Dog breed classification using convolution neural network

2021 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Mrityunjay Singh ◽  
Anjani Kumar Shukla ◽  
Amit Kumar Jakhar
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Dog Breed

Dog breed classification using convolution neural network

2021 ◽  
Vol 6 (2) ◽  
pp. 130
Author(s):  
Amit Kumar Jakhar ◽  
Mrityunjay Singh ◽  
Anjani Kumar Shukla
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Dog Breed

Identification of Violence Images Using Convolution Neural Network in Social Network

2020 ◽  
Vol 5 (3) ◽  
pp. 34-38
Author(s):  
Santhi Baskaran ◽  
Jahnavi Korrapati ◽  
Sooriya K. ◽  
Pavithra R.
Keyword(s):  
Neural Network ◽  
Social Network ◽  
Convolution Neural Network

The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

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