Positional analysis of Brazilian soccer players using GPS data

The professional soccer is always changing and is constantly searching tools and data to help the decision-making,providing tactics and techniques to the team. In Brazil, this sport goes to same way and the investments areconsiderable. The One Sports is a company that capture GPS data from professional soccer players of someBrazilian teams. This set of data has a lot of features and the One Sports asked if was possible to predict the idealposition of a player. Then, was firmed a cooperation between a academic study and a commercial company. Thiswork find to understand a propose methods and techniques to predict the ideal position of the soccer player, usingmachine learning algorithms. The database has more of one million of tuples. It was submitted to preprocessingstep, what is fundamental, because generated new features, removed incomplete and noisy data, generated anew balanced dataset and delete outliers, preparing the data to execution of the algorithms k-NN, decision trees,logistic regression, SVM and neural networks. With the purpose to understand the performance and accuracy,some scenarios were tested. There was poor results when executed multiclass problems. The best results comefrom binary problems. The models k-NN and SVM, specifically to this study, had the best accuracy. It is importantto note that SVM spent more than six hours to finish your execution, and k-NN used less than one and halfminute to end.

BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

This work proposed a binary variant of the recently-proposed Equilibrium Optimizer (EO) to solve binary problems. A v-shaped transfer function is used to map continuous values created in EO to binary. To improve the exploitation of the Binary Equilibrium Optimizer (BEO), the Simulated Annealing is used as one of the most popular local search methods. The proposed BEO algorithm is applied to 18 UCI datasets and compared to a wide range of algorithms. The results demonstrate the superiority and merits of EO when solving feature selection problems.

Continuous variable quantum perceptron

We present a model of Continuous Variable Quantum Perceptron (CVQP), also referred to as neuron in the following, whose architecture implements a classical perceptron. The necessary nonlinearity is obtained via measuring the output qubit and using the measurement outcome as input to an activation function. The latter is chosen to be the so-called Rectified linear unit (ReLu) activation function by virtue of its practical feasibility and the advantages it provides in learning tasks. The encoding of classical data into realistic finitely squeezed states and the use of superposed (entangled) input states for specific binary problems are discussed.

Emphasis Learning (EL), Feature Repetition in Width Instead of Length, to Obtain Better Classification Performance; Case Study: Alzheimer’s Disease Diagnosis

A method for classification is introduced in this article, and it is tested on ADNI database to diagnose alzheimer’s disease (AD). It is obvious that tunning the performance of a classification to get better results is a complicated problem, and when we want model’s accuracy or other peformance measurments higher than 90%, the problem will be more complicated. In this study, we tried and succeeded to discover a method to solve this problem. The final feature set can be used clustering too, because outgrowth feature set of the proposed method is invigorated. In the recent years, a lot of activities is done to develop computer aided systems (CAD) for alzheimer’s disease diagnosis. Most of these recently developed systems concenterated on extracting and combining features from MRI, PET, CSF, and …; in this article, we made attempt to do so and utilized one more technique to increase classification performance. Finding and producing the best features to solve three binary classification problems of AD vs. Normal Control (NC), Mild Cognitive Impairment (MCI) vs. NC, and MCI vs. AD are the purposes of this article. Experiments indicate performance and effectiveness rates of the proposed method, which are accuracies of 98.81%, 81.61%, and 81.40% for AD vs. NC, MCI vs. NC, and AD vs. MCI classification problems, respectively. As can be seen, using this method increased the performance of the three binary problems incredibly.

SHORT INTERVALS ASYMPTOTIC FORMULAE FOR BINARY PROBLEMS WITH PRIME POWERS, II

We improve some results in our paper [A. Languasco and A. Zaccagnini, ‘Short intervals asymptotic formulae for binary problems with prime powers’, J. Théor. Nombres Bordeaux30 (2018) 609–635] about the asymptotic formulae in short intervals for the average number of representations of integers of the forms $n=p_{1}^{\ell _{1}}+p_{2}^{\ell _{2}}$ and $n=p^{\ell _{1}}+m^{\ell _{2}}$, where $\ell _{1},\ell _{2}\geq 2$ are fixed integers, $p,p_{1},p_{2}$ are prime numbers and $m$ is an integer. We also remark that the techniques here used let us prove that a suitable asymptotic formula for the average number of representations of integers $n=\sum _{i=1}^{s}p_{i}^{\ell }$, where $s$, $\ell$ are two integers such that $2\leq s\leq \ell -1$, $\ell \geq 3$ and $p_{i}$, $i=1,\ldots ,s$, are prime numbers, holds in short intervals.

Putting Continuous Metaheuristics to Work in Binary Search Spaces

In the real world, there are a number of optimization problems whose search space is restricted to take binary values; however, there are many continuous metaheuristics with good results in continuous search spaces. These algorithms must be adapted to solve binary problems. This paper surveys articles focused on the binarization of metaheuristics designed for continuous optimization.

Branch and Bound method for binary problems with the procedure that reduces dimension of problems

The relationships between elements aij of coefficient matrix, elements di of vector d and elements cj of vector c in general binary problem are considered. Some of them allow us to establish the values of selected elements of feasible or optimal vector x. This procedure reduces the dimension of basic problem and can be install in branch and bound method. It gives positive effects.