise ratio, the improvement in percentage normalized mean square error ranges between 20 and 50%.(Improved system blind identification based on second-order cyclostationary statistics: A group delay approach) Giridhar, P.V.S.; Narasimhan, S.V.2000Item A TODIM-Based Decision Support Framework for G-Resilient Supplier Selection in Fuzzy Environment(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2016) Sen, D.K.; Datta, S.; Mahapatra, S.S.A novel decision support framework has been proposed herein to solve supplier selection problems by considering green as well as resiliency criteria, simultaneously. In this work subjectivity of evaluation criteria has been tackled by exploring fuzzy set theory. A dominance based approach has been conceptualized which is basically a simplified version of TODIM. Application potential of the proposed dominance based fuzzy decision making approach has been compared to that of fuzzy-TOPSIS, fuzzy-VIKOR and also fuzzy-TODIM. The concept of a unique performance index, i.e. "g-resilient" index has been introduced here to help in assessing suppliers' performance and thereby selecting the best candidate. The work has also been extended to identify the areas in which suppliers are lagging; these seek further improvement towards g-resilient suppliers' performance to be boosted up to the desired level. © 2016 World Scientific Publishing Co.Item A novel GA-ELM model for patient-specific mortality prediction over large-scale lab event data(Elsevier Ltd, 2019) S. Krishnan, G.; Kamath S?, S.Patient-specific mortality prediction models are an essential component of Clinical Decision Support Systems developed for caregivers in Intensive Care Units (ICUs), that enable timely decisions towards effective patient care and optimized ICU resource management. While high prediction accuracy is a fundamental requirement for any mortality prediction application, being able to so with minimal patient-specific data is a major plus point that can help in improving care delivery and cost optimization. Most existing scoring techniques and prediction models utilize a multitude of lab tests and patient events to predict mortality and also suffer from reduced performance when available patient data is less. In this paper, a Genetic Algorithm based Wrapper Feature Selection technique is proposed for determining most-optimal lab events that contribute predominantly to mortality, even for large-scale patient cohorts. Using this, an Extreme Learning Machine (ELM) based neural network is designed for predicting patient-specific ICU mortality. The proposed GA-ELM model was benchmarked against four popular traditional mortality scores and also state-of-the-art machine learning models for experimental validation. The GA-ELM model achieved promising results as it outperformed the traditional scoring systems by 11%–29% and state-of-the-art models by up to 14%, in terms of AUROC. © 2019 Elsevier B.V.Item Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes(Elsevier B.V., 2020) Gangavarapu, T.; Jayasimha, A.; S. Krishnan, G.S.; Kamath S?, S.In hospitals, caregivers are trained to chronicle the subtle changes in the clinical conditions of a patient at regular intervals, for enabling decision-making. Caregivers’ text-based clinical notes are a significant source of rich patient-specific data, that can facilitate effective clinical decision support, despite which, this treasure-trove of data remains largely unexplored for supporting the prediction of clinical outcomes. The application of sophisticated data modeling and prediction algorithms with greater computational capacity have made disease prediction from raw clinical notes a relevant problem. In this paper, we propose an approach based on vector space and topic modeling, to structure the raw clinical data by capturing the semantic information in the nursing notes. Fuzzy similarity based data cleansing approach was used to merge anomalous and redundant patient data. Furthermore, we utilize eight supervised multi-label classification models to facilitate disease (ICD-9 code group) prediction. We present an exhaustive comparative study to evaluate the performance of the proposed approaches using standard evaluation metrics. Experimental validation on MIMIC-III, an open database, underscored the superior performance of the proposed Term weighting of unstructured notes AGgregated using fuzzy Similarity (TAGS) model, which consistently outperformed the state-of-the-art structured data based approach by 7.79% in AUPRC and 1.24% in AUROC. © 2019 Elsevier B.V.Item FarSight: Long-Term Disease Prediction Using Unstructured Clinical Nursing Notes(IEEE Computer Society, 2021) Gangavarapu, T.; S. Krishnan, G.S.; Kamath S?, S.; Jeganathan, J.Accurate risk stratification using patient data is a vital task in channeling prioritized care. Most state-of-the-art models are predominantly reliant on digitized data in the form of structured Electronic Health Records (EHRs). Those models overlook the valuable patient-specific information embedded in unstructured clinical notes, which is the prevalent medium employed by caregivers to record patients' disease timeline. The availability of such patient-specific data presents an unprecedented opportunity to build intelligent systems that provide exclusive insights into patients' disease physiology. Moreover, very few works have attempted to benchmark the performance of deep neural architectures against the state-of-the-art models on publicly available datasets. This article presents significant observations from our benchmarking experiments on the applicability of deep learning models for the clinical task of ICD-9 code group prediction. We present FarSight, a long-term aggregation mechanism intended to recognize the onset of the disease with the earliest detected symptoms. Vector space and topic modeling approaches are utilized to capture the semantic information in the patient representations. Experiments on MIMIC-III database underscored the superior performance of the proposed models built on unstructured data when compared to structured EHR based state-of-the-art model, achieving an improvement of 19.34 percent in AUPRC and 5.41 percent in AUROC. © 2013 IEEE.Item A deep neural network model for content-based medical image retrieval with multi-view classification(Springer Science and Business Media Deutschland GmbH, 2021) Karthik, K.; Kamath S?, S.S.In medical applications, retrieving similar images from repositories is most essential for supporting diagnostic imaging-based clinical analysis and decision support systems. However, this is a challenging task, due to the multi-modal and multi-dimensional nature of medical images. In practical scenarios, the availability of large and balanced datasets that can be used for developing intelligent systems for efficient medical image management is quite limited. Traditional models often fail to capture the latent characteristics of images and have achieved limited accuracy when applied to medical images. For addressing these issues, a deep neural network-based approach for view classification and content-based image retrieval is proposed and its application for efficient medical image retrieval is demonstrated. We also designed an approach for body part orientation view classification labels, intending to reduce the variance that occurs in different types of scans. The learned features are used first to predict class labels and later used to model the feature space for similarity computation for the retrieval task. The outcome of this approach is measured in terms of error score. When benchmarked against 12 state-of-the-art works, the model achieved the lowest error score of 132.45, with 9.62–63.14% improvement over other works, thus highlighting its suitability for real-world applications. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.Item Developing banking intelligence in emerging markets: Systematic review and agenda(Elsevier Ltd, 2021) Arjun, A.; Kuanr, A.; Kr, S.The current banking industry is heavily dependent on technological artifacts supported by intelligent systems for performance on operational and marketing parameters. However, the attributes for enabling practice between such technological interfaces with managerial adoption are been lagging creating a knowledge gap. To address this, present research surveys the prior work from 1970 to 2020 on intelligent decision support models specific to banking. Subsequently, findings are synthesized on quadrant outcomes; technology; employees, customers, and organizations for service ecosystems. In addition, the managerial perceptions of technology on work are captured through short survey. Finally, scope of advancements like big data, internet of things (IoT), virtual reality (VR) along other untapped conceptual relationships into this framework are discussed. © 2021 The AuthorsItem An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images(Springer, 2023) Mayya, V.; Kamath S․, S.K.; Kulkarni, U.; Surya, D.K.; Acharya, U.R.Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Often, overt signs indicative of COD do not manifest until the disease has progressed to an advanced stage. However, if COD is detected early, vision impairment can be avoided by early intervention and cost-effective treatment. Ophthalmologists are trained to detect COD by examining certain minute changes in the retina, such as microaneurysms, macular edema, hemorrhages, and alterations in the blood vessels. The range of eye conditions is diverse, and each of these conditions requires a unique patient-specific treatment. Convolutional neural networks (CNNs) have demonstrated significant potential in multi-disciplinary fields, including the detection of a variety of eye diseases. In this study, we combined several preprocessing approaches with convolutional neural networks to accurately detect COD in eye fundus images. To the best of our knowledge, this is the first work that provides a qualitative analysis of preprocessing approaches for COD classification using CNN models. Experimental results demonstrate that CNNs trained on the region of interest segmented images outperform the models trained on the original input images by a substantial margin. Additionally, an ensemble of three preprocessing techniques outperformed other state-of-the-art approaches by 30% and 3%, in terms of Kappa and F1 scores, respectively. The developed prototype has been extensively tested and can be evaluated on more comprehensive COD datasets for deployment in the clinical setup. © 2022, The Author(s).