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To solve the problem of low performance of deep features used for video classification, this paper proposed a novel method based on the fusion of video temporal and spatial information with two-level encoding method. Firstly, it used two convolutional neural networks (CNN) to respectively extract the video's spatial and temporal information. Then encoding the spatial and temporal information with Fisher vector (FV) and locally aggregating method respectively to get the effective representation of video. Finally, based on the two-level cascaded fusion feature, it used support vector machine (SVM) to classify the videos. Experimental results on UCF101 show that their method has a better performance contrasting to the state of the art methods. [ABSTRACT FROM AUTHOR]

Sentence semantic representation is not only a key problem in natural language processing to be solved at present, but also an important restriction factor whether nature language processing is ability to make deep application. Based on the characteristics of the Chinese text, this paper abandoned the point of separating semantic and grammar,and then put forward a new style of semantic clustering unit, and did a research on information extraction with a deep learning model based on deep belief net,the model took the noun as the core in the sentence and combined the noun with its before and after words to from semantic clustering unit. Then,it used three extraction methods,neural network,support vector machine and depth belief network, to construct the extraction model. Experimentally,there are three groups of experimental, finally results show that under the conditions of large data,deep belief network methods compare with support vector machines and neural networks, which has better effect. [ABSTRACT FROM AUTHOR]

Deep learning method has very excellent ability of image feature extraction. In order to get rid of disadvantages of traditional methods require a priori knowledge, this paper proposed an image feature extraction algorithm based on the fusion AutoEncoder and convolutional neural network (CNN ). The method introduced a fast sparsity control technique to AutoEncod-er and utilized AutoEncoder to train the basic elements of image and initialized the convolution kernel of CNN. Meanwhile, the algorithm added filtering mechanism to the CNN to keep the sparsity of output characteristics. The results of experiments point out that this method achieves good performance on the Minist handwritten digital library and the Yale face database. Furthermore, the advanced experimental outcomes indicate that the feature extraction model included the filtering technique is more effective than the model without filtering mechanism by using cross-validation with T test. [ABSTRACT FROM AUTHOR]

To solve the problem that the loss of image feature information and improve the accuracy of image retrieval, when the convolutional neural network (CNN) was used to extract the feature information of the image, this paper proposed an image retrieval algorithm based on improved convolutional neural network LeNet-L. First, it improved LeNet-5 convolution neural network structure, increased depth of network structure. Then, it pre-trained the deep convolutional neural network LeNet-L to extract the high-level semantic features. At last, it obtained the similar images by distance function between the image being retrieved and the one in image database. In Corel dataset, compared with the original model method and the traditional image retrieval method based on SVM and active learning, the proposed method had a higher accuracy. The experimental results show that the improved convolutional neural network has a better retrieval effect. [ABSTRACT FROM AUTHOR]

For the problem of mobile user activity model over-fitting which leads to the generalization of the problem, this paper put forward an improved mobile user activity recognition method of Dropout DBN (deep belief network). The method randomly changed the probability parameters in algorithms of Dropout, reduced the number of hidden units of network nodes, optimized the network weight in each training. This method improved the accuracy of recognition and generalization when the number of sample decreases. Experimental results show that the average recognition accuracy rate for five activities walking, running, upstairs and downstairs reaches 94. 23% by using random Dropout network, recognition accuracy improves 4. 57% . [ABSTRACT FROM AUTHOR]

The utilization of smart phone’ acceleration sensors to identify human behavior is a big topic in intelligence field. Traditional identification methods, such as Bayes, speed learning and decision tree, must first collected acquisition frequency domain features of acceleration sensor data and extracted preferred features. This paper utilized convolutional neural network algorithm to extract smart phone’ accelerate data in three dimensions, and then automatically found out patterns from the data. Finally it used decision tree to identify human behavior from the pattern. Experimental results show that recognition accuracy improves 1.1% to 5.2% in comparison with traditional machine learning methods, especially data set on the large -scale. [ABSTRACT FROM AUTHOR]

This paper proposed a new method called stacked denoising autoencoder based on collaborative filtering after studying the way of improving precision of recommended algorithm. SDAE was a mul-layer deep learning model which could learn significant dependencies from input information. It used ratings given to items by users to learn the latent feature,got results of dimensionality reduction after PCA to calculate attribute similarities. Then it added the grade similarities computed with concealed feature as the final result. Finally, it emploied similarities between items to generate the top-N recommendation list. Experiments on MovieLens show that the proposed algorithm has higher recall rate and can settle the missing ratings and cold start problems in some way. [ABSTRACT FROM AUTHOR]

In view of solving the problems of poor performance in the traditional collaborative filtering recommendation algorithm in dealing with the cold start and sparse data, this paper proposed a method that combined the deep model of the stacked denoising autoencoders with the nearest neighbor recommendation method. Aiming to form new hybrid recommendation model, treating the autoencoder as the basic unit, training process transformed a unsupervised learning problem. By combining sparse coding algorithm, the denoising criteria, extreme learning machine and using the idea of layer by layer autoencoder. The abstract features extracted from the final model applied to the nearest neighbor algorithm to predict the scoring. The experimental data sets of various models show that when the sparsity is less than 8% , compared with the cosine similarity model and Pearson similarity model, hybrid recommendation model experimental results are increased by 11.3% and 21. 1% , and compared with the potential matrix decomposition model , number of iterations for convergence that mixed model required is less nearly 30%. In the three groups of comparative experiments,the stability of the hybrid model also is the best, thus validating faster convergence speed of mixed model and impressive effect in deal with data sparsity and cold start. [ABSTRACT FROM AUTHOR]

In the era of big data, many of traditional machine learning methods of disposing unlabeled raw voice data have become less applicable. At the same time, deep learning models can directly process unlabeled data because of its powerful capability of modeling to deal with the massive data, and has become a hot research in the field of speech recognition. To begin with, this paper analyzed and summarized the state-of-the-art deep learning of models. And then,it discussed the applications to speech recognition with speech features extraction and acoustic modeling. Finally,it concluded the problems faced and development orientation. [ABSTRACT FROM AUTHOR]

User complaints prediction model could effectively lower complaints rate of telecom users which was crucial for enterprise to improve customer satisfaction and competitiveness. During the process of model training，due to the defects of artificial design features and the unpredictable complexity in the design process，the accuracy of model prediction and the efficiency of the design features couldn't have a lot of ascension. For the above problems，this paper proposed a user complaints prediction model based on deep learning. By deep network characteristics study session，the model could automatically study the senior nonlinear combination features from the original telecommunication users' data which was suitable for classifier classification and import high-level features into traditional classifier to improve the precision of the model. By analyzing the experimental results，the prediction model' s AUC index has increased by 7. 1% compare to previous user complaints model. It has demonstrated that the effectiveness of the model automatic study features and availability of deep learning in telecom big data domain. [ABSTRACT FROM AUTHOR]

In view of the traditional decision tree need to predefine the features before classifying data, and in order to solve the bottleneck problems of processing speed and resource allocation when dealing with massive astronomical data, considering the strong representation learning of deep learning and good performance of processing huge amounts of data on Spark, this paper proposed a parallel deep neural decision tree based on Spark. And then it was applied on the astronomy star/galaxy separation problem. The results show that, this algorithm can scale well with cluster size as it can dramatically decrease the training time of model and enhance the ability of processing massive astronomy data with it. Moreover, it obtains a better classification accuracy on the star/galaxy separation problem by addressing the decision tree to learn the proper representations of input data and the final classifiers in a joint manner. [ABSTRACT FROM AUTHOR]

In ihe task of relation extraction, traditional statistical methods have difficulties in getting perfect features manually, while deep learning methods strongly depend on the representational capacity of single word vector. To solve the problems mentioned above, this paper proposed a novel model called multi-channel convolutional neural network (CNN). Firstly, the model used different word vectors to represent sentence as input for different channels. Secondly, it extracted features automatically through CNN. Finally, it obtained relation types by using softmax classifier. The proposed model could capture more semantic information thus it learned more distinctive features automatically comparing to other models. The experiment results on Sem E-val-2010 Task 8 datasets show that the proposed model is more suitable for relation extraction task than normal models which only utilize single word vector. [ABSTRACT FROM AUTHOR]

In order to reduce the loss of dimension reduction in high dimensional image dataset and improve the effect of data visualization, this paper proposed a new image dataset visualization method based on the combination of deep features and nonlinear dimension reduction. This method firstly designed and trained a convolutional neural network model,and the single model on the MNIST dataset achieved the highest recognition accuracy. Secondly,it used the high precision model to extract the deep middle layer features as an effective representation of the image dataset. Finally,it used nonlinear dimension reduction method to reduce the features into two-dimensional and realized data visualization. Experimental results show that this method can effectively avoid the loss of traditional dimension reduction in image dataset visualization, and the visualization result is very obvious. [ABSTRACT FROM AUTHOR]

In view of the existing prediction methods fail to fully reveal the nature of the traffic flow, this paper proposed a short-term traffic flow prediction model based on deep learning. The method combined the deep belief network( DBN) model and support vector regressionf SVR) classifier as predictive model. It removed the trend of the traffic flow by using data difference, extracted traffic flow features by deep belief network model, carried and the traffic flow prediction out with support vector regression in the top level of the network. Experiment results with actual traffic flow data show that the proposed method has a higher accuracy compared with others, prediction performance increases by 18. 01 % .and it is an effective traffic flow' prediction method. [ABSTRACT FROM AUTHOR]

Partial least squares has been widely used in the multiple variable analysis. However, partial least squares method using principal component analysis, cannot express the nonlinear characteristic. and accuracy is low in the nonlinear data. Therefore, this paper proposed an analysis and predicting method of deep learning combining with PLS. The method could extract nonlinear structure of feature space by sparse autoencoder of deep learning and replace the components in PLS with the extracted components,forming a model which could adapt to nonlinear dose-effect relationship. It analyzed and processed the data of large chengqi decoction.ma xing shi gan tang.puerariae and Scutellariae and coptidis decoction,UCI data set. The experimental results show that,the deep learning and PLS method can well reflect the characteristics of TCM data. [ABSTRACT FROM AUTHOR]

Considering the problems of the traditional facial expression recognition algorithm with poor robustness and easily disturbed by the face identity information, this paper proposed a face recognition algorithm based on denoising AutoEncoders. First, the method detected face area from per picture and made sure all of the face areas were the same scale. Then it constructed the model based on stacked denoising AutoEncoders for pre-training data. Finally, in order to avoid the overfitting problem that was caused by the lack of training samples,it adopted the Dropout technology in the last full connection layer of the deep network model. The experimental results on CK + database, JAFFE database and Yale database achieved the high accuracy rate. So that, this method has stronger robustness and capacity of resisting disturbance of the face identity information. [ABSTRACT FROM AUTHOR]

Due to the most health problems of the building structure damage is cumulative,which is difficult to be detected,the complexity of the actual structure and the ambient noise makes it more difficult to do the structural health monitoring,while the existing method requires a lot of data in the training model,but in practice for the tag data it is very complex. To overcome this problem,by the wireless sensor networks,this paper used the sparse coding to achieve bridge structural health monitoring,and through a large number of unlabeled examples of feature extraction in achieving sparse coding algorithms based on training, it realized the data dimensionality reduction and unlabeled data preprocessing. Secondly, it used the deep learning algorithm to predict the bridge structural health monitoring category,and also used the linear conjugate gradient-based optimization algorithm to improve the Hessian optimization, and used the semi definite Gauss-Newton Hessian matrix to replace uncertain Hessian matrix,which used the secondary target combinations to achieve deep learning algorithm efficiency. Experimental results show that the depth of the structural safety of the bridge learning detection algorithms achieves a high-precision structural health monitoring of ambient noise levels under sparse coding mentioned. [ABSTRACT FROM AUTHOR]

In the recommended process, the traditional algorithm has several problems. For examples, the algorithm ignored the customer preference and the customer’s malevolence, also, it provided a variety of false information, even for time series problems. This paper introduced users’ interests modeling while it built the model. Then, it toke about users’ suspicious degree and time effects used to calculate and updated users’ similarity. In the end, it would highlight the optimum recommended target. In order to avoid data overfitting, it used the greedy algorithm to train the user’s data, which added a preference to the hidden layer and the visible layer. Finally, this method found the optimum solution and improved study ability with deep learning network. It compared the experiments about improvement recommended algorithms and traditional recommended in the Movie-Lens of data collections. The result displays that compare to the old fashioned recommended algorithms, the improved one can greatly improve the accuracy of project recommendation. [ABSTRACT FROM AUTHOR]

Data fusion algorithms widely used BP neural network to extract and classify the node data features in wireless sensor networks. In order to overcome the shortcomings of BP neural network leading to poor performance for data fusion,such as low convergence speed,local optimal and bad generalization ability, this paper proposed a data fusion algorithm SAESMDA combined with deep learning technology and wireless sensor network clustering routing protocol. SAESMDA used deep learning model SAESM based on stacked autoencoder (SAE) instead of the BP neural network, algorithm firstly trained SAESM in sink node and generated clusters for network, then used SAESM to exacted node data features in cluster nodes, finally the data features in the same class would be fused and sent to sink node by cluster heads. Simulation experiments show that compared with BPNDA based on the BP neural network, SAESMDA has a higher feature extraction and classification accuracy with the similar network energy consumption. [ABSTRACT FROM AUTHOR]

Visual sentiment analysis intends to describe images and videos in a subjective and emotional way and to detect and express the information with computers. With the emergence of social media and the rise of plenty of training data, technological revolution like deep learning has brought new opportunities and challenges into this field. This paper reviewed the key technologies including visual feature extraction, emotional space construction and mapping from visual features to emotion in traditional visual sentiment analysis on the basis of the framework for visual emotional semantic extraction. Then it discussed visual sentiment analysis based on middle representation and object detection and the application of deep learning in visual sentiment analysis under the background of big data. Finally it summarized the existing problems of visual sentiment analysis and gave inspirational future research directions. [ABSTRACT FROM AUTHOR]

In order to predict the traffic congestion in city, this paper proposed the prediction model based on the deep learning. The traffic data like flow parameters, environmental situation, times of day and so on were summarized and composed into the feature vector, and defined four kinds of traffic flow pattern. It learned the unlabelled traffic dataset by the auto-encoder network to build the hidden layer function and generate the new feature dataset, and used the softmax regression to classify the traffic pattern based on the labeled feature dataset learning. Demonstrated by the simulation, the prediction model has better performance than the model without the feature learning, the average prediction accuracy can reach 85%. [ABSTRACT FROM AUTHOR]

The classification accuracy of the traditional visual features can not meet the requirements in the application of modern military affairs due to the extremely high similarity of the different objects in the battlefield and low recognition rate in complex conditions. This paper presented a new architecture of the convolutional neural network (CNN) based on PCA whitening for solving the classification problem of large-scale images which contained some specific military objects. It efficiently eliminated the relativity of sample data. enhanced the learning ability and improved the accuracy of the object recognition. It tested and evaluated the new CNN model with the large-scale data from military images and compared with the traditional methods. The experiment results show that the algorithm has higher recognition rate on military object recognition. [ABSTRACT FROM AUTHOR]