检索结果-内蒙古大学图书馆

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Liu Weiyu Zhang Jiasheng Si Zhao Li Xueping Peng Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Evay Info Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Electronic health records (EHRs) play a crucial role in the development of personalized treatment plans for patients. However, EHRs are often highly incomplete, posing significant challenges for predictive modeling. While existing deep learning models employ various imputation techniques to reconstruct missing values, they often fail to represent missing data in a personalized manner and do not learn from the missingness patterns in EHRs data. This limitation reduces their effectiveness in practical personalized healthcare applications. To address this issue, we propose SPIME, a self-supervised model that generates personalized imputations in EHRs data for patient health status prediction. We introduce a personalized missing mask (PMM) based on the frequency of feature measurements. Additionally, we incorporate a masked imputation task (MIT) loss that minimizes the loss of artificially introduced missing values, thereby enhancing the model’s capability to handle missing data. SPIME adopts self-supervised pretraining to learn representations from personalized missing patterns and reconstructs missing data in the latent space. To further enhance representation learning, two independent attention mechanisms are applied separately across the feature and temporal dimensions. Experimental results on two real-world EHRs datasets show that SPIME outperforms existing state-of-the-art methods in predicting in-hospital mortality and decompensation, demonstrating its effectiveness in reconstructing missing data and predicting patient health status. The code will be published at https://***/cling6666/SPIME.

关键词： Training Representation learning Biological system modeling Mortality Predictive models Biomedical measurement Imputation Data models Frequency measurement Electronic medical records

来源：评论

学校读者我要写书评

暂无评论

StarMFND: Star Topology Multi-Domain Fake News Detection

StarMFND: Star Topology Multi-Domain Fake News Detection

引用

Data Science and Information technology (DSIT), International Conference on

作者： Pengfei Gu Yibing Chen Wanlong Bing Yahui Liu Hongliang Ma Wei Wang School of Information Science and Technology Shihezi University Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China School of Computer Science and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection.

关键词： Economics Social networking (online) Computational modeling Stars Feature extraction Data models Topology Internet Fake news Information technology

来源：评论

学校读者我要写书评

暂无评论

MMSFD: Multi-grained and Multi-modal Fusion for Short Video Fake News Detection

MMSFD: Multi-grained and Multi-modal Fusion for Short Video ...

引用

Data Science and Information technology (DSIT), International Conference on

作者： Shuai Ren Yahui Liu Yaping Zhu Wanlong Bing Hongliang Ma Wei Wang School of Information Science and Technology Shihezi University Shihezi China Key Laboratory of Computing Intelligence and Network Information Security Xinjiang Production and Construction Corps Shihezi China School of Computer Science and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Short video platforms have become an essential part of people’s daily lives, while also facing the challenge of the rapid spread of fake news. Therefore, the detection of fake news in short videos has attracted increasing attention. Existing works mostly focus on the integration of textual and visual content, but fail to fully exploit multi-modal information at both fine-grained and coarse-grained levels. Moreover, multi-modal fake news detection is vulnerable to modality ambiguity due to potential conflicts or weak correlations between different modalities. In this paper, we propose a Multi-grained and Multi-modal fusion for Short video Fake news Detection (MMSFD). We encode textual and visual content using pre-trained models at both fine-grained and coarse-grained levels and fuse them through distinct branches. To tackle the ambiguity issue, we design a multi-modal fusion branch based on similarity weighting, as well as separate single-modal branches. Additionally, this paper presents a cross-modal attention mechanism for adaptively aggregating and reweighting features. The experimental results on a real-world short video dataset demonstrate that the proposed method outperforms state-of-the-art short video fake news detection methods.

关键词： Visualization Adaptation models Attention mechanisms Quantization (signal) Computational modeling Feature extraction Rendering (computer graphics) Encoding Real-time systems Fake news

来源：评论

学校读者我要写书评

暂无评论

Wishart Deeplab network for Polarimetric SAR Image Classification 3rd

Wishart Deeplab Network for Polarimetric SAR Image Classific...

引用

3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Shi, Junfei He, Tiansheng Jin, Haiyan Wang, Hua Xu, Weinan State Key Laboratory of Geo-Information Engineering Shaanxi Xi’an710048 China School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9789819903009

Polarimetric Synthetic Aperture Radar (PolSAR) images have attracted much attention with abundant polarimetric scattering information. In recent years, many deep learning models have been proposed and highly expanded to PolSAR image classification. However, how to learn the complex matrix information is an indispensable problem. To address this problem, a Wishart Deeplab network is developed, which can not only learn the polarimetric matrix structure by designing Wishart network level but also learn multi-scale polarimetric scattering features by incorporating dilated convolution. Specifically, the Wishart-Deeplab based PolSAR classification model is constructed by designing the Wishart convolution operation to learn the statistical information of PolSAR data. Then, a deeplabV3 + network is followed to obtain the multi-scale semantic features by dilated convolution. By this way, statistical distrubtion-based and high-level semantic features are adaptively learned to improve the performance of the network. The experiments are conducted on two sets of real PolSAR data and results show that this method can obtain better classification results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

The Initial Screening of Laryngeal Tumors via Voice Acoustic Analysis Based on Siamese network Under Small Samples

引用

Journal of Voice 2025年

作者： You, Zhenzhen Sun, Delong Shi, Zhenghao Du, Shuangli Hei, Xinhong Kong, Demin Du, Xiaoying Yan, Jing Ren, Xiaoyong Hou, Jin Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China Department of Otolaryngology Second Affiliated Hospital of Medical College Xi'an Jiaotong University Xi'an China

Objective: The initial screening of laryngeal tumors via voice acoustic analysis is based on the clinician's experience that is subjective. This article introduces a Siamese network with an auxiliary gender classifier for automated, accurate, and objective initial screening of laryngeal tumors based on voice signals. Methods: The study involved 71 tumor patients and 293 non-tumor subjects of Chinese Mandarin. This dataset was divided into a training set and a test set in a ratio of 4:1. We applied nine data augmentation techniques to enlarge the voice training set and extracted the corresponding mel-frequency cepstral coefficients (MFCC) maps. The MFCC maps were randomly paired and fed into the proposed Siamese network to achieve multitask classification for tumor and non-tumor, woman and man. The performance of the proposed model was compared with one machine learning method and six classical deep learning models with and without the auxiliary gender classifier. Results: Experiments demonstrate the superiority of the proposed network compared with the reference models. The proposed model achieved an overall accuracy of 0.9437, an F score of 0.8462, a precision of 0.9167, a sensitivity of 0.7857, and a specificity of 0.9825. Conclusion: The proposed network can assist in the initial screening of laryngeal tumors through voice acoustic analysis. The initial screening solely through voice acoustic analysis can help individuals seek medical assistance outside the hospitals and reduce the burden on doctors as well. © 2025

关键词： Initial screening of laryngeal tumors—Voice acoustic analysis—Mel-frequency cepstral coefficients—Siamese network

来源：评论

学校读者我要写书评

暂无评论

Adaptive ELM-Based Time-Constrained Control of Uncertain Robotic Manipulators With Minimum Learning Computation

引用

IEEE Transactions on Emerging Topics in computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

来源：评论

学校读者我要写书评

暂无评论

Enhanced YOLOv7 for Lightweight Drone Object Detection

Enhanced YOLOv7 for Lightweight Drone Object Detection

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Chenchen Sui Jiancheng Zhang Liuhui Jin Quanli Lu Jiyang Chen Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Zhengzhong Information Technology Co. LTD. Jinan Shandong China Institute of Marine Science and Technology Shandong University Qingdao China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

A lightweight object detection algorithm based on YOLOv7 is proposed, optimized for deployment on resource-constrained drone platforms. key improvements include optimizing the network structure, adjusting depth and channel dimensions, and integrating the Bi-directional Feature Pyramid network (BiFPN) to enhance multi-scale feature fusion for improved detection accuracy and robustness. A 2-Level Haar Wavelet Transform (2-level HWT) replaces traditional strided convolutions, efficiently preserving spatial information for better feature extraction. Additionally, a novel Dual Cross Stage Partial Fusion (DCSPF) module is introduced to improve feature reuse and representation capacity by facilitating information flow across stages. The detection head is further enhanced with the Group Batch Normalization Reweighting Unit (GNRU) and the Adaptive Grouped Convolution Unit (AGCU) to dynamically adjust feature importance. Experimental results on the Vis-Drone2019 dataset show a 2.6% increase in mAP@50 and a 6.1% reduction in GFLOPs, validating the proposed approach's balance between accuracy and computational efficiency, suitable for drones with limited resources.

关键词： Wavelet transforms Accuracy Computational modeling Object detection Feature extraction Robustness Real-time systems Standards Optimization Drones

来源：评论

学校读者我要写书评

暂无评论

An Multi-feature Fusion Object Detection System for Mobile IoT Devices and Edge computing 3rd

An Multi-feature Fusion Object Detection System for Mobile I...

引用

3rd International Conference on security and Privacy in New computing Environments, SPNCE 2020

作者： Feng, Xingyu Cao, Han Sun, Qindong School of Computer Science and Engineering Xi’an University of Technology Xi’an Shaanxi710048 China Shaanxi Key Laboratory of Network Computing and Security Xi’an University of Technology Xi’an Shaanxi710048 China

ISBN: (纸本)9783030669218

With the increase of data scale and computing power, deep learning algorithm has made a prominent breakthrough in computer vision and other complex problems. However, its high complexity and large memory requirements make it very difficult to run in real time on the Internet of things terminal mobile devices. There is still delay the employing of cloud services cannot meet the real-time requirement. With the popularity of mobile terminal devices and the development of Internet of things, it is of great significance to design a real-time deep learning algorithm on IOT edge mobile devices with limited computing and memory resources. This paper proposes a new object detection method based on the current state-of-the-art object detection deep network model RetinaNet and traditional feature extraction method SIFT. RetinaNet is a one-stage detector with excellent detection speed and accuracy. We use RetinaNet as the object location method, then extract the CNN features and SIFT features of the fixed position image and combine them to train a new classifier. The object classification result will be based on the final classifier. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Feature Selection Method Based on FSA-Choquet Fuzzy Integral

Feature Selection Method Based on FSA-Choquet Fuzzy Integral

引用

Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International Conference on

作者： Zhangyu Xu Huaijun Wang Ruijie Wang Junhuai Li Xunchao Shang School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

With the instant development and wide application of sensor technology, human behavior recognition based on wearable sensors has attracted wide attention and become a hot research topic. The typical Choquet fuzzy integral feature selection method considers the interaction between classes, but the preferred features are not tested in the feature selection process. The selected feature subset is not suitable for different classifiers and different classification actions, and there are redundant features. Consequently, this paper proposes an adaptive feature selection method named Feature Selection based on the Adaptive Choquet fuzzy integral (FSA-Choquet). This proposed method can get the optimal subset by two times of feature selections. First, Choquet fuzzy integral is applied to optimize the current optimal feature subset. Then the feature selection is processed by combining the maximum redundancy calculation with the backward floating search strategy and classifier. At last, Experiments show that the feature based on FSA-Choquet selection has a higher classification recognition rate in behavior recognition. Under the comparison based on SVM classifier, the recognition accuracy of the features selected by FSA-Choquet is as high as 89.6 %, and the recognition rate of the features selected by Choquet is 77.36 %. Besides, Under the classifier based on Naive Bayes (NB), the classification recognition rate of FSA-Choquet is 91.67 %, while that of Choquet is only 82.3 %.

关键词： Support vector machines Redundancy Supply chains Diversity reception Big Data Feature extraction Search problems

来源：评论

学校读者我要写书评

暂无评论

Precise and Efficient:A Hybrid Analysis-Based Stack Overflow Detection Model under MIPS

Precise and Efficient:A Hybrid Analysis-Based Stack Overflow...

引用

Smart World Congress (SWC), IEEE

作者： Yichuan Wang Chenhao Huang Tong Zhang Li Zhao Zhendong Gu Gangyang Li Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Hanjiang.to.Weihan River Valley Water Diversion Project Construction.co.ltd Xi’an China

Although the vulnerability detection techniques under the MIPS platform can detect stack overflow vulnerabilities, the stack overflow vulnerability of the stack frame changes is complex and hard to recover. The input generated by fuzzy testing techniques is inefficient and difficult to recover the site after generating crashes. It is a challenge to preserve the context of runtime stack and establish effective constraints under hybrid analysis techniques, which usually makes the solution path more run inefficient and affects the analysis results in accuracy. This paper proposes a method that combines hybrid analysis including taint analysis to detect the vulnerabilities of stack overflow. The method ensures accuracy while reducing the number of execution paths. The three vulnerabilities of stack overflow in the routed firmware selected for testing are successfully detected and the number of detected paths is controlled to about 10. In addition, the method is designed to decouple the vulnerability location and detection modules, providing a basis for subsequent research on a common vulnerability detection platform.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：