Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects

2020 ◽  
Vol 197 ◽  
pp. 105728
Author(s):  
R. Karthik ◽  
R. Menaka ◽  
Annie Johnson ◽  
Sundar Anand
Keyword(s):  
Deep Learning ◽  
Future Prospects ◽  
Brain Stroke ◽  
Brain Stroke Detection

scholarly journals Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohamed Massaoudi ◽  
Haitham Abu-Rub ◽  
Shady S. Refaat ◽  
Ines Chihi ◽  
Fakhreddine S. Oueslati
Keyword(s):  
Deep Learning ◽  
Smart Grid ◽  
Grid Technology ◽  
Future Prospects ◽  
Smart Grid Technology

scholarly journals Deep Learning and Its Application to LHC Physics

2018 ◽  
Vol 68 (1) ◽  
pp. 161-181 ◽  
Author(s):  
Dan Guest ◽  
Kyle Cranmer ◽  
Daniel Whiteson
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Learning Tools ◽  
Future Prospects ◽  
Higher Dimensional ◽  
Lhc Physics ◽  
Core Concepts ◽  
Energy Physics

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

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