检索结果-内蒙古大学图书馆

14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Yang, Xuemin Gao, Zhihong Li, Yongmin Pan, Chuandi Yang, Ronggen Gong, Lejun Yang, Geng Jiangsu Key Laboratory of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210003 China Zhejiang Engineering Research Center of Intelligence Medicine Wenzhou325035 China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (纸本)9781538680971

Bio-medical entity recognition extracts significant entities, for instance cells, proteins and genes, which is an arduous task in an automatic system that mine knowledge in bioinformatics texts. In this thesis, we utilized a bidirectional long short-term memory (Bi-LSTM) combined with conditional random fields (CRFs) approach to automatically obtain word representation, obliterated the need for a marvelous number of feature engineering tasks. The consequences of this experiment represent the word representation method can effectually acquire potential semantic information. Without relying on any artificial features, the result on the test dataset obtained 76.81 % F-score. Therefore, the proposed method is expected to advance biomedical text mining in bioinformatics entity recognition. © 2018 IEEE.

关键词： Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Efficient EK-means: Extended K-means Clustering for Categorical data with High Processing Speed

引用

Journal of Physics: Conference Series 2020年第1期1584卷

作者： Chen Xi Peng Shuo HunanProvincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of computer and communication engineering Changsha University of Science&technology No. 960 Wanjiali South Road Hunan Province China

The typical representative of the hard clustering algorithm, K-means, is one of the fastest processing algorithms with good scalability. However, it cannot deal with categorical attributes, which is one of the important indicators to measure the pros and cons. Due to the lack of processing capabilities on categorical attributes, k-means has a large limit on data processing capabilities. This paper proposes a clustering algorithm extends from K-means. This algorithm introduces the concept of a Pseudo-mean distance calculation formula and a counting-table so that categorical attributes can be processed while reducing the time cost as much as possible. Experimental results illustrate the proposed Pseudo-means can extend the processing range of k-means to category-type data, and the counting table also effectively reduces the time cost.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Numerical analysis of the magnetic field impact on hydrothermal characteristics of a microchannel heatsink with Fe 3 O 4 ferrofluid and various pin-fin shapes” [J. Magn. Magn. Mater. 585 (2023) 171102]

引用

Journal of Magnetism and Magnetic Materials 2024年 610卷

作者： Tao Hai Kamal Sharma Riyam K. Marjan Babak Farhang Mohamed H. Mahmoud Hassan Fouad Walid El-Shafai School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China School of Electronics and Information Engineering Ankang University China State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang 550025 China Institute of Engineering and Technology GLA University Mathura UP 281406 India Department of Intelligent Medical Systems Al-Mustaqbal University College Babylon 51001 Iraq Department of Energy Technology Aalborg University Denmark Department of Biochemistry College of Science King Saud University Riyadh Saudi Arabia Biomedical Engineering Department Faculty of Engineering Helwan University Cairo P.O. Box 11795 Egypt Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt

来源：评论

学校读者我要写书评

暂无评论

Mapping Heavy Metals in Cultivated Soils Based on Land Use Types and Cokriging 5th

Mapping Heavy Metals in Cultivated Soils Based on Land Use T...

引用

5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Zhao, Jinling Liu, Chuang Song, Qixiang Jiang, Yan Hong, Qi Huang, Linsheng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Hefei230601 China Information Engineering Institute Suzhou University Suzhou234000 China

ISBN: (纸本)9789811308925

It is extremely important to explore the heavy metal content and spatial distribution in different cultivated soils. In our study, cokriging (COK) method was used to investigate the relationship between heavy metals and land use types. A total of six heavy metals including Zn, Fe, Cu, Mn, B, S were selected to forecast the heavy metals in cultivated soils. Five types of cultivated land were considered in the study area, including vegetable land, irrigated land, irrigated paddy field, dry land, orchard. Test of normality was firstly carried out to assure that higher prediction accuracy can be obtained, and then correlation analysis and analysis of variance (ANOVA) were performed to find out the most sensitive heavy metals for spatial interpolation. The analysis results showed that Zn, Cu and Fe were the primary heavy metals in soil of the study area. It was obvious that the effect of Mn and Fe were much greater caused by land use types, while they were smaller for B and S. Three errors of mean standard (MS), root mean square error (RMSE), and root mean square standardized error (RMSSE) were derived to assess the prediction accuracy. In comparison with ordinary kriging (OK), the MS of COK approached to 1, the RMSE was much smaller and the RMSSE also approached to 1, which showed that COK can make better predictions than OK. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Heavy metals

来源：评论

学校读者我要写书评

暂无评论

Retraction notice to “Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial” [Fuel 332 (2023) 125827]

引用

Fuel 2025年 391卷

作者： Tao Hai Kosar Hikmat Hama Aziz Jincheng Zhou Hayder A. Dhahad Kamal Sharma Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Teeba Ismail Kh Sadok Mehrez Anas Abdelrahman School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Mathura Uttar Pradesh 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belv’ed‘ere Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia

来源：评论

学校读者我要写书评

暂无评论

User Heterogeneity and Individualized Recommender

引用

Chinese Physics Letters 2017年第6期34卷 135-138页

作者：王庆先张君杰史晓雨尚明生 School of Information and Software Engineering University of Electronic Science and Technology of China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences

Previous works on personalized recommendation mostly emphasize modeling peoples＇ diversity in potential fa- vorites into a uniform recommender. However, these recommenders always ignore the heterogeneity of users at an individual level. In this study, we propose an individualized recommender that can satisfy every user with a customized parameter. Experimental results on four benchmark datasets demonstrate that the individualized recommender can significantly improve the accuracy of recommendation. The work highlights the importance of the user heterogeneity in recommender design.

关键词： User Heterogeneity and Individualized Recommender

来源：评论

学校读者我要写书评

暂无评论

Adaptive multi-graph hashing for scalable multimedia retrieval

引用

Pattern Recognition Letters 2019年

作者： Xiang, Lingyun Zhao, Guohan Shen, Xiaobo Li, Feng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha410114 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Hashing has become a promising technique to be applied to the large-scale visual retrieval tasks. Multi-view data has multiple views, providing more comprehensive information. Existing hashing methods measure the similarity among the data points by the pre-defined graph Laplacian in each view separately, and thus ignore the underlying common structures across different views. In this paper, we propose a novel hashing method, called adaptive multi-graph hashing (AMGH) to address the above issue. AMGH uses the adaptive weights among the data points, and the weights of each view to characterize the common structure of multi-view data. In addition, AMGH further minimizes the quantization errors. We develop an efficient alternating algorithm to solve the formulated optimization problem. The experiments on three large-scale datasets demonstrate the superiority of the proposed method over the existing multi-view hashing methods. © 2019

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

A hybrid NSGA-II for matching biomedical ontology 14th

A hybrid NSGA-II for matching biomedical ontology

引用

14th International Conference on intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018

作者： Xue, Xingsi Chen, Jie Chen, Junfeng Chen, Dongxu College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Intelligent Information Processing Research Center Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China College of IOT Engineering Hohai University ChangzhouJiangsu China Fujian Medical University Union Hospital FuzhouFujian350001 China

ISBN: (纸本)9783030037444

Over the recent years, ontologies are widely used in the biomedical domains. However, biomedical ontology heterogeneity problem hamper the cooperation between intelligent applications based on biomedical ontologies. It is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Approaches based on Multi-Objective Evolutionary Algorithm (MOEA), such as NSGA-II, are emerging as a new methodology to solve the ontology matching problem. In this paper, to further improve the quality of biomedical ontology alignments, a hybrid NSGA-II is proposed, which modifies the knee solutions in the Pareto front by using a local search method. Experiment utilizes two biomedical ontology matching tracks provided by Ontology Alignment Evaluation Initiative (OAEI 2017). The experimental results show that our approach outperforms the participants of OAEI 2017 and NSGA-II based ontology matching technique. © Springer Nature Switzerland AG 2019.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Optimizing Location Quality in Privacy Preserving Crowdsensing

Optimizing Location Quality in Privacy Preserving Crowdsensi...

引用

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yuhui Zhang Ming Li Dejun Yang Jian Tang Guoliang Xue Colorado School of Mines Golden CO Colorado School of Mines Golden CO USA Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Syracuse University Syracuse NY Arizona State University Tempe AZ

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations. Protecting users' location privacy has been a central issue. The study of various location perturbation techniques for protecting users' location privacy has received widespread attention. Despite the huge promise and considerable attention, the location perturbation operation causes inevitable location errors, which can diminish the location quality of the crowdsensing results. Provable good algorithms that consider location quality in privacy preserving crowdsensing from optimization perspectives are still lacking in the literature. In this paper, we investigate the problem of location quality optimization in privacy preserving crowdsensing, which is to minimize the location quality desegregation, while protecting all users' location privacy. We present an optimal algorithm OLQDM for this problem. Extensive simulations demonstrate that OLQDM significantly outperforms an existing algorithm in terms of the location quality and SSE.

关键词： Privacy Degradation Sensors Perturbation methods Optimization Minimization Mobile handsets

来源：评论

学校读者我要写书评

暂无评论

Adversarial Learning for Distant Supervised Relation Extraction

引用

Computers, Materials & Continua 2018年第4期55卷 121-136页

作者： Daojian Zeng Yuan Dai Feng Li R.Simon Sherratt Jin Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha410114P.R.China Department of Biomedical Engineering University of ReadingUK. School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114P.R.China.

Recently,many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction(DSRE).These approaches generally use a softmax classifier with cross-entropy loss,which inevitably brings the noise of artificial class NA into classification *** address the shortcoming,the classifier with ranking loss is employed to *** randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss ***,the majority of the generated negative class can be easily discriminated from positive class and will contribute little towards the *** by Generative Adversarial Networks(GANs),we use a neural network as the negative class generator to assist the training of our desired model,which acts as the discriminator in *** the alternating optimization of generator and discriminator,the generator is learning to produce more and more discriminable negative classes and the discriminator has to become better as *** framework is independent of the concrete form of generator and *** this paper,we use a two layers fully-connected neural network as the generator and the Piecewise Convolutional Neural Networks(PCNNs)as the *** results show that our proposed GAN-based method is effective and performs better than state-of-the-art methods.

关键词： Relation extraction generative adversarial networks distant supervision piecewise convolutional neural networks pair-wise ranking loss

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：