Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Annappa, B.Subject: search.filters.subject.Neural network

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    Recommending an alternative path of execution using an online decision support system
    (Association for Computing Machinery acmhelp@acm.org, 2017) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
    Prediction of disease severity is highly essential for understanding the progression of disease and initiating an alternative path of execution, which is priceless in treatment planning. An online decision support system (ODeSS) is proposed here for stratification of the patients who may need Endoscopic Retrograde CholangioPancreatography (ERCP) and recommend an alternate path of execution. By this an immediate intervention can be avoided. In this study gallstone disease (GSD) whose prevalence is increasing in India is considered. ODeSS is a versatile non-linear information model which clustered the traces based on the duration of its completion. This is a Retrospective analyses of 575 traces. ODeSS applied the technique of longest common subsequence for identifying the sequence of an online execution and discovering to which cluster of variants it may belong. This discovery assist in taking appropriate clinical decision by recommending an alternative path of execution for such cases which may need emergency interventions. ODeSS performance was evaluated using area under receiver operating characteristic curve (area under ROC curve). This showed an accuracy of 0.9653 in prediction. The proposed model was validated using ROC curve in k-fold cross validation. Hence the proposed ODeSS can be used to conduct a non-linear statistical analysis since, the relationships between the predictive variables are not linear. It can be used as a clinical practice to recommend the path of execution. This would assist in better treatment planning, avoiding future complications. © 2017 ACM.
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    TORA: Text Summarization Using Optical Character Recognition and Attention Neural Networks
    (Springer Science and Business Media Deutschland GmbH, 2022) Sneha, H.R.; Annappa, B.
    Text Summarization is the process of creating a short and coherent version of a longer document that holds the same meaning as that of the original data. This article illustrates the technique to read the text in a printed document (such as newspaper, brochure, web document, etc.) and generate a summary of text. The method proposed is named Text Summarization using Optical Character Recognition and Attention Neural Networks (TORA). TORA can perform extractive summarization of a news article with the aid of Recurrent Neural Networks, Bidirectional Long Short-Term Memory, and Bahadanu Attention Network. The experimental results of the proposed method are promising. The experimental results have shown 80% accuracy in producing the summary from the large text document. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.