Month: December 2024

Rice Classification Using Three-Step Neural Network Integration

Rice Classification Using Three-Step Neural Network Integration Meesri, Sarawut., Amornsamankul, Somkid., Kraipeerapun, Pawalai This paper proposes a three-step neural network integration. In the first step, two neural networks are trained to produce a truth output and a falsity output. The second step uses cascade generalization to improve the results of the first step. A sequence …

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Health Data Classification using Applied Cascade Generalization

Health Data Classification using Applied Cascade Generalization Chatree Nilnumpetch, Somkid Amornsamankul, Pawalai Kraipeerapun This research study introduces two steps to improve the binary classification technique without using threshold value. The first step is to use complementary neural networks to produce the truth data and falsity data. The truth and falsity data are used for decision …

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Special Seminar by Prof. Gretchen L. Matthews

  Title: Hulls and Coding for Quantum Error Correction Speaker: Prof. Gretchen L. Matthews Department of Mathematics, Virginia Tech, USA Date: Thu, 26 December 2024 Time: 3:00-4:00 PM Room: M303 Abstract: Error correcting codes protect information from distortion or degradation so that data can be communicated reliably. Classical error correcting codes accomplish this task for digital …

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Refining a chain theorem from matroids to internally 4-connected graphs

Refining a chain theorem from matroids to internally 4-connected graphs Chanun Lewchalermvongs, Guoli Ding Graph theory and matroid theory are interconnected with matroids providing a way to generalize and analyze the structural and independence properties within graphs. Chain theorems, vital tools in both matroid and graph theory, enable the analysis of matroid structures associated with …

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Enhancing Signed Graph Attention Network by Graph Characteristics: An Analysis

Enhancing Signed Graph Attention Network by Graph Characteristics: An Analysis Panatda Kaewhit, Chanun Lewchalermvongs, Phakaporn Lewchalermvongs – A graph neural network (GNN) is one of successful methods for handling tasks on a graph data structure, e.g. node embedding, link prediction and node classification. GNNs focus on a graph data structure that must aggregate messages on …

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A game-theoretic approach for route planning on traveling by public transportation in the PM2.5 pollution: A case study in Bangkok

A game-theoretic approach for route planning on traveling by public transportation in the PM2.5 pollution: A case study in Bangkok Chanun Lewchalermvongs, Phakaporn Lewchalermvongs, Chettaporn Chuesuphan, Chutchawon Weeranukunjit, Chanon Boonkangwan, Montika Chaikittiporn, Patid Srikirinth, Tanaphoom Glinpatgij Extremely high levels of PM2.5 (particulate matter with a diameter of 2.5 micrometers or less) in Bangkok’s major areas …

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Maximizing vertex rescue: investigating the firefighter problem with edge subdivision

Maximizing vertex rescue: investigating the firefighter problem with edge subdivision Chutchawon Weeranukunjit, Chanun Lewchalermvongs The firefighter problem is a game played on a connected graph where a fire breaks out at a vertex. In each round, a firefighter chooses a vertex to protect, and the fire then spreads to all unprotected neighbors of the burning vertex. …

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A note on an analogue of Hadamard’s theorem for determining the radii of m-meromorphy

A note on an analogue of Hadamard’s theorem for determining the radii of m-meromorphy Nattapong Bosuwan In this paper, we prove an extension of Hadamard’s classical theorem for determining the radius of mmeromorphyof an analytic function in terms of its Taylor coefficients. Our extension is expressed in terms of Fouriercoefficients with respect to an orthonormal …

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