{"id":3447,"date":"2023-11-24T05:39:24","date_gmt":"2023-11-24T05:39:24","guid":{"rendered":"http:\/\/icmc2024.kalasalingam.ac.in\/?page_id=3447"},"modified":"2023-12-15T05:31:24","modified_gmt":"2023-12-15T05:31:24","slug":"abstracts","status":"publish","type":"page","link":"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/abstracts\/","title":{"rendered":"Abstracts"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"3447\" class=\"elementor elementor-3447\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-84e98b1 e-con-boxed e-con\" data-id=\"84e98b1\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c34efeb elementor-widget elementor-widget-heading\" data-id=\"c34efeb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.9.2 - 21-12-2022 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Abstracts<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ad59807 elementor-widget elementor-widget-text-editor\" data-id=\"ad59807\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.9.2 - 21-12-2022 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#818a91;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#818a91;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<strong><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 47<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID : M001<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Babita Mehta\n&amp; P.K. Parida<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Local\nconvergence analysis of a family of third order iterative methods using\nmajorant function in Riemannian Manifold<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: This study\npresents a local convergence analysis of a family of third order iterative\nalgorithms based on the majorant principle for locating a singularity of a\ndifferentiable vector field defined on a complete Riemannian manifold. This\nstudy shows a clear relationship between the vector field under consideration\nand the majorant function, which relaxes the Lipschitz continuity of the\nderivative. Additionally, it enables us to determine the\noptimal&nbsp;convergence radius and the widest range for the uniqueness of the\nsolution.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 54<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM002<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: R Karthika &amp;\nV Renukadevi<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Equivariant\nLS-category and topological complexity of product of several manifolds<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The\nLS-category and the topological complexity are some homotopy invariants of a\ntopological space, and the topological complexity is a close relative of the\nLS-category. In this paper, we calculate the equivariant version of LS-category\nand topological complexity of some Z_2-spaces.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 16<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM003<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Kalaiselvi T\n&amp; Yegnanarayanan Venkataraman<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Computation\nof Various Domination Numbers of a Family of 3-regular Graph<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract : Given a\ngraph G = (V, E), a subset S <span style=\"font-family:&quot;Cambria Math&quot;,serif;\nmso-bidi-font-family:&quot;Cambria Math&quot;\">\u2286<\/span>V is termed as a dominating set if\nevery vertex in V is in S or adjacent to some vertex in S. A dominating set of\nleast size is termed a \u03b3-set and the number of elements of any \u03b3 -set is called\nthe domination number, denoted by \u03b3(G). The task of finding a dominating set is\nin general a hard task. A dominating set S <span style=\"font-family:&quot;Cambria Math&quot;,serif;\nmso-bidi-font-family:&quot;Cambria Math&quot;\">\u2286<\/span> V(G) is called a total dominating\nset if any vertex v <span style=\"font-family:&quot;Cambria Math&quot;,serif;mso-bidi-font-family:\n&quot;Cambria Math&quot;\">\u2208<\/span> V(G) has at least one adjacent element in S. The size\nof a minimal total dominating set is referred as the&nbsp; total domination number of G and it is\ndenoted by \u03b3_t(G). A total dominating set S is called a captive dominating set\nif any vertex in S is adjacent with at least one element in V \u2013 S. The size of\na minimal captive dominating set is referred as the captive domination number\nof G and denoted by \u03b3_ca(G). Suppose that uv <span style=\"font-family:&quot;Cambria Math&quot;,serif;\nmso-bidi-font-family:&quot;Cambria Math&quot;\">\u2208<\/span> E(G) of G. u is said to dominate\nv strongly if deg(u) \u2265 deg(v). Clearly any vertex of V(G) dominates strongly\nitself. S is called a strong dominating set if each v <span style=\"font-family:\n&quot;Cambria Math&quot;,serif;mso-bidi-font-family:&quot;Cambria Math&quot;\">\u2208<\/span> V &#8211; S is\nstrongly dominated by some u in S. The strong domination number \u03b3std(G) of G is\nthe minimum size of a strong captive dominating set. S is termed a strong\ncaptive dominating set if it is both strong and a captive dominating set. The\nleast number of elements in such a S is called strong captive domination\nnumber, \u03b3sca(G). A captive dominating set S is called a half certified captive\ndominating set, if S is a captive dominating set and every vertex in S has at\nleast two neighbors in V-S. The size of a minimal half certified captive\ndominating set is called as the half certified captive domination number of G\nand denoted by \u03b3_hcca(G). The neighborhood of v is the set NG(v) = N(v) = {u <span style=\"font-family:&quot;Cambria Math&quot;,serif;mso-bidi-font-family:&quot;Cambria Math&quot;\">\u2208<\/span>\nV(G): uv <span style=\"font-family:&quot;Cambria Math&quot;,serif;mso-bidi-font-family:\n&quot;Cambria Math&quot;\">\u2208<\/span> E(G)}. If S<span style=\"font-family:&quot;Cambria Math&quot;,serif;\nmso-bidi-font-family:&quot;Cambria Math&quot;\">\u2286<\/span> V(G), then the open neighborhood\nof S is the set NG(S) = N(S) =&nbsp; <span style=\"font-family:&quot;Cambria Math&quot;,serif;mso-bidi-font-family:&quot;Cambria Math&quot;\">\u22c3<\/span>_(v\n<span style=\"font-family:&quot;Cambria Math&quot;,serif;mso-bidi-font-family:&quot;Cambria Math&quot;\">\u2208<\/span>&nbsp; S)<span style=\"font-family:&quot;Arial&quot;,sans-serif\">\u2592<\/span><span lang=\"ZH-TW\" style=\"font-family:&quot;Cambria Math&quot;,serif;mso-fareast-font-family:\n&quot;Cambria Math&quot;;mso-bidi-font-family:&quot;Cambria Math&quot;\">\u3016<\/span>N_G (v)<span lang=\"ZH-TW\" style=\"font-family:&quot;Cambria Math&quot;,serif;mso-fareast-font-family:\n&quot;Cambria Math&quot;;mso-bidi-font-family:&quot;Cambria Math&quot;\">\u3017<\/span>. The closed\nneighborhood of S is NG[S] = N[S] = S<span style=\"font-family:&quot;Cambria Math&quot;,serif;\nmso-bidi-font-family:&quot;Cambria Math&quot;\">\u222a<\/span> N(S). A subset S of V(G) is a\nmajority dominating set if at least half of the vertices of V(G) are either\nbelong to S or adjacent to the elements of S. That is |N[S]|&nbsp; \u2265 <span style=\"font-family:&quot;Cambria Math&quot;,serif;\nmso-bidi-font-family:&quot;Cambria Math&quot;\">\u2308<\/span>(V(G))\/2<span style=\"font-family:\n&quot;Cambria Math&quot;,serif;mso-bidi-font-family:&quot;Cambria Math&quot;\">\u2309<\/span>. The minimum\nsize of a majority dominating set of G is called as the majority domination\nnumber of G and is denoted by \u03b3_m(G).Some interesting results about the\ncomputation of these parameters are reported here for flower snark graphs, that\nare one pertinent family of 3-regular graphs.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 49<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM004<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Hidenori\nOgata<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Method of\nfundamental solutions for doubly periodic potential problems<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: In this\npaper, we propose a method of fundamental solution for two-dimensional doubly\nperiodic problems, especially potential flow problems with a doubly-periodic\narray of obstacles. In the proposed method, we approximates the solution, which\ninvolves doubly periodic functions, by a linear combination of the logarithmic\npotentials consisting of the theta functions. The method inherits the\nefficiency of the ordinary method of fundamental solutions and gives an\napproximate solution which has the same periodicity as the one of the exact\nsolution. Numerical examples show the efficiency of the presented method.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 63<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM005<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: R. Deb and\nA.K. Das<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: On the\nsolution set of semi-infiite tensor complementarity problem<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: In this\npaper, we introduce semi-infinite tensor complementarity problem to provide an\napproach for considering a more realistic situation of the problem. We prove\nthe necessary and sufficient conditions for the existence of the solution set.\nIn this context, we study the error bounds of the solution set in terms of residual\nfunction.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 29<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM006<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Biman Sarkar,\nPriya Sharma and Soumen De<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Analysis of\noblique wave scattering by a thick bottom-standing barrier placed in between a\npair of thin partially immersed barriers<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The study\ninvestigates the interaction of oblique water waves with a configuration\nconsisting of a pair of partially immersed thin vertical barriers on the two\nsides of a bottom-standing rectangular thick barrier. The eigenfunction\nexpansion method is employed to analyze the system, leading to weakly singular\nFredholm-type integral equations. Singularities near the edges of the barriers\nare addressed using Chebyshev and ultraspherical Gegenbauer polynomials as\nbasis functions. Numerical estimations of reflection and transmission\ncoefficients are presented, demonstrating excellent agreement with existing\nliterature and validating the theory\u2019s reliability and applicability in\npractical wave interaction scenarios.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 112<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS001<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Po-An Shih,\nCheng-Che Wu ,Chia-Hsin Huang and Arijit Karati<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Effective\nData Transmission in NDN-Assisted Edge-Cloud Computing Model<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The\nproliferation of data generated by Internet of Things (IoT) devices has\nprompted the pursuit of streamlined data retrieval as a fundamental objective.\nEdge computing performs computations locally while significantly reducing cloud\noverhead. However, it lacks providing location anonymity while integrating multiple\nclouds. Named Data Networking (NDN) as a novel Internet architecture provides\nlocation anonymity and enhances the efficacy of data exchange through caching.\nIn this paper, we develop an efficient data retrieval system that protects file\nlocation privacy across multiple cloud platforms by leveraging NDN and edge computing.\nConsumers perform data queries on the local network without connecting to the\ncloud server in our Edge-NDN architecture. The first stage entails a search for\nedge-cacheable data. If content cannot be located, it is retrieved via an\nexternal network connection to the cloud. We estimate the performance of the work\nusing NDN Forwarding Daemon (NFD), ndn-cxx, and jNDN tools. The empirical\nfindings indicate that the proposed framework for facilitating anonymous data\ncommunication outperforms the conventional cloud-centric approach.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 85<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM007<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Phani Kumar\nNyshadham, Levin Dabhi, Archie Mittal&nbsp;\nand Harsh Kedia<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Geometric\nAlgorithm for Generalized Inverse of Rank<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Deficient Real\nMatrices<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: An inverse\nof a matrix which is not necessarily square or is square, but nevertheless\nsingular called rank deficient matrices, is applied to solve ill-conditioned\nproblems such as large sized matrix computations. Such an inverse is referred\nto as a generalized inverse. Generalized inverses have many applications in engineering\nproblems, such as data analysis, electrical networks, character recognition,\nand so on. The most frequently used one is a Moore-Penrose type inverse.\nSeveral algorithms to compute generalized inverses have been proposed. Many\nalgorithms require to solve large least square systems in minimum-norm sense. Moore-Penrose\ninverse matrices allow for solving such systems, even with rank deficiency, and\nthey provide minimum-norm vectors as solutions. In this paper, we propose novel\ngeometric algorithm for computing generalized inverse of rank deficient real\nmatrices. While some of the approaches for the formulations are purely based on\nLU-factorization, the other variations are based on LU and QR factorizations.\nThe uniqueness of the generalized inverse are also proved for the proposed\nformulations.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 66<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM008<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Manikandan V\nand Monikandan S<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Algorithm for Reconstruction\nNumber of Split Graphs<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: A card G-v\nof a graph G is obtained by deleting the vertex v and all edges incident with\nv. The multiset of all cards of G is called the deck of G. A graph is\nreconstructible if it is determined up to isomorphism from the collection of\nall its cards. The Reconstruction Conjecture asserts that all graphs of order at\nleast three are reconstructible. The minimum number of cards of G that do not\nbelong to the deck of any graph not isomorphic to G is called the\nreconstruction number of G. A split graph is a graph in which the vertices can\nbe partitioned into an independent set and a clique. In this paper, we prove\nthat the degree sequence of a split graph G can be found by using some six\ncards of G. We give an algorithm to find the reconstruction number of split\ngraphs G which uses only six cards of G for most of the cases.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 250<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS002<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: &nbsp;Joyanta Kumar Majhi&nbsp; and A. K. Das<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Pricing and\nretailer service strategy in a closed-loop supply Chain as a response to show rooming\neffect<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Increased\nawareness of environmental and social responsibility has prompted many\nmanufacturers to adopt product recycling programs. In addition, with the\ndevelopment of the internet and e-commerce, the supply chain structure has\nchanged from a single physical store to a mode in which online and offline\noutlets coexist. At the same time, consumers usually obtain a product\ninformation in a physical store before purchasing it online which is recognized\nas free-riding or showrooming behaviour. In contrast, the free-riding rate has\nthe same impacts on online and offline price decisions in different decision\nmodes. More specifically, the free-riding rate has both positive as well as\nnegative impact on the online and offline price modes. This study investigates\nthe influence of the showrooming effect on firms pricing and service effort in\na dual-channel supply chain. The no-service, ex-ante and ex-post service effort\nstrategies are considered. The equilibrium results show that the showrooming\neffect enables the firms to benefit the most from the ex-post service efforts.\nMoreover, the showrooming effect makes the manufacturer set both high and low\nwholesale prices for retailers in the ex-ante and ex-post strategy. This study\nfurther extends the three strategies by considering no show rooming effect.\nResults show that the greater the show rooming effect, the higher profits firms\nwill obtain using the ex-post service effort strategy.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 153<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM009<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Helda\nRajendran and Kalpana Mahalingam<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title :&nbsp; Properties of m-bonacci words<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The\n$m$-bonacci word is the unique fixed point of the morphism $\\varphi_m:$\n$0\\rightarrow 01,~1\\rightarrow 02,~2\\rightarrow 03,\\ldots,(m-2)\\rightarrow\n0(m-1),~(m-1)\\rightarrow 0$. The finite $m$- bonacci word $w_{n,m}$ is defined\nas $w_{n,m}=\\varphi_m^n(0)$. We study some combinatorial properties of finite\n$m$-bonacci words. We find the values of $n$, such that $w_{n,m}$ is square\nfree. We prove that $w_{n,m}$ is primitive and have a unique representation as\na product of two palindromes. We also show that the language\n$W_m^0=\\{w_{n,m}:~n\\ge 0\\}$ is context-free free and not dense.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 141<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM010<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Ishwariya R<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Robust\nNumerical technique for a class of singularly perturbed nonlinear system of\nn-differential equations with Robin boundary conditions<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: In this\narticle, a class of singularly perturbed nonlinear system of n-differential\nequations with unperturbed Robin boundary conditions is considered. The\nnumerical method considered in this work consists of the classical finite\ndifference operator over a piecewise uniform Shishkin mesh and a continuation\nalgorithm is constructed to solve the problems. The method suggested is proved\nto be essentially first order convergent uniformly with respect to all\nperturbation parameters. Numerical experiments are carried out for two\ndifferent types of Robin boundary conditions with and without perturbation\nparameters.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 254<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM011<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors : Amit\nTripathi, Rachna Bhatia, Pratibha Joshi and Anand Kumar Tiwari<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : A\ncomputational study of time dependent nonlinear Schrodinger equation with cubic\nnonlinearity<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: This\nresearch study presents a computational method to solve one dimensional\nSchrodinger equation with cubic non-linearity, which describes many important\nphysical phenomena such as propagation of classical waves in dispersive\nnonlinear media, nonlinear optics, water waves etc. We use modified trigonometric\ncubic B-spline functions in collocation method to discretize the equation in\nspace variable. This approach converts the equation into system of ordinary\ndifferential equations, which has been solved using stability preserving\nRunge-Kutta method. The computational complexity is observed as linear in size\nof partition. The implementation of developed approach is easy and the required\ncomputational work is also very less. Additionally, the solutions using this\napproach can be found not only at the discretized mesh points xi but also at\nany point in solution domain.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 36<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS003<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Redwan\nWalid, Lavanya Elluri and Karuna Joshi<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Secure and\nPrivacy-Compliant Data Sharing: An Essential Framework for Healthcare\nOrganizations<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Data\nintegration from multiple sources can improve decision-making and predict\nepidemiological trends. While there are many benefits to data integration,\nthere are also privacy concerns, especially in healthcare. The Health Insurance\nPortability and Accountability Act (HIPAA) is one of the essential regulations in\nhealthcare, and it sets strict standards for the privacy and security of\npatient data. Often, data integration can be complex because different rules\napply to different companies. Many existing data integration technologies are\ndomain-specific and theoretical, while others rigorously adhere to unified data\nintegration. Moreover, the integration systems do not have semantic access\ncontrol, which causes privacy breaches. We propose a framework for sharing and\nintegrating data across healthcare providers by protecting data privacy. We use\nan ontology to provide Attribute-Based Access Control (ABAC) for preventing\nexcess or unwanted access based on the user attributes or central organization\nrules. The data is shared by removing sensitive attributes and anonymizing the\nrest using k-anonymity to strike a balance between data utility and secret information.\nA metadata layer is used to describe the schema mapping to integrate data from\nmultiple sources. Our framework is a promising approach to data integration in\nhealthcare, and it addresses some of the critical challenges of data\nintegration in this domain.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 276<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS004<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: &nbsp;Anisha Mitra and Dipanwita Roy Chowdhury<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Guarding the\nBeats by Defending Resource Depletion Attacks on Implantable Cardioverter\nDefibrillators<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Implantable\nMedical Devices (IMDs) have revolutionized the treatment of critical diseases.\nHowever, the increasing reliance on these life-saving devices&amp;#39; wireless\nfunctionality has made them vulnerable to cyber attacks. Implantable\nCardioverter Defibrillator (ICD) has emerged as a leading IMD owing to the worldwide\nsurge in cardiac diseases. Given the resource-constrained ICD environment,\nthere&amp;#39;s a pressing need to develop tailored security measures for\nprotection, moving beyond traditional approaches. In this paper, we present\nresource depletion attack scenarios in an ICD environment where attackers can\nexploit ICD&amp;#39;s wireless connectivity function. We propose some\ncomprehensive approaches to mitigate such attacks, offering a significant step\nforward in safeguarding the well-being of patients. This research contributes\nto the ongoing efforts to secure the Internet of Medical Things (IoMT)\necosystem and underscores the importance of cybersecurity in modern healthcare.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 164<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS005<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Debranjan\nPal, Mainak Chaudhury, Abhijit Das and Dipanwita Roy Chowdhury<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Deep Learning\nBased Differential Distinguishers for NIST Standard Authenticated Encryption\nand Permutations<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Deep\nlearning-based cryptanalysis is one of the new ideas that has emerged in recent\nyears. By using deep learning-based methodologies, researchers are currently\nmodeling conventional differential cryptanalysis. We use deep learning models,\nCNN, LSTM, LGBM, DenseNet and LeNet, to generate deep learning-based\ndifferential distinguishers that can reveal weaknesses in the encryption\nschemes. We focus on National Institute of Standards and Technology (NIST)\nstandard lightweight authenticated encryption (AE), such as TGIF-TBC, and,\nLIMDOLEN- 128, along with permutation methods like SPARKLE-256, ACE-128 and\nSPONGENT-160. Our research has led us to found that deep learning techniques\ncan generate differential distinguishers for these cryptographic elements.\nSpecifically, we were able to develop differential distinguishers for the\nSPONGENT-160 permutation up to seven rounds, for the SPARKLE-256 permutation up\nto three rounds, for the ACE-128 permutation up to four rounds, for the TGIF- TBC\nAE up to five rounds, and for the LIMDOLEN-128 AE up to fourteen rounds.\nNotably, this marks the first instance of a deep learning-based differential\nclassifier for the authenticated encryptions TGIF- TBC, LIMDOLEN-128, as well\nas the permutations SPARKLE-256, ACE-128, and SPONGENT-160, based on our current\nunderstanding. When considering various models, both DenseNet and CNN\ndemonstrate strong performance. However, it is the LightGBM (LGBM) model that truly\nshines as the optimal choice, primarily attributed to its minimal parameter\nrequirements and rapid response speed.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 107<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM012<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: &nbsp;J Jenifa, and J Christy Roja<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Overlapping\nIterative Numerical Method for Solving Systems of Singularly Perturbed\nConvection Diffusion Problems with Mixed Type Boundary Conditions<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: We\ninvestigate in this article convergence of the overlapping iterative numerical\nmethod on a Shiskin type mesh for a system of singularly perturbed\nconvection-diffusion equations with mixed type boundary conditions. The\nanalysis is based on defining some auxiliary problems that allow to prove the\nuniform convergence of the method in two steps, splitting the discretization\nerror and the iteration error. An error estimate is derived by using supremum\nnorm and it is of order O(N^(\u22121) ln^(2)N). Numerical experiments are given to\ndemonstrate the theoretical results.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 209<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM013<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Ali\nAl-Sharadqah and Giuliano Piga<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : &nbsp;Concentric Ellipse Fitting Problem: Theory and\nNumerical Implementations<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The problem\nof fitting ellipses has been popular since the 1970&amp;#39;s, and remains a\nprominent area of research in statistics, computer vision, and engineering.\nThis paper aims to address the problem of fitting concentric ellipses under\ngeneral assumptions which started paying more attention recently due to its applications\nin engineering. We study two methods of obtaining an estimator of the\nconcentric ellipse parameters under this model, namely, the least squares (LS)\nand the gradient weighted algebraic fits (GRAF). We address some practical\nissues in obtaining these estimators. Since our model is nonlinear, obtaining\nan estimate for the concentric ellipse parameters requires the implementation\nof numerical minimization schemes. We propose and compare several minimization\nschemes, and provide several initial guesses which yield the best convergence\nrates.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 104<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS020<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Vishnu\nPendyala, Samhita Konduri and Kriti Pendyala<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Analysis of\nMulti-language Regional Music Tracks using Representation Learning Techniques\nin Lower Dimensions<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Machine\nunderstanding of music requires digital representation of the music using\nmeaningful features and then analyzing the features. The work in this paper is\nunique in using representation learning techniques in lower dimensions for\nanalyzing the effectiveness of mel-spectrogram features of assorted music\ntracks in multiple languages. The features are plotted in three different\ntransformed feature spaces for visual inspection of the fine-grained attributes\nof the music rendition such as the vocal artist, their gender, language, and\nstanding in the industry. The analysis of the music tracks in a chosen dataset\nusing spectral and non-spectral algorithms such as Principal Component Analysis\n(PCA), t-distributed stochastic neighbor embedding (t-SNE), and Uniform\nManifold Approximation and Projection (UMAP) provide valuable insights into the\nrepresentation learning of the selected music tracks. UMAP performs better than\nthe other two algorithms and is able to reasonably discern the various subtler\naspects of a music rendition.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 157<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS007<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Amit Sardar\nand Dipanwita Roy Chowdhury<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : &nbsp;Key Dependent Dynamic Sbox for Kasumi Block\nCipher<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The core\nstrength of a block cipher lies in its nonlinear substitution operation, known\nas the SBox. However, the presence of static parameters within the SBox can\npotentially lead to the exposure of certain information in the ciphertext. In\nthis paper, we present a methodology for the construction of key-dependent SBoxes.\nThese key-dependent SBoxes exhibit resistance against linear and differential\ncryptanalysis. In this paper, we generate key-dependent 7-bit and 9-bit S-Boxes\nfor the Kasumi block cipher. Furthermore, we demonstrate their resistance\nagainst known differential fault attacks.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 100<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM014<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: &nbsp;Samar Idris and Rifat Colak<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Lambda &#8211;\nstatistical derivative<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: In this\nstudy, following recent and new studies, we extend recently introduced\nstatistical derivative and Ces\u00e0ro derivative to \u03bb\u2212statistical derivative, (V,\n\u03bb)\u2212 derivative and strongly (V, \u03bb)\u2212 derivative respectively. We also give the\nrelationship between the \u03bb\u2212 statistical derivative and strongly (V, \u03bb)\u2212\nderivative.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 218<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS008<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: &nbsp;Shuddhashil Ganguly, Himadri Mukherjee ,\nAnkita Dhar , Matteo Marciano and Kaushik Roy<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : ChiBa &#8211; A\nChirrup and Bark detection system for Urban Environment<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The World\nis developing at a tremendous pace which has been catapulted by large-scale technological\nadvancements. Building mega structures has never been easier and modes of\ncommute have also developed thereby shortening travel-time. Such advancements\nhave also brought along newer sources of pollution which are harming our planet\nat an even faster pace. Sound pollution is one such agent which has a long-term\neffect on not only humans but the entire biodiversity. Its effect on life is\nnot immediately observed but the damage becomes visible over time. Birds are\none of the most affected creatures due to sound pollution. This is one of the\nmajor reasons for declining bird population in the Urban areas. It is very\nimportant to preserve biodiversity for a sustainable future. Animals have calls\nthat are melodious and rhythmic and these calls tend to change when they are in\ndistress. An automated system can be very useful in this context which can\nmonitor animal sounds and detect changes in their calls. Deployment of such a\nsystem in Urban areas is challenging due to the presence of ambient sounds\nwhich is extremely diverse. Thus it is essential to initially detect animal\ncalls in the Urban environment prior to monitoring them. ChiBa is a system\nproposed to address this problem. Experiments were initially performed with the\ndetection of birds and dogs (most common and loudest creatures in cities) calls\nin the Urban environment. Tests were performed with over 7K clips comprising of\nthe animal calls as well as Urban ambient sounds. The audios were modeled using\na deep learning-based approach wherein the highest accuracy of 99.91% was\nobtained.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 212<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS009<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: &nbsp;Kokila R &nbsp;and Thangavelu P<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : FFT based image\nregistration using Stationary Wavelet Transform and edge features<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Image\nregistration plays a pivotal role in many image processing applications that\ninvolve multiple images for comparison, integration (or) analysis such as image\nfusion, image mosaics, scene change detection and medical imaging. We propose\nFFT based image registration using Stationary Wavelet Transform (SWT) and edge\nfeatures. Two sets of experiments were conducted on a number of images to\nmeasure the effectiveness and robustness of the proposed schemes. From our\nextensive experimental results, it was found that SWT based image registration\nscheme performs better than edge feature based schemes and Normalised Gradient Correlation\n(NGC) approach [21] and able to recover scale factor up to 8.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 87<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM016<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Ramkumar S B\nand Renukadevi V<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Stabilizer\ngroup of set ideals<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: We derive a\nnecessary and sufficient condition for the stabilizer groups of set ideal on an\ninfinite set X containing a moiety of X is to be isomorphic. Also, we prove\nthat the outer automorphism group of S_{I} is a group of order atmost two if I\nis isomorphic to its polar.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 35<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM017<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Rachna\nSachdeva and Ashok Agarwal<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Modified\nlattice paths and Gordon-McIntosh eighth order mock theta functions<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: In 2004,\nthe second author gave the combinatorial interpretations of four mock theta\nfunctions of S. Ramanujan using (n+t)-color partitions introduced by himself\nand G.E. Andrews in 1987. Very recently, Agarwal and Sood defined split\n(n+t)-color partitions which generalize the (n+t)-color partitions. Using split\n(n+t)-color partitions they provided combinatorial meaning to two eighth order\nmock theta functions of Gordon-McIntosh found in 2000. In this paper, we modify\nthe definition of Agarwal-Bressoud weighted lattice paths and restate\nAgarwal-Sood results in terms of modified lattice paths. This results in two\nnew combinatorial identities.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 22<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS010<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Aadi Gupta,\nPriya Gulati and Siddhartha Chakrabarty<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : &nbsp;Classification based credit risk analysis: The\ncase of Lending Club<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: In this\npaper, we performs a credit risk analysis, on the data of past loan applicants\nof a company named Lending Club. The calculation required the use of\nexploratory data analysis and machine learning classification algorithms,\nnamely, Logistic Regression and Random Forest Algorithm. We further used the calculated\nprobability of default to design a credit derivative based on the idea of a\nCredit Default Swap, to hedge against an event of default. The results on the\ntest set are presented using various performance measures.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 50<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS011<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors:Parvathi\nPradeep, Premjith B, Nimal Madhu M &amp; Gopalakrishnan E.a<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : &nbsp;A Transformer-based Stock Market Price\nPrediction by incorporating BERT Embedding<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The stock\nmarket trend is known to be volatile, dynamic and nonlinear. Therefore,\naccurate prediction of the trend and forecasting the stock prices in\ntoday&amp;#39;s world is one of the most complex tasks. It is because of the\nevents and preconditions, macro or micro, a few being politics, global economic\nconditions, and unexpected events which affect the stock market trend. Since it\nis difficult to predict all the contingencies, how long the effect of such\nparameters last can not be predicted. In this work, we studied the efficacy of different\ndeep learning algorithms to learn the trend in the stock market price to\npredict the price for the next few days. We considered the stock price, stock\nindex and dollar index and related news data to predict the stock closing price\nof Apple Inc. Sentence embedding and sentiment scores were extracted from the\nnews data and fed to the deep learning model along with stock price, stock\nindex and dollar index values. The deep learning model was designed using a\nTransformer consisting of an Encoder stack with attention layers and a set of\nMLP layers to reshape the predictions. The experiments showed that\nincorporating sentence embedding improved the prediction rate compared to the\nstate-of-the-art model.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 57<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM018<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Sapan Kumar\nNayak and Pradip Kumar Parida<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : &nbsp;Real dynamics of a sixth-order family of\nderivative free iterative method without memory<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: This\nmanuscript introduces the dynamical behavior of a family of sixth-order\nderivative-free iterative method. When, the proposed method applied on a\nquadratic equation, and the presence of a parameter $\\mu\\in \\mathbb{R}$, the\niterative method creates the dynamical plane. Also, the reliability and\nstability of the iterative method have been studied using different tools.\nMoreover, information like convergence to $n$- cycles, different types of fixed\npoints, and the chaotic nature of polynomials are all studied using the convergence\nplane.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 167<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS012<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">30. Christopher\nSamuel Raj Balraj and Nagaraj P<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Prediction of\nmental health issues and challenges using hybrid learning techniques<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Mental\nhealth issues like melancholy, anxiety, and a lack of sleep-in young children,\nteenagers, and adults are the root cause of emotional stress. It affects how\nsomeone feels, thinks, or responds to a certain circumstance or situation.\nBeing in good physical and mental health is a prerequisite for productive work\nand realizing one&amp;#39;s full potential. From childhood to maturity,\nmaintaining one&amp;#39;s mental health is crucial. The various causes of\nmental health concerns that lead to mental illness include stress, social\nanxiety, depression, obsessive-compulsive disorder, substance addiction,\nemployment issues, and personality disorders. We used openly accessible web\ndatasets to collect the data. The data was label-encoded to improve prediction.\nThe methods employed include logistic regression, Nave Bayes, decision trees,\nneural networks, and support vector machines. The Decision Tree, the Support\nVector Machine, and the neural network, in that order, are the most trustworthy\nmodels for stress, depression, and anxiety. The data is put through several\nmachine-learning techniques to produce labels. Based on these classified\ncategories, a model will be created to forecast the mental state of an\nindividual. People over 18 who are working class make up our main market. After\nfinishing, based on the information a user submitted on the website.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 151<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID : S013<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Sakthidevi\nI, S J Subhashini, Jeyaraj Jane Rubel Angelina, Venkataraman Yegnanarayanan and\nKundakarla Syam Kumar<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Leveraging\nMeta-Learning for Dynamic Anomaly Detection in Zero&nbsp;Trust&nbsp;Clouds<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: In the\nrapidly evolving landscape of cloud computing, ensuring the security of data\nand services remains an imperative challenge. The Zero Trust framework,\nadvocating continuous verification and access control, presents a pivotal\nparadigm to mitigate risks. This research introduces a pioneering approach\nnamed &amp;quot;DeepMetaGuard&amp;quot; for addressing dynamic anomaly\ndetection within Zero Trust cloud environments. By amalgamating the\nModel-Agnostic Meta-Learning (MAML) and Variational Autoencoders (VAEs) &#8211; a\nDeep Anomaly Detection model, DeepMetaGuard stands as a promising innovation.\nDeepMetaGuard harnesses the potential of meta-learning through MAML, which\nexpedites the model&amp;#39;s adaptation to diverse cloud scenarios, thereby\nenhancing its adaptability to anomalous behaviours. Simultaneously, its\nintegration with VAEs equips the model to identify anomalies across various\ncloud environments by acquiring generalized knowledge while accommodating\ndistinct traits. To assess DeepMetaGuard&amp;#39;s efficacy, a comprehensive\nsimulation analysis is conducted, comparing its performance against existing\nanomaly detection algorithms. The evaluation encompasses a spectrum of\nsimulation metrics, including Area Under Curve &#8211; Precision Recall Metric\n(AUC-PR), Detection Time, Precision-Recall Gain Curves, and Matthews\nCorrelation Coefficient (MCC). AUC-PR gauges precision-recall trade-offs,\nDetection Time measures response speed, Precision-Recall Gain Curves visualize\nincremental performance gains, and MCC balances overall model performance. In\nthis pioneering study, DeepMetaGuard emerges as a proficient contender in\ndynamic anomaly detection within Zero Trust cloud environments. The\namalgamation of meta-learning and deep anomaly detection techniques, as\nevidenced through the comprehensive evaluation, underscores its potential in\nredefining cloud security. By introducing DeepMetaGuard and substantiating its\neffectiveness against established benchmarks, this research contributes to the\nadvancement of cybersecurity strategies in the realm\nof&nbsp;cloud&nbsp;systems.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 165<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS014<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: &nbsp;Sumathi Ganesan and Mahalakshmi G<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : ANALYSIS OF\nBLOOD TRANSFUSION DATASET USING DATA MINING TECHNIQUES<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Data mining\nis the practice of using large data sets to analyze and learn from. Here the\nBlood Transfusion data set is being processed and the required knowledge is\ngained through data mining classification techniques. The blood Transfusion\ndata set is first being processed through different classification algorithms.\nThe top five algorithms with greater accuracy are noted. Then, the dataset is normalized\nwithin the value 0.0 to 1.0 and the same process is carried out. Again, the\naccuracy is then being checked for any changes. If changes were to be found,\nthey are justified. The top five algorithms that were finalized before\nnormalizing are bagging algorithm, LogitBoost algorithm, J48 algorithm, ClassificationViaRegression\nand Random Forest algorithm.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 127<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM019<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Sheela Rani\nM and Dhanasekar S<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Fuzzy MCDM techniques\nfor analysing the risk factors of COVID-19 and FLU<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Decision\nmaking is inevitable in day today life. Fuzzy Multi Criteria Decision Making is\nincorporating for better decision making in almost all kind of complexity\nproblems. In this research notable advantages of VIKOR and TOPSIS techniques\nemployed with fuzzy triangular numbers to analyse the risk factors of COVID-19 and\nFLU. The comparative analysis is illustrated to find the most influencing risk\nfactors of COVID-19 and FLU by comparing each and every situation of patients.\nAt last, the resistance test also included to check the final rankings and\noutcome.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 182<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM020<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Narmada Devi\nR and Sowmiya S<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Solving of\nAssignment Problem by Pythagorean Octagonal Neutrosophic Fuzzy Number<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The article\naims to introduce the Pythagorean Octagonal Neutrosophic Fuzzy Number (PONFN)\nand its operations. In this paper, deals with the solving of assignment problem\nby the various ranking procedure based on Pythagorean Octagonal Neutrosophic\nFuzzy Number. Comparative Analysis also performed to ensure the framework\u2019s\nrobustness.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 258<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID : S015<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Rajalaxmi G\n, Vimal S E&nbsp; and Janani Selvaraj<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Water Body\nSegmentation for Satellite Images Using U-Net++<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Satellite\nimages are important for both monitoring and managing natural resources. The\nability to identify and manage water resources is made possible by the\nsegmentation of water bodies in satellite data. In this study, U-Net++(Nested\nU-Net) model was used to separate water bodies in satellite data. The dataset\nfor the project was collected using USGS Earth Explorer and QGIS, and it was\ndivided into 20% for testing and 80% for training. After 70 cycles of training,\nthe U- Net++ model had an accuracy of 97.66%. The U-Net++ model builds on the\noriginal U-Net model, which has been widely used for segmentation tasks. The\nU-Net++ model incorporates skip connections and dense connections to improve\nmodel performance. This study&amp;#39;s ability to segment the water body opens\nup a lot of possibilities for controlling and monitoring water supplies, among other\nthings. The accuracy reached with the U-Net++ model demonstrates its capacity\nfor accurate water body segmentation in satellite pictures.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 268<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS016<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Janani\nSelvaraj and Prashanthi Devi Marimuthu<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: &nbsp;Modeling Vegetation Dynamics: Insights from\nDistributed Lag Model and Spatial Interpolation of Satellite Derived\nEnvironmental Data<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The study\nproposes a method for modelling vegetation dynamics by combining time series\nanalysis of the Normalised Difference Vegetation Index (NDVI) with spatial\ninterpolation of environmental data. The goal is to provide a comprehensive\nunderstanding of how vegetation responds to changing environmental conditions\nby taking both temporal and spatial aspects into account. To investigate the\ntemporal patterns of NDVI, advanced time series analysis techniques are used in\nthe temporal domain. Distributed Lag Models, in particular, are utilised for\nmodelling to discover the complex interactions between satellite derived NDVI\nand environmental factors such as Land Surface Temperature and precipitation.\nThis method aids in assessing the delayed impacts of environmental influences\non vegetation providing information on both short-term and long-term responses.\nSimultaneously, spatial interpolation methods are used in the spatial domain to\nbuild continuous maps of environmental variables across the study area. These\nspatial surfaces provide useful information on the geographic variation of\nenvironmental conditions. These findings have implications for ecosystem\nmanagement, assessing climate change, and planning land use, providing a solid\nplatform for informed decision-making in complex ecological systems.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 267<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS017<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Sundareswaran\nN , Sasirekha S, Vijay M and Vivek Rabinson Rabinson<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Supporting\nSmart Meter Context Management using OWL Ontology and Hyperledger Fabric\nBlockchain<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The usage\nof electrical and electronic appliances is on the rise in both homes and\nbusinesses. The smart energy device has various potential applications,\nincluding power measurement, power control, and data exchange between smart\npower plants and individual customer endpoints. However, the current smart\nenergy meters primarily provide data on the overall electricity consumption of\na home or business, without considering context or information security. To\neffectively manage energy, it is essential to have a knowledge interpreter and\na secure information storage system, as most households and industries lack\nawareness of energy consumption, data privacy, and actions that can reduce\ndemand. Hence, this study proposes a context- aware smart energy metering\nsystem and a secure information storage management system based on blockchain.\nMoreover, we analyzed the Sustainable Data for Energy Disaggregation\n(SustDataED2) dataset. Similarly, the Hyper Ledger Fabric (HLF) blockchain\nsystem functions as a storage ledger, ensuring the integrity<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">of information and\nprotecting it against malicious attacks.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 79<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS018<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Jaya Sudha ,\nHariprasath C and Senthil Kumaran R<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Design of\nMicrostrip Rectangular Dual Band Antenna for MIMO 5G Applications<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: In\ntoday&amp;#39;s wireless communication networks, microwave antennas play a\npivotal role in ensuring efficient and reliable connectivity. This is centered\non the creation of small rectangular patches for multiband applications using\nseveral sorts of flawed soil structure methodologies. This study introduces a\nproposed design for a microstrip rectangular dual-band antenna specifically\ntailored for MIMO (Multiple-Input Multiple- Output) applications in the context\nof 5G technology. The antenna is designed to operate at two frequency bands of\n3.5 GHz and 6 GHz. The proposed antenna is composed of a rectangular patch with\na slit and a rectangular ground plane. The proposed antenna is compact,\nlow-cost, and suitable for 5G MIMO applications. The rectangular patch antenna\npresented in this study demonstrates a remarkable return loss of -20.02 dB,\nindicating its excellent impedance matching capabilities. This antenna operates\nefficiently at a frequency of 5.8 GHz. The suggested antenna performs admirably\nand has high radiation efficiency. The U-shaped Defected Ground Structure (DGS)\nemployed in this study exhibits an impressive bandwidth of 500 MHz at two\ncentral frequencies: 5.7 GHz and 8.8 GHz. Additionally, it achieves a bandwidth\nof 300 MHz within the frequency range of 8.7-9 GHz. The simulated Frequency\nDependent Ground Structure (FDGS) analysis reveals that the 10 dB return loss\nbandwidth percentage is 5.26%, covering the frequency range of 7.4-7.8 GHz.\nMoreover, the FDGS achieves an 8.94% bandwidth (11.6-12.7 GHz), demonstrating\nits effectiveness in providing a wide operating range for the antenna. The FDGS\nenhances the return loss bandwidth, the radiation characteristics, and the\nmaximum gain by 10 dB, whereas the gain of the dual-band antenna is between 3\nand 6 dB. Radiation properties, maximum gain, and 10dB return loss bandwidth\nhave been improved with rectangular DGS. A typical FR4 substrate with a cheap\ncost and a thickness of 1.6 mm was chosen as the dielectric material to design\nand construct the fault grounding structure. It has a dielectric constant of\n4.4. Validating the modelling findings allows for experimental testing of the\nfabricated antennas.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 121<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nS019<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Sk Hafizul\nIslam,<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Purnendu Vashistha,\nAman P.Singh, Aman Kishore and Jitesh Pradhan<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: ResNet-CPDS:\nColonoscopy Polyp Detection and Segmentation Using Modified ResNet101V2<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: Colorectal\ncancer (CRC) is a global public health concern, and early detection through\nscreening reduces mortality rates. It is one of the common types of cancer with\na high mortality rate. Traditionally, colonoscopy is used to detect CRC, which\nis inefficient. Therefore, an automated Colonoscopy Polyp Detection and\nSegmentation (CPDS) system can significantly increase the efficiency of\ncolonoscopy. We propose an automated model: ResNet-CPDS, using the modified\nResNet101V2 model. We evaluate the performance of ResNet-CPDS and other CPDS\nmodels, and compare their accuracy. We also demonstrate that the ResNet-CPDS\nmodel outperforms other models for the CVC-ClinicDB dataset.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 38<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :\nM021<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Mohammad\nAlakhrass<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: LIEB FUNCTIONS\nAND PPT MATRICES<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: In this\nnote, we present several inequalities that govern the components of a &nbsp;2 \u00d7 2 PPT matrices. The utilization of Lieb\nfunctions enables us to present concise and straightforward proofs for these\ninequalities.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 279<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID : M023<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: Ayyappan G\nand Arulmozhi N<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Analysis and\nempirical investigation of queueing inventory system: Two classes of customer\nunder non-preemptive priority, single vacation, and (s, S) replenishment policy<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract: The arrival\nof priority and regular customers is governed by the Markovian Arrival Process,\nwith two distinct categories of customers being observed. The duration of their\nservice times is determined by the Phase-type distribution. The system consists\nof an infinite capacity for ordinary customers and priority customers having\nfinite buffer capacity of N size. Maximum S items in the inventory. Arriving\npriority customer which find the inventory depleted is lost (lost sales). If a\nordinary customer discovers the inventory to be empty, they may be waiting to\nqueue. The utilization of the (s, S) policy is also implemented within the\nsystem. The number of priority and regular customers in the system is analyzed\nusing the Matrix analytic method. Furthermore, a thorough examination of\nsteady-state analysis, busy period, cost analysis, and numerical\nexemplifications are all carried out.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><br>\nPaper ID : 281<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID : M022<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: G. Ayyappan ,\nS. Sankeetha<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Enhancing\nQueue Management: Dynamic Server Allocation and Optional Services in Stochastic\nModeling<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract : Consider a\nqueueing system with a single server, where customer arrivals follow a\nMarkovian arrival process and service times follow a phase-type distribution.\nThe main server has the capability to recruit an additional server when the\nnumber of customers in the system exceeds a certain threshold, denoted as $L$.\nBoth servers provide normal service to customers, and optional service is\nprovided upon request. The main server takes multiple vacations, with the\ndurations following an exponential distribution with rate parameter $\\eta$,\nuntil there is at least one customer in the system. This system can be\nrepresented as a Markov chain process, and its steady state can be analyzed\nusing matrix analytic methods. Performance measures such as the average number\nof customers, waiting time, and system throughput can be evaluated using the\nsteady state probabilities. Numerical and graphical representations can be\nestablished to visualize the system&#8217;s behavior. By studying this system, we can\ngain insights into its efficiency, identify areas for improvement, and make\ninformed decisions to enhance overall performance.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 282<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID : M024<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: G. Ayyappan\nand S. Nithya<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Analysis of\nM[X1], M[X2]\/G1, G2\/1 Retrial Queue with Priority Services, Differentiate\nBreakdown, Delayed Repair, Bernoulli Feedback, Balking and Working Vacation<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract : In this\nstudy, we construct a single server retrial queueing system with two types of\nbreakdowns, delayed repair, Bernoulli feedback, balking and working vacation.\nTwo different categories of customers like priority and ordinary are to be\nconsidered. This model proposes non-pre-emptive priority discipline. Ordinary\nand priority customers arrive as per Poisson processes. For both ordinary and\npriority customers, the server consistently affords a single service that\nfollows to general distribution. During certain periods, an arriving ordinary\ncustomer may balk the system. When the orbit and priority queue are empty after\nthe service is ended, the server takes a single working vacation. In this\nstudy, we used probability generating function and supplementary variable\ntechnique to solve the Laplace transforms of time-dependent probabilities of\nsystem states. In order to accelerate the sensitivity analysis of system\ndescriptions, numerical data are obtained and also examined.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : AA1<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID : M025<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: &nbsp;G. Ayyappan, S. Kalaiarasi<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: Efficacy Of A\nFlexible Group Service Queueing Model With Server Malfunction<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract : Queueing\nmodels in which the services are provided in groups (or blocks or batches) have\nfound to be very useful in real-world applications and such queues been\nextensively analysed in the literature. In this paper we see one such group\nservice queueing model with server malfunction. The arrival processes is a\nMarkovian arrival. Customers are provided service in groups of varying size\nfrom 1 to the fixed constant, say, N. The service time of a batch follows the\nphase type distribution corresponding to the each size of the group. A group\u2019s\nservice time is taken as the highest of the service times of each customers who\nmake up the group. The server may experience a malfunction at any time, in\nwhich case the server will continue to provide service at a slower rate for\nthat particular customer only, rather than moving on to repair and when that\nparticular customer\u2019s service is completed, the server will immediately go through\nthe repair process for rejuvenation. We calculated the steady state\nprobabilities by using the matrix geometric method, then by using it we\ncomputed few performance measures. We have studied the busy period and the\ndistribution of waiting time is derived. Results are illustrated with some\ngraphical representations.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 45<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID : M026<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors: &nbsp;Nathan Chane De la Cruz, Rocky Bigcas and\nJerico Bacani<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title: The search for\nsolutions of the Diophantine equation a^x + b^y + c^z = w^2 with Pythagorean\ntriple bases<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract : This paper\nexplores the solvability in the nonnegative integers of the Diophantine\nequation having the form a^x + b^y + c^z = w2, where a=2mn, b=m^2\u2212n^2 and c=m^2\n+n^2, such that m and n are positive integers with m &gt; n, and a, b, and c\nare primitive Pythagorean triples. Specifically, the study focuses on the case\nwhere m is odd and n is even.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><a name=\"Submission_46\">Paper ID : 46<o:p><\/o:p><\/a><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID : M015<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors : Shahin\nShaikh and Rupal Shroff<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : On The Line\nZero Divisor Graph Of Small Finite Commutative Rings<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract : In this\narticle,the list of line zero divisor graph on n = 1, 2, 3, &#8230;, 9 vertices\ncorresponding to zero divisor graph and extended zero divisor graph of commutative\nrings with unity (up to isomorphism) is provided. List is classified based on\nthe nature of rings as reduced ring or local ring. The conditions on m and n\nsuch that Km,n is line zero divisor graph and Kn is line zero divisor graph of\nstar graph Sn (on n + 1 vertices) are given.<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\"><o:p>&nbsp;<\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Paper ID : 77<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Presentation ID :S006<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Authors\n:Krishnamugundh P, Karmel Arockiasamy,Kanimozhi G &amp; Karthika P<o:p><\/o:p><\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Title : Analysis on\nFertility of Soil Parameters using Machine Learning Algorithms<o:p><\/o:p><\/p><p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<\/p><p class=\"MsoNormal\" align=\"center\" style=\"text-align:center\">Abstract : The\nprediction of soil fertility is critical for effective agricultural management,\nand traditional methods for determining soil fertility are time-consuming and\nrisk-intensive. However, with the advent of machine learning and AI techniques,\nit is now possible to accurately predict soil fertility using soil parameters,\nweather data, and other relevant factors. In this article, an analysis of soil\nfertility prediction using Machine Learning (ML) and AI algorithms is\npresented.&nbsp;&nbsp; The analysis highlights the\nimportance of adopting a comprehensive approach to soil fertility prediction,\nincorporating soil parameters such as pH, temperature, moisture content,\nhumidity, NPK (nitrogen, phosphorus, and potassium), organic matter, carbon\ncontent, weather, and climatic circumstances. The proposed method offers a\nquick and precise outcome, enabling farmers to make informed decisions and\noptimize soil fertility. Overall, the study demonstrates the significant\npotential of machine learning and AI algorithms for soil fertility prediction\nand offers practical implications for agricultural management. With the help of\nensemble models, it has been observed that Random Forest gave an accuracy of\naround 92% followed by Extra Tress classifier and other classifiers.<o:p><\/o:p><\/p><\/strong>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Abstracts Paper ID : 47 Presentation ID : M001 Authors: Babita Mehta &amp; P.K. Parida Title: Local convergence analysis of a family of third order iterative methods using majorant function in Riemannian Manifold &nbsp; Abstract: This study presents a local convergence analysis of a family of third order iterative algorithms based on the majorant principle &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/abstracts\/\"> <span class=\"screen-reader-text\">Abstracts<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-global-header-display":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"class_list":["post-3447","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/wp-json\/wp\/v2\/pages\/3447","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/wp-json\/wp\/v2\/comments?post=3447"}],"version-history":[{"count":12,"href":"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/wp-json\/wp\/v2\/pages\/3447\/revisions"}],"predecessor-version":[{"id":3507,"href":"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/wp-json\/wp\/v2\/pages\/3447\/revisions\/3507"}],"wp:attachment":[{"href":"http:\/\/icmc2024.kalasalingam.ac.in\/index.php\/wp-json\/wp\/v2\/media?parent=3447"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}