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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 197-208页

作者： Yang, Jing Jiang, Liufeng Shen, Anbo Wang, Aiguo Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of Electronic and Information Engineering Foshan University Foshan528011 China

Aimedat the problem of dynamic causal discovery in the era of artificial intelligence, this article combines partial rank correlation coefficients and streaming features in the field of Bayesian network structure learning and proposes a new online streaming feature causal discovery algorithm based on partial rank correlation named the partial rank casual discovery streaming feature based algorithm. This algorithm is not only suitable for Bayesian causal structure learning in dynamic feature spaces generated by sequential streams of features but can also effectively process multivariate linear Gaussian and nonlinear non-Gaussian data. We present three main contributions. First, for arbitrarily distributed datasets, which can be generated by the additive noise model, we proved that the partial rank correlation coefficient can be used as the criterion for the conditional independence test and explored the distribution of corresponding statistics. Second, the PCSDSF algorithm redefined the relevance based on partial rank statistic prospects and then redefined conditional dependence or independence. This method can significantly reduce the number of conditional independence tests and achieves a good time performance. Finally, theoretical analysis and several experiments proved the reliability of the algorithm. A simulation showed that on average, the PCSDSF algorithm outperforms existing algorithms in terms of both the accuracy and time performance. © 2022 IEEE.

关键词： Additive noise

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FRMT: RMT with Frequency Feature learning Framework for land cover classification 3

FRMT: RMT with Frequency Feature learning Framework for land...

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3rd International Conference on Environmental Remote Sensing and Geographic Information technology, ERSGIT 2024

作者： Ou, Zhihui Chen, Fang Wang, Lei Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510690219

The classification of land cover is of great significance in the management and sustainable development of land resources. In recent years, deep learning methods have made remarkable progress in the field of remote sensing image processing. However, due to the complex difference in structure and scale of surface objects in remote sensing images, most of the current methods are prone to the problem of unclear boundary extraction, and the classification results of different scale surface objects are poor. In this letter, we proposed FRMT by introducing frequency domain information into RMT framework. A frequency feature learning module (FFLM) is proposed. The frequency domain features are decomposed into high frequency and low frequency features, and the frequency features are learned by spiking convolution to strengthen the model's learning of boundary and different scale features. We performed experimental validation on the very challenging multi-class remote sensing dataset GID-15 and Huawei Ascend Cup competition dataset, and the experimental results show that our model achieves the best results compared to five other recently proposed models, which mIoU achieves 77.91% and 71.85%, respectively. © 2025 SPIE.

关键词： Frequency domain analysis

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Research on Deep Learning-Based Algorithms for Medical Image Characterisation 2

Research on Deep Learning-Based Algorithms for Medical Image...

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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Yu, Yunxia Qi, Zhiyong Xu, Ling Zhou, Xiong Jingchu University Of Technology Big Data Research Center Jingmen China College Of Computer Science Wuhan Vocational College Of Software And Engineering Wuhan China Shayang People's Hospital Jingmen China

ISBN: (纸本)9798350381665

Lung disease is a worldwide health problem, and accurate image diagnosis of lung disease is crucial for patient treatment and management. In recent years, deep learning has made significant progress in the field of medical image analysis, in which Graph Neural Networks (GNNs), as a special deep learning model, show great potential in lung disease diagnosis. In this study, we propose a graph neural network-based model for lung disease diagnosis, aiming to automate the analysis of lung images and lesion detection. First, we represent the lung images as a graph structure, where each pathogenic region represents a node in the graph. Then, the adjacency matrix and node features of the graph are constructed, and the image is subjected to feature extraction and representation learning using a graph neural network, which enables the model to capture the structural and contextual information in the image. To further improve the performance of the model, we introduce multilayer graph convolution and multilayer perceptron to enhance the sensitivity and differentiation ability of the graph neural network for lung lesions. We conducted experimental evaluations based on publicly available lung image datasets and compared them with traditional machine learning methods as well as other deep learning models. The experimental results show that the proposed graph neural network model achieves excellent performance on the lung disease diagnosis task. © 2024 IEEE.

关键词： Multilayers

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Predicting Employee Attrition Using Machine Learning Approaches 9

Predicting Employee Attrition Using Machine Learning Approac...

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9th International Symposium on Computer and Information Processing technology, ISCIPT 2024

作者： Jin, Aitong Yang, Zicong Liu, Xiaoshan Lou, Shang Zhuang, Zhipeng Zhang, Shixuan Zhang, Chunfu Zhuhai College of Science and Technology Department of Big Data Zhuhai China Zhuhai College of Science and Technology Department of Computer Zhuhai China The Chinese University of Hong Kong Hkstr Department of Mechanical and Automation Engineering China Zhuhai College of Science and Technology Department of Life Sciences Zhuhai China

ISBN: (纸本)9798350388404

Employee attrition has become a focal point of interest for researchers and human resource managers alike. Analyzing factors leading to employee attrition plays a vital role in organizational stability, cost management, and reputation. This study aims to analyze such factors and predict employee attrition using machine learning and ensemble learning methods. A comparison of four machine learning methods and three ensemble learning methods was conducted, utilizing grid search for hyperparameter tuning to identify the optimal model. Additionally, exploratory data analysis of employee data was employed to identify factors contributing to employee attrition. The results indicate that Random Forest exhibits the highest accuracy, approximately 95%. Overtime, stock options level, and job satisfaction were identified as key factors leading to employee attrition. These findings can assist organizations in reducing employee attrition by addressing key factors contributing to turnover, thereby enhancing job satisfaction and improving organizational stability and reputation. © 2024 IEEE.

关键词： Job satisfaction

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FastPlane: A Fully Convolutional Network for Real-time 3D Plane Segmentation

FastPlane: A Fully Convolutional Network for Real-time 3D Pl...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Zhang, Haijun Ren, Qingji Dong, Li Tsang, Kim Fung Harbin Institute of Technology Shenzhen China Department of Computer Science Shenzhen China Shenzhen Technology University The College of Big Data and Internet Shenzhen China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou China

ISBN: (纸本)9798350324136

3D plane segmentation is a challenging computer vision task that involves the detection of planar regions in a scene and the prediction of their spatial position. Recent methods have addressed this problem as a multi-task learning problem in which convolutional neural networks with separate branches predict the plane segmentation mask and the dense depth maps, or plane parameters. However, existing methods often rely on a multi-stage object detection framework for plane segmentation, resulting in slow inference speeds that hinder real-time applications. To address this issue, we propose FastPlane, a real-time 3D plane segmentation network based on a fully convolutional structure. FastPlane comprises a shared backbone, a fully convolutional plane segmentation network, and a lightweight depth estimation network. Given a single image as input, FastPlane outputs both the plane segmentation mask and dense depth maps. Extensive experiments on public datasets demonstrate the effectiveness and efficiency of FastPlane, outperforming existing methods in terms of plane segmentation and depth estimation metrics. Notably, FastPlane achieves an impressive inference speed of 45.0 FPS, making it suitable for real-time applications in robotics, augmented reality, and virtual reality. © 2024 IEEE.

关键词： 3D plane segmentation depth estimation multitask learning scene reconstructing

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Feature extraction and clustering of electricity market data based on GCN and graph filtering

Feature extraction and clustering of electricity market data...

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2024 International Conference on Smart Grid and Artificial Intelligence, SGAI 2024

作者： Huang, Longda Xu, Pan Weng, Liguo China Electric Power Research Institute Co. Ltd Jiangsu Nanjing210000 China Jiangsu Key Laboratory of Big Data Analysis Technology CICAEET Nanjing University of Information Science and Technology Nanjing210044 China

As the power grid undergoes transformation and the Internet's influence grows, the electricity market is evolving towards informatization. Traditional power marketing methods struggle to cope with the vast and intricate data of the modern power market. To address this challenge, this study employs random forest and PBF models for processing electricity market data. Additionally, it introduces the spectral clustering algorithm for a comprehensive analysis. The experimental results successfully extract diverse electricity consumption characteristics. This approach aids in advancing the informatization of power grid enterprises, boosting their capacity to perceive power data, and furnishing decision-makers with dependable support. © Published under licence by IOP Publishing Ltd.

关键词： Power markets

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Water body extraction in Nansi Lake Basin by integrating surface object types and spectral features 6

Water body extraction in Nansi Lake Basin by integrating sur...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Xing, Jun Cui, Binge Li, Peng Wang, Xiaohui Song, Lijuan Big Data College Qingdao Huanghai University Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Marine Geosciences Institute of Estuarine and Coastal Zone Ocean University of China Qingdao China

ISBN: (纸本)9781510688018

Water body extraction is the preliminary step to define the range of remote sensing for water quality, which is of great significance to define the monitoring boundary of conventional water pollutants in Nansi Lake Basin. The original Sentinel-2 L2A multispectral image was processed with super resolution, and its spatial resolution of 13 bands was unified to 10m. According to the difference of spectral features of surfaces objects, characteristics of NDWI, NDWI3, MNDWI, NDPI and other water indexes were analyzed. By comparing effects of water body extraction with four water body indexes, it was found that the applicability of different water indexes in different surface object types was different. The remote sensing image of Nansi Lake Basin is mainly divided into five surface object types, including vegetation, towns, small rivers, mountains and shadows. The EM algorithm was used to classify surface objects of remote sensing images and complete the definition of five surface object types. In different areas of surface object types, the water index with better applicability was respectively used to extract water body. The results showed that the method of water body extraction integrating surface object types and spectral characteristics is highly efficient, and can obtain better accuracy of water body extraction than the one using only a single water index, and effect of water body extraction for large-scale remote sensing images is effectively improved. © 2025 SPIE.

关键词： Lakes

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Auxiliary Attributes-Guided Face Age Estimation 11th

Auxiliary Attributes-Guided Face Age Estimation

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： You, Fan Zhong, Fujin Yu, Hong Hu, Jun Yang, Yan Liu, Mengqi School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China

ISBN: (纸本)9783031927409

Facial age estimation is a critical task within computer vision, with wide-ranging applications in demographic analysis, video retrieval, and access control. Prior research has highlighted that facial aging is significantly influenced by attributes such as gender and ethnicity, which present substantial challenges to achieving accurate and robust age prediction. To tackle this challenge, we propose MSAN, an innovative end-to-end age estimation framework that integrates auxiliary information, including gender and ethnicity, during the prediction phase and dynamically models their interactions with age. Extensive evaluations on multiple public datasets demonstrate that our model outperforms several state-of-the-art approaches. This study provides an effective solution for enhancing the accuracy of facial age estimation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Face recognition

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A Unified Framework Integrating Knowledge and data for Collaborative Root Cause Identification 5

A Unified Framework Integrating Knowledge and Data for Colla...

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5th International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Yu, Jiefei Cao, Zicheng He, Siyi Gu, Zuyi Xu, Yingcheng Zhong, Kai Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui China School of Big Data and Statistics Anhui University Anhui China

ISBN: (纸本)9798331529192

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller. © 2024 IEEE.

关键词： Knowledge graph

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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