Faculty Publications
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Publications by NITK Faculty
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Item Smart home environment: Artificial intelligence-enabled IoT framework for smart living and smart health(IGI Global, 2020) Geetha, V.; Kamath S․, S.; Salvi, S.S.Increase in population year by year is making the living status of the urban people difficult as resource-saving and sharing become more challenging. A smart home, which is part of smart city development, provides a better way of handling available resources. Smart home also provides a better way of living with smart devices, which can monitor various activities autonomously. It is also essential to have a smart health system that monitors day to the activity of a person and provides health statistics and indicates health issues at an early stage. The home or devices become smart using artificial Intelligence to analyze the activities. Artificial intelligence provides a way to analyze the data and provide recommendations or solutions based on personalization. In this regard, developing a smart home is essential in the current urban area. This chapter identifies various challenges present in developing a smart home for smart living and smart health and also proposes an AI-based framework for realizing a system with user peronalization and autonomous decision making. © 2021, IGI Global.Item Ensemble deep neural models for automated abnormality detection and classification in precision care applications(Elsevier, 2023) Karthik, K.; Mayya, V.; Kamath S․, S.Radiological imaging is one of the most relied upon modalities in the clinical diagnosis and treatment planning process. Conventional diagnosis involves the manual analysis of radiology images by experienced radiologists, which is often a time-consuming and labor-intensive process. The scarcity of experienced radiologists and necessity of large-scale X-rays image analysis given the huge diagnosis workload at most hospitals stresses the need for automated clinical diagnosis systems capable of fast and accurate identification of abnormalities, disease characteristic identification, disease classification, and others. Such automated methods are thus a fundamental requirement in clinical workflow management applications. In this work, we present an approach for multitask clinical objectives such as disease classification and detection of abnormalities. The proposed model leverages the predictive power of deep neural models for enabling evidence-based diagnosis. During validation experiments, the model achieved an accuracy of 89.58% along with sensitivity and specificity of 85.83% and 90.83%, respectively, with an AUC (area under the ROC curve) of 95.84% for normal/no findings versus COVID-19 chest radiograph classification and an accuracy of 73.19% for upper extremity musculoskeletal images. The performance of the model for the classification and abnormality identification tasks, when benchmarked over multiple standard datasets, emphasizes its suitability and adaptability in real-world clinical settings, with significant improvements in radiology-based diagnosis workflow and patient care. © 2023 Elsevier Inc. All rights reserved.Item Deep neural models for automated multi-task diagnostic scan management - Quality enhancement, view classification and report generation(IOP Publishing Ltd, 2022) Karthik, K.; Kamath S․, S.The detailed physiological perspectives captured by medical imaging provides actionable insights to doctors to manage comprehensive care of patients. However, the quality of such diagnostic image modalities is often affected by mismanagement of the image capturing process by poorly trained technicians and older/poorly maintained imaging equipment. Further, a patient is often subjected to scanning at different orientations to capture the frontal, lateral and sagittal views of the affected areas. Due to the large volume of diagnostic scans performed at a modern hospital, adequate documentation of such additional perspectives is mostly overlooked, which is also an essential key element of quality diagnostic systems and predictive analytics systems. Another crucial challenge affecting effective medical image data management is that the diagnostic scans are essentially stored as unstructured data, lacking a well-defined processing methodology for enabling intelligent image data management for supporting applications like similar patient retrieval, automated disease prediction etc. One solution is to incorporate automated diagnostic image descriptions of the observation/findings by leveraging computer vision and natural language processing. In this work, we present multi-task neural models capable of addressing these critical challenges. We propose ESRGAN, an image enhancement technique for improving the quality and visualization of medical chest x-ray images, thereby substantially improving the potential for accurate diagnosis, automatic detection and region-of-interest segmentation. We also propose a CNN-based model called ViewNet for predicting the view orientation of the x-ray image and generating a medical report using Xception net, thus facilitating a robust medical image management system for intelligent diagnosis applications. Experimental results are demonstrated using standard metrics like BRISQUE, PIQE and BLEU scores, indicating that the proposed models achieved excellent performance. Further, the proposed deep learning approaches enable diagnosis in a lesser time and their hybrid architecture shows significant potential for supporting many intelligent diagnosis applications. © 2021 IOP Publishing Ltd.Item Swarm optimisation-based bag of visual words model for content-based X-ray scan retrieval(Inderscience Publishers, 2022) Karthik, K.; Kamath S․, S.Classification and retrieval of medical images (MedIR) are emerging applications of computer vision for enabling intelligent medical diagnostics. Medical images are multi-dimensional and require specialised processing for the extraction of features from their manifold underlying content. Existing models often fail to consider the inherent characteristics of data and have thus often fallen short when applied to medical images. In this paper, we present a MedIR approach based on the bag of visual words (BoVW) model for content-based medical image retrieval. When it comes to any medical approach models, an imbalance in the dataset is one of the issues. Hence the perspective is also considering a balanced set of categories from an imbalanced dataset. The proposed work on BoVW model extracts features from each image are used to train supervised machine learning classifier for X-ray medical image classification and retrieval. During the experimental validation, the proposed model performed well with the classification accuracy of 89.73% and a good retrieval result using our filter-based approach. © © 2022 Inderscience Enterprises Ltd.Item Stacking Deep learning and Machine learning models for short-term energy consumption forecasting(Elsevier Ltd, 2022) Sujan Reddy, A.; Akashdeep, S.; Harshvardhan, R.; Kamath S․, S.Accurate prediction of electricity consumption is essential for providing actionable insights to decision-makers for managing volume and potential trends in future energy consumption for efficient resource management. A single model might not be sufficient to solve the challenges that result from linear and non-linear problems that occur in electricity consumption prediction. Moreover, these models cannot be applied in practice because they are either not interpretable or poorly generalized. In this paper, a stacking ensemble model for short-term electricity consumption is proposed. We experimented with machine learning and deep models like Random Forests, Long Short Term Memory, Deep Neural Networks, and Evolutionary Trees as our base models. Based on the experimental observations, two different ensemble models are proposed, where the predictions of the base models are combined using Gradient Boosting and Extreme Gradient Boosting (XGB). The proposed ensemble models were tested on a standard dataset that contains around 500,000 electricity consumption values, measured at periodic intervals, over the span of 9 years. Experimental validation revealed that the proposed ensemble model built on XGB reduces the training time of the second layer of the ensemble by a factor of close to 10 compared to the state-of-the-art, and also is more accurate. An average reduction of approximately 39% was observed in the Root mean square error. © 2022 Elsevier LtdItem Automated Health Insurance Processing Framework with Intelligent Fraud Detection, Risk Classification and Premium Prediction(Springer, 2024) Devaguptam, S.; Gorti, S.S.; Akshaya, T.L.; Kamath S․, S.Private insurance represents one of the sectors poised for significant growth. There are insurance solutions available for most high-value assets such as homes, jewelry, vehicles, and other valuable items. To optimize profitability while managing client claims, insurance companies have embraced advanced operations, procedures, and mathematical models to assess risks and prioritize customer satisfaction, all while maximizing profits. This article introduces a machine learning-driven automated framework designed to reduce human intervention, safeguard insurance operations, identify high-risk clients, detect fraudulent claims, and mitigate financial losses within the insurance sector. Initially, the framework focuses on fraud detection to determine the legitimacy of claims. Genuine claims leverage the patient’s medical history to calculate associated risk factors and premiums. Various machine learning-based classification models and ensemble techniques were employed and evaluated for each of the three insurance processing tasks. Performance assessments using relevant metrics are presented and thoroughly discussed. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.Item Ensemble neural models for ICD code prediction using unstructured and structured healthcare data(Elsevier Ltd, 2024) Merchant, A.M.; Shenoy, N.; Lanka, S.; Kamath S․, S.Disease coding is the process of assigning one or more standardized diagnostic codes to clinical notes that are maintained by health practitioners (e.g. clinicians) to track patient condition. Such a coding process is often expensive and error-prone, as human medical coders primarily perform it. Automating diagnostic coding using Artificial Intelligence is seen as an essential solution in Hospital Information Management Systems and approaches built on Convolutional Neural Networks currently perform best. In this work, a neural model built on unstructured clinical text for enabling automatic diagnostic coding for given patient discharge summaries is proposed. We incorporate a structured self-attention mechanism designed to boost learning of label-specific vectors and the significant clinical text snippets associated with a certain label for this purpose. These vectors are then combined with a novel code description pipeline leveraging the descriptions provided for each standardized diagnostic code. The proposed model achieved best performance in terms of standard metrics over the MIMIC-III dataset, outperforming models based on Longformers and Knowledge graphs. Furthermore, to leverage structured clinical data to enhance the proposed model, and to enable improved diagnostic code prediction, model ensembling is considered. A neural model built on structured data by leveraging supervised machine learning algorithms such as random forest and boosting, is designed for multi-class code classification. Experimental results revealed that the proposed ensemble models show promising performance compared to traditional models that rely solely on unstructured or structured clinical data, emphasizing their suitability for real-world deployment. © 2024 The Author(s)