id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
esrj-105296	Seyrek, Eren Can; Uysal, Murat	Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification	2024	14	.pdf	application/pdf	9304	497	46	It was observed that the performance of classification algorithms varied across datasets, with factors such as insufficient ground truth data, mixed pixels, and spectral similarity between classes affecting classification performances. HybridSN CNN (Roy et al., 2019) was chosen as the CNN architecture, but in light of literature suggesting that employing various activation functions and optimizers can enhance classification performance (Agarwal et al., 2021; Bera & Shrivastava, 2020; Dubey et al., 2022; Hao et al., 2020; Seyrek & Uysal, 2024; Vani & Rao, 2019), a modified version called Modified HybridSN was developed, integrating Mish activation function (Misra, 2019) and Adamax optimizer (Kingma & Ba, 2014).	cache/esrj-105296.pdf	txt/esrj-105296.txt