Combining single-pixel wavefront imaging with a custom neural network improves the photon imaging in cloudy environments.

This study assesses the performance of CustomNet, a lightweight neural network model trained using NumPy and Pandas, compared to the VGG-16 architecture on the datasets of MNIST, Fashion MNIST, and ...

This study assesses the performance of CustomNet, a lightweight neural network model trained using NumPy and Pandas, compared to the VGG-16 architecture on the datasets of MNIST, Fashion MNIST, and ...

The innovative multi-task neural network achieves simultaneous depth estimation and soft-edge detection in a single network, producing clear 3D reconstructed images of relief-type cultural ...

This study evaluates the performance and reliability of a vision transformer (ViT) compared to convolutional neural networks (CNNs) using the ResNet50 model in classifying lung cancer from CT images ...

In the experiment, the dataset was divided with 50% used for training and the remaining 50% for testing. Furthermore, the performance of neural network models mainly depends on the knowledge learned ...

Next, the demo creates and trains a neural network model using the ... politics # Anaconda3-2022.10 Python 3.9.13 scikit 1.0.2 # Windows 10/11 import numpy as np from sklearn.neural_network import ...

In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned ...

When using the scikit library for multi-class classification, the main alternative to the MLPClassifier neural network module is the scikit DecisionTree module. Decision trees are useful for ...