Aiming at the problem of inaccurate prediction of edges and the farthest region in monocular image depth estimation， a monocular depth estimation method based on Pyramid Split attention Network （PS-Net） was proposed. Firstly， based on Boundary-induced and Scene-aggregated Network （BS-Net）， Pyramid Split Attention （PSA） module was introduced in PS-Net to process the spatial information of multi-scale features and effectively establish the long-term dependence between multi-scale channel attentions， thereby extracting the boundary with sharp change depth gradient and the farthest region. Then， the Mish function was used as the activation function in the decoder to further improve the performance of the network. Finally， training and evaluation were performed on NYUD v2 （New York University Depth dataset v2） and iBims-1 （independent Benchmark images and matched scans v1） datasets. Experimental results on iBims-1 dataset show that the proposed network reduced 1.42 percentage points compared with BS-Net in measuring Directed Depth Error （DDE）， and has the proportion of correctly predicted depth pixels reached 81.69%. The above proves that the proposed network has high accuracy in depth prediction.

High Utility Itemsets Mining （HUIM） is able to mine the items with high significance from transaction database， thus helping users to make better decisions. In view of the fact that the application of intelligent optimization algorithms can significantly improve the mining efficiency of high utility itemsets in massive data， a survey of intelligent optimization algorithm-based HUIM methods was presented. Firstly， detailed analysis and summary of the intelligent optimization algorithm-based HUIM methods were performed from three aspects： swarm intelligence optimization-based， evolution-based and other intelligent optimization algorithms-based methods. Meanwhile， the Particle Swarm Optimization （PSO）-based HUIM methods were sorted out in detail from the aspect of particle update methods， including traditional update strategy-based， sigmoid function-based， greedy-based， roulette-based and ensemble-based methods. Additionally， the swarm intelligence optimization algorithm-based HUIM methods were compared and analyzed from the perspectives of population update methods， comparison algorithms， parameter settings， advantages and disadvantages， etc. Next， the evolution-based HUIM methods were summarized and outlined in terms of both genetic and bionic aspects. Finally， the next research directions were proposed for the problems of the existing intelligent optimization algorithm-based HUIM methods.

The traditional classifiers are difficult to cope with the challenges of complex types of data streams with concept drift， and the obtained classification results are often unsatisfactory. Aiming at the methods of dealing with concept drift in different types of data streams， classification methods for complex data streams with concept drift were summarized from four aspects： imbalance， concept evolution， multi-label and noise-containing. Firstly， classification methods of four aspects were introduced and analyzed： block-based and online-based learning approaches for classifying imbalanced concept drift data streams， clustering-based and model-based learning approaches for classifying concept evolution concept drift data streams， problem transformation-based and algorithm adaptation-based learning approaches for classifying multi-label concept drift data streams and noisy concept drift data streams. Then， the experimental results and performance metrics of the mentioned concept drift complex data stream classification methods were compared and analyzed in detail. Finally， the shortcomings of the existing methods and the next research directions were given.

As a kind of Graph Convolutional Neural Network （GCNN）， Topology Optimization based Graph Convolutional Network （TOGCN） model adopts auxiliary information in the network to optimize topological structure of the network， thereby helping to reflect the relational degrees between the nodes. However， TOGCN model only focuses on the association between local nodes， and not enough on the potential global structure information. Fusing global feature information， the model will help to improve performance as well as its robustness in dealing with incomplete information. A Global structure information Enhanced-TOGCN （GE-TOGCN） model was proposed， the attributes of neighboring nodes were utilized to optimize the topological graph， and the class information was regarded as the global structure information to maintain intra-class aggregation and inter-class separation. Firstly， the center vector of each class was calculated by the labeled nodes， then some unlabeled nodes were selected to update these class center vectors. Finally， all the nodes were assigned to the corresponding class according to their similarity to class center vectors， and a semi-supervised loss function was adopted to optimize the class center vector of each class and the final representation vectors of the nodes. On Cora and Citeseer datasets， node classification task and node visualization task were performed by using the obtained node representation vectors with the loss of label information. Experimental results show that compared with Graph Convolutional Network （GCN）， Graph Learning-Convolutional Network （GLCN） and other models， GE-TOGCN has the classification accuracy increased by 1.2-12.0 percentage points on Cora dataset， and the classification accuracy increased by 0.9-9.9 percentage points on Citeseer dataset. In node visualization task， the proposed model has higher degree of intra-class node aggregation and more obvious boundaries between class clusters. In summary， the fusion of class global information can reduce the negative influence of label information loss on learning effects of the model， and the node representations obtained by the proposed model have better performance in downstream tasks.

Concerning the problem that the linear eigentransformation method cannot capture the statistical properties of the nonlinear facial image, a Data-driven Local Eigentransformation (DLE) method for face hallucination was proposed. Firstly, some samples most similar to the input image patch were searched. Secondly, a patch-based eigentransformation method was used for modeling the relationship between the Low-Resolution (LR) and High-Resolution (HR) training samples. Finally, a post-processing approach refined the hallucinated results. The experimental results show the proposed method has better visual performance as well as 1.81dB promotion over method of locality-constrained representation in objective evaluation criterion for face image especially with noise. This method can effectively hallucinate surveillant facial images.