Feature data processing: Making medical data fit deep neural networks

2020 ◽  
Vol 109 ◽  
pp. 149-157
Author(s):  
Yingying Xu ◽  
Zhi Liu ◽  
Yujun Li ◽  
Haixia Hou ◽  
Yankun Cao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Deep Neural Networks ◽  
Medical Data

Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

2021 ◽  
Author(s):  
Jafar A. Alzubi ◽  
Omar A. Alzubi ◽  
Majdi Beseiso ◽  
Anil Kumar Budati ◽  
K. Shankar
Keyword(s):  
Neural Networks ◽  
Data Transmission ◽  
Deep Neural Networks ◽  
Homomorphic Encryption ◽  
Medical Data

Real-Time Downhole Geosteering Data Processing Using Deep Neural Networks On FPGA

2021 ◽  
Author(s):  
Qiyu Wan ◽  
Yuchen Jin ◽  
Xuqing Wu ◽  
Jiefu Chen ◽  
Xin Fu
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Real Time ◽  
Deep Neural Networks

scholarly journals Survey of deployment locations and underlying hardware architectures for contemporary deep neural networks

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986866
Author(s):  
Miloš Kotlar ◽  
Dragan Bojić ◽  
Marija Punt ◽  
Veljko Milutinović
Keyword(s):  
Neural Networks ◽  
Integrated Circuits ◽  
Data Processing ◽  
Deep Neural Networks ◽  
Multicore Processors ◽  
Two Dimensions ◽  
Processing Unit ◽  
Central Processing ◽  
Trade Offs ◽  
Dew Computing

This article overviews the emerging use of deep neural networks in data analytics and explores which type of underlying hardware and architectural approach is best used in various deployment locations when implementing deep neural networks. The locations which are discussed are in the cloud, fog, and dew computing (dew computing is performed by end devices). Covered architectural approaches include multicore processors (central processing unit), manycore processors (graphics processing unit), field programmable gate arrays, and application-specific integrated circuits. The proposed classification in this article divides the existing solutions into 12 different categories, organized in two dimensions. The proposed classification allows a comparison of existing architectures, which are predominantly cloud-based, and anticipated future architectures, which are expected to be hybrid cloud-fog-dew architectures for applications in Internet of Things and Wireless Sensor Networks. Researchers interested in studying trade-offs among data processing bandwidth, data processing latency, and processing power consumption would benefit from the classification made in this article.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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