检索结果-内蒙古大学图书馆

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma Jun Wang Wenchang Yu Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable *** available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal *** order to address the aforementioned issues,this paper delves into the study of attribute reduction *** of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute *** newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute ***,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute ***,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy *** effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information ***,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is *** experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction *** comprehensive analysis o

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

来源：评论

学校读者我要写书评

暂无评论

An autoencoder-based feature level fusion for speech emotion recognition

引用

Digital Communications and Networks 2024年第5期10卷 1341-1351页

作者： Peng Shixin Chen Kai Tian Tian Chen Jingying National Engineering Research Center for E-Learning National Engineering Laboratory for Educational Big DataCentral China Normal UniversityHubei430079China

Although speech emotion recognition is challenging,it has broad application prospects in human-computer *** a system that can accurately and stably recognize emotions from human languages can provide a better user ***,the current unimodal emotion feature representations are not distinctive enough to accomplish the recognition,and they do not effectively simulate the inter-modality dynamics in speech emotion recognition *** paper proposes a multimodal method that utilizes both audio and semantic content for speech emotion *** proposed method consists of three parts:two high-level feature extractors for text and audio modalities,and an autoencoder-based feature *** audio modality,we propose a structure called Temporal Global Feature Extractor(TGFE)to extract the high-level features of the timefrequency domain relationship from the original speech *** that text lacks frequency information,we use only a Bidirectional Long Short-Term Memory network(BLSTM)and attention mechanism to simulate an intra-modal *** these steps have been accomplished,the high-level text and audio features are sent to the autoencoder in parallel to learn their shared representation for final emotion *** conducted extensive experiments on three public benchmark datasets to evaluate our *** results on Interactive Emotional Motion Capture(IEMOCAP)and Multimodal EmotionLines dataset(MELD)outperform the existing ***,the results of CMU Multi-modal Opinion-level Sentiment Intensity(CMU-MOSI)are ***,experimental results show that compared to unimodal information and autoencoderbased feature level fusion,the joint multimodal information(audio and text)improves the overall performance and can achieve greater accuracy than simple feature concatenation.

关键词： Attention mechanism Autoencoder Bimodal fusion Emotion recognition

来源：评论

学校读者我要写书评

暂无评论

Method for wheat ear counting based on frequency domain decomposition of MSVF-ISCT

引用

Information Processing in Agriculture 2023年第2期10卷 240-255页

作者： Wenxia Bao Ze Lin Gensheng Hu Dong Liang Linsheng Huang Xin Zhang National Engineering Research Center for Agro-ecological Big Data Analysis and Application Anhui UniversityHefei 230601China

Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield *** frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine *** based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat *** order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature ***,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the *** number of wheat ears in the image can be accurately *** show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.

关键词： Wheat ear counting Frequency domain decomposition Multiscale support value filter Improved sampled contourlet transform Image segmentation Morphological processing

来源：评论

学校读者我要写书评

暂无评论

An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical data:A COVID-19 Case Study

引用

Journal of Bionic engineering 2024年第1期21卷 426-446页

作者： Ali Fatahi Mohammad H.Nadimi-Shahraki Hoda Zamani Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad ***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad ***Iran

Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic ***,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction *** paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic *** proposed IBQANA’s contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary *** the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates *** combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local *** effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,***'s effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic ***,IBQANA is utilized to detect *** results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical *** proposed method presents a promising solution to the FSS problem in medical data preprocessing.

关键词： Feature subset selection Optimization Binary metaheuristic algorithms Bioinspired Machine learning Medical datasets

来源：评论

学校读者我要写书评

暂无评论

A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

引用

Computers, Materials & Continua 2025年第4期83卷 1195-1218页

作者： Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and TechnologyChizhou247000China

Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关键词： Fuzzy rough sets feature selection cross-similarity fuzzy relations

来源：评论

学校读者我要写书评

暂无评论

big data in support of the sustainable development goals(continued):a celebration of the establishment of the International research center of big data for Sustainable Development Goals(CBAS)

引用

big Earth data 2021年第4期5卷 443-444页

作者： Huadong Guo Heide Hackmann Ke Gong International Research Center of Big Data for Sustainable Development Goals World Federation of Engineering Organizations

The rapid development of analytics and concepts in big data enables us to diversify our efforts and enhance opportunities to implement the Sustainable Development Goals(SDGs).big data improves the extent to which scientific evidence and innova-tive technological solutions can be adopted to meet these ***,the tools and methods of big data are still somewhat of a novelty in this respect.

关键词： Sustainable continued enable

来源：评论

学校读者我要写书评

暂无评论

IoTDQ: An Industrial IoT data Analysis Library for Apache IoTDB

引用

big data Mining and Analytics 2024年第1期7卷 29-41页

作者： Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang School of Software Tsinghua UniversityBeijing 100084China National Engineering Research Center for Big Data Software(NERCBDS) Tsinghua UniversityBeijing 100084China

There is a growing demand for time series data analysis in industry *** loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O *** User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB *** satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache *** library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,*** enables users to conduct a wide range of analyses,such as monitoring,error diagnosis,equipment reliability *** provides a framework for users to examine loT time series with data quality *** show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.

关键词： industrial big data data quality data mining and analytics

来源：评论

学校读者我要写书评

暂无评论

UnifiedGEC: Integrating Grammatical Error Correction Approaches for Multi-languages with a Unified Framework 31

UnifiedGEC: Integrating Grammatical Error Correction Approac...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Zhao, Yike Wang, Xiaoman Lan, Yunshi Qian, Weining School of Data Science and Engineering East China Normal University Shanghai Engineering Research Center of Big Data Management China

ISBN: (纸本)9798891761988

Grammatical Error Correction is an important research direction in NLP field. Although many models of different architectures and datasets across different languages have been developed to support the research, there is a lack of a comprehensive evaluation on these models, and different architectures make it hard for developers to implement these models on their own. To address this limitation, we present UnifiedGEC, the first open-source GEC-oriented toolkit, which consists of several core components and reusable modules. In UnifiedGEC, we integrate 5 widely-used GEC models and compare their performance on 7 datasets in different languages. Additionally, GEC-related modules such as data augmentation, prompt engineering are also deployed in it. Developers are allowed to implement new models, run and evaluate on existing benchmarks through our framework in a simple way. Code, documents and detailed results of UnifiedGEC are available at https://***/AnKate/UnifiedGEC. ©2025 Association for Computational Linguistics.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

引用

Science China(Information Sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

来源：评论

学校读者我要写书评

暂无评论

big data in support of the Sustainable Development Goals:a celebration of the establishment of the International research center of big data for Sustainable Development Goals(CBAS)

引用

big Earth data 2021年第3期5卷 259-262页

作者： Huadong Guo Heide Hackmann Ke Gong International Research Center of Big Data for SDGs International Science Council World Federation of Engineering Organizations

In the last century the impacts of human activity on natural processes that sustain the Earth’s biosphere,atmosphere,hydrosphere and lithosphere and that provide the bed-rock of human life support systems,have grown to the extent that they pose a credible existential threat to ***,the biggest challenge for science,technology and innovation(STI)is to contribute to the pursuit of global sustainability as exemplified in the Sustainable Development Goals(SDGs)that were adopted by the United Nations(UN)in 2015.

关键词： Sustainable pursuit humanity

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：