… While our best result is achieved with a combination of molecular feature types (gene …
model predicted combination effect and recover 80% of the top pairs with enhanced activity. …

… Despite the fact that deep learning algorithms can reach … Another challenge with deep
learning for time series … and increase the performance of deep learning models in time series …

… suggest combining 3D stereophotogrammetry with the more modern machine learning technique
‘deep learning… Deep learning has shown promising results in various fields of research, …

… based on deep learning have gained an increased attention in the recent years in particular
Remote Sensing. Convolutional Neural Networks (CNNs) as one of these deep learning …

… This study compares human-coded images posted during 2017's Hurricane Harvey to
machine-learned ‘deep learning’ classification methods. Our framework for feature extraction uses …

… deep learning has greater potential for shallow learning and may aid in solving small sample
problems, while traditional deep learning … are not small sample deep learning algorithms. …

… To address these challenges, this paper presents a deep learning approach to the OPF. The
learning model exploits the information available in the similar states of the system (which is …

… We present NeuroLKH, a novel algorithm that combines deep learning with the strong … (SGN)
with supervised learning for edge scores and unsupervised learning for node penalties, …

… This study includes an effective deep learning based framework for PQD classification. The
proposed framework has the ability to analyze in 1D and 2D domains in accordance with the …

… The main intent of this paper is to develop a novel robust model for detecting and preventing
HELLO flooding attacks using optimized deep learning approach. In this proposed research …