scholarly journals Learning-Based Detection of Harmful Data in Mobile Devices

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Skin Color ◽  
Multimedia Content ◽  
Body Parts ◽  
Human Face ◽  
Neural Network Learning ◽  
Network Learning ◽  
Tablet Pcs ◽  
Artificial Neural

The Internet has supported diverse types of multimedia content flowing freely on smart phones and tablet PCs based on its easy accessibility. However, multimedia content that can be emotionally harmful for children is also easily spread, causing many social problems. This paper proposes a method to assess the harmfulness of input images automatically based on an artificial neural network. The proposed method first detects human face areas based on the MCT features from the input images. Next, based on color characteristics, this study identifies human skin color areas along with the candidate areas of nipples, one of the human body parts representing harmfulness. Finally, the method removes nonnipple areas among the detected candidate areas using the artificial neural network. The experimental results show that the suggested neural network learning-based method can determine the harmfulness of various types of images more effectively by detecting nipple regions from input images robustly.

scholarly journals Comparative investigation of artificial neural network learning algorithms for modeling solar still production

2015 ◽  
Vol 5 (4) ◽  
pp. 480-493 ◽  
Author(s):  
Ahmed F. Mashaly ◽  
A. A. Alazba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithms ◽  
Ambient Air ◽  
Feed Water ◽  
Solar Still ◽  
Neural Network Learning ◽  
Network Learning ◽  
Artificial Neural ◽  
Lm Algorithm

Three artificial neural network learning algorithms were utilized to forecast the productivity (MD) of a solar still operating in a hyper-arid environment. The learning algorithms were the Levenberg–Marquardt (LM), the conjugate gradient backpropagation with Fletcher–Reeves restarts, and the resilient backpropagation. The Julian day, ambient air temperature, relative humidity, wind speed, solar radiation, temperature of feed water, temperature of brine water, total dissolved solids (TDS) of feed water, and TDS of brine water were used in the input layer of the developed neural network model. The MD was located in the output layer. The developed model for each algorithm was trained, tested, and validated with experimental data obtained from field experimental work. Findings revealed the developed model could be utilized to predict the MD with excellent accuracy. The LM algorithm (with a minimum root mean squared error and a maximum overall index of model performance) was found to be the best in the training, testing, and validation stages. Relative errors in the predicted MD values of the developed model using the LM algorithm were mostly in the vicinity of ±10%. These results indicated that the LM algorithm is the most ideal and accurate algorithm for the prediction of the MD with the developed model.

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