This study aims to predict the direction of US stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE ...

With the widespread application of deep learning and Convolutional Neural Networks (CNN) in image classification, how to effectively improve model performance and reduce its complexity has become a ...

Recently, deep learning based classification method is popularly used for brain tumor detection from 2D Magnetic Resonance (MR) images. In this article, several transfer learning based deep learning ...

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning (RL) framework to transform the ...

Intelligent machine fault diagnosis methods that leverage machine learning techniques have received widespread attention owing to their proven efficacy in enhancing production efficiency and quality ...

Muscle fatigue impacts upper extremity function but is often overlooked in biomechanical models. The present work leveraged a transfer learning approach to improve torque predictions during fatiguing ...

According to the World Report by WHO on Hearing published in March, 2021, nearly 2.5 billion people will be deaf or hard of hearing by the year 2050. In 2020, the uncorrected hearing loss in the world ...

Precise and efficient coil inductance calculations in wireless power transfer (WPT) systems are crucial for accelerating the coil design process and optimizing system performances. However, commonly ...

The deep convolutional neural network (DeCNN) is considered one of promising techniques for classifying the high-spatial-resolution remote sensing (HSRRS) scenes, due to its powerful feature ...

Therefore, the recognition method for small sample ferrographic images based on the convolutional neural network (CNN) and transfer learning (TL) is proposed. Based on the similarity of samples, the ...

When a learned model has high accuracy under familiar settings (internal testing) and a big drop in accuracy under slightly different circumstances (external testing) we suspect it is using shortcuts ...

In this paper, we propose a novel framework, namely Q-TRANSFER, to address the insufficiency problem of the actual training data sets in modern networking platforms in order to push the application of ...