Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling

Wang, Shuihua; Lv, Yiding; Sui, Yuxiu; Liu, Shuai; Wang, Sujing; Zhang, Yudong

doi:10.1007/s10916-017-0845-x

Cited by 148 publications

(52 citation statements)

References 31 publications

(35 reference statements)

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“…On the one hand, the achievements of the model can be further improved by data enhancement. The generalization ability of the model can be improved by increasing the number of images via rotation and translation and so on, while the robustness of the model can be further improved by images added noise [12,13]. On the other hand, it expands the range of data sets, which makes the features mined by the network own more invariance to operations such as rotation and scaling.…”

Section: Data Augmentation and Oversamplingmentioning

confidence: 99%

A CNN-LSVM Model for Imbalanced Images Identification of Wheat Leaf

Su¹,

Min²,

Shi³

et al. 2019

NNW

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In order to improve the accuracy of convolutional neural networks (CNN) in imbalanced dataset classification, a novel hierarchical CNN-LSVM is proposed. Considering the imbalance in the number and spatial distribution of wheat leaf disease images, the improved local support vector machine (LSVM) replaces Softmax as the classifier of the model, and meanwhile a cost sensitive matrix is designed to assign the value for penalty factors in the optimized objective function of LSVM. It effectively improves the sensitivity of misclassification caused by imbalanced data. To verify the validity and practicability of CNN-LSVM, 6028 wheat leaf disease images containing 8 species are collected from planting bases in Shandong Agricultural University. Then the imbalanced and balanced standard image sets are generated by data augmentation and Borderline-Synthetic Minority Oversampling (Borderline-SMOTE). They have 36168 and 46176 images, respectively. The experimental results show that the average identification accuracies of the CNN-LSVM obtained on imbalanced and balanced standard datasets are 90.32 % and 93.68 %, respectively. And it starts to converge when the iteration times are close to 13000. CNN-LSVM has higher classification accuracy and lower iteration times, compared with CNN-Softmax, CNN-SVM, LSVM and support vector machine (SVM).

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Section: Data Augmentation and Oversamplingmentioning

confidence: 99%

A CNN-LSVM Model for Imbalanced Images Identification of Wheat Leaf

Su¹,

Min²,

Shi³

et al. 2019

NNW

View full text Add to dashboard Cite

show abstract

“…In addition, due to the continuous development of deep learning in various fields [34], the research of sentiment analysis gradually begins to tend to unsupervised classification [35][36][37][38]. Such methods are quite difficult and of great research significance also can save labor, but the method is not mature enough yet and the classified accuracy is relatively low which can't be used to the application now.…”

Section: Related Workmentioning

confidence: 99%

A Method of Fine-Grained Short Text Sentiment Analysis Based on Machine Learning

Chang¹,

Huo²

2018

NNW

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Text sentiment analysis plays an important role in social network information mining. It is also the theoretical foundation and basis of personalized recommendation, circle of interest classification and public opinion analysis. In view of the existing algorithms for feature extraction and weight calculation, we find that they fail to fully take into account the influence of sentiment words. Therefore, this paper proposed a fine-grained short text sentiment analysis method based on machine learning. To improve the calculation method of feature selection and weighting and proposed a more suitable sentiment analysis algorithm for features extraction named N-CHI and weight calculation named W-TF-IDF, increasing the proportion and weight of sentiment words in the feature words Through experimental analysis and comparison, the classification accuracy of this method is obviously improved compared with other methods.

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“…In recent years, Convolutional Neural Network (CNN) [5] has played a very important role in the remote sensing field for image classification, detection, description and segmentation [6][7][8][9][10][11][12], and it also has been widely used in many other fields [13][14][15][16]. CNN constructs multiple layers to learn high-level image features with better discrimination and robustness, as opposed to that in traditional methods [17][18][19], where features have to be handcrafted.…”

Section: Introductionmentioning

confidence: 99%

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

Shi

Zou

et al. 2018

Remote Sensing

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Raft-culture is a way of utilizing water for farming aquatic product. Automatic raft-culture monitoring by remote sensing technique is an important way to control the crop's growth and implement effective management. This paper presents an automatic pixel-wise raft labeling method based on fully convolutional network (FCN). As rafts are always tiny and neatly arranged in images, traditional FCN method fails to extract the clear boundary and other detailed information. Therefore, a homogeneous convolutional neural network (HCN) is designed, which only consists of convolutions and activations to retain all details. We further design a dual-scale structure (DS-HCN) to integrate higher-level contextual information for accomplishing sea-land segmentation and raft labeling at the same time in a uniform framework. A dataset with Gaofen-1 satellite images was collected to verify the effectiveness of our method. DS-HCN shows a satisfactory performance with a better interpretability and a more accurate labeling result.

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Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling

Cited by 148 publications

References 31 publications

A CNN-LSVM Model for Imbalanced Images Identification of Wheat Leaf

A CNN-LSVM Model for Imbalanced Images Identification of Wheat Leaf

A Method of Fine-Grained Short Text Sentiment Analysis Based on Machine Learning

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

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