Deep learning in diverse intelligent sensor based systems
Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …
virtually all fields of science and engineering. The increasing complexity and the large …
A survey on artificial intelligence in pulmonary imaging
Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer
vision and image recognition creating widespread opportunities of using artificial …
vision and image recognition creating widespread opportunities of using artificial …
Weapon detection in real-time cctv videos using deep learning
Security and safety is a big concern for today's modern world. For a country to be
economically strong, it must ensure a safe and secure environment for investors and tourists …
economically strong, it must ensure a safe and secure environment for investors and tourists …
A fast dense spectral–spatial convolution network framework for hyperspectral images classification
W Wang, S Dou, Z Jiang, L Sun - Remote sensing, 2018 - mdpi.com
Recent research shows that deep-learning-derived methods based on a deep convolutional
neural network have high accuracy when applied to hyperspectral image (HSI) …
neural network have high accuracy when applied to hyperspectral image (HSI) …
An in-field automatic wheat disease diagnosis system
Crop diseases are responsible for the major production reduction and economic losses in
agricultural industry worldwide. Monitoring for health status of crops is critical to control the …
agricultural industry worldwide. Monitoring for health status of crops is critical to control the …
Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Surrogate strategies are used widely for uncertainty quantification of groundwater models in
order to improve computational efficiency. However, their application to dynamic multiphase …
order to improve computational efficiency. However, their application to dynamic multiphase …
Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical information. We
describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative …
describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative …
A differentiable physics engine for deep learning in robotics
An important field in robotics is the optimization of controllers. Currently, robots are often
treated as a black box in this optimization process, which is the reason why derivative-free …
treated as a black box in this optimization process, which is the reason why derivative-free …
A deep learning approach for mild depression recognition based on functional connectivity using electroencephalography
X Li, R La, Y Wang, B Hu, X Zhang - Frontiers in neuroscience, 2020 - frontiersin.org
Early detection remains a significant challenge for the treatment of depression. In our work,
we proposed a novel approach to mild depression recognition using …
we proposed a novel approach to mild depression recognition using …
Deep learning neural networks trained with MODIS satellite-derived predictors for long-term global solar radiation prediction
Solar energy predictive models designed to emulate the long-term (eg, monthly) global solar
radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets …
radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets …