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            <name>Title</name>
            <description>A name given to the resource</description>
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              <elementText elementTextId="44496">
                <text>Book Chapter</text>
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    <name>Book Chapter</name>
    <description>Faculty Publications- Book Chapter</description>
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          <name>Title</name>
          <description>A name given to the resource</description>
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              <text>Adaptive artificial bee colony (aabc)-based malignancy pre-diagnosis</text>
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          <name>Subject</name>
          <description>The topic of the resource</description>
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              <text>Adaptive artificial bee colony; Feed-forward back-propagation neural network; Risk factor and symptoms and modification rate</text>
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          <name>Description</name>
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              <text>Lung cancer is one of the leading causes of death. The survival rate of the patients diagnosed with lung cancer depends on the stage of the detection and the timely prognosis. Hence, early detection of anomalous malignant cells is needed for pre-diagnosis of lung cancer as it plays a major role in the prognosis and treatment. In this work, an innovative pre-diagnosis approach is suggested, wherein the size of the dataset comprising risk factors and symptoms is considerably decreased and optimized by means of an Adaptive Artificial Bee Colony (AABC) algorithm. Subsequently, the optimized dataset is fed to the Feed-Forward Back-Propagation Neural Network (FFBNN) to perform the training task. For the testing, supplementary data is furnished to well-guided FFBNN-AABC to authenticate whether the supplied investigational data is competent to effectively forecast the lung disorder or not. The results obtained show a considerable improvement in the classification performance compared to other approaches like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).  The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.</text>
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          <name>Creator</name>
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              <text>Kokatnoor S.A.; Krishnan B.</text>
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          <name>Source</name>
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              <text>Lecture Notes in Networks and Systems, Vol-132, pp. 149-157.</text>
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          <name>Publisher</name>
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              <text>Springer</text>
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          <name>Date</name>
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              <text>2021-01-01</text>
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          <name>Identifier</name>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-15-5309-7_15" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-15-5309-7_15&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372979&amp;amp;doi=10.1007%2F978-981-15-5309-7_15&amp;amp;partnerID=40&amp;amp;md5=d507f04568db1956a97bf2305991bfea" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372979&amp;amp;doi=10.1007%2f978-981-15-5309-7_15&amp;amp;partnerID=40&amp;amp;md5=d507f04568db1956a97bf2305991bfea&lt;/a&gt;</text>
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          <name>Rights</name>
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            <elementText elementTextId="158827">
              <text>Restricted Access</text>
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              <text>ISSN: 23673370</text>
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          <name>Format</name>
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              <text>Online</text>
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          <name>Language</name>
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              <text>English</text>
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          <name>Type</name>
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              <text>Book chapter</text>
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          <name>Coverage</name>
          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <text>Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Bangalore, 560074, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Bangalore, 560074, India</text>
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