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    <pubDate>Tue, 27 Oct 2026 19:23:24 GMT</pubDate>
    <dc:date>2026-10-27T19:23:24Z</dc:date>
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      <title>Finite element method for the stationary generalized Burgers-Huxley equation</title>
      <link>http://localhost:8081/jspui/handle/123456789/21157</link>
      <description>Title: Finite element method for the stationary generalized Burgers-Huxley equation
Authors: Shukla, Aishwarya
Abstract: In this work we deal with the evaluation of the generalized Burgers-Huxley equation which is a&#xD;
nonlinear elliptic problem . The existence and uniqeness of weak solutions are mentioned in detail&#xD;
by the use of a Faedo-Galerkin approach and also by the use of Fixed point theory. A priory error&#xD;
estimates for conforming finite element method are also derived.</description>
      <pubDate>Sun, 01 May 2022 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://localhost:8081/jspui/handle/123456789/21157</guid>
      <dc:date>2022-05-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>FUZZY RELATION PROGRAMMING PROBLEM WITH INTUTIONISTIC FUZZY COEFFICIENT</title>
      <link>http://localhost:8081/jspui/handle/123456789/21156</link>
      <description>Title: FUZZY RELATION PROGRAMMING PROBLEM WITH INTUTIONISTIC FUZZY COEFFICIENT
Authors: Kumar, Ajay
Abstract: The use of max-product composition in relational geometric programming with &#xD;
intuitionistic fuzzy coefficients is addressed in this article. We use a linear &#xD;
ranking function to convert the problem into a fuzzy relation geometric &#xD;
programming problem since the object is an intuitionistic fuzzy relation function &#xD;
with intuitionistic fuzzy coefficients. After that, we look at intuitionistic fuzzy &#xD;
relation geometric programming and try to find the best solution.</description>
      <pubDate>Sun, 01 May 2022 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://localhost:8081/jspui/handle/123456789/21156</guid>
      <dc:date>2022-05-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>DYNAMICS OF ANTITUMOR IMMUNE RESPONSES-A  STUDY BASED ON MATHEMATICAL MODELLING</title>
      <link>http://localhost:8081/jspui/handle/123456789/21155</link>
      <description>Title: DYNAMICS OF ANTITUMOR IMMUNE RESPONSES-A  STUDY BASED ON MATHEMATICAL MODELLING
Authors: Singh, Anurag
Abstract: This work focuses on the interaction between tumor and immune components, namely, &#xD;
immature and mature lymphocytes, using mathematical modelling as a tool. The model is kept &#xD;
simple but realistic, which is able to capture the dynamics, like , unlimited expansion of tumor, &#xD;
stable endemic equilibrium , periodic oscillation and tumor free equilibrium, as the quantity of &#xD;
mature immune cells are increased .Stability analysis of the system is performed  and numerical &#xD;
results agree with the theoretical analysis.</description>
      <pubDate>Sun, 01 May 2022 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://localhost:8081/jspui/handle/123456789/21155</guid>
      <dc:date>2022-05-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>Using Information Theoretic Concepts to Understand  Autoencoders</title>
      <link>http://localhost:8081/jspui/handle/123456789/21154</link>
      <description>Title: Using Information Theoretic Concepts to Understand  Autoencoders
Authors: Ashish
Abstract: Deep neural networks currently require theory - building approaches for analysis, despite &#xD;
their extensive usage in practical uses. We present a new information-theoretic model to &#xD;
understand learning dynamics and building autoencoders, a type of deep learning architecture &#xD;
that resembles a communication channel. "&#xD;
By generalizing the information plane to any cost &#xD;
function and analyzing the roles and dynamics of various levels using layer-wise information &#xD;
quantity, we emphasize the importance of mutual information in measuring knowledge from &#xD;
data. "&#xD;
 Using the data processing inequality, we also suggest and scientifically illustrate three &#xD;
essential hypotheses relating to the layer-wise flow of information and intrinsic &#xD;
dimensionality of the bottleneck layer for mean square error training and the recognition of &#xD;
a data-controlled bifurcation point in the information plane. Our findings have a direct &#xD;
influence on the best design of autoencoders, alternative feedforward training approaches, &#xD;
and even the generalization problem.</description>
      <pubDate>Sun, 01 May 2022 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://localhost:8081/jspui/handle/123456789/21154</guid>
      <dc:date>2022-05-01T00:00:00Z</dc:date>
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