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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3858-3871页

作者： Yang, Jing Lu, Ting Zhu, Youjie Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Discovering causal relationships from a large amount of observational data is an important research direction in data mining. To address the challenges of discovering and constructing causal networks on nonlinear and high-dimensional data, this article proposes a new structural learning algorithm called spatial coordinates based (SCB). The SCB algorithm effectively discovers causal networks from large scale nonlinear data. In this article, we make three main contributions. Firstly, based on the Hilbert-Schmidt independence criterion (HSIC), we propose new independence test coefficients: spatial coordinate (SC) coefficient and conditional spatial coordinate (CSC) coefficient. We also prove that the statistical distribution of the CSC coefficient follows a standard normal distribution. Secondly, using the statistical distribution of the CSC coefficient, we redefine the correlation of variables and combine it with local learning to propose the SCB algorithm. Finally, we demonstrate the effectiveness of the algorithm through experiments on data generated from various nonlinear function dependencies. Compared to existing algorithms, the causal network models constructed using the SCB algorithm exhibit significant improvements in accuracy and time performance. The effectiveness of the proposed method is further validated on real power plant data. © 2020 IEEE.

关键词： Additive noise

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Belief Combination of Classifiers for Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 652-667页

作者： Zuowei Zhang Songtao Ye Yiru Zhang Weiping Ding Hao Wang IEEE the Research Center for Optical Fiber Sensing Zhejiang Laboratory the School of Automation Northwestern Polytechnical University the School of Cyber Engineering Xidian University the Department of Computer Science CY Cergy Paris Université the School of Information Science and Technology Nantong University the School with of Cyber Engineering Xidian University the Department of Computer Science Norwegian University of Science and Technology

Data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of *** this paper,we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier(BCC)method based on the evidence *** proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by *** BCC,different attributes are regarded as independent sources,and the collection of each attribute is considered as a ***,multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query ***,these sub-classification results with different weights(discounting factors)are used to provide supplementary information to jointly determine the final classes of query *** weights consist of two aspects:global and *** global weight calculated by an optimization function is employed to represent the reliability of each classifier,and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern *** comparative experiments including seven methods on twelve datasets are executed,demonstrating the out-performance of BCC over all baseline methods in terms of accuracy,precision,recall,F1 measure,with pertinent computational costs.

关键词： Classifier fusion classification evidence theory incomplete data missing values

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M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Network for Segmenting Pathology Lesion of the Focal Lymphocytic Sialadenitis 1

M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Ne...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Han, Hao Lu, Feng Deng, Yanhan Luo, Xiaofang Jin, Hai Tu, Wei Xie, Xia School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan430074 China Tongji Medical College Huazhong University of Science and Technology Tongji Hospital Wuhan430022 China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350358780

In the medical realm, the pivotal role of pathological Whole Slide Images (WSIs) in detecting cancer, tracking disease progression, and evaluating treatment efficacy is indisputable. Nevertheless, the identification and quantification of lesion areas in these gigapixel WSIs present a significant challenge due to their substantial size and the intricate details of lesions. To address these issues, we developed a novel multi-resolution and multi-scale cross fusion network (M2CF-Net), adept at managing large-scale pathological WSIs and capturing both fine details and context. Our model particularly focuses on segmenting local lymphocyte infiltration lesions in pathological WSIs of patients diagnosed with primary Sjogren's syndrome. By employing a patch-based training approach and combining interconnected elements via a multi-scale fusion technique, we enhance our model's capacity to detect and analyze structures and features in minor salivary gland section WSIs. Extensive experiments and ablation studies conducted on real-world clinical datasets affirm our model's superior accuracy in identifying lymphocyte-infiltrated regions over state-of-the-art models, with a performance improvement of up to 4.32% in terms of the Dice Similarity Coefficient. © 2023 IEEE.

关键词： Pathology

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A Prognostic Framework for Post-Operative Patient Survival Prediction in IoMT 16

A Prognostic Framework for Post-Operative Patient Survival P...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mittal, Shubh Rahman, Saifur Pal, Shantanu Karmakar, Chandan School of Computer Science Vellore Institute of Technology India School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9798350383119

The study presents an Internet of Medical Things (IoMT) framework designed to predict patient survival outcomes through the evaluation of a post-thoracic surgery scenario. We employ a multi-layered IoMT framework that integrates various sensors and medical devices for real-time data collection, efficient data transmission, and data analysis. Utilizing a set of eight traditional and ensemble machine learning classifiers, along with neural networks optimized using grid search, we establish a baseline performance for the framework's capability in predicting post-surgical survival rates. However, as individual machine learning classifiers exhibit suboptimal performance across the performance metrics used, we combine the individual strengths of these classifiers to construct a stacking approach. The stacked classifier which incorporates a multi-layer perceptron as the final estimator achieved significant results, including a high accuracy of 0.90, precision of 0.87, and recall of 0.93. These metrics not only indicate a high post-operative survival detection rate but also demonstrate a balance of low bias and high variance performance, ensuring that the model is both accurate and reliable in varying IoMT scenarios. © 2024 IEEE.

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EMD-TCN-TimeGAN: A Data Augmentation Model for Enhancing the Accuracy of Agricultural Product Price Prediction

EMD-TCN-TimeGAN: A Data Augmentation Model for Enhancing the...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Wang, Yinbing Wang, Xingfen Li, Libo Beijing Information Science & Technology University School of Computer Beijing China Beijing Information Science & Technology University Institute of Business Intelligence Beijing China Beijing Information Science & Technology University School of Management Science and Engineering Beijing China

ISBN: (纸本)9798350362480

Accurately predicting agricultural product prices is crucial for the stability of the agricultural market. However, due to the collection intervals of agricultural price data, which are typically daily, weekly, or monthly, the data volume is relatively small. This limitation affects the accuracy of deep learning models in price prediction. Data augmentation is an effective method to improve the accuracy of agricultural price predictions. This research proposes a novel data augmentation model, EMD-TCN-TimeGAN. Based on TimeGAN, this model has been significantly improved by replacing the RNN-based components with TCN, which enhances the model's ability to capture long-term dependencies. Additionally, the model incorporates an EMD module to decompose the original price series, thereby better capturing the intrinsic structural information of the time series and further improving the quality of the generated data. Experiments conducted on a collected corn dataset show that adding the augmented data to the training set of the prediction model and retraining it significantly improves the prediction accuracy. The coefficient of determination (R2) increased from 0.928831 to 0.968742, and the mean absolute error (MAE) decreased from 0.026023 to 0.016628. Comparisons with the TimeGAN model using RNN components validate the effectiveness of replacing the components with TCN;comparisons with TCN-TimeGAN validate the effectiveness of the EMD module in further improving prediction performance. The EMD-TCN-TimeGAN proposed in this study generates sequences that better preserve the temporal dynamics of the time series data, maintaining the temporal relationships between the original variables. This improves the accuracy of small-sample agricultural price predictions, which is of significant importance and benefit to agricultural production and related activities. © 2024 IEEE.

关键词： Prediction models

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Stochastic Block Models for Complex Network Analysis: A Survey

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ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷 1-35页

作者： Liu, Xueyan Song, Wenzhuo Musial, Katarzyna Li, Yang Zhao, Xuehua Yang, Bo College of Computer Science and Technology Jilin University Changchun China School of Information Science and Technology Northeast Normal University Changchun China Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Sydney Australia Aviation University of the Air Force Changchun China School of Digital Media Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Changchun China

Complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Complex Networks Structural Pattern Discovery Stochastic Block models Learning Methods of SBMs

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Design an optimization based ensemble machine learning framework for detecting rice leaf diseases

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Multimedia Tools and Applications 2024年第36期83卷 84401-84424页

作者： Rajasekhar, Veeramreddy Arulselvi, Gnanasekaran Babu, K. Suresh Department of Computer Science Engineering Annamalai University Tamilnadu Chidambaram India Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu Chidambaram India International School of Technology and Sciences for Women East Gonagudem Rajanagaram A.P Rajamahendravaram India

The agriculture industry is currently dealing with serious issues with rice plants as a result of illnesses that decrease the quantity and output of the harvest. Numerous fungi and bacteria diseases harm plants that affect the growth of yield. So identification of the detection of crop diseases is essential for enhancing the productivity of crops. Many techniques have been developed to improve disease detection in crops but there are still some issues such as noise, time consumption, delay, poor segmentation, and less accuracy. To overcome these issues, this paper, designed a Spider Monkey-based Random Forest (SMbRF) model for accurate detection and classification of rice leaf diseases and the designed model was implemented in the Python system. The main aim is to develop an optimization-based random forest technique for accurate prediction of rice leaf disease by exact segmentation. Initially, preprocessing is performed to enhance the data quality and remove errors. Furthermore, separate the lesions using mean shift image segmentation and extract the shape and color features to improve the detection results of the developed model. As well, spider monkey fitness is updated in the RF classification layer for accurate classification of diseases and improves accuracy with less execution time. The experiment results show the reliability and efficiency of the designed model, it attains better performance in accuracy, sensitivity, specificity, and precision. The designed model attained 99.29% accuracy, 99.52% sensitivity, 98.76% precision, and 99% specificity proving the scalability of the designed model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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A Dynamic Bayesian-Based Comprehensive Trust Evaluation Model for Dispersed Computing Environment

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China Communications 2023年第2期20卷 278-288页

作者： Hongwen Hui Zhengxia Gong Jianwei An Jianzhong Qi School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Graduate School of University of Science and Technology Beijing Guangdong 528399China China Academy of Information and Communications Technology(CAICT) Beijing 100191China Department of Information Engineering North China University of TechnologyBeijing 100144China

Dispersed computing is a new resourcecentric computing *** to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its *** trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing *** this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing ***,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the *** the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding ***,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary *** experiments are introduced to verify the performance of the *** with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.

关键词： dispersed computing trust evaluation model malicious node interaction success rate detection rate

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Stretchable wireless optoelectronic synergistic patches for effective wound healing

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npj Flexible Electronics 2024年第1期8卷 233-242页

作者： Qian Wang Siyuan Cai Guang Yao Liyuan Zhang Wenhao Lou Youxin Chen Qingqing Li Maowen Xie Xingyi Gan Chenzheng Zhou Taisong Pan Min Gao Kangning Zhao Zhen Cai Yuan Lin School of Materials and Energy University of Electronic Science and Technology of ChinaChengduSichuanChina Department of Plastic Surgery Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuanChina Department of Plastic and Cosmetic Surgery Xinqiao HospitalArmy Medical UniversityChongqingChina State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of ChinaChengduSichuanChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenGuangdongChina State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and EngineeringWuhan University of TechnologyWuhanHubeiChina Medico-Engineering Cooperation on Applied Medicine Research Center University of Electronic Science and Technology of ChinaChengduSichuanChina

Physiotherapies play a crucial role in noninvasive tissue engineering for wound ***,challenges such as the implementation of complex interventions and unsatisfactory treatment outcomes impede widespread ***,we proposed a stretchable and wirelessly-powered optoelectronic synergistic patch with a dual-layer serpentine wireless receiver circuit to drive the optoelectronic modulation *** structure and impedance matching enable the patch to seamlessly attach to irregular skin surfaces and operate robustly over a 30%tensile strain *** on Sprague-Dawley rat wound *** wound closure rate of the optoelectronic synergistic group significantly outperformed both monointervention and blank control ***,optoelectronic synergistic intervention enhances the secretion of vascular endothelial marker proteins and growth factors,and stabilizes mitochondrial function during oxidative ***,the scalable amalgamation of flexible electronics,wireless transmission,and synergistic interventions promise to improve wound care.

关键词： optoelectronic synergistic healing

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Optimizing AES S-Box Implementation:A SAT-Based Approach with Tower Field Representations

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computers, Materials & Continua 2025年第4期83卷 1515-1531页

作者： Jingya Feng Ying Zhao Tao Ye Wei Feng School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Computing and Information Systems The University of MelbourneMelbourne3010Australia Guangxi Wangxin Information Technology Co. Ltd.Nanning530000China

The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data *** paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)*** research makes several significant contri-butions to the ***,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known ***,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local *** approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box ***,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior *** implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy *** approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy *** optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.

关键词： AES S-box SAT optimization tower field hardware implementation area efficiency energy consumption

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