Activation functions in deep learning: A comprehensive survey and benchmark
Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …
problems. Various types of neural networks have been introduced to deal with different types …
Deep reinforcement learning for resource management on network slicing: A survey
JA Hurtado Sánchez, K Casilimas… - Sensors, 2022 - mdpi.com
Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …
The influence of the activation function in a convolution neural network model of facial expression recognition
Y Wang, Y Li, Y Song, X Rong - Applied Sciences, 2020 - mdpi.com
The convolutional neural network (CNN) has been widely used in image recognition field
due to its good performance. This paper proposes a facial expression recognition method …
due to its good performance. This paper proposes a facial expression recognition method …
A review of practical ai for remote sensing in earth sciences
Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for
revolutionizing data analysis and applications in many domains of Earth sciences. This …
revolutionizing data analysis and applications in many domains of Earth sciences. This …
Automatic detection of sewer defects based on improved you only look once algorithm
The drainage system is an important part of civil infrastructure. However, the underground
sewage pipe will gradually suffer from defects over time, such as tree roots, deposits …
sewage pipe will gradually suffer from defects over time, such as tree roots, deposits …
A novel direct trajectory planning approach based on generative adversarial networks and rapidly-exploring random tree
Trajectory planning is essential for self-driving vehicles and has stringent requirements for
accuracy and efficiency. The existing trajectory planning methods have limitations in the …
accuracy and efficiency. The existing trajectory planning methods have limitations in the …
Prediction and evaluation of fuel properties of hydrochar from waste solid biomass: Machine learning algorithm based on proposed PSO–NN model
L Mu, Z Wang, D Wu, L Zhao, H Yin - Fuel, 2022 - Elsevier
Hydrothermal carbonization is an effective and environmentally friendly biomass
pretreatment technology, which converts high moisture biomass into homogeneous, carbon …
pretreatment technology, which converts high moisture biomass into homogeneous, carbon …
Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework
Effective access to obtain the complex flow fields around an airfoil is crucial in improving the
quality of supercritical wings. In this study, a systematic method based on generative deep …
quality of supercritical wings. In this study, a systematic method based on generative deep …
Optimized leaky ReLU for handwritten Arabic character recognition using convolution neural networks
Object classification, such as handwritten Arabic character recognition, is a computer vision
application. Deep learning techniques such as convolutional neural networks (CNNs) are …
application. Deep learning techniques such as convolutional neural networks (CNNs) are …
[HTML][HTML] A survey of convolutional neural network in breast cancer
Aims A large number of clinical trials have proved that if breast cancer is diagnosed at an
early stage, it could give patients more treatment options and improve the treatment effect …
early stage, it could give patients more treatment options and improve the treatment effect …