Aiming at the problems of the corresponding features between different modals easy to be fused and mislocated， the subjective empirical parameter adjustment of recognition model experts， and the high computational cost， a self-optimized dual-modal （“contrast enhanced T1 weighting” and “high resolution enhanced T2 weighting”） multi-channel non-deep vestibular schwannoma recognition model was proposed. Firstly， a vestibular schwannoma recognition model was constructed to further explore the multi-modal image features of vestibular schwannoma and the complex nonlinear complementary information among the modals. Then， a model optimization strategy with global parallel sparrow search algorithm based on game theory was designed to realize the adaptive optimization of key hyperparameters of the model， so that the model had a better recognition effect. Experimental results show that compared with the deep learning-based model， the proposed model reduces the number of parameters by 27.9% with an improvement of 4.19 percentage points in recognition accuracy， which verifies the effectiveness and adaptability of the proposed model.

To further improve the efficiency of hyperparameter multi-objective adaptive optimization of deep classification models， a Filter Enhanced Dropout Agent （FEDA） model was proposed. Firstly， a dual-channel Dropout neural network with enhanced point-to-point mutual information constraint was constructed， to enhance the fitting of high-dimensional hyperparameter deep classification model， and the selection of candidate solution sets was accelerated by combining the aggregation solution selection strategy. Secondly， an FEDA model-A novel preference-based dominance Relation for Multi-Objective Evolutionary Algorithm （FEDA-ARMOEA） combined with model management strategy was designed to balance the convergence and diversity of population individuals， and to assist FEDA in improving the efficiency of deep classification model training and hyperparameter self optimization. Comparative experiments were conducted between FEDA-ARMOEA， EDN-ARMOEA （Efficient Dropout neural Network-assisted AR-MOEA）， HeE-MOEA （Heterogeneous Ensemble-based infill criterion for Multi-Objective Evolutionary Algorithm）， and other algorithms. Experimental results show that FEDA-ARMOEA performs well on 41 sets in all 56 sets of testing problems. Experiments on industrial application weld data set MTF and public data set CIFAR-10 show that the accuracy of FEDA-ARMOEA optimized classification model is 96.16% and 93.79%， respectively， and the training time is decreased by 6.94%-47.04% and 4.44%-39.07% compared with the contrast algorithms， respectively. All of them are superior to those of the contrast algorithms， which verifies the effectiveness and generalization of the proposed algorithm.

An important strategy for lightweighting a 3D model is to use the mesh simplification algorithm to reduce the number of triangular meshes on the model surface. The widely used edge collapse algorithm is more efficient and has better simplification effect than other mesh simplification algorithms， but some detailed geometric features may be damaged or lost during the simplification process of this algorithm. Therefore， the approximate curvature of curve and the average area of the first-order neighborhood triangle of the edge to be collapsed were added as penalty factors to optimize the edge collapse cost of the original algorithm. First， according to the definition of curve curvature in geometry， the calculation formula of the approximate curvature of curve was proposed. Then， in the calculation process of vertex normal vector， two stages - area weighting and interior angle weighting were used to modify the initial normal vector， thereby considering more abundant geometric information of the model. The performance of the optimized algorithm was verified by experiments. Compared with the classical Quadratic Error Metric （QEM） algorithm and the mesh simplification algorithm considering the angle error， the optimized algorithm has the maximum error reduced by 73.96% and 49.77% at least and respectively. Compared with the QEM algorithm， the optimized algorithm has the Hausdorff distance reduced by 17.69% at least. It can be seen that in the process of model lightweighting， the optimized algorithm can reduce the deformation of the model and better maintain its own detailed geometric features.

Traditional stock prediction methods are mostly based on time-series models， which ignore the complex relations among stocks， and the relations often exceed pairwise connections， such as stocks in the same industry or multiple stocks held by the same fund. To solve this problem， a stock trend prediction method based on temporal HyperGraph Convolutional neural Network （HGCN） was proposed， and a hypergraph model based on financial investment facts was constructed to fit multiple relations among stocks. The model was composed of two major components： Gated Recurrent Unit （GRU） network and HGCN. GRU network was used for performing time-series modeling on historical data to capture long-term dependencies. HGCN was used to model high-order relations among stocks to learn intrinsic relation attributes， and introduce the multiple relation information among stocks into traditional time-series modeling for end-to-end trend prediction. Experiments on real dataset of China A-share market show that compared with existing stock prediction methods， the proposed model improves prediction performance， e.g. compared with the GRU network， the proposed model achieves the relative increases in ACC and F1_score of 9.74% and 8.13%， respectively， and is more stable. In addition， the simulation back-testing results show that the trading strategy based on the proposed model is more profitable， with an annual return of 11.30%， which is 5 percentage points higher than that of Long Short-Term Memory （LSTM） network.

With the rapid development of the Internet of Things (IoT), security of constrained devices suffer a serious challenge. LightWeight Cryptography (LWC) as the main security measure of constrained devices is getting more and more attention of researchers. The recent advance in issues of lightweight cryptography such as design strategy, security and performance were reviewed. Firstly, design strategies and the key issues during the design were elaborated, and many aspects such as principle and implementation mechanisms of some typical and common lightweight cryptography were analyzed and discussed. Then not only the commonly used cryptanalysis methods were summarized but also the threat of side channel attacks and the issues should be noted when adding resistant mechanism were emphasized. Furthermore, detailed comparison and analysis of the existing lightweight cryptography from the perspective of the important indicators of the performance of lightweight cryptography were made, and the suitable environments of hardware-oriented and software-oriented lightweight cryptography were given. Finally, some unresolved difficult issues in the current and possible development direction in the future of lightweight cryptography research were pointed out. Considering characteristics of lightweight cryptography and its application environment, comprehensive assessment of security and performance will be the issues which worth depth researching in the future.

Strong-loop, weak-loop, jump and invisible tasks are ubiquitous between tasks in the business process, and mining them from the event logs is a problem to be solved. The features of strong-loop, weak-loop, jump and invisible tasks were analyzed, and the determinant theorems were offered, on the basis of which the γ+ algorithm was proposed with synchronized-manager models. The γ+algorithm overcomes the limitation of the converntional mining algorithms, executes the mining of management actions along with the mining of process structure，and manages to solve problems such as strong-loop, weak-loop, jump and invisible tasks. Experimental results indicate the effectiveness and applicability.

Non-certain choice and parallel is a ubiquitous relation between tasks in the business process. Modeling this kind of relation by traditional WF_Net based method will cause duplicated tasks in the model, which brings difficulties to the process mining. Based on event logs, in this paper the non-certain choice and parallel relation was defined, and meanwhile co-operating with synchronized-manager a theorem and the γ algorithm that can determine this relation was proposed. The γ algorithm overcame the limits of current mining algorithm, and it could mine management operation actions while mining the process structure, which greatly improved the applicatory of process mining. The experimental results show that this γ algorithm is effective.

Word embedding models can map words to low-dimensional vector space for analyzing word semantics， which provides an effective way for computer understanding and text processing. Traditional Chinese word embedding models learn semantic information through the internal compositional information of Chinese words， however， for the utilization degree of Chinese characters and information of their different levels of components， different models have insufficient or excessive utilization problems. Thus， in order to utilize the information of different levels of components of Chinese characters better to generate high-quality word embeddings， a Multilevel-component Joint Chinese Word Embedding （MJWE） model was proposed to integrate the characteristics of words， Chinese characters， and multilevel-components， combine word embeddings with positional information， and construct multilevel-component embeddings composed of radicals and finer-grained components to capture the internal composition information of Chinese words more comprehensively. Meanwhile， a non-compositional word list was constructed to prevent the over utilization of the internal information of Chinese words. Experimental results show that MJWE model has the accuracy improved by 2.11% compared to JWE （Joint learning Word Embeddings） model on the word similarity task “WS-295”， by 2.52% compared to Skip-Gram （SG） model on the word analogy task “state”， by 6.58% compared to CBOW （Continuous Bag Of Words） model on the word analogy task “family”， by 0.71% compared to JWE model on the emotion classification task （two classes）， and by 8.60% compared to SG model on the emotion classification task （seven classes）. Meanwhile， MJWE model was applied to analyse literature on traditional Chinese medicine， for core drug identification in traditional Chinese medicine formulae， and MJWE model was able to identify the core drugs for treating different symptoms of chronic glomerulonephritis. It can be seen that MJWE can generate Chinese word embeddings with good quality， and combined with community detection algorithm， it can identify core drugs for treating different syndromes of chronic glomerulonephritis， which is conducive to assisting traditional Chinese medicine doctors in clinical decision-making.