The online banking transaction system is the application system with the most complex business, the most demanded, and frequent version updates in the software engineering application system. The existing online banking business sub-module is intelligent and faces major challenges in security. Traditional online banking systems cannot meet this capability. This article combines machine learning and online banking business module design to implement a business agent online banking system based on a new architecture. The article first proposes new features and new challenges of the online banking system, discusses the technical problems solved by the intelligent online banking system, and analyzes HERCULES Architecture business intelligent machine learning algorithm model, smart deposits and white-collar loans and other core processes, and then designed and implemented an intelligent online banking system business model, focusing on the issues of the intelligent online banking system, soft load balancing implementation and transaction security, through business implementation The effectiveness of the proposed business agent is verified. The actual use results show that the intelligent online banking of the HERCULES architecture has greatly improved the intelligence and security of the traditional online banking system. Finally, we summarize and analyze the value and innovation of the intelligent online banking system, and look forward to the shortcomings of the system.

Traditional Chinese Medicine (TCM) clinical informatization focuses on serving user-oriented health knowledge and facilitating online diagnosis. Regularities are hidden in clinical knowledge play a significant role in the improvement of the TCM informatization service. However, many regularities can hardly be discovered because of specific data-challenges in TCM prescriptions at present. Therefore, in this article, we propose an end-to-end model, called Semantic-aware Graph Convolutional Networks (SaGCN) model, to learn the latent regularities in three steps: (1) We first construct a heterogeneous graph based on prescriptions; (2) We stack Semantic-aware graph convolution to learn effective low-dimensional representations of nodes by meta-graphs and self-attention; (3) With the learned representations, we can detect regularities accurately by clustering and linked prediction. To the best of our knowledge, this is the first study to use metagraph and graph convolutional networks for modeling TCM clinical data and diagnosis prediction. Experimental results on three real datasets demonstrate SaGCN outperforms the state-of-the-art models for clinical auxiliary diagnosis and treatment.INDEX TERMS Tranditional Chinese medicine, clinical knowledge discovery, metagraph, graph convolutional networks.

Background Identification of epistatic interactions provides a systematic way for exploring associations among different single nucleotide polymorphism (SNP) and complex diseases. Although considerable progress has been made in epistasis detection, efficiently and accurately identifying epistatic interactions remains a challenge due to the intensive growth of measuring SNP combinations. Results In this work, we formulate the detection of epistatic interactions by a combinational optimization problem, and propose a novel evolutionary-based framework, called GEP-EpiSeeker, to detect epistatic interactions using Gene Expression Programming. In GEP-EpiSeeker, we propose several tailor-made chromosome rules to describe SNP combinations, and incorporate Bayesian network-based fitness evaluation into the evolution of tailor-made chromosomes to find suspected SNP combinations, and adopt the Chi-square test to identify optimal solutions from suspected SNP combinations. Moreover, to improve the convergence and accuracy of the algorithm, we design two genetic operators with multiple and adjacent mutations and an adaptive genetic manipulation method with fuzzy control to efficiently manipulate the evolution of tailor-made chromosomes. We compared GEP-EpiSeeker with state-of-the-art methods including BEAM, BOOST, AntEpiSeeker, MACOED, and EACO in terms of power, recall, precision and F1-score on the GWAS datasets of 12 DME disease models and 10 DNME disease models. Our experimental results show that GEP-EpiSeeker outperforms comparative methods. Conclusions Here we presented a novel method named GEP-EpiSeeker, based on the Gene Expression Programming algorithm, to identify epistatic interactions in Genome-wide Association Studies. The results indicate that GEP-EpiSeeker could be a promising alternative to the existing methods in epistasis detection and will provide a new way for accurately identifying epistasis.

With the continuous breakthroughs in artificial intelligence technology, it has become easier to extract general-purpose knowledge using machine learning, but it is a challenging task to extract and learn small samples of knowledge in medical expertise. On the one hand, it is difficult to represent medical expertise entities, and on the other hand, the training samples of such expertise are small, and deep learning methods often require a large number of samples to complete the learning task. To this end, we proposes a graph network learning method for specialized vocabulary representation. Specifically, a contextual knowledge representation model based on graph meta-learning is proposed, which combines text, phrase, vocabulary, and other information to solve the problem of sparse data of medical electronic medical record entities that cannot be extracted and learned. In this method, a text-independent lexical representation learning method, a context-aware graph neural network, and a combined LSTM language model are used to model information from different perspectives as a way to learn semantic representations of professional discourse entities. The experimental results show that the accuracy of the method outperforms other similar methods and proves its effectiveness.

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