Search results for: explainability.

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explainability.</title> <meta name="description" content="Search results for: explainability."> <meta name="keywords" content="explainability."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="explainability."> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: explainability.</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> DeClEx-Processing Pipeline for Tumor Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Gaurav%20Shinde">Gaurav Shinde</a>, <a href="https://publications.waset.org/search?q=Sai%20Charan%20Gongiguntla"> Sai Charan Gongiguntla</a>, <a href="https://publications.waset.org/search?q=Prajwal%20Shirur"> Prajwal Shirur</a>, <a href="https://publications.waset.org/search?q=Ahmed%20Hambaba"> Ahmed Hambaba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline which ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx. </p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Machine%20learning" title="Machine learning">Machine learning</a>, <a href="https://publications.waset.org/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=explainability." title=" explainability."> explainability.</a> </p> <a href="https://publications.waset.org/10013804/declex-processing-pipeline-for-tumor-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013804/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013804/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013804/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013804/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013804/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013804/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013804/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013804/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013804/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013804/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013804.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">66</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Metrology-Inspired Methods to Assess the Biases of Artificial Intelligence Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Belkacem%20Laimouche">Belkacem Laimouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>With the field of Artificial Intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20intelligence" title="Artificial intelligence">Artificial intelligence</a>, <a href="https://publications.waset.org/search?q=metrology" title=" metrology"> metrology</a>, <a href="https://publications.waset.org/search?q=measurement%20uncertainty" title=" measurement uncertainty"> measurement uncertainty</a>, <a href="https://publications.waset.org/search?q=prediction%20error" title=" prediction error"> prediction error</a>, <a href="https://publications.waset.org/search?q=bias" title=" bias"> bias</a>, <a href="https://publications.waset.org/search?q=machine%20learning%20algorithms" title=" machine learning algorithms"> machine learning algorithms</a>, <a href="https://publications.waset.org/search?q=probabilistic%20models" title=" probabilistic models"> probabilistic models</a>, <a href="https://publications.waset.org/search?q=inter-laboratory%20comparison" title=" inter-laboratory comparison"> inter-laboratory comparison</a>, <a href="https://publications.waset.org/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/search?q=data%20reliability" title=" data reliability"> data reliability</a>, <a href="https://publications.waset.org/search?q=bias%20impact%20assessment" title=" bias impact assessment"> bias impact assessment</a>, <a href="https://publications.waset.org/search?q=bias%20measurement." title=" bias measurement."> bias measurement.</a> </p> <a href="https://publications.waset.org/10013629/metrology-inspired-methods-to-assess-the-biases-of-artificial-intelligence-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013629/apa" 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Search results for: explainability.