Prediction of Attendance Demand in European Football Games: Comparison of ANFIS, Fuzzy Logic, and ANN.

An artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS) models, and fuzzy rule-based system (FRBS) models are developed to predict the attendance demand in European football games, in this paper. To determine the most successful method, each of the methods is analyzed under different situations. The Elman backpropagation, feed-forward backpropagation, and cascade-forward backpropagation network types are developed to determine the outperforming ANN model. The backpropagation and hybrid optimization methods are used for training fuzzy inference system (FIS) to determine the outperforming ANFIS model. The fuzzy logic model is developed after experimenting different forms of membership functions. To this end, the data of 236 soccer games are used to train the ANN and ANFIS models, and 2017/2018 season's data of these clubs are used to test all of the models. The results of all models are compared with each other and real past data. To assess the performance of each model, two error measures that are Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) are implemented. These measures reveal that the ANN model that has Elman network type outperforms the other models. Finally, the results emphasize that the proposed ANN model can be effectively used for prediction purposes.

An optimization model for locating and sizing emergency medical service stations.

Emergency medical services (EMS) play a crucial role in the overall quality and performance of health services. The performance of these systems heavily depends on operational success of emergency services in which emergency vehicles, medical personnel and supporting equipment and facilities are the main resources. Optimally locating and sizing of such services is an important task to enhance the responsiveness and the utilization of limited resources. In this study, an integer optimization model is presented to decide locations and types of service stations, regions covered by these stations under service constraints in order to minimize the total cost of the overall system. The model can produce optimal solutions within a reasonable time for large cities having up to 130 districts or regions. This model is tested for the EMS system of Adana metropolitan area in Turkey. Case study and computational findings of the model are discussed in detail in the paper.

Optimal resource allocation model to mitigate the impact of pandemic influenza: a case study for Turkey.

Pandemic influenza has been considered as a serious international health risk by many health authorities in the world. In mitigating pandemic influenza, effective allocation of limited health resources also plays a critical role along with effective use of medical prevention and treatment procedures. A national resource allocation program for prevention and treatment must be supported with the right allocation decisions for all regions and population risk groups. In this study, we develop a multi-objective mathematical programming model for optimal resource allocation decisions in a country where a serious risk of pandemic influenza may exist. These resources include monetary budget for antivirals and preventive vaccinations, intensive care unit (ICU) beds, ventilators, and non-intensive care unit (non-ICU) beds. The mathematical model has three objectives: minimization of number of deaths, number of cases and total morbidity days during a pandemic influenza. This model can be used as a decision support tool by decision makers to assess the impact of different scenarios such as attack rates, hospitalization and death ratios. These factors are found to be very influential on the allocation of the total budget among preventive vaccination, antiviral treatment and fixed resources. The data set collected from various sources for Turkey is used and analyzed in detail as a case study.

Neural network-based computer-aided diagnosis in classification of primary generalized epilepsy by EEG signals.

Epilepsy is a disorder of cortical excitability and still an important medical problem. The correct diagnosis of a patient's epilepsy syndrome clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. The aim of this study is to classify subgroups of primary generalized epilepsy by using Multilayer Perceptron Neural Networks (MLPNNs). This is the first study classifying primary generalized epilepsy using MLPNNs. MLPNN classified primary generalized epilepsy with the accuracy of 84.4%. This model also classified generalized tonik-klonik, absans, myoclonic and more than one type seizures epilepsy groups correctly with the accuracy of 78.5%, 80%, 50% and 91.6%, respectively. Moreover, new MLPNNs were constructed for determining significant variables affecting the classification accuracy of neural networks. The loss of consciousness in the course of seizure time variable caused the largest decrease in the classification accuracy when it was left out. These outcomes indicate that this model classified the subgroups of primary generalized epilepsy successfully.

A radial basis function neural network model for classification of epilepsy using EEG signals.

Epilepsy is a disorder of cortical excitability and still an important medical problem. The correct diagnosis of a patient's epilepsy syndrome clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. The aim of this study is to evaluate epileptic patients and classify epilepsy groups such as partial and primary generalized epilepsy by using Radial Basis Function Neural Network (RBFNN) and Multilayer Perceptron Neural Network (MLPNNs). Four hundred eighteen patients with epilepsy diagnoses according to International League against Epilepsy (ILAE 1981) were included in this study. The correct classification of this data was performed by two expert neurologists before they were executed by neural networks. The neural networks were trained by the parameters obtained from the EEG signals and clinic properties of the patients. Experimental results show that the predictions of both neural network models are very satisfying for learning data sets. According to test results, RBFNN (total classification accuracy = 95.2%) has classified more successfully when compared with MLPNN (total classification accuracy = 89.2%). These results indicate that RBFNN model may be used in clinical studies as a decision support tool to confirm the classification of epilepsy groups after the model is developed.

A radial basis function neural network (RBFNN) approach for structural classification of thyroid diseases.

The thyroid is a gland that controls key functions of body. Diseases of the thyroid gland can adversely affect nearly every organ in human body. The correct diagnosis of a patient's thyroid disease clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. This study investigates Multilayer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) for structural classification of thyroid diseases. A data set for 487 patients having thyroid disease is used to build, train and test the corresponding neural networks. The structural classification of this data set was performed by two expert physicians before the input variables and results were fed into the neural networks. Experimental results show that the predictions of both neural network models are very satisfying for learning data sets. Regarding the evaluation data, the trained RBFNN model outperforms the corresponding MLPNN model. This study demonstrates the strong utility of an artificial neural network model for structural classification of thyroid diseases.

A hierarchical multiple criteria mathematical programming approach for scheduling general surgery operations in large hospitals.

Limited staff and equipment within surgical services require efficient use of these resources among multiple surgeon groups. In this study, a set of hierarchical multiple criteria mathematical programming models are developed to generate weekly operating room schedules. The goals considered in these models are maximum utilization of operating room capacity, balanced distribution of operations among surgeon groups in terms of operation days, lengths of operation times, and minimization of patient waiting times. Because of computational difficulty of this scheduling problem, the overall problem is broken down into manageable hierarchical stages: (1) selection of patients, (2) assignment of operations to surgeon groups, and (3) determination of operation dates and operating rooms. Developed models are tested on the data collected in College of Medicine Research Hospital at Cukurova University as well as on simulated data sets using MPL optimization package.