Shuqiao Liu¹, Zhao Zhang^2,*, Hongyan Zhou¹, Xuebo Chen¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 929-948, 2025, DOI:10.32604/cmc.2024.057319 - 03 January 2025

Abstract This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems. Combining Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis data with Variation Autoencoder (VAE) and Generative Adversarial Network (GAN) the network framework model (SAE-GAN), is proposed for environmental data reconstruction. The model combines two popular generative models, GAN and VAE, to generate features conditional on categorical data embedding after SWOT Analysis. The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data. Reconstructed data is… More >

Liviu Marin¹

CMC-Computers, Materials & Continua, Vol.17, No.3, pp. 233-274, 2010, DOI:10.3970/cmc.2010.017.233

Abstract We investigate two algorithms involving the relaxation of either the given boundary temperatures (Dirichlet data) or the prescribed normal heat fluxes (Neumann data) on the over-specified boundary in the case of the iterative algorithm of Kozlov91 applied to Cauchy problems for two-dimensional steady-state anisotropic heat conduction (the Laplace-Beltrami equation). The two mixed, well-posed and direct problems corresponding to every iteration of the numerical procedure are solved using the method of fundamental solutions (MFS), in conjunction with the Tikhonov regularization method. For each direct problem considered, the optimal value of the regularization parameter is chosen according More >