Aiming at the poor effect of general new word discovery method on the recognition of patent long words, the low flexibility of part of speech collocation template of patent terminology, and the lack of unsupervised methods for Chinese patent long word recognition, a novel bidirectional aggregation degree feature extraction method for patent new word discovery was proposed.Firstly, a bidirectional conditional probability was introduced on the statistical information between the first and last words on a double word term. Secondly, a word boundary filtering rule was extendedly introduced by using the above feature. Finally, new patent words were able to be extracted by combining the above aggregation degree feature and word boundary filtering rule. Experimental analysis show that, the new method improves the overall F-score by 6.7 percentage points compared with the new word discovery method in the general field, improves the overall F-score by 19.2 and 17.2 percentage points respectively compared with two latest patent terminology collocation template methods, and significantly increase the F-score for the discovery of new words with 4 to 8 characters. In summary, the proposed method greatly improves the performance of patent new word discovery, and can extract high compound long words in patent documents more effectively, while reducing the reliance on pre-training processes and extra complex rule base, with better practicality.

Ultrasound image segmentation of left ventricle is very important for doctors in clinical practice. As the ultrasound images contain a lot of noise and the contour features are not obvious, current Convolutional Neural Network (CNN) method is easy to obtain unnecessary regions in left ventricular segmentation, and the segmentation regions are incomplete. In order to solve these problems, keypoint location and image convex hull method were used to optimize segmentation results based on Fully Convolutional neural Network (FCN). Firstly, FCN was used to obtain preliminary segmentation results. Then, in order to remove erroneous regions in segmentation results, a CNN was proposed to locate three keypoints of left ventricle, by which erroneous regions were filtered out. Finally, in order to ensure that the remained area were able to be a complete ventricle, image convex hull algorithm was used to merge all the effective areas together. The experimental results show that the proposed method can greatly improve left ventricular segmentation results of ultrasound images based on FCN. Under the evaluation standard, the accuracy of results obtained by this method can be increased by nearly 15% compared with traditional CNN method.

Since it was not ideal for Extreme Learning Machine (ELM) to deal with non-linear data, and the parameter randomization of ELM was not conducive for generalizing the model, an improved version of ELM algorithm was proposed. The parameters of ELM were optimized by Adaptive Chaotic Particle Swarm Optimization (ACPSO) algorithm to increase the stability of the algorithm and improve the accuracy of ELM for gene expression data classification. The simulation experiments were carried out on the UCI gene data. The results show that Adaptive Chaotic Particle Swarm Optimization-Extreme Learning Machine (ACPSO-ELM) has good stability and reliability, and effectively improves the accuracy of gene classification over existing algorithms, such as Detecting Particle Swarm Optimization-Extreme Learning Machine (DPSO-ELM) and Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM).