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classification framework and semantic labeling for Big Earth data

Big Earth data 2023年第3期7卷 886-903页

作者： Juanle Wang Kun Bu Dongmei Yan Jingyue Wang Bowen Duan Min Zhang Guojin He State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciencesand Natural Resources ResearchChinese Academy of SciencesBeijingChina Collaborative InnovationCenter for Development and Utilization of Geographic Information Resources in Jiangsu Province NanjingChina Northeast Institute of Geography and Agroecology Chinese Academy of SciencesChangchunChina International Research Center of Big data for Sustainable Development Goals BeijingChina Schoolof Civil and Architectural Engineering Shandong University of TechnologyZiboChina College of Resourceand Environment University of Chinese Academy of SciencesBeijingChina

Big Earth data refers to the multidimensional integration and association of scientific data,including geography,resources,environment,ecology,and *** effective data classification system and label management strategy are important foundations for long-term management of data *** objective of this study was to construct a classification system and realize multidimensional semantic data label management for the Big Earth data Science Engineering Program(CASEarth).This study constructed two sets of classification and coding systems that realize classification by mapping each other;namely,the geosphere-level and Sustainable Development Goals(SDGs)indicator *** technique was based on natural language processing technology and solved problems with subject-word segmentation,weight calculation,and dynamic matching.A prototype system for classification and label management was constructed based on existing CASEarth datasets of more than 1,***,we expect our study to provide the methodology and technical support for useroriented classification and label management services for Big Earth data.

关键词： Big Earth data CASEarth scientific engineering data classification data labeling data management

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data Flow&Transaction Mode classification and An Explorative Estimation on data Storage&Transaction Volume

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China Economist 2022年第6期17卷 78-112页

作者： Cai Yuezhou Liu Yuexin Institute of Quantitative&Technological Economics(IQTE) Chinese Academy of Social Sciences(CASS)BeijingChina University of Chinese Academy of Social Sciences(CASS)

The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at data *** deliver a complete picture of data flaw and transaction,this paper presents a systematic overview of the flow and transaction of personal,corporate and public data on the basis of data factor classification from various *** utilizing various sources of information,this paper estimates the volume of data generation&storage and the volume&trend of data market transactions for major economies in the world with the following findings:(i)data classification is diverse due to a broad variety of applying scenarios,and data transaction and profit distribution are complex due to heterogenous entities,ownerships,information density and other attributes of different data types.(ii)Global data transaction has presented with the characteristics of productization,servitization and platform-based mode.(iii)For major economies,there is a commonly observed disequilibrium between data generation scale and storage scale,which is particularly striking for China.(i^v)The global data market is in a nascent stage of rapid development with a transaction volume of about 100 billion US dollars,and China s data market is even more underdeveloped and only accounts for some 10%of the world *** sectors of the society should be flly aware of the diversity and complexity of data factor classification and data transactions,as well as the arduous and long-term nature of developing and improving relevant institutional *** to such features,efforts should be made to improve data classification,enhance computing infrastructure development,foster professional data transaction and development institutions,and perfect the data governance system.

关键词： data factor data classification data transaction mode data generation&storage volume data transaction volume

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The need for disciplinarity,interdisciplinarity and scientific societies-the cartographic perspective

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Geo-Spatial Information Science 2021年第1期24卷 160-166,I0017页

作者： Jochen Schiewe Lab for Geoinformatics and Geovisualization(G2lab) HafenCity University of HamburgHamburgGermany

Do we still need Cartography?Do we still need a cartographic society?These questions,which can also be transferred to other disciplines,are answered from disciplinary and interdisciplinary perspectives-which creates an ideal connection to the networked and interdisciplinary thinking of Gottfried *** need for Cartography as a discipline is discussed based on a view on historic,current and future functions of maps and changing research and development *** respect to interdisciplinarity,Cartography shows connections to various domains while more and more relationships with young areas from the field of Computer Science are *** regard to cartographic societies,their central role and importance as networking platforms are ***,geographical and thematic granularities of societies as well as networking formats(including online versions)need to be evaluated and further developed on a constant basis.

关键词： Cartography map functions interdisciplinarity data classification point generalization professional societies

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一种基于并行遗传算法的数据分类方法

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计算机科学 2002年第Z1期29卷 148-151页

作者：蒙祖强蔡自兴孙国荣李枚毅石跃祥中南大学信息科学与工程学院长沙410083

　　1引言　　随着信息技术的高速发展和信息管理技术的广泛应用,自从九十年代以来,数据挖掘(data Mining)已成为计算机科学中一个引人注目的新的研究方向[1].目前,数据挖掘技术在各大中型企业、电信业、商业、银行等部门中表现出极强... 详细信息

　　1引言　　随着信息技术的高速发展和信息管理技术的广泛应用,自从九十年代以来,数据挖掘(data Mining)已成为计算机科学中一个引人注目的新的研究方向[1].目前,数据挖掘技术在各大中型企业、电信业、商业、银行等部门中表现出极强的应用潜力.……

关键词：并行遗传算法数据挖掘技术分类方法 Parallel Genetic Algorithm data classification 信息管理技术计算机科学应用研究方向信息技术型企业电信业银行商业潜力

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The materials data ecosystem： Materials data science and its role in data-driven materials discovery

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Chinese Physics B 2018年第11期27卷 120-125页

作者： Hai-Qing Yin Xue Jiang Guo-Quan Liu Sharon Elder Bin Xu Qing-Jun Zheng Xuan-Hui Qu Collaborative Innovation Center of Steel Technology University of Science and Technology Beijing Beijing 100083 China Beijing Laboratory of Metallic Materials and Processing for Modern Transportation University of Science and Technology Beijing Beijing 100083 China Beijing Key Laboratory of Materials Genome Engineering University of Science and Technology Beijing Beijing 100083 China Computer Science and Engineering The Pennsylvania State University University Park PA 16802 USA Kennametal Inc. 1600 Technology Way Latrobe PA 15650 USA Institute of Advanced Materials and Technology University of Science and Technology Beijing Beijing 100083 China

Since its launch in 2011, the Materials Genome Initiative（MGI） has drawn the attention of researchers from academia,government, and industry worldwide. As one of the three tools of the MGI, the use of materials data, for the first time, has emerged as an extremely significant approach in materials discovery. data science has been applied in different disciplines as an interdisciplinary field to extract knowledge from data. The concept of materials data science has been utilized to demonstrate its application in materials science. To explore its potential as an active research branch in the big data era, a three-tier system has been put forward to define the infrastructure for the classification, curation and knowledge extraction of materials data.

关键词： Materials Genome Initiative materials data science data classification life-cycle curation

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An artificial immune and incremental learning inspired novel framework for performance pattern identification of complex electromechanical systems

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Science China(Technological Sciences) 2020年第1期63卷 1-13页

作者： WANG RongXi GAO Xu GAO JianMin GAO ZhiYong CHEN Kun PENG CaiYuan State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong UniversityXi'an 710049China Xi'an Thermal Power Research Institute Co. Ltd.Xi'an 710054China

Performance pattern identification is the key basis for fault detection and condition prediction,which plays a major role in ensuring safety and reliability in complex electromechanical systems(CESs).However,there are a few problems related to the automatic and adaptive updating of an identification *** to solve the problem of identification model updating,a novel framework for performance pattern identification of the CESs based on the artificial immune systems and incremental learning is proposed in this paper to classify real-time monitoring data into different performance ***,an unsupervised clustering technique is used to construct an initial identification ***,the artificial immune and outlier detection algorithms are applied to identify abnormal data and determine the type of immune ***,incremental learning is employed to trace the dynamic changes of patterns,and operations such as pattern insertion,pattern removal,and pattern revision are designed to realize automatic and adaptive updates of an identification *** effectiveness of the proposed framework is demonstrated through experiments with the benchmark and actual pattern identification *** an unsupervised and self-adapting approach,the proposed framework inherits the preponderances of the conventional methods but overcomes some of their drawbacks because the retraining process is not required in perceiving the pattern ***,this method can be flexibly and efficiently used for performance pattern identification of the ***,the proposed method provides a foundation for fault detection and condition prediction,and can be used in other engineering applications.

关键词： performance pattern identification complex electromechanical systems artificial immune incremental learning data classification

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A generalized rough set-based information flling technique for failure analysis of thruster experimental data

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Chinese Journal of Aeronautics 2013年第5期26卷 1182-1194页

作者： Han Shan Zhu Qiang Li Jianxun Chen Lin Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired from the simulation and evaluation system formed as intervalvalued information system （IIS） is classified by the interval similarity relation. Then, as an improvement of the classical rough set, a new kind of generalized information entropy called ＂H＇-information entropy＂ is suggested for the measurement of uncertainty and the classification ability of IIS. There is an innovative information filling technique using the properties of H＇-information entropy to replace missing data by some smaller estimation intervals. Finally, an improved method of failure analysis synthesized by the above achievements is presented to classify the thruster experimental data, complete the information, and extract the failure rules. The feasibility and advantage of this method is testified by an actual application of failure analysis, whose performance is evaluated by the quantification of E-condition entropy.

关键词： data acquisition data classification Failure analysis Information filling Rough set

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal classification Model

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Computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

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Computers, Materials & Continua 2022年第3期70卷 6257-6270页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi Mohammed Alamgeer Anwer Mustafa Hilal Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from *** this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be *** studies have developed different approaches for the detection of spam bots in *** those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be *** this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as *** proposed SBD-HDL technique focuses on the detection of spam bots that exist in *** technique has different stages of operations such as pre-processing,classification,and parameter ***,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification *** order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this *** experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.

关键词： Cybersecurity spam bot data classification social networks twitter deep learning

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Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News classification

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Computer Systems Science & Engineering 2023年第3期44卷 2395-2409页

作者： Ashit Kumar Dutta Basit Qureshi Yasser Albagory Majed Alsanea Manal Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake *** recently developed machine learning(ML)models can be employed for the detection and classification of fake *** study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and *** goal of the CAS-WELM technique is to discriminate news into fake and *** CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding ***,N-gram based feature extraction technique is derived to gen-erate feature ***,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS *** performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several *** experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

关键词： Cybersecurity cybercrime fake news data classification machine learning metaheuristics

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