Tamer Akkan

Classical time series methods are widely employed to analyze linear time series with a limited number of observations; however, their effectiveness relies on several strict assumptions. In contrast, artificial neural networks are particularly suitable for forecasting problems due to their data-driven nature and ability to address both linear and nonlinear challenges. Furthermore, recurrent neural networks feed the output back into the network as input, utilizing this feedback mechanism to enrich the information provided to the model. This study proposes a novel recurrent hybrid intuitionistic forecasting method utilizing a modified pi–sigma neural network, principal component analysis (PCA), and simple exponential smoothing (SES). In the proposed framework, lagged time series variables and principal components derived from the membership and non-membership values of an intuitionistic fuzzy clustering method are used as inputs. A modified particle swarm optimization (PSO) algorithm is employed to train this new hybrid network. By integrating PCA, modified pi–sigma neural networks (MPS-ANNs), and SES within a recurrent hybrid structure, the model simultaneously captures linear and nonlinear dynamics, thereby enhancing forecasting accuracy and stability. The performance of the proposed model is evaluated using diverse financial and environmental datasets, including CMC-Open (I–IV), NYC water consumption, OECD freshwater use, and ROW series. Comparative results indicate that the proposed method achieves superior accuracy and stability compared to other fuzzy-based approaches.

Identifying and measuring potential sources of pollution is essential for water management and pollution control. Using a range of artificial intelligence models to analyze water quality (WQ) is one of the most effective techniques for estimating water quality index (WQI). In this context, machine learning–based models are introduced to predict the WQ factors of Southeastern Black Sea Basin. The data comprising monthly samples of different WQ factors were collected for 12 months at eight locations of the Türkiye region in Southeastern Black Sea. The traditional evaluation with WQI of surface water was calculated as average (i.e. good WQ). Single multiplicative neuron (SMN) model, multilayer perceptron (MLP) and pi-sigma artificial neural networks (PS-ANNs) were used to predict WQI, and the accuracy of the proposed algorithms were compared. SMN model and PS-ANNs were used for WQ prediction modeling for the first time in the literature. According to the results obtained from the proposed ANN models, it was found to provide a highly reliable modeling approach that allows capturing the nonlinear structure of complex time series and thus to generate more accurate predictions. The results of the analyses demonstrate the applicability of the proposed pi-sigma model instead of using other computational methods to predict WQ both in particular and other surface water resources in general.

A significant problem in the sustainable management of water resources is the lack of funding and long-term monitoring. Today, this problem has been greatly reduced by innovative, adaptive, and sustainable learning methods. Therefore, in this study, a sample river was selected and 14 variables observed at 5 different points for 12 months, traditionally reference values, were calculated by multivariate statistical analysis methods to obtain the water quality index (WQI). The WQI index was estimated using different algorithms including the innovatively used multiple linear regression (MLR), multilayer perceptron artificial neural networks (MLP-ANN) and various machine learning estimation algorithms including neural networks (NN), support vector machine (SVM), gaussian process regression (GPR), ensemble and decision tree approach. By comparing the results, the most appropriate method was selected. The determination of water quality was best estimated by the multiple linear regression (MLR) model. As a result of this MLR modeling, high prediction performance was obtained with accuracy values of R ² = 1.0, RMSE = 0.0025, and MAPE = 0.0296. The root mean square error (RMSE), percent mean absolute error (MAE), and coefficient of determination (R ² ) were used to determine the accuracy of the models. These results confirm that both MLR model can be used to predict WQI with very high accuracy. It seems that it can contribute to strengthening water quality management. As a result, as with the powerful results of the innovative approaches (MLR and MLP-ANN) and other assessments, it was found that the presence of intense anthropogenic pressure in the study area and the current situation needs immediate remediation.

The present study evaluates the protective properties of boric acid (BA) against the toxic effects induced by ochratoxin A (OTA) in human embryonic kidney cells (HEK293). The focus is on various parameters such as cytotoxicity, genotoxicity, oxidative stress, and apoptosis. OTA is a known mycotoxin that has harmful effects on the liver, kidneys, brain, and nervous system. BA, on the other hand, a boron-based compound, is known for its potential as a vital micronutrient with important cellular functions. The results show that BA administration not only increases cell viability but also mitigates the cytotoxic effects of OTA. This is evidenced by a reduction in the release of lactate dehydrogenase (LDH), indicating less damage to cell membranes. In addition, BA shows efficacy in reducing genotoxic effects, as the frequency of micronucleus (MN) and chromosomal aberrations (CA) decreases significantly, suggesting a protective role against DNA damage. In addition, the study shows that treatment with BA leads to a decrease in oxidative stress markers, highlighting its potential as a therapeutic intervention against the deleterious effects of OTA. These results emphasize the need for further research into the protective mechanisms of boron, particularly BA, in combating cell damage caused by OTA.

Methods based on artificial neural networks for intuitionistic fuzzy time series forecasting can produce successful forecasting results. In the literature, exponential smoothing methods are hybridised with artificial neural networks due to their simple and efficient structures to improve the forecasting performance. The contribution of this paper is to propose a new forecasting approach combining exponential smoothing methods and intuitionistic fuzzy time series. In this study, a forecasting algorithm based on the dendrite neuron model and simple exponential smoothing methods is proposed for modelling intuitionistic fuzzy time series. In the fuzzification stage of the proposed method, the intuitionistic fuzzy c-means method is used. The proposed method is a modular method using two separate dendrite neuron model neural networks and the grey wolf optimisation algorithm is used to estimate all parameters of the method. The performance of the proposed method is applied on four different random time series obtained for Index of Coin Market Cap and the performance of the method is compared with some other fuzzy forecasting methods. As a result of the analyses, it is concluded that the proposed modular method has better forecasting results than other methods.

Bu çalışmada, Harşit Çayı’ndan örneklenen 202 adet tatlı su kefali Squalius orientalis Heckel, 1847 bireyinin kondisyon faktörü değerleri, boy-ağırlık ve boy-boy ilişkileri tespit edilmiştir. Örneklemde dişi: erkek oranı 1,00:1,26 olarak hesaplanmıştır. Örneklerin total boy ve ağırlık değerleri sırasıyla 9,0-30,3 cm ve 6,54-325,26 g olarak tespit edilmiştir. Tüm örnekler için boy-ağırlık ilişkisi W=0,0056xTB3,227 (r2=0,995) olarak hesaplanmıştır. Boy-ağırlık ilişkisinin b değerine göre Squalius orientalis ’in tüm bireylerinde pozitif allometrik büyüme bulunmuştur (b>3, P 0,05). Populasyon genelinde ortalama kondisyon faktörü 1,004±0,0067 olarak tespit edilmiştir. Total, çatal ve standart boylar arasında kuvvetli ilişkiler tespit edilmiştir (P<0,001).