SEMINAR SERIES: Part 1: Spaces and moduli spaces of (Riemannian) metrics Part 2: Quantifying molecular similarity via Gromov-Hausdorff distances Speaker: Prof. Wilderich Tuschmann Department of Mathematics, Karlsruhe Institute of Technology, Germany Date: Thursday, 13 February 2025 Time: 10:30 AM. -12:30 PM. Room: M303 Abstract Part 1: Consider a smooth manifold with a Riemannian metric satisfying …
An analytical solution to the time fractional Navier–Stokes equation based on the Katugampola derivative in Caputo sense by the generalized Shehu residual power series approach Wannika. Sawangtong, Patcharee Dunnimit, Benchawan Wiwatanapataphee, Panumart Sawangtong The Navier–Stokes equations, independently derived by Stokes and Navier, describe fluid motion and are widely applied in engineering and science. Their complexity …
List of students accepted to B.Sc. Actuarial Science and B.Sc. Programme in Industrial Mathematics & Data Science Academic Year 2025 (Direct Admission Round 1) List of accepted students AS List of accepted students IMDS Attendance Confirmation Form AS Attendance Confirmation Form IMDS Details Dates Submit the enrollment confirmation form to the Department of Mathematics, Faculty …
The interview for the direct admission (round 1) to the B.Sc. Programme in Actuarial Science (International Programme) and the B.Sc. Programme in Industrial Mathematics and Data Science (International Programme) will be held on January 19, 2025, at the Department of Mathematics, Faculty of Science, Mahidol University. Please download the list of candidates to be interviewed …
Title: The trivial units property and the unique product property for torsion free groups Speaker: Prof. Andre Nies School of Computer Science, University of Auckland, New Zealand Date: Fri, 31 January 2024 Time: 1:30-2:30 PM. Room: B201
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 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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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 …
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 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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