The ship behavior recognition by human supervision in complex marine environment is inefficient. In order to solve the problem, a new ship behavior recognition method based on multi-scale convolutional neural network was proposed. Firstly, massive ship driving data were obtained from the Automatic Identification System (AIS), and the discriminative ship behavior trajectories were extracted. Secondly, according to the characteristics of the trajectory data, the behavior recognition network for ship trajectory data was designed and implemented by multi-scale convolution, and the feature channel weighting and Long Short-Term Memory network (LSTM) were used to improve the accuracy of algorithm. The experimental results on ship behavior dataset show that, the proposed recognition network can achieve 92.1% recognition accuracy for the ship trajectories with specific length, which is 5.9 percentage points higher than that of the traditional convolutional neural network. In addition, the stability and convergence speed of the proposed network are significantly improved. The proposed method can effectively improve the ship behavior recognition accuracy, and provide efficient technical support for the marine regulatory authority.

If cloud data is not deleted in time after expiration, it may lead to unauthorized access and privacy leakage. For above issue, a cloud data assured deletion scheme based on key distribution and ciphertext sampling was proposed. It was composed of the encryption algorithm and Distributed Hash Table (DHT) network. Firstly, the plaintext was encrypted into the ciphertext. The ciphertext was sampled by random sampling algorithm. The incomplete ciphertext was uploaded to the cloud. Secondly, The trust value of each node in the DHT network was evaluated by evaluative method. The encryption key was processed into the subkeys by Shamir secret sharing algorithm, and the subkeys were distributed into the nodes with high trust degree. Finally, the encryption key was automatically deleted by the periodic self-updating function of the DHT network. The ciphertext in the cloud was overwritten by uploading random data through the Hadoop Distributed File System (HDFS)'s interface. Assured deletion of cloud data was done by deleting the encryption key and the ciphertext. The security analysis and performance analysis demonstrate that the proposed scheme is secure and efficient.

Due to redundancy of ultrasound image samples, high similarity between different planes caused by disease, and inaccurate positioning of region-of-interest, a classification method of TransEsophageal Echocardiography (TEE) standard plane was proposed by combining with Bag of Features (BOF) model, active learning and improved multi-class AdaBoost algorithm. Firstly, BOF model was constructed to describe ultrasound image. Secondly, active learning was adopted to select the most informative samples for classifiers as training data set. Lastly, improved multi-class AdaBoost algorithm was proposed, where the weight update rule of multi-class AdaBoost was modified according to the classfication results of temporary strong learner, and the TEE standard plane was classified by the improved multi-class AdaBoost algorithm. The experimental results on TEE data set and three UCI data sets showed that, compared with AdaBoost.SAMME, multi-class Support Vector Machine (SVM), BP neural network and AdaBoost.M2, the G-mean value, the total classification accuracy and the classification accuracy in most classes of the proposed method were improved in varying degrees, the classification accuracy of easily misclassified class was improved most significantly. The experimental results illustrate that the improved multi-class AdaBoost algorithm can significantly improve the G-mean value and accuracy of easily misclassified class in the datasets containing similar samples between classes.

An imbalance multi-class AdaBoost algorithm with active learning was proposed to improve the recognition accuracy of minority class in imbalance classification. Firstly, active learning was adopted to select the most informative samples for classifiers through multiple iterations of sampling. Secondly, a new sample selection strategy based on uncertainty of dynamic margin was proposed to tackle the problem of data imbalance in the multi-class case. Finally, the cost sensitive method was adopted to improve the multi-class AdaBoost algorithm: giving different class with different misclassification cost, adjusting sample weight update speed, and forcing weak learners to "concern" minority class. The experimental results on clinical TransThoracic Echocardiography (TTE) data set illustrate that, when compared with multi-class Support Vector Machine (SVM), the total recognition accuracy of heart disease increases by 5.9%, G-mean improves by 18.2%, the recognition accuracy of Valvular Heart Disease (VHD) improves by 0.8%, the recognition accuracy of Infective Endocarditis (IE) (minority class) improves by 12.7% and the recognition accuracy of Coronary Artery Disease (CAD) (minority class) improves by 79.73%; compared with SMOTE-Boost, the total recognition accuracy of heart disease increases by 6.11%, the G-mean improves by 0.64%, the recognition accuracy of VHD improves by 11.07%, the recognition accuracy of Congenital Heart Disease (CHD) improves by 3.67%. The experiment results on TTE data and 4 UCI data sets illustrate that when used in imbalanced multi-class classification, the proposed algorithm can improve the recognition accuracy of minority class effectively, and upgrade the overall classifier performance while guaranteeing the recognition accuracy of other classes not to be decreased dramatically.

A deep model for automatic recognition of echocardiographic standard views based on deep convolutional neural network was proposed, and the effectiveness of the deep model was analyzed by visualize class activation maps. In order to overcome the shortcomings of the fully connected layer occupying most of the parameters of the model, the spatial pyramid mean pool was used to replace the fully connected layer, and more spatial structure information was obtained. The model parameters and the over-fitting risk were reduced.The attention mechanism was introduced into the model visualization process by the class significance region. The robustness and effectiveness of the deep convolution neural network model were explained by the case recognizing echocardiographic standard views. Visualization analysis on echocardiography show that the decision basis made by the improved depth model is consistent with the standard view classification by the sonographer which indicates the validity and practicability of the proposed method.

To solve the multi-label classification problem that a target belongs to multiple classes, a new multi-label classification algorithm based on floating threshold classifiers combination was proposed. Firstly, the theory and error estimation of the AdaBoost algorithm with floating threshold (AdaBoost.FT) were analyzed and discussed, and it was proved that AdaBoost.FT algorithm could overcome the defect of unstabitily when the fixed segmentation threshold classifier was used to classify the points near classifying boundary, the classification accuracy of single-label classification algorithm was improved. And then, the Binary Relevance (BR) method was introduced to apply AdaBoost.FT algorithm into multi-label classification problem, and the multi-label classification algorithm based on floating threshold classifiers combination was presented, namely multi-label AdaBoost.FT. The experimental results show that the average precision of multi-label AdaBoost. FT outperforms the other three multi-label algorithms, AdaBoost.MH (multiclass, multi-label version of AdaBoost based on Hamming loss), ML-kNN (Multi-Label k-Nearest Neighbor), RankSVM (Ranking Support Vector Machine) about 4%, 8%, 11% respectively in Emotions dataset, and is just little worse than RankSVM about 3%, 1% respectively in Scene and Yeast datasets. The experimental analyses show that multi-label AdaBoost. FT can obtain the better classification results in the datasets which have small number of labels or whose different labels are irrelevant.

It is called labels matching problem when two labels of an instance come from two labelsets respectively in multi-label classification, however there is no any specific algorithm for solving such problem. Although the labels matching problem could be solved by tranditional multi-label classification algorithms, but this problem has its own particularity. After analyzing the labels matching problem, a new labels matching algorithm based on pairwise labelsets was proposed using adaptive method, which considered the real Adaptive Boosting (real AdaBoost) and the global optimization idea. This algorithm could learn the rule of labels matching well and complete matching. The experimental results show that, compared with the traditional algorithms, the new algorithm can not only reduce searching scope of the labels space, but also decrease the minimum learning error as the number of weak classifiers increases, and make the classification more accurate and faster.