scholarly journals Machine learning analysis of lifeguard flag decisions and recorded rescues

2019 ◽  
Vol 19 (11) ◽  
pp. 2541-2549
Author(s):  
Chris Houser ◽  
Jacob Lehner ◽  
Nathan Cherry ◽  
Phil Wernette
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Machine Learning Techniques ◽  
Public Health Issue ◽  
Rip Currents ◽  
Effective Strategies ◽  
Wave Forcing ◽  
Learning Techniques ◽  
Yellow Flag ◽  
The Impact

Abstract. Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow, and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard(s) monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag is not consistent with the beach user perception of the risk, which may increase the potential for rescues or drownings. In this study, machine learning is used to determine the potential for error in the flags used at Pensacola Beach and the impact of that error on the number of rescues. Results of a decision tree analysis indicate that the colour flag chosen by the lifeguards was different from what the model predicted for 35 % of days between 2004 and 2008 (n=396/1125). Days when there is a difference between the predicted and posted flag colour represent only 17 % of all rescue days, but those days are associated with ∼60 % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.

scholarly journals Machine Learning Analysis of Lifeguard Flag Decisions and Recorded Rescues

2019 ◽  
Author(s):  
Chris Houser ◽  
Jacob Lehner ◽  
Phil Wernette
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Machine Learning Techniques ◽  
Public Health Issue ◽  
Effective Strategies ◽  
Wave Forcing ◽  
Learning Techniques ◽  
Yellow Flag ◽  
The Impact ◽  
Learning Analysis

Abstract. Rips currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags and signs are important and to varying degrees they are effective strategies to minimize risk. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag does not accurately reflect the potential risk, which may increase the potential for rescues or drownings. In this study, machine learning used to determine the potential for error in the flags used at Pensacola Beach, and the impact of that error on the number of rescues. A decision tree analysis suggests that the wrong flag was flown on ~ 35 % of days between 2004 and 2008 (n = 396/1125), and that those differences account for only 17 % of all rescue days and ~ 60 % of the total number of rescues. Further analysis reveals that the largest number of rescue days and total number of rescues is associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model would suggest a green flag would be more appropriate based on the wind and wave forcing. Regardless whether this is a result of the lifeguards being overly cautious or the rip and surf hazard is associated with weak rips forced by a transverse-bar and rip morphology, the results suggest that beach users are discounting the lifeguard warnings if it isn't consistent with how they perceive the surf hazard. Results suggest that machine learning techniques have the potential to support lifeguard and thereby reduce the number of rescues and drownings.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

Primary Factors Influencing the Decision to Vaccinate Against COVID-19 in the United States: A Predictive Analytics Approach (Preprint)

2021 ◽  
Author(s):  
Serkan Varol ◽  
Serkan Catma ◽  
Diana Reindl ◽  
Elizabeth Serieux
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Predictive Analytics ◽  
The United States ◽  
Machine Learning Techniques ◽  
Vaccine Hesitancy ◽  
Willingness To Accept ◽  
Perceptions And Attitudes ◽  
Learning Techniques ◽  
Vaccine Decision Making

BACKGROUND Vaccine refusal still poses a risk to reaching herd immunity in the United States. The existing literature focuses on identifying the predictors that would impact the willingness to accept (WTA) vaccines using survey data. These variables range from the socio-demographic characteristics of the participants to the perceptions and attitudes towards the vaccines so each variable’s statistical relationship with the WTA a vaccine can be investigated. However, while the results of these studies may have important implications for understanding vaccine hesitancy by offering interpretation of the statistical relationships, the prediction of vaccine decision-making has rarely been investigated OBJECTIVE We aimed to identify the factors that contribute to the prediction of COVID-19 vaccine acceptors and refusers using machine learning METHODS A nationwide survey was administered online in November, 2020 to assess American public perceptions and attitudes towards COVID-19 vaccines. Seven machine learning techniques were utilized to identify the model with the highest predictive power. Moreover, a set of variables that would contribute the most to the predictions of vaccine acceptors and refusers was identified using Gini importance based on Random Forest structure RESULTS The resulting machine learning algorithm has better prediction ability for willingness to accept (82%) versus reject (51%) a COVID-19 vaccine. In terms of predictive success, the Random Forest model outperformed the other machine learning techniques with a 69.52% accuracy rate. Worrying about (re) contracting Covid 19 and opinions regarding mandatory face covering were identified as the most important predictors of vaccine decision-making CONCLUSIONS The complexity of vaccine hesitancy needs to be investigated thoroughly before the threshold needed to reach population immunity can be achieved. Predictive analytics can help the public health officials design and deliver individually tailored vaccination programs that would increase the overall vaccine uptake.

Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals

2021 ◽  
pp. 136-160
Author(s):  
Mercedes Barrachina ◽  
Laura Valenzuela López
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Adult Population ◽  
The United States ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Learning Techniques ◽  
Control And Prevention ◽  
Future Research Directions

Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.

scholarly journals Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

scholarly journals Iterative machine learning applied to annotation of text datasets

2021 ◽  
Author(s):  
Thiago Abdo ◽  
Fabiano Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
The Creation ◽  
The Impact ◽  
High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

A Novel Data Mining on Breast Cancer Survivability Using MLP Ensemble Learners

2019 ◽  
Vol 63 (3) ◽  
pp. 435-447
Author(s):  
Mohsen Salehi ◽  
Jafar Razmara ◽  
Shahriar Lotfi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Mining ◽  
Cancer Patients ◽  
Cancer Incidence ◽  
The United States ◽  
Machine Learning Techniques ◽  
Breast Cancer Patients ◽  
Stacked Generalization ◽  
Learning Techniques

Abstract Breast cancer survivability has always been an important and challenging issue for researchers. Different methods have been utilized mostly based on machine learning techniques for prediction of survivability among cancer patients. The most comprehensive available database of cancer incidence is SEER in the United States, which has been frequently used for different research purposes. In this paper, a new data mining has been performed on the SEER database in order to investigate the ability of machine learning techniques for survivability prediction of breast cancer patients. To this end, the data related to breast cancer incidence have been preprocessed to remove unusable records from the dataset. In sequel, two machine learning techniques were developed based on the Multi-Layer Perceptron (MLP) learner machine including MLP stacked generalization and mixture of MLP-experts to make predictions over the database. The machines have been evaluated using K-fold cross-validation technique. The evaluation of the predictors revealed an accuracy of 84.32% and 83.86% by the mixture of MLP-experts and MLP stacked generalization methods, respectively. This indicates that the predictors can be significantly used for survivability prediction suggesting time- and cost-effective treatment for breast cancer patients.

scholarly journals Machine learning approach for simulation of heavy metal concentration in river water: the Crimean peninsula case study

2020 ◽  
Vol 163 ◽  
pp. 06009
Author(s):  
Evgeniy Malygin ◽  
Mikhail Lychagin
Keyword(s):  
Machine Learning ◽  
Heavy Metal ◽  
Metal Concentration ◽  
Heavy Metal Concentration ◽  
Machine Learning Techniques ◽  
Crimean Peninsula ◽  
River Waters ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
The Impact

This study proposes an approach for simulation of heavy metal concentration in river waters using machine learning techniques. A regression model was built and it captured the relationship between the concentration of heavy metal and metalloids (HMM) and several characteristics of studied catchment. Machine learning techniques allowed to simulate the annual concentration variability of HMM. This approach allows exploring the impact of different factors on studied processes.

Automatic Routing in Triage Process

2013 ◽  
Author(s):  
Dinesh Rathi
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Domain Experts ◽  
Digital Reference ◽  
Learning Techniques ◽  
The Impact

This study investigates and characterizes the impact of different features of email on effective routing of email to domain experts. The findings of the study would help in understanding how machine learning techniques such as classification could be applied effectively to develop better automatic triage process in digital reference service.Cette étude examine et caractérise l'impact de différentes caractéristiques des courriels sur leur acheminement efficace aux experts du domaine. Les résultats de l'étude permettraient de comprendre comment les techniques d'apprentissages machine comme la classification pourraient être appliquées efficacement afin de développer de meilleurs processus de triage automatique pour les services de référence numérique. 

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