scholarly journals Automated detection of cardiac arrhythmia using deep learning techniques

2018 ◽  
Vol 132 ◽  
pp. 1192-1201 ◽  
Author(s):  
Swapna G ◽  
Soman K P ◽  
Vinayakumar R
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmia ◽  
Automated Detection ◽  
Learning Techniques

scholarly journals Detection of Disease in Maize Plant Using Deep Learning

2021 ◽  
Vol 36 (2) ◽  
pp. 82-88
Author(s):  
Dr.B. Rama Subba Reddy ◽  
Dr.G. Bindu Madhavi ◽  
C.H. Sri Lakshmi ◽  
Dr.K. Venkata Nagendra ◽  
Dr.R. Sridevi
Keyword(s):  
Deep Learning ◽  
Food Production ◽  
Automated Detection ◽  
Plant Diseases ◽  
Maize Plant ◽  
Small Scale ◽  
Traditional Farming ◽  
Cercospora Leaf Spot ◽  
Detection Model ◽  
Learning Techniques

Agriculture is vital to the Indian economy as over 17 percent of total GDP and employs more than 60 percent of the population relies on agriculture. This research focuses on plant diseases as they create a major threat to food production as well as for small-scale farmer’s livelihood. Expert workers are employed in traditional farming to visually evaluate row by row to identify plant diseases, which is a time-consuming, labor-intensive activity that is potentially error-prone because it is done by humans. The aim of this research is to develop an automated detection model that uses a combination of image processing and deep learning techniques (Faster R-CNN+ResNet50) to analyze real-time images and detect and classify the three common maize plant diseases: Common Rust, Cercospora Leaf Spot, and Northern Leaf Blight. The proposed system achieved 91% accuracy and successfully detects three maize diseases.

scholarly journals Automated Detection of COVID-19 using Deep Learning

2021 ◽  
Vol 10 (2) ◽  
pp. , 824-830
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Global Economy ◽  
Imaging Techniques ◽  
Automated Detection ◽  
Diagnostic Tools ◽  
Radiological Imaging ◽  
Daily Lives ◽  
Learning Techniques ◽  
The World

Corona virus 2019 (COVID-2019), has first appeared in Wuhan, China in December 2019, spread around the world rapidly causing thousands of fatalities. It is caused a devastating result in our daily lives, public health, and also the global economy. It is important to sight the positive cases as early as possible therefore forestall any unfoldment of this epidemic and to quickly treat affected patients. The necessity for auxiliary diagnostic tools has increased as there aren't any accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Coupling deep learning techniques with radiological imaging may end up within the accurate detection of this disease. This assistance will help to beat the matter of an absence of specialized physicians in the remote villages.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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