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Sofía Ramos-Pulido, Neil Hernández-Gress and Gabriela Torres-Delgado
Current research on the career satisfaction of graduates limits educational institutions in devising methods to attain high career satisfaction. Thus, this study aims to use data science models to understand and predict career satisfaction based on infor...
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N. Aidossov, Vasilios Zarikas, Aigerim Mashekova, Yong Zhao, Eddie Yin Kwee Ng, Anna Midlenko and Olzhas Mukhmetov
Breast cancer comprises a serious public health concern. The three primary techniques for detecting breast cancer are ultrasound, mammography, and magnetic resonance imaging (MRI). However, the existing methods of diagnosis are not practical for regular ...
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Egor I. Chetkin, Sergei L. Shishkin and Bogdan L. Kozyrskiy
Bayesian neural networks (BNNs) are effective tools for a variety of tasks that allow for the estimation of the uncertainty of the model. As BNNs use prior constraints on parameters, they are better regularized and less prone to overfitting, which is a s...
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Elliot Pachniak, Yongzhen Fan, Wei Li and Knut Stamnes
The Ocean Color?Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations to provide accurate remote sens...
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Yu-Ting Tsai and Ching-Piao Tsai
Deep learning techniques have revolutionized the field of artificial intelligence by enabling accurate predictions of complex natural scenarios. This paper proposes a novel convolutional neural network (CNN) model that involves deep learning technologies...
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