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Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1046-1058 doi: 10.1631/FITEE.1500085

Abstract: In this paper, we employ a metric learning approach which helps find the active elements correctly byAfter the metric has been learned, a neighborhood graph is constructed in the projected space.

Keywords： Face recognition     Sparse representation     Manifold structure     Metric learning     Subset selection    

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A software defect prediction method with metric compensation based on feature selection and transferlearning Research Article

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 715-731 doi: 10.1631/FITEE.2100468

Abstract: training efficiency and thus decrease the prediction accuracy of the model; (2) the distribution of metricbetter results on area under the receiver operating characteristic curve (AUC) value and F1-measure metric

Keywords： Defect prediction     Feature selection     Transfer learning     Metric compensation    

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A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing Article

Yaoyao Bao, Yuanming Zhu, Feng Qian

Engineering 2022, Volume 18, Issue 11,   Pages 186-196 doi: 10.1016/j.eng.2022.04.025

Abstract:

Inspired by the tremendous achievements of meta-learning in various fields, this paper proposes thelocal quadratic embedding learning (LQEL) algorithm for regression problems based on metric learningFirst, Mahalanobis metric learning is improved by optimizing the global consistency of the metrics betweenThen, we further prove that the improved metric learning problem is equivalent to a convex programming

Keywords： Local quadratic embedding     Metric learning     Regression machine     Soft sensor    

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Image-based 3D model retrieval using manifold learning None

Pan-pan MU, San-yuan ZHANG, Yin ZHANG, Xiu-zi YE, Xiang PAN

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1397-1408 doi: 10.1631/FITEE.1601764

Abstract: Thus, the image-based 3D model retrieval is reduced to a problem of Euclid-to-Riemann metric learningFinally, we design an optimization algorithm to learn a metric in this Hilbert space using a kernel trickAny new image descriptors, such as the features from deep learning, can be easily embedded in our framework

Keywords： Model retrieval     Euclidean space     Riemannian manifold     Hilbert space     Metric learning    

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Discoverymethod for distributed denial-of-service attack behavior inSDNs using a feature-pattern graphmodel Special Feature on Future Network-Research Article

Ya XIAO, Zhi-jie FAN, Amiya NAYAK, Cheng-xiang TAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 9,   Pages 1195-1208 doi: 10.1631/FITEE.1800436

Abstract: The similarity between nodes is modeled by metric learning and the Mahalanobis distance.

Keywords： Software-defined network     Distributed denial-of-service (DDoS)     Behavior discovery     Distance metric learning    

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A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 187-223 doi: 10.1007/s11708-021-0722-7

Abstract: In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

Keywords： forecasting techniques     hybrid models     neural network     solar forecasting     error metric     support vector machine    

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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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Calculation of the Behavior Utility of a Network System: Conception and Principle Article

Changzhen Hu

Engineering 2018, Volume 4, Issue 1,   Pages 78-84 doi: 10.1016/j.eng.2018.02.010

Abstract:

The service and application of a network is a behavioral process that is oriented toward its operations and tasks, whose metrics and evaluation are still somewhat of a rough comparison. This paper describes scenes of network behavior as differential manifolds. Using the homeomorphic transformation of smooth differential manifolds, we provide a mathematical definition of network behavior and propose a mathematical description of the network behavior path and behavior utility. Based on the principle of differential geometry, this paper puts forward the function of network behavior and a calculation method to determine behavior utility, and establishes the calculation principle of network behavior utility. We also provide a calculation framework for assessment of the network’s attack-defense confrontation on the strength of behavior utility. Therefore, this paper establishes a mathematical foundation for the objective measurement and precise evaluation of network behavior.

Keywords： Network metric evaluation     Differential manifold     Network behavior utility     Network attack-defense confrontation    

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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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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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Deep learning based water leakage detection for shield tunnel lining

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 6,   Pages 887-898 doi: 10.1007/s11709-024-1071-5

Abstract: A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced

Keywords： water leakage detection     deep learning     deconvolutional-feature pyramid     spatial attention    

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Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 12, doi: 10.1007/s11783-023-1752-7

Abstract:

● Online learning models accurately predict influent flow rate at

Keywords： Wastewater prediction     Data stream     Online learning     Batch learning     Influent flow rates    

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Advancing agriculture with machine learning: a new frontier in weed management

Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2024564

Abstract:

● Machine learning offers innovative and sustainable weed management

Keywords： Weed management     herbicides     machine learning     agricultural practices     environmental impact    

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Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov

Frontiers of Engineering Management doi: 10.1007/s42524-024-0082-1

Abstract: This study proposes a method, the online hidden Markov model (OHMM), which combines online learning with

Keywords： geological risk prediction     machine learning     online learning     hidden Markov model     borehole logging    

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning

Keywords： machine learning     flowsheet simulations     constraints     exploration    

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Title Author Date Type Operation

Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Journal Article

A software defect prediction method with metric compensation based on feature selection and transferlearning

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Journal Article

A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing

Yaoyao Bao, Yuanming Zhu, Feng Qian

Journal Article

Image-based 3D model retrieval using manifold learning

Pan-pan MU, San-yuan ZHANG, Yin ZHANG, Xiu-zi YE, Xiang PAN

Journal Article

Discoverymethod for distributed denial-of-service attack behavior inSDNs using a feature-pattern graphmodel

Ya XIAO, Zhi-jie FAN, Amiya NAYAK, Cheng-xiang TAN

Journal Article

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Journal Article

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

Journal Article

Calculation of the Behavior Utility of a Network System: Conception and Principle

Changzhen Hu

Journal Article

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

Journal Article

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

Journal Article

Deep learning based water leakage detection for shield tunnel lining

Journal Article

Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

Journal Article

Advancing agriculture with machine learning: a new frontier in weed management

Journal Article

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article