Python’s been the language of data science since before machine learning was trendy, and now you can use it for building AI ...

You could sift through websites, but some Python code and a little linear regression could make the job easier. ...

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 ...

Researchers proposed machine learning or deep learning methods to predict seizures. However, automatic screening of electrode channels and improvement of predictive accuracy remain a challenge. A ...

This study develops the traffic prediction model based on real traffic data in congested hours of expressways in Bangkok, Thailand. Unlike most studies, this model utilizes data from 40 nodes along ...

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 ...

This research is composed of two approaches. In the first part, we propose a hybrid model named TimeDistributed-CNN-LSTM (TD- CNN-LSTM) combining 3D Convolutional Neural Network (CNN) and Long Short ...

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 ...