scholarly journals A machine learning perspective on responsible gambling

2018 ◽  
Author(s):  
Arman Hassanniakalager ◽  
Philip Warren Stirling Newall
Keyword(s):  
Machine Learning ◽  
Salient Feature ◽  
Responsible Gambling ◽  
Quantitative Information ◽  
Random Strategy ◽  
Large Spread ◽  
Relative Product ◽  
Product Risk ◽  
Average Loss ◽  
Betting Odds

Gamblers are frequently reminded to “gamble responsibly.” But these qualitative reminders come with no quantitative information for gamblers to judge relative product risk in skill-based gambling forms. By comparison, consumers purchasing alcohol are informed of product strength by alcohol by volume (ABV %) or similar labels. This paper uses mixed logistic regression machine learning to uncover the potential variation in soccer betting outcomes. This paper uses data from four bet types and eight seasons of English Premier League soccer, ending in 2018. Outcomes across each bet type were compared using three betting strategies: the most-skilled prediction, a random strategy, and the least-skilled prediction. There was a large spread in betting outcomes, with for example the per-bet average loss varying by a factor of 54 (from 1.1% to 58.9%). Gamblers’ losses were positively correlated with the observable betting odds across all bets, indicating that betting odds are one salient feature which could be used to inform gamblers about product risk. Such large differences in product risk are relevant to the promotion of responsible gambling.

scholarly journals A machine learning perspective on responsible gambling

2019 ◽  
pp. 1-24 ◽  
Author(s):  
ARMAN HASSANNIAKALAGER ◽  
PHILIP W.S. NEWALL
Keyword(s):  
Machine Learning ◽  
Salient Feature ◽  
Responsible Gambling ◽  
Quantitative Information ◽  
Volume Percentage ◽  
Random Strategy ◽  
Large Spread ◽  
Product Risk ◽  
Average Loss ◽  
Betting Odds

AbstractGamblers are frequently reminded to ‘gamble responsibly’. But these qualitative reminders come with no quantitative information for gamblers to judge relative product risk in skill-based gambling forms. By comparison, consumers purchasing alcohol are informed of product strength by alcohol by volume percentage (ABV%) or similar labels. This paper uses mixed logistic regression machine learning to uncover the potential variation in soccer betting outcomes. This paper uses data from four bet types and eight seasons of English Premier League soccer, ending in 2018. Outcomes across each bet type were compared using three betting strategies: the most-skilled prediction, a random strategy and the least-skilled prediction. There was a large spread in betting outcomes, with, for example, the per-bet average loss varying by a factor of 54 (from 1.1% to 58.9%). Gamblers’ losses were positively correlated with the observable betting odds across all bets, indicating that betting odds are one salient feature that could be used to inform gamblers about product risk. Such large differences in product risk are relevant to the promotion of responsible gambling.

Exploring multi-modalities in weather prediction using a univariate graph based on machine learning techniques

2021 ◽  
Author(s):  
Natacha Galmiche ◽  
Nello Blaser ◽  
Morten Brun ◽  
Helwig Hauser ◽  
Thomas Spengler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Probability Distributions ◽  
Weather Prediction ◽  
A Priori ◽  
Clustering Algorithms ◽  
Quantitative Information ◽  
Machine Learning Techniques ◽  
Topological Data Analysis ◽  
Learning Techniques

<p>Probability distributions based on ensemble forecasts are commonly used to assess uncertainty in weather prediction. However, interpreting these distributions is not trivial, especially in the case of multimodality with distinct likely outcomes. The conventional summary employs mean and standard deviation across ensemble members, which works well for unimodal, Gaussian-like distributions. In the case of multimodality this misleads, discarding crucial information. </p><p>We aim at combining previously developed clustering algorithms in machine learning and topological data analysis to extract useful information such as the number of clusters in an ensemble. Given the chaotic behaviour of the atmosphere, machine learning techniques can provide relevant results even if no, or very little, a priori information about the data is available. In addition, topological methods that analyse the shape of the data can make results explainable.</p><p>Given an ensemble of univariate time series, a graph is generated whose edges and vertices represent clusters of members, including additional information for each cluster such as the members belonging to them, their uncertainty, and their relevance according to the graph. In the case of multimodality, this approach provides relevant and quantitative information beyond the commonly used mean and standard deviation approach that helps to further characterise the predictability.</p>

Imaging-based prediction of histological clot composition from admission CT imaging

2021 ◽  
pp. neurintsurg-2020-016774
Author(s):  
Uta Hanning ◽  
Peter B Sporns ◽  
Marios N Psychogios ◽  
Astrid Jeibmann ◽  
Jens Minnerup ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vascular Tissue ◽  
Large Diameter ◽  
Small Diameter ◽  
Area Under The Curve ◽  
Quantitative Information ◽  
Youden Index ◽  
Quantitative Image ◽  
Characteristic Area ◽  
Contrast Ct

BackgroundThrombus composition has been shown to be a major determinant of recanalization success and occurrence of complications in mechanical thrombectomy. The most important parameters of thrombus behavior during interventional procedures are relative fractions of fibrin and red blood cells (RBCs). We hypothesized that quantitative information from admission non-contrast CT (NCCT) and CT angiography (CTA) can be used for machine learning based prediction of thrombus composition.MethodsThe analysis included 112 patients with occlusion of the carotid-T or middle cerebral artery who underwent thrombectomy. Thrombi samples were histologically analyzed and fractions of fibrin and RBCs were determined. Thrombi were semi-automatically delineated in CTA scans and NCCT scans were registered to the same space. Two regions of interest (ROIs) were defined for each thrombus: small-diameter ROIs capture vessel walls and thrombi, large-diameter ROIs reflect peri-vascular tissue responses. 4844 quantitative image markers were extracted and evaluated for their ability to predict thrombus composition using random forest algorithms in a nested fivefold cross validation.ResultsTest set receiver operating characteristic area under the curve was 0.83 (95% CI 0.80 to 0.87) for differentiating RBC-rich thrombi and 0.84 (95% CI 0.80 to 0.87) for differentiating fibrin-rich thrombi. At maximum Youden-Index, RBC-rich thrombi were identified at 77% sensitivity and 74% specificity; for fibrin-rich thrombi the classifier reached 81% sensitivity at 73% specificity.ConclusionsMachine learning based analysis of admission imaging allows for prediction of clot composition. Perspectively, such an approach could allow selection of clot-specific devices and retrieval procedures for personalized thrombectomy strategies.

Research Methods to Study and Empower Crowd Workers

2021 ◽  
pp. 164-184
Author(s):  
Saiph Savage ◽  
Carlos Toxtli ◽  
Eber Betanzos-Torres
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Research Methods ◽  
Real World ◽  
Intelligent Systems ◽  
Quantitative Information ◽  
Learning Models ◽  
Professional Goals ◽  
Career Growth ◽  
Machine Learning Models

The artificial intelligence (AI) industry has created new jobs that are essential to the real world deployment of intelligent systems. Part of the job focuses on labelling data for machine learning models or having workers complete tasks that AI alone cannot do. These workers are usually known as ‘crowd workers’—they are part of a large distributed crowd that is jointly (but separately) working on the tasks although they are often invisible to end-users, leading to workers often being paid below minimum wage and having limited career growth. In this chapter, we draw upon the field of human–computer interaction to provide research methods for studying and empowering crowd workers. We present our Computational Worker Leagues which enable workers to work towards their desired professional goals and also supply quantitative information about crowdsourcing markets. This chapter demonstrates the benefits of this approach and highlights important factors to consider when researching the experiences of crowd workers.

scholarly journals Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

2019 ◽  
Vol 10 (1) ◽  
pp. 46 ◽  
Author(s):  
Johannes Stübinger ◽  
Benedikt Mangold ◽  
Julian Knoll
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Home Team ◽  
Ensemble Strategy ◽  
The World ◽  
European Football ◽  
The Individual ◽  
Football Betting ◽  
Betting Odds

In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.

scholarly journals Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

2018 ◽  
Author(s):  
Iason-Zois Gazis ◽  
Timm Schoening ◽  
Evangelos Alevizos ◽  
Jens Greinert
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Autonomous Underwater Vehicle ◽  
Predictive Modelling ◽  
Quantitative Information ◽  
Training Data ◽  
Optical Data ◽  
Small Scale ◽  
Ground Truth Data ◽  
Median Size

Abstract. In this study, high-resolution bathymetric multibeam and optical image data, both obtained within the Belgian manganese (Mn) nodule mining license area by the autonomous underwater vehicle (AUV) Abyss, were combined in order to create a predictive Random Forests (RF) machine learning model. AUV bathymetry reveals small-scale terrain variations, allowing slope estimations and calculation of bathymetric derivatives such as slope, curvature, and ruggedness. Optical AUV imagery provides quantitative information regarding the distribution (number and median size) of Mn-nodules. Within the area considered in this study, Mn-nodules show a heterogeneous and spatially clustered pattern and their number per square meter is negatively correlated with their median size. A prediction of the number of Mn-nodules was achieved by combining information derived from the acoustic and optical data using a RF model. This model was tuned by examining the influence of the training set size, the number of growing trees (ntree) and the number of predictor variables to be randomly selected at each RF node (mtry) on the RF prediction accuracy. The use of larger training data sets with higher ntree and mtry values increases the accuracy. To estimate the Mn-nodule abundance, these predictions were linked to ground truth data acquired by box coring. Linking optical and hydro-acoustic data revealed a non-linear relationship between the Mn-nodule distribution and topographic characteristics. This highlights the importance of a detailed terrain reconstruction for a predictive modelling of Mn-nodule abundance. In addition, this study underlines the necessity of a sufficient spatial distribution of the optical data to provide reliable modelling input for the RF.

scholarly journals Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

2019 ◽  
Vol 52 (6) ◽  
pp. 387-396 ◽  
Author(s):  
Marcel Koenigkam Santos ◽  
José Raniery Ferreira Júnior ◽  
Danilo Tadao Wada ◽  
Ariane Priscilla Magalhães Tenório ◽  
Marcello Henrique Nogueira Barbosa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Diagnostic Imaging ◽  
Precision Medicine ◽  
Quantitative Information ◽  
Computer Aided Diagnosis ◽  
Rapid Expansion ◽  
Computer Aided ◽  
The Impact ◽  
Aided Diagnosis

Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

Quantitative Demonstration of a High-Fidelity Oil-Based Mud Resistivity Imager Using a Controlled Experiment

2021 ◽  
Vol 62 (4) ◽  
pp. 407-421
Author(s):  
Baris Guner ◽  
◽  
Ahmed E. Fouda ◽  
Wei-Bin Ewe ◽  
David Torres ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Estimation ◽  
Hybrid Approach ◽  
Measurement Data ◽  
Quantitative Information ◽  
Inversion Algorithm ◽  
Data Set ◽  
Test Fixture ◽  
Imaging Tool ◽  
Formation Resistivity

The objective of this paper is to describe and validate a new approach for acquiring images that provides both qualitative and quantitative information on the formation electrical properties using a high-resolution, oil-based mud imager (HROBMI) tool. This new multifrequency imaging tool is able to function at high frequencies (in the MHz range) in oil-based muds. To allow for the quantitative estimation of formation and mud properties from the HROBMI data, a hybrid machine-learning/inversion approach was implemented. In this hybrid approach, machine-learning models corresponding to different candidate mud properties are trained, and the resulting regression functions are stored. For a given measurement data set, predictions of these different models are used to quickly identify an optimum mud candidate. This information is then fed into an inversion algorithm that provides accurate quantitative information on the logging environment of the HROBMI. The accuracy of this algorithm has been verified using a test fixture that enables the change of formation properties in different mud environments. The measurements from the HROBMI are a function of the formation properties: resistivity and permittivity, frequency, and mud properties. The hybrid algorithm can untangle HROBMI data from multiple frequencies to obtain true formation resistivity images independent of the other parameters that affect the tool measurements. In addition, the algorithm provides formation permittivity images as well as a standoff image. The results have been provided from both the controlled experiments in the test fixture and from field logs.

scholarly journals Random forest model identifies serve strength as a key predictor of tennis match outcome

2021 ◽  
pp. 1-8
Author(s):  
Zijian Gao ◽  
Amanda Kowalczyk
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Sports Analytics ◽  
Record Keeping ◽  
Machine Learning Methods ◽  
Simple Machine ◽  
Similar Information ◽  
Betting Odds ◽  
Potential Use ◽  
Tennis Match

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80%accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

scholarly journals Does quantity matter to a stingless bee?

2021 ◽  
Author(s):  
Johanna Eckert ◽  
Manuel Bohn ◽  
Johannes Spaethe
Keyword(s):  
Numerical Cognition ◽  
Training Stimulus ◽  
Salient Feature ◽  
Quantitative Information ◽  
Stimulus Dimension ◽  
Stingless Bee ◽  
Quantity Discrimination ◽  
Body Of Knowledge ◽  
Wide Range ◽  
Growing Body

AbstractQuantitative information is omnipresent in the world and a wide range of species has been shown to use quantities to optimize their decisions. While most studies have focused on vertebrates, a growing body of research demonstrates that also insects such as honeybees possess basic quantitative abilities that might aid them in finding profitable flower patches. However, it remains unclear if for insects, quantity is a salient feature relative to other stimulus dimensions, or if it is only used as a “last resort” strategy in case other stimulus dimensions are inconclusive. Here, we tested the stingless bee Trigona fuscipennis, a species representative of a vastly understudied group of tropical pollinators, in a quantity discrimination task. In four experiments, we trained wild, free-flying bees on stimuli that depicted either one or four elements. Subsequently, bees were confronted with a choice between stimuli that matched the training stimulus either in terms of quantity or another stimulus dimension. We found that bees were able to discriminate between the two quantities, but performance differed depending on which quantity was rewarded. Furthermore, quantity was more salient than was shape. However, quantity did not measurably influence the bees' decisions when contrasted with color or surface area. Our results demonstrate that just as honeybees, small-brained stingless bees also possess basic quantitative abilities. Moreover, invertebrate pollinators seem to utilize quantity not only as "last resort" but as a salient stimulus dimension. Our study contributes to the growing body of knowledge on quantitative cognition in invertebrate species and adds to our understanding of the evolution of numerical cognition.

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