Search

In colposcopy-assisted diagnosis, the difference between the different lesion grades of colposcopic images is small, and the visual similarity is high. Therefore, it is a very challenging task to accurately diagnose cervical lesions through colposcopic images. This paper proposes a new risk assessment net of cervical lesions in colposcopic images (RACNet). The RACNet mainly consists of two parts. At first, the location and grade of lesions in different scales are detected through a multi-scale detection network. Then, these lesions are classified by designing a multi-branch convolutional neural network (CNN) to improve the performance of risk assessment. The RACNet was compared with the most advanced methods and colposcopists under the same condition. The experimental results show that the RACNet in this paper is superior to other methods, with an accuracy rate of 84.5%, which is 15% higher than the average level of colposcopists. It can provide clinicians with auxiliary diagnosis and reduce missed diagnosis and misdiagnosis. [ABSTRACT FROM AUTHOR]

In colposcopy-assisted diagnosis, the difference between the different lesion grades of colposcopic images is small, and the visual similarity is high. Therefore, it is a very challenging task to accurately diagnose cervical lesions through colposcopic images. This paper proposes a new risk assessment net of cervical lesions in colposcopic images (RACNet). The RACNet mainly consists of two parts. At first, the location and grade of lesions in different scales are detected through a multi-scale detection network. Then, these lesions are classified by designing a multi-branch convolutional neural network (CNN) to improve the performance of risk assessment. The RACNet was compared with the most advanced methods and colposcopists under the same condition. The experimental results show that the RACNet in this paper is superior to other methods, with an accuracy rate of 84.5%, which is 15% higher than the average level of colposcopists. It can provide clinicians with auxiliary diagnosis and reduce missed diagnosis and misdiagnosis. [ABSTRACT FROM AUTHOR]

This paper introduces a malware detection method based on the reorganisation of API instruction sequence and image representation in an effort to address the challenges posed by current methods of malware detection in terms of feature extraction and detection accuracy. In the first step, APIs of the same type are grouped into an API block. Each API block is reorganised according to the first invocation order of each type of API. As a measure of the API's devotion to the software sample, the number of API block entries is recorded. Second, the API codes, API devotions, and API sequential indexes are extracted based on the reorganised API instruction sequence to generate the feature image. The feature image is then fed into the self-built lightweight malware feature image convolution neural network. The experimental results indicate that the detection accuracy of this method is 98.66% and that it has high performance indicators and detection speed for malware detection. [ABSTRACT FROM AUTHOR]

In recent years, image aesthetic quality assessment has attracted considerable attention due to the massive growth of digital images in social platforms and the Internet. However, automatically assessing aesthetic quality of an image is a challenging task, because image aesthetic is affected by various factors, and the criteria for judging the aesthetic of images with diverse semantic information are different. To this end, a Semantic-Aware Multi-task convolution neural network (SAM-CNN) for evaluating image aesthetic quality is proposed in this paper. The network can fuse intermediate features of different layers at different scales in CNN to obtain a more comprehensive and accurate aesthetic expression, under the joint supervision of image aesthetic quality assessment task and semantic classification task in a multi-task learning manner. Besides, by applying the attention mechanism, semantic information with a large receptive field extracted from deep layers is utilised to guide the network to focus on the key parts of features to be fused, to improve the effectiveness of feature fusion. Experimental results on the AVA dataset and Photo.net dataset demonstrate the effectiveness and superiority of the proposed SAM-CNN. [ABSTRACT FROM AUTHOR]

The word embedding model word2vec tends to ignore the importance of a single word to the entire document, which affects the accuracy of the news text classification method. To address this problem, a method that combines word2vec, a topic-based TF-IDF algorithm, and an improved convolutional neural network is proposed in this paper, which is named WTL-CNN. Firstly, word2vec is used to convert text data into word vectors. Secondly, an improved TF-IDF algorithm is proposed to weight word vectors. The improved TF-IDF algorithm introduces LDA topic generation model to enhance the topic semantic information of TF-IDF values. Thirdly, according to the location distribution law of important features of news texts, the location information is converted into weights and integrated into the pooling process convolutional neural network to further improve the accuracy of classification. At last, WTL-CNN has been evaluated and compared with seven contrast models on datasets THUCNews and SogouCS under the environment of TensorFlow. The experimental results show that the precision rate, recall rate and F1 value of WTL-CNN model reach 95.76%, 93.43%, 94.98% respectively on the THUCNews, and reach 94.61%, 93.43%, 94.01 respectively on the SogouCS. [ABSTRACT FROM AUTHOR]

Network pruning facilitates the deployment of convolutional neural networks in resource-limited environments by reducing redundant parameters. However, most of the existing methods ignore the differences in the contributions of the output feature maps. In response to the above, we propose a novel neural network pruning method based on the channel attention mechanism. In this paper, we firstly utilise the principal component analysis algorithm to reduce the influence of noisy data on feature maps. Then, we propose an improved Leaky-Squeeze-and-Excitation block to evaluate the contribution of each output feature map using the channel attention mechanism. Finally, we effectively remove lower contribution channels without reducing the model performance as much as possible. Extensive experimental results show that our proposed method achieves significant improvements over the state-of-the-art in terms of FLOPs and parameters reduction with similar accuracies. For example, with VGG-16-baseline, our proposed method reduces parameters by 83.3% and FLOPs by 66.3%, with only a loss of 0.13% in top-5 accuracy. Furthermore, it effectively balances pruning efficiency and prediction accuracy. [ABSTRACT FROM AUTHOR]

Traffic forecasting is highly challenging due to its complex spatial and temporal dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been effectively used for traffic forecasting due to its excellent performance in modelling spatial dependencies. In most existing approaches, GCN models spatial dependencies in the traffic network with a fixed adjacency matrix. However, the spatial dependencies change over time in the actual situation. In this paper, we propose a graph learning-based spatial-temporal graph convolutional neural network (GLSTGCN) for traffic forecasting. To capture the dynamic spatial dependencies, we design a graph learning module to learn the dynamic spatial relationships in the traffic network. To save training time and computation resources, we adopt dilated causal convolution networks with a gating mechanism to capture long-term temporal correlations in traffic data. We conducted extensive experiments using two real-world traffic datasets. Experimental results demonstrate that the proposed GLSTGCN achieves superior performance than all state-of-art baselines. [ABSTRACT FROM AUTHOR]

Stocks price prediction is a current hot spot with great promise and challenges. Recently, there have been many stock price prediction methods. However, the prediction accuracy of these methods is still far from satisfactory. In this paper, we propose a stock price prediction method that incorporates multiple data sources and the investor sentiment, which can be called SILSTM. Firstly, we crawl multiple data sources on the Internet and preprocess them respectively. These data involve stock historical data, technical indicators, and non-traditional data sources, such as stock posts and financial news. Then, we use the sentiment analysis method based on convolutional neural network for the non-traditional data, which can calculate the investors' sentiment index. Finally, we combine sentiment index, technical indicators and stock historical transaction data as the feature set of stock price prediction and adopt the long short-term memory network for predicting the China Shanghai A-share market. The experiments show that the predicted stock closing price is closer to the true closing price than the single data source, and the mean absolute error can achieve 2.386835, which is better than traditional methods. We verified the effectiveness on the real data sets of five listed companies. [ABSTRACT FROM AUTHOR]

In recent years, image aesthetic quality assessment has attracted considerable attention due to the massive growth of digital images in social platforms and the Internet. However, automatically assessing aesthetic quality of an image is a challenging task, because image aesthetic is affected by various factors, and the criteria for judging the aesthetic of images with diverse semantic information are different. To this end, a Semantic-Aware Multi-task convolution neural network (SAM-CNN) for evaluating image aesthetic quality is proposed in this paper. The network can fuse intermediate features of different layers at different scales in CNN to obtain a more comprehensive and accurate aesthetic expression, under the joint supervision of image aesthetic quality assessment task and semantic classification task in a multi-task learning manner. Besides, by applying the attention mechanism, semantic information with a large receptive field extracted from deep layers is utilised to guide the network to focus on the key parts of features to be fused, to improve the effectiveness of feature fusion. Experimental results on the AVA dataset and Photo.net dataset demonstrate the effectiveness and superiority of the proposed SAM-CNN. [ABSTRACT FROM AUTHOR]

This paper introduces a malware detection method based on the reorganisation of API instruction sequence and image representation in an effort to address the challenges posed by current methods of malware detection in terms of feature extraction and detection accuracy. In the first step, APIs of the same type are grouped into an API block. Each API block is reorganised according to the first invocation order of each type of API. As a measure of the API's devotion to the software sample, the number of API block entries is recorded. Second, the API codes, API devotions, and API sequential indexes are extracted based on the reorganised API instruction sequence to generate the feature image. The feature image is then fed into the self-built lightweight malware feature image convolution neural network. The experimental results indicate that the detection accuracy of this method is 98.66% and that it has high performance indicators and detection speed for malware detection. [ABSTRACT FROM AUTHOR]

The word embedding model word2vec tends to ignore the importance of a single word to the entire document, which affects the accuracy of the news text classification method. To address this problem, a method that combines word2vec, a topic-based TF-IDF algorithm, and an improved convolutional neural network is proposed in this paper, which is named WTL-CNN. Firstly, word2vec is used to convert text data into word vectors. Secondly, an improved TF-IDF algorithm is proposed to weight word vectors. The improved TF-IDF algorithm introduces LDA topic generation model to enhance the topic semantic information of TF-IDF values. Thirdly, according to the location distribution law of important features of news texts, the location information is converted into weights and integrated into the pooling process convolutional neural network to further improve the accuracy of classification. At last, WTL-CNN has been evaluated and compared with seven contrast models on datasets THUCNews and SogouCS under the environment of TensorFlow. The experimental results show that the precision rate, recall rate and F1 value of WTL-CNN model reach 95.76%, 93.43%, 94.98% respectively on the THUCNews, and reach 94.61%, 93.43%, 94.01 respectively on the SogouCS. [ABSTRACT FROM AUTHOR]

Network pruning facilitates the deployment of convolutional neural networks in resource-limited environments by reducing redundant parameters. However, most of the existing methods ignore the differences in the contributions of the output feature maps. In response to the above, we propose a novel neural network pruning method based on the channel attention mechanism. In this paper, we firstly utilise the principal component analysis algorithm to reduce the influence of noisy data on feature maps. Then, we propose an improved Leaky-Squeeze-and-Excitation block to evaluate the contribution of each output feature map using the channel attention mechanism. Finally, we effectively remove lower contribution channels without reducing the model performance as much as possible. Extensive experimental results show that our proposed method achieves significant improvements over the state-of-the-art in terms of FLOPs and parameters reduction with similar accuracies. For example, with VGG-16-baseline, our proposed method reduces parameters by 83.3% and FLOPs by 66.3%, with only a loss of 0.13% in top-5 accuracy. Furthermore, it effectively balances pruning efficiency and prediction accuracy. [ABSTRACT FROM AUTHOR]

Traffic forecasting is highly challenging due to its complex spatial and temporal dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been effectively used for traffic forecasting due to its excellent performance in modelling spatial dependencies. In most existing approaches, GCN models spatial dependencies in the traffic network with a fixed adjacency matrix. However, the spatial dependencies change over time in the actual situation. In this paper, we propose a graph learning-based spatial-temporal graph convolutional neural network (GLSTGCN) for traffic forecasting. To capture the dynamic spatial dependencies, we design a graph learning module to learn the dynamic spatial relationships in the traffic network. To save training time and computation resources, we adopt dilated causal convolution networks with a gating mechanism to capture long-term temporal correlations in traffic data. We conducted extensive experiments using two real-world traffic datasets. Experimental results demonstrate that the proposed GLSTGCN achieves superior performance than all state-of-art baselines. [ABSTRACT FROM AUTHOR]

Stocks price prediction is a current hot spot with great promise and challenges. Recently, there have been many stock price prediction methods. However, the prediction accuracy of these methods is still far from satisfactory. In this paper, we propose a stock price prediction method that incorporates multiple data sources and the investor sentiment, which can be called SILSTM. Firstly, we crawl multiple data sources on the Internet and preprocess them respectively. These data involve stock historical data, technical indicators, and non-traditional data sources, such as stock posts and financial news. Then, we use the sentiment analysis method based on convolutional neural network for the non-traditional data, which can calculate the investors' sentiment index. Finally, we combine sentiment index, technical indicators and stock historical transaction data as the feature set of stock price prediction and adopt the long short-term memory network for predicting the China Shanghai A-share market. The experiments show that the predicted stock closing price is closer to the true closing price than the single data source, and the mean absolute error can achieve 2.386835, which is better than traditional methods. We verified the effectiveness on the real data sets of five listed companies. [ABSTRACT FROM AUTHOR]