Endoscopy-based deep learning algorithms achieve higher sensitivity, specificity, and overall diagnostic accuracy than endoscopists for early ESCC detection.

Abstract: Redundant soft sensors are used to provide information on physical parameters in industrial manufacturing processes to accommodate conventional sensor failure. In this article, a ...

Motivated by "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" by Jiang et. al. 2017 [1]. In this project: Implement two state-of-art continous deep ...

Abstract: Deep learning approach is used to predict the chance of acquiring several types of malignancies, including breast, brain, lung, colon, oral, kidney, and cervical cancer. To find patterns and ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

These include such learning paradigms as Q-Learning and the Deep Q-Networks setups. Reinforcement Learning paradigms essentially aim at teaching robots to undertake certain actions that will be used ...

Image recognition has made significant progress in recent years, majorly in the development of powerful algorithms that can analyze and interpret visual data with unparalleled accuracy. In this ...

Optimization of pattern-synthesis algorithms. Applying a deep-learning network to generate antenna element weights. Using a convolution neural network to perform pattern synthesis with deep learning.

1 College of Engineering and Computing, Florida International University, Miami, USA. 2 College of Intelligent Equipment, Shandong University of Science and Technology, Qingdao, China. 3 Steven J.

Cerebral venous thrombosis (CVT) is a rare cerebrovascular disease. Routine brain magnetic resonance imaging is commonly used to diagnose CVT. This study aimed to develop and evaluate a novel deep ...

Terminologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning are hype these days. People, however, often use these terms interchangeably. Although these terms highly ...