Search

The smarter world needs more efforts to purposeful manage and usage of technologies, science, artificial intelligence, and artificial neural networks, as their product. One of the main tools to facilitate collecting beneficial information is reviewing the publications of the target domain. To this end, the present paper attempts to categorize recent trends and innovations in the application of artificial neural networks in water quality and pollution research. The most prevalent methods for water pollution and quality prediction modeling during 2011–2020 were BPNN and normal MLP.. Conducting and comparing other analytical methods such as ANN, RBFNN, LSTM, and CNN to find accurate results are in the next orders. Moreover, along with the emerging and development of the internet of things (IoT), it was observed that IoT as a real-time technology for collecting relevant data, which appeared in the water quality analysis since 2016–2017, was used increasingly in 2019 and 2020. Furthermore, reviewed papers revealed that pH, chemical oxygen demand, and biochemical oxygen demand were the most water quality indicators. Regionally, drivers in China were the most investigated sites during the last decade, and then India and Turkey were the second and third place. [ABSTRACT FROM AUTHOR]

Forecasting the future energy demand accurately is a critical issue, especially for countries like Turkey where the energy dependency ratio is high. This paper presents a neural network based on the particle swarm optimization algorithm with mutation (PSOM-NN) to enhance the prediction accuracy of the energy demand of Turkey. Unlike some studies in the field which are using all the observed data for training purpose, the proposed network used only a part of these data for training. Approximately 63 % and 37 % of the mentioned data are used for the training and test, respectively. Detrending is applied to the data to avoid nonlinear transfer functions that constrain the model to the input range values. The analysis of the results revealed that PSOM-NN produced better forecasts of energy demand compared to the earlier studies in terms of root-mean-square error, mean absolute percentage error and mean absolute deviation. Finally, future projections under different scenarios are also employed and discussed. It is believed that the proposed model could be applied to any country that needs accurate forecasts of the energy demand for sustainable energy policies. [ABSTRACT FROM AUTHOR]

Abstract: Training neural networks (NNs) is a complex task of great importance in the supervised learning area. However, performance of the NNs is mostly dependent on the success of training process, and therefore the training algorithm. This paper addresses the application of harmony search algorithms for the supervised training of feed-forward (FF) type NNs, which are frequently used for classification problems. In this paper, five different variants of harmony search algorithm are studied by giving special attention to Self-adaptive Global Best Harmony Search (SGHS) algorithm. A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on six benchmark classification problems and a real-world problem. Among these benchmark problems two of them have binary classes and remaining four are n-ary classification problems. Real-world problem is related to the classification of most frequently encountered quality defect in a major textile company in Turkey. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm, is compared with the other harmony search algorithms and the most widely used standard back-propagation (BP) algorithm. The experiments presented that the SGHS algorithm lends itself very well to the training of NNs and also highly competitive with the compared methods in terms of classification accuracy. [Copyright &y& Elsevier]

This paper looks into the Greek-Turkish arms race a decade after an earlier contribution to the issue that relied heavily on artificial neural networks. The time period between the two papers contributes to the reliability of the results derived, not just by increasing the number of observations, but also mainly by incorporating the progress made in the realm of artificial intelligence. The focus on the case of both countries unlike the paper mentioned above that dealt with just the Greek side provides ample room for comparative purposes regarding the determinants of defense expenditure on both sides. The results derived in terms of input significance estimation support the findings of the earlier research as indicated above, pointing to the leading role of the demographic preponderance of Turkey over Greece. The paper also points to the fact that 10 years later, Turkey continues to set the arms race rules against its rival by determining the defense expenditure of Greece, whereas the role of the latter in affecting the military spending of Turkey is non-existent. [ABSTRACT FROM AUTHOR]

This study deals with the relationship between the current account deficit and the budget deficit in Turkey. Artificial Neural Networks are employed to investigate this relationship. According to the results of the application the twin deficits hypothesis which indicates a positive correlation between the two deficits isn't valid in Turkey. As this paper tries to analyze the factors accounting for the absence of this kind of correlation, it develops a flexible understanding which doesn't refuse a hypothesis or a theory (the twin deficits hypothesis) completely, on the contrary, emphasizes the importance of investigating the conditions in which a hypothesis, theory or a model is valid. [ABSTRACT FROM AUTHOR]

Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary and pneumonia diseases are two of the most important chest diseases. And these are very common illnesses in Turkey. In this paper, a comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis was realized by using neural networks and artificial immune systems. For this purpose, three different neural networks structures and one artificial immune system were used. Used neural network structures in this study were multilayer, probabilistic, and learning vector quantization neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on chronic obstructive pulmonary and pneumonia diseases diagnosis. The chronic obstructive pulmonary and pneumonia diseases dataset were prepared from a chest diseases hospital’s database using patient’s epicrisis reports. [ABSTRACT FROM AUTHOR]

Abstract: The performance of neural networks in evaluating and forecasting banking crises have been examined in this paper. An artificial neural network model which works with the banking data belonging to the same date and another artificial neural network model which works with cross sectional banking data have been formed and tested. The optimal topologies of these models have been determined by Taguchi approach which is a design of experiments method. Both models can forecast the values of the output neurons consisting of Non-performing Loans/Total loans, Capital/Assets, Profits/Assets and Equity/Assets ratios by using 25 input neurons consisting of macroeconomic variables, the variables related to the external balanced financial system’s structure, and time with very small errors. Consequently, it has been seen that artificial neural networks which are capable of producing successful solutions for semi-structural and non-structural problems, can be used effectively in evaluating and forecasting banking crises. [Copyright &y& Elsevier]

IIn this paper the capability of two different feed-forward back-propagation neural network algorithms, namely Levenberg-Marquardt and gradient-descent, in solving complex nonlinear problems is utilized for suspended sediment prediction. The monthly streamflow and suspended sediment data from two stations, Palu and Çayağzi, in the Firat Basin in Turkey are used as case studies. The first part of the study involves the prediction of sediment data for the two stations. The second part of the study focuses on the prediction of the downstream station sediment data using upstream data. The effect of the periodicity on model performance is also investigated in each application. [ABSTRACT FROM AUTHOR]