Deep Learning Frameworks for COVID-19 Detection

2021 ◽  
Author(s):  
Ankit Vijayvargiya ◽  
Akshit Panchal ◽  
Abhijeet Parashar ◽  
Ayush Gautam ◽  
Jayesh Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Frameworks

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequences ◽  
Experimental Methods ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Genomic Features ◽  
Disease Mechanisms ◽  
Evaluation Strategies ◽  
Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

Algorithmically-consistent deep learning frameworks for structural topology optimization

2021 ◽  
Vol 106 ◽  
pp. 104483
Author(s):  
Jaydeep Rade ◽  
Aditya Balu ◽  
Ethan Herron ◽  
Jay Pathak ◽  
Rishikesh Ranade ◽  
...  
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Learning Frameworks

scholarly journals Approach for Improving User Interface Based on Gesture Recognition

2021 ◽  
Vol 297 ◽  
pp. 01030
Author(s):  
Issam Elmagrouni ◽  
Abdelaziz Ettaoufik ◽  
Siham Aouad ◽  
Abderrahim Maizate
Keyword(s):  
Deep Learning ◽  
User Interface ◽  
Research Methods ◽  
Gesture Recognition ◽  
Visual Detection ◽  
Interface Control ◽  
Learning Frameworks

Gesture recognition technology based on visual detection to acquire gestures information is obtained in a non-contact manner. There are two types of gesture recognition: independent and continuous gesture recognition. The former aims to classify videos or other types of gesture sequences that only contain one isolated gesture instance in each sequence (e.g., RGB-D or skeleton data). In this study, we review existing research methods of visual gesture recognition and will be grouped according to the following family: static, dynamic, based on the supports (Kinect, Leap…etc), works that focus on the application of gesture recognition on robots and works on dealing with gesture recognition at the browser level. Following that, we take a look at the most common JavaScript-based deep learning frameworks. Then we present the idea of defining a process for improving user interface control based on gesture recognition to streamline the implementation of this mechanism.

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