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One of the significant challenges in the sports industry is identifying the factors influencing match results and their respective weightage. For appropriate recommendations to the team management and the team players, there is a need to predict the match and quantify the important factors for which prediction models need to be developed. The second thing required is identifying talented and emerging players and performing an associative analysis of the important factors to the match-winning outcome. This paper formulates a hybrid machine learning-clustering-associative rules model. This paper also implements the framework for cricket matches, one of the most popular sports globally watched by billions around the world. We predict the match outcome for One day Internationals (ODIs) and Twenty 20 s (T20s) (two formats of Cricket representing fifty over and twenty over versions respectively) adopting state-of-the-art machine learning algorithms, Random Forest, Gradient Boosting, and Deep neural networks. The variable importance is computed using machine-learning techniques and further statistically validated through the regression model. The emerging talented players are identified by clustering. Association rules are generated for determining the best possible winning outcome. The results show that environmental conditions are equally crucial for determining a match result, as are internal quantitative factors. The model is thus helpful for both team management and for players to improve their winning strategy and also for discovering emerging players to form an unbeatable team. [ABSTRACT FROM AUTHOR]

This study discusses the relationship between Polycystic Ovary Syndrome (PCOS) and diabetes in women, which has become increasingly prevalent due to changing lifestyles and environmental factors. The characteristic that distinguishes women with PCOS is hyperandrogenism which results from abnormal ovarian or adrenal function, which leads to the overproduction of androgens. Excessive androgens in women increase the risk of Type 2 diabetes (T2D) and insulin resistance (IR). Nowadays, diabetes affects people of all ages and is linked to factors such as lifestyle, genetics, stress, and aging. Diabetes, the uncontrolled high blood sugar level can potentially harm kidneys, nerves, eyes, and other organs and there is no cure, making it a concerning disease in developing nations. This research tried to submit the evidence through feature-wise correlation analyses between PCOS and diabetes. Hence, this model utilized the Exploratory Data Analysis (EDA) and the Elbow clustering algorithms for the experimental purpose in which the EDA deeply analyzed the features of PCOS and diabetes and recorded a positive correlation of 95%. The Elbow clustering technique is employed for verifying the correlations identified through EDA. Although limited research exists on this specific disease, this work provides potential evidence for the research community by evaluating the clustering results using Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. [ABSTRACT FROM AUTHOR]

In this paper we present a novel method for genome ranking according to gene lengths. The main outcomes described in this paper are the following: the formulation of the genome ranking problem, presentation of relevant approaches to solve it, and the demonstration of preliminary results from prokaryotic genomes ordering. Using a subset of prokaryotic genomes, we attempted to uncover factors affecting gene length. We have demonstrated that hyperthermophilic species have shorter genes as compared with mesophilic organisms, which probably means that environmental factors affect gene length. Moreover, these preliminary results show that environmental factors group together in ranking evolutionary distant species. [ABSTRACT FROM AUTHOR]

Dimensionality reduction techniques such as principal component analysis and factor analysis are used to discover a linear mapping between high-dimensional data samples and points in a lower-dimensional subspace. Previously, Frey and Jojic introduced transformation-invariant component analysis (TCA) to learn a linear mapping, invariant to a set of known form of global transformations. However, parameter estimation in that model using the previously-proposed expectation maximization (EM) algorithm required scalar operations in the order of N 2 where N is the dimensionality of each training example. This is prohibitive for many applications of interest such as modeling mid-to large-size images, where, for instance, N may be as high as 786432 (512×512 RGB image). In this paper, we present an efficient algorithm that reduces the computational requirements to order of Nlog N. With this speedup, we show the effectiveness of transformation-invariant component analysis in various applications including tracking, learning video textures, clustering, object recognition and object detection in images. Software for TCA can be downloaded from . [ABSTRACT FROM AUTHOR]

Abstract: Recently, a representative approach, named mixtures of common factor analyzers (MCFA), was proposed for clustering high-dimensional observed data. Existing model-parameter estimation methods for this approach is based on the maximum likelihood criterion and performed by the expectation–maximization algorithm. In this paper, we consider the MCFA from a Bayesian perspective and propose the Bayesian mixtures of common factor analyzers (BMCFA) model, which replaces the deterministic model parameters in the MCFA by stochastic variables. Then we present a variational inference algorithm for this BMCFA model. Moreover, the proposed BMCFA model and the associated variational inference algorithm are used for clustering the high-dimensional synthetic data, the wine data from the UCI machine learning repository and the gene expression data. Experimental results illustrate that the BMCFA has good generalization capacities, automatically determining the appropriate number of clusters from high-dimensional observed data. [Copyright &y& Elsevier]