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Danial Sharifrazi
Researcher at Islamic Azad University
Publications - 13
Citations - 328
Danial Sharifrazi is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 5, co-authored 10 publications receiving 68 citations.
Papers
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Journal ArticleDOI
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.
Danial Sharifrazi,Roohallah Alizadehsani,Mohamad Roshanzamir,Javad Hassannataj Joloudari,Afshin Shoeibi,Afshin Shoeibi,Mahboobeh Jafari,Sadiq Hussain,Zahra Alizadeh Sani,Fereshteh Hasanzadeh,Fahime Khozeimeh,Abbas Khosravi,Saeid Nahavandi,Maryam Panahiazar,Assef Zare,Sheikh Mohammed Shariful Islam,Sheikh Mohammed Shariful Islam,Sheikh Mohammed Shariful Islam,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +20 more
TL;DR: In this article, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images.
Journal ArticleDOI
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.
Nooshin Ayoobi,Danial Sharifrazi,Roohallah Alizadehsani,Afshin Shoeibi,Afshin Shoeibi,Juan Manuel Górriz,Hossein Moosaei,Abbas Khosravi,Saeid Nahavandi,Abdoulmohammad Gholamzadeh Chofreh,Feybi Ariani Goni,Jirí Jaromír Klemeš,Amir Mosavi,Amir Mosavi +13 more
TL;DR: In this article, the authors evaluated the performance of three deep learning methods and their bidirectional extensions to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days.
Journal ArticleDOI
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.
Fahime Khozeimeh,Danial Sharifrazi,Navid Hoseini Izadi,Javad Hassannataj Joloudari,Afshin Shoeibi,Afshin Shoeibi,Roohallah Alizadehsani,Juan Manuel Górriz,Juan Manuel Górriz,Sadiq Hussain,Zahra Alizadeh Sani,Hossein Moosaei,Abbas Khosravi,Saeid Nahavandi,Sheikh Mohammed Shariful Islam,Sheikh Mohammed Shariful Islam,Sheikh Mohammed Shariful Islam +16 more
TL;DR: In this article, the authors proposed a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a convolutional neural network trained with clinical information.
Posted ContentDOI
CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering
Danial Sharifrazi,Roohallah Alizadehsani,Javad Hassannataj Joloudari,Shahab Shamshirband,Sadiq Hussain,Zahra Alizadeh Sani,Fereshteh Hasanzadeh,Afshin Shoaibi,Abdollah Dehzangi,Hamid Alinejad-Rokny +9 more
TL;DR: This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose the Myocarditis and demonstrates that CNNKCL achieves 92.3% in terms of diagnosis myocarditis prediction accuracy which is significantly better than those reported in previous studies.
Journal ArticleDOI
Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit
TL;DR: To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modelling techniques, as an alternative to CFD simulation, to predict discharge coefficient based on an experimental dataset.