Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the ...Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the VGG-Inspired stochastic pooling neural network(VISPNN)model based on three components:(i)a VGG-inspired mainstay network,(ii)the stochastic pooling technique,which aims to outperform traditional max pooling and average pooling,and(iii)an improved 20-way data augmentation(Gaussian noise,salt-and-pepper noise,speckle noise,Poisson noise,horizontal shear,vertical shear,rotation,Gamma correction,random translation,and scaling on both raw image and its horizontally mirrored image).In addition,two networks(Net-I and Net-II)are proposed in ablation studies.Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling.Net-II removes the 20-way data augmentation.Results The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32,a specificity of 97.80±1.35,a precision of 97.78±1.35,an accuracy of 97.89±1.11,an F1 score of 97.87±1.12,an MCC of 95.79±2.22,an FMI of 97.88±1.12,and an AUC of 0.9849,respectively.Conclusion The performance of our VISPNN model is better than two internal networks(Net-I and Net-II)and ten state-of-the-art alcoholism recognition methods.展开更多
Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of users.The use of SNS often inflicts the physical and the mental h...Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of users.The use of SNS often inflicts the physical and the mental health of the people.Nowadays,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative influence.The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy.To overcome these issues,the proposed methodology utilizes a Modified Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky Rectified Linear Unit(LReLU).Here,each word in the social media text is analyzed based on its meaning.The stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overfitting.The LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation functions.It also doesn’t stop the learning process when the values are negative.The MCNN computes a specified score value using a novel integrated anomaly detection tech-nique.Based on the score value,the anomalies are identified.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modified CNN and fast convergence is offered.In this way,the performance of the model is enhanced in terms of classification accuracy.The efficiency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,specificity,recall,and precision.The proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter,and Reddit respectively.展开更多
基金This paper is partially supported by the Royal Society International Exchanges Cost Share Award,UK(RP202G0230)Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)+3 种基金Hope Foundation for Cancer Research,UK(RM60G0680)British Heart Foundation Accelerator Award,UKSino-UK Industrial Fund,UK(RP202G0289)Global Challenges Research Fund(GCRF),UK(P202PF11).In addition,we acknowledge the help of Dr.Hemil Patel and Dr.Qinghua Zhou for their help in English correction.
文摘Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the VGG-Inspired stochastic pooling neural network(VISPNN)model based on three components:(i)a VGG-inspired mainstay network,(ii)the stochastic pooling technique,which aims to outperform traditional max pooling and average pooling,and(iii)an improved 20-way data augmentation(Gaussian noise,salt-and-pepper noise,speckle noise,Poisson noise,horizontal shear,vertical shear,rotation,Gamma correction,random translation,and scaling on both raw image and its horizontally mirrored image).In addition,two networks(Net-I and Net-II)are proposed in ablation studies.Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling.Net-II removes the 20-way data augmentation.Results The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32,a specificity of 97.80±1.35,a precision of 97.78±1.35,an accuracy of 97.89±1.11,an F1 score of 97.87±1.12,an MCC of 95.79±2.22,an FMI of 97.88±1.12,and an AUC of 0.9849,respectively.Conclusion The performance of our VISPNN model is better than two internal networks(Net-I and Net-II)and ten state-of-the-art alcoholism recognition methods.
文摘Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of users.The use of SNS often inflicts the physical and the mental health of the people.Nowadays,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative influence.The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy.To overcome these issues,the proposed methodology utilizes a Modified Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky Rectified Linear Unit(LReLU).Here,each word in the social media text is analyzed based on its meaning.The stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overfitting.The LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation functions.It also doesn’t stop the learning process when the values are negative.The MCNN computes a specified score value using a novel integrated anomaly detection tech-nique.Based on the score value,the anomalies are identified.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modified CNN and fast convergence is offered.In this way,the performance of the model is enhanced in terms of classification accuracy.The efficiency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,specificity,recall,and precision.The proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter,and Reddit respectively.
