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Home healthcare services are public or private service that aims to provide health services at home to socially disadvantaged, sick, needy, disabled, and elderly individuals. This study aims to increase the quality of home healthcare practice by analyzing the factors affecting it. In Megacity Istanbul, data from 1707 patients were used by considering 14 different input variables affecting home healthcare practice. The demographic, geographic, and living conditions of patients and healthcare professionals who take an active role in home healthcare practice constituted the central theme of the input parameters of this study. The regression method was used to look at the factors that affect the length of time a patient needs home healthcare, which is the study's output variable. This article provides short planning times and flexible solutions for home healthcare practice by showing how to avoid planning patient healthcare applications by hand using methods that were developed for home health services. In addition, in this research, the AB, RF, GB, and NN algorithms, which are among the machine learning algorithms, were developed using patient and personnel data with known input parameters to make home healthcare application planning correct. These algorithms' accuracy and error margins were calculated, and the algorithms' results were compared. For the prediction data, the AB model showed the best performance, and the R2 value of this algorithm was computed as 0.903. The margins of error for this algorithm were found to be 0.136, 0.018, and 0.043 for the RMSE, MSE, and MAE, respectively. This article provides short planning times and flexible solutions in home healthcare practice by avoiding manual patient healthcare application planning with the methods developed in the context of home health services. [ABSTRACT FROM AUTHOR]

ANFIS is an artificial intelligence technique which is composed of a combination of artificial neural networks and fuzzy inference system. Due to its structure, it is used in modeling and identifying numerous systems in various fields. The training process of ANFIS is important to obtain effective results with it. So, a successful training algorithm should be used. In this study, the ANFIS is trained by using ABC algorithm for the solution of the real-world problem. For this purpose, the number of foreign visitors coming to Turkey from the USA, Germany, Bulgaria, France, Georgia, the Netherlands, England, Iran and Russia is estimated. In addition, total number of visitors coming to Turkey is also predicted. In applications, 150 months of data between July 2002 and December 2014 are utilized and a time series is created using these data. The results obtained using ABC algorithm are compared with GA, DE, HS and PSO. As a conclusion, it is seen that the results obtained using ABC algorithm for estimation of number of foreign visitors are more successful than other optimization methods. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS. [ABSTRACT FROM AUTHOR]

Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load. [ABSTRACT FROM AUTHOR]

The article presents the study results on the use of multilayer neural networks (MLNNs) with genetic algorithm on the diagnosis of tuberculosis disease. Topics discussed include the reference to the mycobacterium tuberculosis with its limited treatment capacities in developing countries, the two hidden layers with the training algorithm and the success of classification in comparison with reference studies. Also mentioned was the Levenberg-Marquardt training algorithm.

Copyright of Transport (16484142) is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently various algorithms such as artificial neural networks (ANNs) is applied to air quality modeling. The present work aims to predict tropospheric ozone concentration by the ANN with three pollutant parameters and eight meteorological factors in selected areas. We have preferred three-layer perceptron type of ANNs, which consists of input, hidden, and output layers, respectively. To evaluate the performance of the ANN model, selected statistical performance parameters are used. The overall system finds correlation parameter, r between 0.8 and 0.9 for the test data sets. Therefore, results show the successful follow of estimated ozone concentrations by the model with the observed values. Finally, it was seen that the ANN is one of the compromising methods in estimation of environmental complex air pollution problems. [ABSTRACT FROM AUTHOR]