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

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 ...

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 ...

Abstract: This study introduces a deep learning approach for network intrusion detection (NIDS), which excels in both binary and multi-classification tasks. This approach combines the strengths of six ...

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.

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 ...

We explore propagation of seismic interpretation by deep learning in stacked 2D sections. We show the application of state-of-the-art image classification algorithms on seismic data. These algorithms ...

Every time you pick up your smartphone, you’re summoning algorithms. They’re used for everything from unlocking your phone with your face to deciding what videos you see on TikTok to updating your ...