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Natural Language processing (ICNLP), International Conference on

作者： Fangyuan Du Hao Chen Kunjun Chen Xuelian Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

To address the challenge of predicting survival rates for patients with Glioblastoma (GBM) after radiation therapy, we propose a two-stage integrated learning method that is based on Stacking and multi-objective differential evolution algorithm. Our aim is to achieve a balance between accuracy and interpretability. We have created a framework for survival prediction using radiomics features of gliomas that are imaged in subregions. Our method constructs a model for predicting GBM survival and visualizes decision rules that are easy to interpret. The method involves two primary steps: 1) Various combinations of MRI sequences and glioma subregions were evaluated to determine the optimal sequence, and the GBM long and short survival classification task in the first stage of the task was achieved by compressing features using spectral clustering. 2) An evolutionary integrated learning method based on Stacking was proposed. It uses a Random Forest classifier as the basic learning module of the integrated model and relies on the multi-objective optimization algorithm NSGA-II to continuously iterate with the aim of maximizing the regression accuracy while minimizing the model's integration complexity. We used 119 GBM patients in the BraTS2020 dataset as our study data. Based on the five-fold cross-validation, the survival classification Accuracy and Recall of our long- and short-term classification algorithm for GBM patients were 0.981 and 0.943, respectively; and the Mean-Square Error (MSE) of the survival prediction of GBM patients with the stack-based evolutionary integration method was 55713.08. The experimental results showed that our proposed method for GBM survival days prediction is significantly better than existing Machine Learning (ML) methods. This finding enables us to stratify patients more effectively and provides a more reliable basis for achieving personalized treatment for GBM patients.

关键词： Learning systems Accuracy Protocols Stacking Semantics Classification algorithms Radiation therapy Reliability Random forests Radiomics

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Causal Logic Discovery and Rule Validation Using Evolutionary Integration Model Global Fit U-Net

Causal Logic Discovery and Rule Validation Using Evolutionar...

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Natural Language processing (ICNLP), International Conference on

作者： Xuelian Yang Fangyuan Du Kunjun Chen Hao Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to explore the correlation between different MRI sequences and the results of U-Net segmentation of glioma subregions, this paper proposes an interpretable method based on an evolutionary integration algorithm for logical discovery of the segmentation process of U-Net. Our approach consists of three steps: 1) Global fitting of the U-Net model to the segmentation results of gliomas using a dual evolutionary algorithm to generate a fitted model with both accuracy and interpretability.2) Extracting decision rules from the fitted model according to a specific target interpretable region and generating a complete set of interpretable rules after optimisation.3) Proposing a decision path integrator modeling method for the target region of decision paths for experimental validation. In this study, 293 patients from the BraTS2020 dataset are used as research data, and the accuracy of the fitted model is obtained to be 0.92, which is basically the same as that of Random Forest, but the model in this study has a better and simpler internal structure. At the same time, this study validated the relationship between Flair sequence and the edema region of glioma, and the experimental results showed that our extracted decision paths have a certain auxiliary effect on the segmentation of U-Net, and also proved the effectiveness of our proposed interpretability method.

关键词： Accuracy Correlation Magnetic resonance imaging Fitting Evolutionary computation data models Natural language processing Logic Random forests

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3D Hand Pose Estimation Based on External Attention

3D Hand Pose Estimation Based on External Attention

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Natural Language processing (ICNLP), International Conference on

作者： Shoukun Li Xiaoying Pan Beibei Wang Jialong Gao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

3D hand pose for a single depth image is an important topic in computer vision and human-computer interaction, and although significant progress has been made in this field in recent years, there is still room for improvement in accuracy for some specific application scenarios. To address this problem, a 3D hand pose estimation algorithm based on external attention is proposed. First, the target features are extracted by an hourglass network; then, a HEM (Hard Example Mining) loss based on a mean-variance loss function is proposed, which firstly calculates the L2 loss values of all N keypoints, and then sorts these loss values, and back-propagates the gradient only to the first m loss values. Meanwhile, external attention is introduced to enhance the ability to perceive the global information of the target, and the recognition ability of the features is improved by giving the features different influences through the attention weights. Experimental results show that the algorithm achieves an average distance error of 5.42 mm on the ICVL dataset and 7.11 mm on the MSRA dataset, which further improves the detection performance of the 3D hand pose estimation algorithm.

关键词： Human computer interaction Computer vision Three-dimensional displays Accuracy Target recognition Pose estimation Feature extraction Prediction algorithms Natural language processing

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TSARNet :Integrated spatio-temporal attention mechanism for multivariate time series prediction deep learning framework

TSARNet :Integrated spatio-temporal attention mechanism for ...

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Natural Language processing (ICNLP), International Conference on

作者： Xiaoying Pan Hao Wang Jia Sun Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Multivariate time series forecasting is particularly critical in a number of areas involving air forecasting, electricity consumption and exchange rate transformations. The time series data in these fields often contain present multivariate nature, fusing long and short-term patterns, and traditional forecasting methods are often difficult to work. To address this challenge, this paper proposes a new deep learning framework, TSARNet, which combines gated recurrent units (GRUs) and an adaptive scale processing layer to effectively capture short-term localized dependency patterns among multivariate variables and reveal long-term trends through a spatio-temporal attention mechanism. In addition, an autoregressive model is incorporated to enhance the network's sensitivity to time series scale changes. Comprehensive experiments on four publicly available datasets show that the proposed TSARNet algorithm outperforms other comparative methods in most cases in terms of mean absolute error (MAE) and root mean square error (RMSE) in the long term prediction of different datasets. The TSARNet method also achieves optimal results for doing long-term prediction for each dimension of the power transformer temperature dataset.

关键词： Deep learning Attention mechanisms Accuracy Sensitivity Time series analysis Predictive models Market research Prediction algorithms Forecasting Root mean square

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Trust-based distributed kalman filter estimation fusion under malicious cyber attacks 21

Trust-based distributed kalman filter estimation fusion unde...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Chen, Yanping Ma, Long Xia, Hong Gao, Cong Wang, Zhongmin Yu, Zhong School of Computer Science AND Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Shananxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an710121 China School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781728120584

We consider distributed Kalman filter for dynamic state estimation over wireless sensor networks. It is promising but challenging when network is under cyber attacks. Since the information exchange between nodes, the malicious attacks quickly spread across the entire network, which causing large measurement errors and even to the collapse of sensor networks. Aiming at the malicious network attack, a trust-based distributed processing frame is proposed. Which allows neighbor nodes to exchange information, and a series of trusted nodes are found using truth discovery. As a demonstration, distributed Cooperative Localization is considered, and numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks. © 2019 IEEE.

关键词： Kalman filters

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Msfe-Panet: Improved Yolov4-Based Small Object Detection Method in Complex Scenes

SSRN

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SSRN 2022年

作者： Pan, Xiaoying Jia, Ningxin Mu, Yuanzhen Bai, Weidong School of Computer Science and Technology Xi’an University of Posts and Telecommunications GuoDu Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China

Purpose With the rapid development of computer vision and artificial intelligence technology, visual object detection has made unprecedented progress, and small object detection in complex scenes has attracted more and more increasing *** of the limited feature information of small objects, the resolution is low. Therefore, in the actual complex scene, the effect of detecting small objects is not ideal. Methods To solve the problems of ambiguity, overlap and occlusion in small object detection in complex scenes. In this paper, a multiscale fusion feature enhanced path aggregation network MSFEPANet is proposed. By adding an attention mechanism and feature fusion, the fusion of strong positioning information of the deep feature map and strong semantic information of the shallow feature map is enhanced, which helps the network find interesting areas in complex scenes and improves its sensitivity to small objects. The rejection loss function and network prediction scale are designed © 2022, The Authors. All rights reserved.

关键词： Object detection

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Emotional analysis Based on Dynamic Functional Brain network

Emotional Analysis Based on Dynamic Functional Brain Network

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International Conference on networking and network Applications (NaNA)

作者： Zhongmin Wang Yue Tong Xia Heng Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

The human brain dynamically changes in the second and millisecond levels, so constructing a functional brain network using static connections can result in the absence of some time-related effective features. In this paper, a dynamic network model construction method is proposed. The basic idea of the proposed dynamic emotion analysis method is to construct different network modes based on the channel correlation mode and band power distribution method, and use the sliding window method to estimate the dynamic correlation mode and dynamic power distribution changes, Then the multivariate features of network-level brain dynamics are extracted and classified. We conducted experiments on the SEED database to study network-level dynamic transformations in the process of positive, neutral, and negative emotions. The results verify the feasibility of the emotional assessment method based on dynamic functional connection, and provide a new way to establishing brain dynamic model under different emotional states. The results show that emotion analysis based on dynamic networks is more accurate than static networks.

关键词： Correlation Electroencephalography Power distribution Windows Feature extraction Time series analysis databases

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An Efficient Image Captioning Method Based on Generative Adversarial networks 4

An Efficient Image Captioning Method Based on Generative Adv...

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4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Lin, Zhenxian Feng, Feirong Yang, Xiaobao Ding, Chen School of Science Xi'an University of Posts and Telecommunications China School of Telecommunication and Information Engineering Xi'an University of Posts and Telecommunications China School of Computer Science and Technology Northwestern Polytechnical University China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

Image captioning is usually based on the mainstream encoder-decoder framework, the accuracy of the encoders and the generation capability of the decoders directly affect the quality of image captions. But no matter how the encoders and the decoders are combined, the generated captions will only imitate the ground-truth captions, and its diversity is suppressed. To generate more accurate and diverse captions while exploring the impact of different encoder-decoder frameworks for the quality of the generated captions, we conducted an exploration of an efficient image captioning method based on GAN. Specifically, we use ResNet152, EfficientNet-B0, and EfficientNet-B2 as the encoders to extract image features, GRU or Transformer as the decoders to generate captions, respectively. Based on MS COCO2014 dataset, a large number of experiment results show that the EfficientNet-B2+GRU framework has best performance, training time is reduced, BLUE-4 score increases by 0.6, parameters reduce by 0.66 million, which can be used as an efficient encoder-decoder framework for generating diverse captions. © 2021 ACM.

关键词： Generative adversarial networks

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A Hybrid Prediction Method Based on MODWT-EMD for Time Series in Wireless Sensor networks

A Hybrid Prediction Method Based on MODWT-EMD for Time Serie...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Zhongmin Wang Jingwen Jia Cong Gao Yanping Chen Hong Xia Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an University of Posts and Telecommunications Xi’an China School of Computer Science & Technology Xi’an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (纸本)9781665405546

Time series is the main form of sensor data. Time series prediction is conducive to reduce the energy consumption of sensor nodes and increase the service life. As time cost and computation cost of the existing hybrid prediction methods for time series are not good, this paper proposes a hybrid prediction method based on MODWT-EMD for time series. Firstly, the original time series is decomposed into multiple frequency components by Maximal Overlapping Discrete Wavelet Transform (MODWT). Secondly, the multiple frequency components are decomposed successively by Empirical Mode Decomposition (EMD) to obtain IMF components and residuals. IMFs and residuals contain the feature information of time series. Then, the importance of the extracted feature information is scored and sorted by random forest. We select feature information with a higher score. Finally, the selected feature information is input into Bi-GRU for training. We predicted the original time series by the trained Bi-GRU and obtained the prediction results. The experimental results show that the method proposed in this paper can predict the time series effectively.

关键词： Training Wireless communication Wireless sensor networks Time-frequency analysis Costs Time series analysis Predictive models

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Probabilistic Adaptive Genetic Algorithm for Pre-treatment Radiomics Features Predict Patient Survival

Probabilistic Adaptive Genetic Algorithm for Pre-treatment R...

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2018 International Conference on Computer, Communications and Mechatronics Engineering（CCME 2018）

作者： Xiao-ying PAN Ting ZHANG Qing-ping YANG Liu CHEN School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications

In order to provide personalized treatment for patients with tongue carcinoma, a probabilistic adaptive genetic algorithm neural network(PAGA-BP) model is proposed in this paper. The PAGA-BP model ameliorates selection sorting operator, adaptive crossover operator and u-adaptive mutation operator to optimize the initial weight of BP neural network. By comparing with traditional GA-BP neural network and BP neural network survival prediction model, the results show that PAGA-BP prediction model has the highest approximation accuracy and better survival period prediction.

关键词： PGA-BP neural network Radiomics features Survival Prediction

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