scholarly journals Unsupervised Machine Learning as a Tool for Exploratory Analysis of Acoustic Telemetry Data: A Case Study With Northern Pike in Toronto Harbour

2021 ◽  
Author(s):  
Adogbeji Agberien
Keyword(s):  
Machine Learning ◽  
Acoustic Telemetry ◽  
Northern Pike ◽  
Exploratory Analysis ◽  
Telemetry Data ◽  
Unsupervised Machine Learning

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

2019 ◽  
Author(s):  
Jun Xia ◽  
Pei-Jie Chen ◽  
Ji-Hong Wang ◽  
Jie Zhuang ◽  
Zhen-Bo Cao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Schools ◽  
Analysis Of Covariance ◽  
School Type ◽  
Combined Approach ◽  
Combined Method ◽  
Unsupervised Machine Learning ◽  
Individual School ◽  
Indoor And Outdoor

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.

scholarly journals A Novel Framework to Predict Relative Habitat Selection in Aquatic Systems: Applying Machine Learning and Resource Selection Functions to Acoustic Telemetry Data From Multiple Shark Species

2021 ◽  
Vol 8 ◽  
Author(s):  
Lucas P. Griffin ◽  
Grace A. Casselberry ◽  
Kristen M. Hart ◽  
Adrian Jordaan ◽  
Sarah L. Becker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Habitat Selection ◽  
Acoustic Telemetry ◽  
Resource Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Shark Species ◽  
Telemetry Data ◽  
Resource Selection Functions ◽  
Resource Managers

Resource selection functions (RSFs) have been widely applied to animal tracking data to examine relative habitat selection and to help guide management and conservation strategies. While readily used in terrestrial ecology, RSFs have yet to be extensively used within marine systems. As acoustic telemetry continues to be a pervasive approach within marine environments, incorporation of RSFs can provide new insights to help prioritize habitat protection and restoration to meet conservation goals. To overcome statistical hurdles and achieve high prediction accuracy, machine learning algorithms could be paired with RSFs to predict relative habitat selection for a species within and even outside the monitoring range of acoustic receiver arrays, making this a valuable tool for marine ecologists and resource managers. Here, we apply RSFs using machine learning to an acoustic telemetry dataset of four shark species to explore and predict species-specific habitat selection within a marine protected area. In addition, we also apply this RSF-machine learning approach to investigate predator-prey relationships by comparing and averaging tiger shark relative selection values with the relative selection values derived for eight potential prey-species. We provide methodological considerations along with a framework and flexible approach to apply RSFs with machine learning algorithms to acoustic telemetry data and suggest marine ecologists and resource managers consider adopting such tools to help guide both conservation and management strategies.

scholarly journals Unsupervised Machine Learning for Co/Multimorbidity Analysis

2018 ◽  
Vol 7 (6) ◽  
pp. 23 ◽  
Author(s):  
Shatrunjai P. Singh ◽  
Swagata Karkare ◽  
Sudhir M. Baswan ◽  
Vijendra P. Singh
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Patient Population ◽  
Exploratory Analysis ◽  
Agglomerative Hierarchical Clustering ◽  
Specific Population ◽  
Unsupervised Machine Learning ◽  
Depth Analysis ◽  
Study Population ◽  
Multimorbid Patients

Although co/multimorbidities are associated with a significant increase in mortality, lack of quantitative exploratory techniques often impedes an in-depth analysis of their association. In the current study, we explore the clustering of co/multimorbid patients in the Texas patient population. We employ unsupervised agglomerative hierarchical clustering to find clusters of co/multimorbid patients within this population. Our analysis revealed the presence of nine distinct, clinically relevant clusters of co/multimorbidities within the study population of interest. This technique provides a quantitative exploratory analysis of the co/multimorbidities present in a specific population.

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