Performance Evaluation of Deep Learning Frameworks over Different Architectures

2019 ◽  
pp. 92-104
Author(s):  
Rafael Gauna Trindade ◽  
João Vicente Ferreira Lima ◽  
Andrea Schwerner Charão
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Learning Frameworks

Object Recognition Using Deep Learning

2019 ◽  
Vol 16 (9) ◽  
pp. 4044-4052 ◽  
Author(s):  
Rohini Goel ◽  
Avinash Sharma ◽  
Rajiv Kapoor
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Object Recognition ◽  
Deep Neural Network ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Learning Frameworks

The deep learning approaches have drawn much focus of the researchers in the area of object recognition because of their implicit strength of conquering the shortcomings of classical approaches dependent on hand crafted features. In the last few years, the deep learning techniques have been made many developments in object recognition. This paper indicates some recent and efficient deep learning frameworks for object recognition. The up to date study on recently developed a deep neural network based object recognition methods is presented. The various benchmark datasets that are used for performance evaluation are also discussed. The applications of the object recognition approach for specific types of objects (like faces, buildings, plants etc.) are also highlighted. We conclude up with the merits and demerits of existing methods and future scope in this area.

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.

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