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A Survey of Tax Risk Detection Using Data Mining Techniques

Qinghua Zheng,Yiming Xu,Huixiang Liu,Bin Shi,Jiaxiang Wang,Bo Dong,

Engineering doi: 10.1016/j.eng.2023.07.014

Abstract: In recent years, tax risk detection, driven by information technology such as data mining and artificialcomprehensive overview and summary of existing tax risk detection methods worldwide.It then focuses on data-mining-based tax risk detection methods utilized around the world.Finally, four major technical bottlenecks of current data-driven tax risk detection methods are analyzedAfter investigating these issues, it is concluded that knowledge-guided and data-driven big data knowledge

Keywords： Tax risk detection     Data mining     Knowledge guide     Informatization     Intellectualization    

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An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and groupmethod of data handling surrogate model

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 4,   Pages 907-929 doi: 10.1007/s11709-020-0628-1

Abstract: In this study, the performance of an efficient two-stage methodology which is applied in a damage detectionMoreover, a modal property change vector is evaluated using the group method of data handling (GMDH)

Keywords： two-stage method     modal strain energy     surrogate model     GMDH     optimization damage detection    

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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

Frontiers of Structural and Civil Engineering 2015, Volume 9, Issue 1,   Pages 1-16 doi: 10.1007/s11709-014-0277-3

Abstract: recently performed by applying statistical and machine learning techniques for vibration-based damage detectionThe present paper aims at detecting this type of damage by using static SHM data and by assuming thatTo achieve this objective a data driven strategy is proposed, consisting of the combination of advancedstatistical and machine learning methods such as principal component analysis, symbolic data analysis

Keywords： structural health monitoring     early-damage detection     principal component analysis     symbolic data     symbolic    

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Joint DOA and channel estimation with data detection based on 2D unitary ESPRITin massive MIMO systems Article

Jing-ming KUANG, Yuan ZHOU, Ze-song FEI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 6,   Pages 841-849 doi: 10.1631/FITEE.1700025

Abstract: novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with datadetection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMOIn addition, a low-complexity approach toward data detection is presented by reducing the dimension of

Keywords： Two-dimensional (2D) direction-of-arrival (DOA) estimation     Channel impulse response estimation     Datadetection     Uniform rectangular array (URA)     Massive multiple-input multiple-output (MIMO)    

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Asecure data sharing scheme with cheating detection based onChaum-Pedersen protocol for cloud storage Research Papers

Xin WANG, Bo YANG, Zhe XIA, Hong-xia HOU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 787-800 doi: 10.1631/FITEE.1800066

Abstract:

With the development of cloud computing technology, data can be outsourced to the cloud and convenientlyHowever, in many circumstances, users may have concerns about the reliability and integrity of their dataIt is crucial to provide data sharing services that satisfy these security requirements.We introduce a reliable and secure data sharing scheme, using the threshold secret sharing techniqueIt is particularly suitable for application to protect users’ medical insurance data over the cloud.

Keywords： Data sharing     Chaum-Pedersen proof     Cheating detection     Cloud storage    

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Underwater object detection by fusing features from different representations of sonar data Research Article

Fei WANG, Wanyu LI, Miao LIU, Jingchun ZHOU, Weishi ZHANG,feiwang@dlmu.edu.cn,zhoujingchun@dlmu.edu.cn,teesiv@dlmu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 6,   Pages 828-843 doi: 10.1631/FITEE.2200429

Abstract: Modern methods recognize objects from sonar data based on their geometric shapes.However, the distortion of objects during data acquisition and representation is seldom considered.In this paper, we present a detailed summary of representations for sonar data and a concrete analysisof the geometric characteristics of different data representations.Three strategies are presented to investigate the impact of on different components of the detection

Keywords： Underwater object detection     Sonar data representation     Feature fusion    

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The research of detection of outliers based on manifold lear ning

Xu Xuesong,Song Dongming,Zhang Xu,Xu Manwu,Liu Fengyu

Strategic Study of CAE 2009, Volume 11, Issue 2,   Pages 82-87

Abstract:

The data dimensionality reduction is the main method that can enhancethe outliers mining efficiency based on higher- dimension data set.The research of detection of outliers based on manifold learning is proposed after analyzing the advantagesset after data dimensionality reduction, so as to improve efficiency of detection of outliers.Our method gives a new way for the solution of detection of outliers.

Keywords： manifold learning     detection of outliers     high dimensional data     dimensionality reduction     outliers    

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Data-Driven Anomaly Diagnosis for Machining Processes Article

Y.C. Liang, S. Wang, W.D. Li, X. Lu

Engineering 2019, Volume 5, Issue 4,   Pages 646-652 doi: 10.1016/j.eng.2019.03.012

Abstract: To address this issue, this paper presents a novel data-driven diagnosis system for anomalies.In this system, power data for condition monitoring are continuously collected during dynamic machininganalysis, preprocessing mechanisms have been designed to denoise, normalize, and align the monitored dataBased on historical data, the values of thresholds are optimized using a fruit fly optimization (FFO)algorithm to achieve more accurate detection.

Keywords： Computer numerical control machining     Anomaly detection     Fruit fly optimization algorithm     Data-driven    

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Edge Detection Based on a Uniform B-Spline With Shape Parameter by Modifying Profit and Loss Data

Zhao Yanli ,Wang Zhan ,Guo Chenghao ,Liu Fengyu

Strategic Study of CAE 2007, Volume 9, Issue 7,   Pages 65-70

Abstract:

This paper puts forward a novel image edge detection method based onuniform B-spline with shape parameter by modifying profit and loss data.A smooth surface of the digital image is presented by the new modified data.

Keywords： uniform B-spline with shape parameter     edge detection     computer vision     profit and loss modifying    

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Outliers detection algorithm based on nonlinear data transformation

Xu Xuesong,Zhang Xu,Song Dongming,Zhang Hong,Liu Fnegyu

Strategic Study of CAE 2008, Volume 10, Issue 9,   Pages 74-78

Abstract: outliers mining efficiency based on higher-dimension data set.A novel outlier detection algorithm is proposed after analyzing the advantages and disadvantages of theinto linear data in the feature space,and introduce a nonlinear data transformation to reduce data dimensionOn the basis of each resulting vector,it determins whether the data is outlier data or not one by oneused to detect nonlinear inseparable outlier data.

Keywords： dimension reduction     kernel function     principal component     outliers    

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FAAD: an unsupervised fast and accurate anomaly detectionmethod for amulti-dimensional sequence over data Regular Papers

Bin LI, Yi-jie WANG, Dong-sheng YANG, Yong-mou LI, Xing-kong MA

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3,   Pages 388-404 doi: 10.1631/FITEE.1800038

Abstract: Sequence data in these fields are usually multi-dimensional over the data stream.It is a challenge to design an anomaly detection method for a multi-dimensional sequence over the datafor sequence modeling; (2) Anomaly detection cannot adapt to the high-speed nature of the data streamTo improve the performance of anomaly detection for a multi-dimensional sequence over the data streamSecond, to speed up model construction and ensure the detection rate for the sequence over the data stream

Keywords： Data stream     Multi-dimensional sequence     Anomaly detection     Concept drift     Feature selection    

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A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars Article

Ziyi Liu,Siyu Yu,Nanning Zheng

Engineering 2018, Volume 4, Issue 4,   Pages 479-490 doi: 10.1016/j.eng.2018.07.010

Abstract: Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-drivingcars based on fusing pixel information from a monocular camera with spatial information from a light detection

Keywords： Drivable area     Self-driving     Data fusion     Co-point mapping    

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Usability perceptions and beliefs about smart thermostats by chi-square test, signal detection theory, and fuzzy detection theory in regions of Mexico

Pedro PONCE, Therese PEFFER, Arturo MOLINA

Frontiers in Energy 2019, Volume 13, Issue 3,   Pages 522-538 doi: 10.1007/s11708-018-0562-2

Abstract: This paper proposes the use of a signal detection theory (SDT), fuzzy detection theory (FDT), and chi-square

Keywords： thermostats     perceptions     beliefs     signal detection theory (SDT)     fuzzy signal detection theory (FSDT)     chi-square    

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Advances in airborne microorganisms detection using biosensors: A critical review

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 3, doi: 10.1007/s11783-021-1420-8

Abstract: In recent years, the detection technology for airborne microorganisms has developed rapidly; it can be

Keywords： Biosensor     Airborne microorganisms     Microbiological detection technology    

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Recent advances in SERS detection of perchlorate

Jumin Hao, Xiaoguang Meng

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3,   Pages 448-464 doi: 10.1007/s11705-017-1611-9

Abstract: Development of novel detection methods for perchlorate with the potential for field use has been an urgentperchlorate in water and other media with an emphasis on the development of SERS substrates for perchlorate detection

Keywords： perchlorate     SERS     detection     substrate     modification     nanostructure    

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Title Author Date Type Operation

A Survey of Tax Risk Detection Using Data Mining Techniques

Qinghua Zheng,Yiming Xu,Huixiang Liu,Bin Shi,Jiaxiang Wang,Bo Dong,

Journal Article

An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and groupmethod of data handling surrogate model

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

Journal Article

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

Journal Article

Joint DOA and channel estimation with data detection based on 2D unitary ESPRITin massive MIMO systems

Jing-ming KUANG, Yuan ZHOU, Ze-song FEI

Journal Article

Asecure data sharing scheme with cheating detection based onChaum-Pedersen protocol for cloud storage

Xin WANG, Bo YANG, Zhe XIA, Hong-xia HOU

Journal Article

Underwater object detection by fusing features from different representations of sonar data

Fei WANG, Wanyu LI, Miao LIU, Jingchun ZHOU, Weishi ZHANG,feiwang@dlmu.edu.cn,zhoujingchun@dlmu.edu.cn,teesiv@dlmu.edu.cn

Journal Article

The research of detection of outliers based on manifold lear ning

Xu Xuesong,Song Dongming,Zhang Xu,Xu Manwu,Liu Fengyu

Journal Article

Data-Driven Anomaly Diagnosis for Machining Processes

Y.C. Liang, S. Wang, W.D. Li, X. Lu

Journal Article

Edge Detection Based on a Uniform B-Spline With Shape Parameter by Modifying Profit and Loss Data

Zhao Yanli ,Wang Zhan ,Guo Chenghao ,Liu Fengyu

Journal Article

Outliers detection algorithm based on nonlinear data transformation

Xu Xuesong,Zhang Xu,Song Dongming,Zhang Hong,Liu Fnegyu

Journal Article

FAAD: an unsupervised fast and accurate anomaly detectionmethod for amulti-dimensional sequence over data

Bin LI, Yi-jie WANG, Dong-sheng YANG, Yong-mou LI, Xing-kong MA

Journal Article

A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars

Ziyi Liu,Siyu Yu,Nanning Zheng

Journal Article

Usability perceptions and beliefs about smart thermostats by chi-square test, signal detection theory, and fuzzy detection theory in regions of Mexico

Pedro PONCE, Therese PEFFER, Arturo MOLINA

Journal Article

Advances in airborne microorganisms detection using biosensors: A critical review

Journal Article

Recent advances in SERS detection of perchlorate

Jumin Hao, Xiaoguang Meng

Journal Article