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18th IEEE International Conference on Pervasive Intelligent and Computing (PICom)

作者： Xiao, Da Jiang, Meiyi Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China

ISBN: (纸本)9781728166094

Spam and phishing emails are very troublesome problems for mailbox users. Many enterprises, departments and individuals are harmed by them. Moreover, the senders of these malicious emails are in a hidden position and occupy an initiative position. The existing mailbox services can only filter and shield some malicious mails, which is difficult to reverse the disadvantage of users. To solve these problems, we propose a secure mail system using k-nearest neighbor(KNN) algorithm and improved long short-term memory(lstm) algorithm(Bilstm-Attention algorithm). KNN classifier can effectively distinguish normal emails, spam and phishing emails, and has a high accuracy. Bi-lstm-Attention classifier classifies phishing emails according to the similarity of the malicious mail text from the same attacker to some extent. By classifying and identifying the source of malicious emails, we can grasp the characteristics of the attacker, provide materials for further research, and improve the passive status of users. Experiments show that the classification results of attack sources reach 90%, which indicate the value of further research and promotion.

关键词： phishing mails KNN improved lstm algorithm classifier

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Research on Main Steam Temperature Prediction Model Based on improved lstm algorithm

Research on Main Steam Temperature Prediction Model Based on...

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作者： Ling Zheng Xin Ye Fei Chen Department of Computer Science and Technology North China Electric Power University Faculty of Control and Computer Engineering

In order to improve the reliability and accuracy of the main steam temperature trend prediction, a main steam temperature prediction model based on improved lstm is proposed. Firstly, uses the grey correlation analysis method to select the important influencing factors. Then, a linear structure is introduced into the lstm structure to construct a main steam temperature prediction model. Finally, based on the historical operating data of the thermal power unit, a simulation experiment is performed to compare the prediction error of the output of the RNN model, the lstm model, and the improved lstm model. The results show that the method has higher prediction accuracy for the main steam temperature. At the same time, compared with other traditional methods, this method has better fitting effect, which can be well applied in practical engineering.

关键词： Main steam temperature grey correlation analysis deep learning improved lstm algorithm regression prediction

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A nonlinear inversion method for predicting the in-situ stress field in deep coal seam based on improved long short-term memory neural network

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INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 2025年 186卷

作者： Zhou, Jiaxing Wu, Bisheng Nie, Yuanxun Zhang, Haitao Tsinghua Univ State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Tsinghua Univ Dept Hydraul Engn Beijing 100084 Peoples R China Tsinghua Univ Key Lab Hydrosphere Sci Minist Water Resources Beijing 100084 Peoples R China

Existence of discontinuous geological structures, such as folds and fault, poses a great challenge in predicting the in-situ stress fields (ISSF). This paper proposes a discontinuous intelligent inversion method to predict the ISSFs in the deep coal seam area (DCSA) of the Shanghai Temple, which exhibits distinct discontinuous geological features. The proposed method consists of three key components. First, a discontinuous loading model was developed to address the problem of accuracy in the numerical simulation of discontinuous tectonic regions such as folds and faults. The simulation data generated is used as a sample dataset for the training of the inversion algorithm and their completeness is fully guaranteed. Second, the statistical distribution patterns of horizontal, maximum and minimum lateral pressure coefficients (LPCs) of the ISSF in the typical DCSAs of China is statistically calculated. By applying Gaussian- and Cauchy-type fuzzy membership functions, the degree of influence of faults and folds on the local ISSF is quantified and the geological structure influence model is constructed. The influence value enriches the input data dimension of the algorithm and lays a more detailed data foundation for the stress inversion. Third, the improved Long Short-Term Memory (lstm) network algorithm was constructed by optimizing the network hierarchy and multi-parameter cyclic learning. An inversion analysis is carried out using the ISSF around the borehole as an example, and the relative error strictly controlled within 1 %. The improved lstm algorithm achieves an accuracy of 88.58 % at each measurement point in the Shanghai Temple deep coal seam project, which is significantly higher than that of the back propagation neural network (BPNN). The discontinuous intelligent inversion method proposed in this study can provide an effective tool for predicting the ISSF in DCSA.

关键词： In-situ stress field Discontinuous Nonlinearity improved lstm algorithm

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