Performance Analysis of Intelligent Neural-Based Deep Learning System on Rank Images Classification  

作　　者：Muhammad Hameed Siddiqi Asfandyar Khan Muhammad Bilal Khan Abdullah Khan Madallah Alruwaili Saad Alanazi 

机构地区：[1]College of Computer and Information Sciences,Jouf University,Sakaka,Aljouf,73211,Kingdom of Saudi Arabia [2]Institute of Computer Science and Information Technology,ICS/IT FMCS,University of Agriculture,Peshawar,25000,Pakistan

出　　处：《Computer Systems Science & Engineering》2023年第11期2219-2239,共21页计算机系统科学与工程（英文）

基　　金：funded by the Deanship of Scientific Research at Jouf University under Gran Number DSR–2022–RG–0101.

摘　　要：The use of the internet is increasing all over the world on a daily basis in the last two decades.The increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child pornography.Various research has been done for pornographic image detection and classification.Most of the used models used machine learning techniques and deep learning models which show less accuracy,while the deep learning model ware used for classification and detection performed better as compared to machine learning.Therefore,this research evaluates the performance analysis of intelligent neural-based deep learning models which are based on Convolution neural network(CNN),Visual geometry group(VGG-16),VGG-14,and Residual Network(ResNet-50)with the expanded dataset,trained using transfer learning approaches applied in the fully connected layer for datasets to classify rank(Pornographic vs.Nonpornographic)classification in images.The simulation result shows that VGG-16 performed better than the used model in this study without augmented data.The VGG-16 model with augmented data reached a training and validation accuracy of 0.97,0.94 with a loss of 0.070,0.16.The precision,recall,and f-measure values for explicit and non-explicit images are(0.94,0.94,0.94)and(0.94,0.94,0.94).Similarly,The VGG-14 model with augmented data reached a training and validation accuracy of 0.98,0.96 with a loss of 0.059,0.11.The f-measure,recall,and precision values for explicit and non-explicit images are(0.98,0.98,0.98)and(0.98,0.98,0.98).The CNN model with augmented data reached a training and validation accuracy of 0.776&0.78 with losses of 0.48&0.46.The f-measure,recall,and precision values for explicit and non-explicit images are(0.80,0.80,0.80)and(0.78,0.79,0.78).The ResNet-50 model with expanded data reached with training accuracy of 0.89 with a loss of 0.389 and 0.86 of validation accuracy and a loss of 0.47.The f-measure,recall,and precision values for explicit and non-explicit images are(0.86,0.97,0.91)and(0.86,0.93,0.89).W

关 键 词：VGG-16 VGG-14 pornography detection EXPANSION ResNet-50 convolution neural network(CNN) machine learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]