A Novel High-performance Deep Learning Framework for Load Recognition: Deep-shallow Model based on Fast Backpropagation

Author(s):  
Chen Li ◽  
Guo Chen ◽  
Gaoqi Liang ◽  
Zhao Yang Dong
2021 ◽  
Vol 2078 (1) ◽  
pp. 012019
Author(s):  
Gonghan Liu ◽  
Yue Li ◽  
Xiaoling Wang

Abstract If the traditional deep learning framework needs to support a new operator, it usually needs to be highly optimized by experts or hardware vendors to be usable in practice, which is inefficient. The deep learning compiler has proved to be an effective solution to this problem, but it still suffers from unbearably long overall optimization time. In this paper, aiming at the XGBoost cost model in Ansor, we train a cost model based on LightGBM algorithm, which accelerates the optimization time without compromising the accuracy. Experimentation with real hardware shows that our algorithm provides 1.8× speed up in optimization over XGBoost, while also improving inference time of the deep networks by 6.1 %.


Author(s):  
Mohammed Y. Kamil

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.


2019 ◽  
Vol 14 (16) ◽  
pp. 5775-5781
Author(s):  
Muayad S. Croock ◽  
Ayad E. Korial ◽  
Tara F. Kareem ◽  
Qusay Sh. Hamad ◽  
Ghaidaa M. Abdulsaheb

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Aditya Balu ◽  
Sahiti Nallagonda ◽  
Fei Xu ◽  
Adarsh Krishnamurthy ◽  
Ming-Chen Hsu ◽  
...  

AbstractBioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life directly from medical images is difficult. Analyzing the valve performance can provide better guidance for personalized valve design. However, such analyses are often computationally intensive. In this work, we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with the same fidelity. We present statistical results that demonstrate the high performance of the DLFEA framework and the applicability of the framework to predict bioprosthetic aortic valve deformations. With further development, such a tool can provide fast decision support for designing surgical bioprosthetic aortic valves. Ultimately, this framework could be extended to other BHVs and improve patient care.


2019 ◽  
Vol 13 ◽  
Author(s):  
Michelle Livne ◽  
Jana Rieger ◽  
Orhun Utku Aydin ◽  
Abdel Aziz Taha ◽  
Ela Marie Akay ◽  
...  

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ping Yi ◽  
Yuxiang Guan ◽  
Futai Zou ◽  
Yao Yao ◽  
Wei Wang ◽  
...  

Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity. It will lead to information disclosure and property damage. This paper mainly focuses on applying a deep learning framework to detect phishing websites. This paper first designs two types of features for web phishing: original features and interaction features. A detection model based on Deep Belief Networks (DBN) is then presented. The test using real IP flows from ISP (Internet Service Provider) shows that the detecting model based on DBN can achieve an approximately 90% true positive rate and 0.6% false positive rate.


2020 ◽  
Author(s):  
Raniyaharini R ◽  
Madhumitha K ◽  
Mishaa S ◽  
Virajaravi R

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