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A machine learning approach to query generation in plagiarism source retrieval Article

Lei-lei KONG, Zhi-mao LU, Hao-liang QI, Zhong-yuan HAN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1556-1572 doi: 10.1631/FITEE.1601344

Abstract: This paper paves the way for a new statistical machine learning approach to select the best queries fromThe statistical machine learning approach to query generation for source retrieval is formulated as aThe proposed method exploits learning to rank to generate queries from the candidates.To our knowledge, our work is the first research to apply machine learning methods to resolve the problemTo solve the essential problem of an absence of training data for learning to rank, the building of training

Keywords： Plagiarism detection     Source retrieval     Query generation     Machine learning     Learning to rank    

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Study on Rank Reversals in Liner Allocation Method

Zhang Ling,Zhou Dequn

Strategic Study of CAE 2008, Volume 10, Issue 2,   Pages 50-53

Abstract:

The problem that the liner allocation method can result in rank reversalconcepts of sorting vectors and their relativity distribute ra tios are proposed to explain the causes of rank

Keywords： decision making     liner allocation method     rank reversal     rank preservation    

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Using heterogeneous patent network features to rank and discover influential inventors

Yong-ping DU,Chang-qing YAO,Nan LI

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 7,   Pages 568-578 doi: 10.1631/FITEE.1400394

Abstract: Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not applicable to a heterogeneous network. Registered patent history data denotes the innovations and the achievements in different research fields. In this paper, we present an iteration algorithm called inventor-ranking, to sort the influences of patent inventors in heterogeneous networks constructed based on their patent data. This approach is a flexible rule-based method, making full use of the features of network topology. We sort the inventors and patents by a set of rules, and the algorithm iterates continuously until it meets a certain convergence condition. We also give a detailed analysis of influential inventor’s interesting topics using a latent Dirichlet allocation (LDA) model. Compared with the traditional methods such as PageRank, our approach takes full advantage of the information in the heterogeneous network, including the relationship between inventors and the relationship between the inventor and the patent. Experimental results show that our method can effectively identify the inventors with high influence in patent data, and that it converges faster than PageRank.

Keywords： Heterogeneous patent network     Influence     Rule-based ranking    

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A critical review of ash slagging mechanisms and viscosity measurement for low-rank coal and bio-slags

Md Tanvir ALAM, Baiqian DAI, Xiaojiang WU, Andrew HOADLEY, Lian ZHANG

Frontiers in Energy 2021, Volume 15, Issue 1,   Pages 46-67 doi: 10.1007/s11708-020-0807-8

Abstract: In particular, this paper focuses on low-rank coal and biomass that have been receiving increased attentionand slagging indices can only satisfactorily predict the viscosity and slagging propensity of high-rankcoals but cannot predict the slagging propensity and slag viscosity of low-rank coal, and especiallymethod, which can predict/measure the slagging propensity and slag viscosity correctly for all low-rank

Keywords： slag     viscosity     biomass     low-rank coal     combustion     gasification    

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Study on Rank Preservation of the MCDM With Normalizing Formula

Zhang Ling,Zhou Dequn,Li Hongwei,Zhu Peifeng

Strategic Study of CAE 2006, Volume 8, Issue 12,   Pages 85-88

Abstract: normalizing formula will result in violations of independence from irrelevant alternatives and lead to rankIn the paper, the two methods that improve the original normalizing formula to erase the rank reversal

Keywords： MCDM decision     normalizing     violations of independence from irrelevant alternatives     rank preservation     rank    

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An intuitive general rank-based correlation coefficient Research Articles

Divya PANDOVE, Shivani GOEL, Rinkle RANI

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6,   Pages 699-711 doi: 10.1631/FITEE.1601549

Abstract: We propose a predictive metric to calculate correlations between paired values, known as the general rank-basedWe have compared it to Spearman’s rank correlation coefficient.

Keywords： General rank-based correlation coefficient     Multivariate analysis     Predictive metric     Spearman’s rank correlation    

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Fuzzy Rank Methodology for Risk Assessment of Environmental Pollution Accidents

Xiong Deqi,Chen Gang,Li Qiong

Strategic Study of CAE 2001, Volume 3, Issue 8,   Pages 46-50

Abstract: factories is a multi-criteria and multi-stage process, and is of relativity and fuzziness, the fuzzy rank

Keywords： pollution accidents     risk     fuzzy     assessment     rank    

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A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties

Frontiers in Energy 2023, Volume 17, Issue 2,   Pages 198-210 doi: 10.1007/s11708-023-0874-8

Abstract: To address this challenge, a rank-based multiple-choice secretary algorithm (RMSA) was proposed for microgrid

Keywords： energy management systems     demand response     scheduling under uncertainty     renewable energy sources     multiple-choice secretary algorithm    

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Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint Article

Yu-tang ZHU,Yong-bo ZHAO,Jun LIU,Peng-lang SHUI

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 11,   Pages 1245-1252 doi: 10.1631/FITEE.1601112

Abstract: We propose a low complexity robust beamforming method for the general-rank signal model, to combat against

Keywords： Beamforming     General-rank     Low complexity     Positive semidefinite (PSD) constraint     Model mismatches    

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Changes in hourly precipitation may explain the sharp reduction of discharge in the middle reach of the Yellow River after 2000

Lin LUO, Zhongjing WANG

Frontiers of Environmental Science & Engineering 2013, Volume 7, Issue 5,   Pages 756-768 doi: 10.1007/s11783-013-0563-7

Abstract: The Hekou-Longmen reach, together with local floods, is the main source area for coarse sedimentations into the Yellow River. When total rainfall slightly increased in the area, discharge dramatically decreased by 40%–70% after the year of 2000, and attracting extensive attention in the context of global climate change. High temporal resolution precipitation (timescales between 1 and 4 h) data from the June to September period from 270 rain gauges over the past three decades was mined in order to help explain the phenomenon. Each rainfall event was classified as light/moderate rain, large rain, heavy rain or rainstorm by the event’s rainfall amount, and further classified as low intensity rain, medium intensity rain and high intensity rain by the event’s rainfall intensity. The Mann-Kendall trend test was applied to detect the presence and significance of monotonic trends, and to find the change points in the mean and variance of the precipitation characteristics series, including the amount, intensity, frequency and duration of each rainfall category. Results show that although the total amount of precipitation has slightly increased, the average rainfall intensity has significantly decreased. The larger change happened in light/moderate rain events and low/medium intensity rain events, and the intensity changes have a great extent occurred around the threshold of Non-Runoff Rainfall regime, which was proposed for the approximate calculation of initial losses. Changes in rainfall distribution between different classes of the Runoff Rainfall regime in the 2000s could lead to 0.9 mm less runoff depth (17.3% of the total reduction) than the 1980–1999 period. The study indicates that changes in hourly precipitation may be responsible for the sharp reduction of discharge.

Keywords： precipitation intensity     Mann-Kendall rank statistic     spatial and temporal distribution     climatic change    

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Ensemble unit and AI techniques for prediction of rock strain

Pradeep T; Pijush SAMUI; Navid KARDANI; Panagiotis G ASTERIS

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 858-870 doi: 10.1007/s11709-022-0831-3

Abstract: The behavior of rock masses is influenced by a variety of forces, with measurement of stress and strain playing the most critical roles in assessing deformation. The laboratory test for determining strain at each location within rock samples is expensive and difficult but rock strain data are important for predicting failure of rock material. Many researchers employ AI technology in order to solve these difficulties. AI algorithms such as gradient boosting machine (GBM), support vector regression (SVR), random forest (RF), and group method of data handling (GMDH) are used to efficiently estimate the strain at every point within a rock sample. Additionally, the ensemble unit (EnU) may be utilized to evaluate rock strain. In this study, 3000 experimental data are used for the purpose of prediction. The obtained strain values are then evaluated using various statistical parameters and compared to each other using EnU. Ranking analysis, stress-strain curve, Young’s modulus, Poisson’s ratio, actual vs. predicted curve, error matrix and the Akaike’s information criterion (AIC) values are used for comparing models. The GBM model achieved 98.16% and 99.98% prediction accuracy (in terms of values of R2) in the longitudinal and lateral dimensions, respectively, during the testing phase. The GBM model, based on the experimental data, has the potential to be a new option for engineers to use when assessing rock strain.

Keywords： prediction     strain     ensemble unit     rank analysis     error matrix    

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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research Article

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Engineering 2021, Volume 7, Issue 12,   Pages 1725-1731 doi: 10.1016/j.eng.2020.05.028

Abstract: Therefore, in this article, we introduce the reduced rank regression method and its extensions, sparsereduced rank regression and subspace assisted regression with row sparsity, which hold potential to

Keywords： Multivariate regression methods     Reduced rank regression     Sparsity     Dimensionality reduction     Variable    

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Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords： Ozone     Integrated method     Machine learning    

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Title Author Date Type Operation

A machine learning approach to query generation in plagiarism source retrieval

Lei-lei KONG, Zhi-mao LU, Hao-liang QI, Zhong-yuan HAN

Journal Article

Study on Rank Reversals in Liner Allocation Method

Zhang Ling,Zhou Dequn

Journal Article

Using heterogeneous patent network features to rank and discover influential inventors

Yong-ping DU,Chang-qing YAO,Nan LI

Journal Article

A critical review of ash slagging mechanisms and viscosity measurement for low-rank coal and bio-slags

Md Tanvir ALAM, Baiqian DAI, Xiaojiang WU, Andrew HOADLEY, Lian ZHANG

Journal Article

Study on Rank Preservation of the MCDM With Normalizing Formula

Zhang Ling,Zhou Dequn,Li Hongwei,Zhu Peifeng

Journal Article

An intuitive general rank-based correlation coefficient

Divya PANDOVE, Shivani GOEL, Rinkle RANI

Journal Article

Fuzzy Rank Methodology for Risk Assessment of Environmental Pollution Accidents

Xiong Deqi,Chen Gang,Li Qiong

Journal Article

A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties

Journal Article

Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint

Yu-tang ZHU,Yong-bo ZHAO,Jun LIU,Peng-lang SHUI

Journal Article

Changes in hourly precipitation may explain the sharp reduction of discharge in the middle reach of the Yellow River after 2000

Lin LUO, Zhongjing WANG

Journal Article

Ensemble unit and AI techniques for prediction of rock strain

Pradeep T; Pijush SAMUI; Navid KARDANI; Panagiotis G ASTERIS

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article