Phishing has grown into one of the major and supreme operative in cyber threats, triggering millions of data breaches and security failures every year. This paper proposes a CNN based prediction ...

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning ...

This paper proposes a deep-learning model for automated sleep stage scoring with single-channel EEG recordings that combines CNN and LSTM. Several of the methods in use today rely on qualities that ...

Deep convolutional neural networks have significantly advanced color image denoising. However, existing models often apply grayscale denoising techniques to color images without accounting for ...

Epilepsy is the unstable state caused by excessive discharge of brain cells. In more than 30 percent of epilepsy cases, seizures cannot be controlled with medication or surgery. Refractory epilepsy ...

Traffic state prediction methods have been considered by many researchers since accurate traffic prediction is an important part of the successful implementation of the Intelligent Transportation ...

The dilated convolution algorithm, which is widely used for image segmentation, is applied in the image classification field in this paper. In many traditional image classification algorithms, ...

In this work, we propose a hybrid classification model using the CNN-SVM that focuses on K-12 learning materials. We combine the Word2Vec feature and the hidden layer feature of CNN. In response to a ...

Therefore, the model is developed with optimal configuration performing ablation study for layer architecture and hyper-parameters. In the second part, a 3D CNN model is trained respectively with each ...

In face-to-face interactions, facial expressions convey nonverbal details. Since the early 1990s, researchers have been increasingly interested in automatic facial expression recognition, which is ...

Food grains are sensitive to deterioration owing to precipitation temperature, humidity, and several other variables; thus, researchers are working toward novel approaches to preserve the dietary ...

Object-oriented convolutional neural network (CNN) has been proven to be an effective classification method for very fine spatial resolution remotely sensed imagery. It can obtain higher accuracy and ...