Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques

Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering belongs to the group of unsupervised machine learning techniques, meaning that there is no information about the labels of the elements. However, when knowledge of data points is known in advance, it will be beneficial to use a semi-supervised algorithm. Within many clustering techniques available, fuzzy C-means clustering (FCM) is a common one. To make the FCM algorithm a semi-supervised method, it was proposed in the literature to use an auxiliary matrix to adjust the membership grade of the elements to force them into certain clusters during the computation. In this study, instead of using the auxiliary matrix, we proposed to use multiple fuzzification coefficients to implement the semi-supervision component. After deriving the proposed semi-supervised fuzzy C-means clustering algorithm with multiple fuzzification coefficients (sSMC-FCM), we demonstrated the convergence of the algorithm and validated the efficiency of the method through a numerical example.

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Predictive Analysis of IPL Match Winner using Machine Learning Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1043.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 430-435

Keyword(s):

Machine Learning ◽

Multivariate Regression ◽

Time Lapse ◽

Machine Learning Techniques ◽

Past Performance ◽

The Past ◽

Premier League ◽

Learning Techniques ◽

Time Problem ◽

Machine Learning Models

Artificial intelligence (AI) can be implemented using Machine Learning which allows the computing to potentially robotically study and improve from its previous experiences without being manually typed. Data can be accessed and used by the computer programs developed using Machine learning. This paper mainly focused on implementation of machine learning in the arena of sports to predict the captivating team of an IPL match. Cricket is a popular uncertain sport, particularly the T-20 format, there’s a possibility of the complete game play to change with the effect of any single over. Millions of spectators watch the Indian Premier League (IPL) every year, hence it becomes a real-time problem to compose a technique that will forecast the conclusion of matches. Many aspects and features determine the result of a cricket match each of which has a weighted impact on the result of a T20 cricket match. This paper describes all those features in detail. A multivariate regression-based approach is proposed to measure the team's points in the league. The past performance of every team determines its probability of winning a match against a particular opponent. Finally, a set of seven factors or attributes is identified that can be used for predicting the IPL match winner. Various machine learning models were trained and used to perform within the time lapse between the toss and initiation of the match, to predict the winner. The performance of the model developed are evaluated with various classification techniques where Random Forest and Decision Tree have given good results.

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Determination of Cup to Disc Ratio Using Unsupervised Machine Learning Techniques for Glaucoma Detection

Molecular & Cellular Biomechanics ◽

10.32604/mcb.2021.014622 ◽

2021 ◽

Vol 18 (2) ◽

pp. 69-86

Author(s):

R. Praveena ◽

T. R. GaneshBabu

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Unsupervised Machine Learning ◽

Glaucoma Detection ◽

Learning Techniques ◽

Cup To Disc Ratio

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Breathlessness in COPD: linking symptom clusters with brain activity

European Respiratory Journal ◽

10.1183/13993003.04099-2020 ◽

2021 ◽

pp. 2004099

Author(s):

Sarah L. Finnegan ◽

Olivia K. Harrison ◽

Catherine J. Harmer ◽

Mari Herigstad ◽

Najib M. Rahman ◽

...

Keyword(s):

Machine Learning ◽

Functional Neuroimaging ◽

Brain Activity ◽

Symptom Burden ◽

Functional Brain Imaging ◽

Self Report ◽

Machine Learning Techniques ◽

Chronic Obstructive ◽

Unsupervised Machine Learning ◽

Learning Techniques

RationaleCurrent models of breathlessness often fail to explain disparities between patients' experiences of breathlessness and objective measures of lung function. While a mechanistic understanding of this discordance has thus far remained elusive, factors such as mood, attention and expectation have all been implicated as important modulators of breathlessness. Therefore, we have developed a model to better understand the relationships between these factors using unsupervised machine learning techniques. Subsequently we examined how expectation-related brain activity differed between these symptom-defined clusters of participants.MethodsA cohort of 91 participants with mild-to-moderate chronic obstructive pulmonary disease (COPD) underwent functional brain imaging, self-report questionnaires and clinical measures of respiratory function. Unsupervised machine learning techniques of exploratory factor analysis and hierarchical cluster modelling were used to model brain-behaviour-breathlessness links.ResultsWe successfully stratified participants across four key factors corresponding to mood, symptom burden and two capability measures. Two key groups resulted from this stratification, corresponding to high and low symptom burden. Compared to the high symptom load group, the low symptom burden group demonstrated significantly greater brain activity within the anterior insula, a key region thought to be involved in monitoring internal bodily sensations (interoception).ConclusionsThis is the largest functional neuroimaging study of COPD to date and is the first to provide a clear model linking brain, behaviour and breathlessness expectation. Furthermore, it was possible to stratify participants into groups, which then revealed differences in brain activity patterns. Together, these findings highlight the value of multi-modal models of breathlessness in identifying behavioural phenotypes, and for advancing understanding of differences in breathlessness burden.

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