Evaluation on Sports Facility Resource in Primary School Using a Combined Approach of Unsupervised Machine Learning: a Case Study of Shanghai, China

The aim of this study is (a) to develop, test, and employ a combined method of unsupervised machine learning to objectively assess the condition of sports facility in primary schools (PSSFC) and (b) examine the examine the geographical and typological association with PSSFC. Based on the Sixth National Sports Facility Census (NSFC), six PSSFC indicators (indoor and outdoor facility included) were selected as the measurements and decomposed by using the t-stochastic neighbor embedding (t-SNE). Thereafter, the Fuzzy C-mean (FCM) algorithm was used to cluster the same type of PSSFC with selecting the optimum numbers of evaluation level. Overall 845 primary schools in Shanghai, China were recruited and tested by this combined approach of unsupervised machine learning. In addition, the two-way analysis of covariance was used to examine the location and types of school associated with PSSFC variables in each level. The combined method was found to have acceptable reliability and good interpretability, differentiating PSSFC into five gradient levels. The characteristics of PSSFC differ by the location and school type of individual school. Our findings are conducive to the regionalized and personalized intervention and promotion on the children’s physical activity (PA) upon the practical situation of particular schools.

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Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: A case study from Ag–Pb–Zn deposits in north-western Zhejiang, China

Applied Geochemistry ◽

10.1016/j.apgeochem.2020.104679 ◽

2020 ◽

Vol 120 ◽

pp. 104679 ◽

Cited By ~ 2

Author(s):

Jun Wang ◽

Yongzhang Zhou ◽

Fan Xiao

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Unsupervised Machine Learning ◽

Geochemical Anomalies ◽

North Western

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Mind the Queue: A Case Study in Visualizing Heterogeneous Behavioral Patterns in Livestock Sensor Data Using Unsupervised Machine Learning Techniques

Frontiers in Veterinary Science ◽

10.3389/fvets.2020.00523 ◽

2020 ◽

Vol 7 ◽

Cited By ~ 1

Author(s):

Catherine McVey ◽

Fushing Hsieh ◽

Diego Manriquez ◽

Pablo Pinedo ◽

Kristina Horback

Keyword(s):

Machine Learning ◽

Sensor Data ◽

Machine Learning Techniques ◽

Behavioral Patterns ◽

Unsupervised Machine Learning ◽

Learning Techniques

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A combined approach of atom probe tomography and unsupervised machine learning to understand phase transformation in (AlxGa1−x)2O3

Applied Physics Letters ◽

10.1063/5.0002049 ◽

2020 ◽

Vol 116 (15) ◽

pp. 152101 ◽

Cited By ~ 3

Author(s):

Jith Sarker ◽

Scott Broderick ◽

A. F. M. Anhar Uddin Bhuiyan ◽

Zixuan Feng ◽

Hongping Zhao ◽

...

Keyword(s):

Machine Learning ◽

Phase Transformation ◽

Atom Probe Tomography ◽

Atom Probe ◽

Combined Approach ◽

Unsupervised Machine Learning

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Unsupervised machine learning in industrial applications: a case study in iron mining

2020 IEEE Bombay Section Signature Conference (IBSSC) ◽

10.1109/ibssc51096.2020.9332174 ◽

2020 ◽

Author(s):

Leonardo Santiago Benitez Pereira ◽

Rafael Nilson Rodrigues ◽

Edison Antonio Cardoso Aranha Neto

Keyword(s):

Machine Learning ◽

Industrial Applications ◽

Unsupervised Machine Learning ◽

Iron Mining

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Intrusion Detection in Network Systems Through Hybrid Supervised and Unsupervised Machine Learning Process: A Case Study on the ISCX Dataset

2018 1st International Conference on Data Intelligence and Security (ICDIS) ◽

10.1109/icdis.2018.00043 ◽

2018 ◽

Cited By ~ 7

Author(s):

Saeid Soheily-Khah ◽

Pierre-Francois Marteau ◽

Nicolas Bechet

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Learning Process ◽

Network Systems ◽

Unsupervised Machine Learning

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Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

Forests ◽

10.3390/f12121663 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1663

Author(s):

Shaoqing Dai ◽

Xiaoman Zheng ◽

Lei Gao ◽

Chengdong Xu ◽

Shudi Zuo ◽

...

Keyword(s):

Machine Learning ◽

Spatial Statistics ◽

Forest Biomass ◽

Absolute Error ◽

Support Vector ◽

Combined Approach ◽

Combined Method ◽

Statistic Model ◽

Combined Models ◽

Level Model

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.

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