Tomatoes are preferred by farmers because of their high productivity. This fruit has a fibrous structure and contains plenty of vitamins. Tomato diseases are generally observed on stem, fruit, and leaves. Early diagnosis of the disease in plants is of vital importance for the plant. This is very important for farmers who expect economic gain from that plant. Because if the disease is not treated early, these tomatoes should be destroyed. For these reasons, systems to diagnose the disease early are very important. In this study, a tomato leaf diseases classification model developed with deep learning methods, which is one of the most popular artificial intelligence techniques, is proposed in order to eliminate the possibility of the human eye being mistaken. In this study, 6 different Convolutional Neural Network (CNN) architectures were used. In the first stage of this study, which consists of two stages, the classification process was carried out with the Alexnet, Googlenet, Shufflenet, Efficientb0, Resnet50, and Inceptionv3 architectures that were previously trained. In the second stage, feature maps of tomato leaf images in the dataset were obtained using the six pre-trained deep learning architectures. In the hybrid model proposed in this study, the feature maps extracted using the best two of the six deep learning models are concatenated. Then, the Neighborhood Component Analysis (NCA) method was applied to the extracted features in order to speed up the system, unnecessary features were removed and optimized. The optimized feature map is classified by traditional intelligent classification models. As a result of experimental studies, the average accuracy rate of the proposed model is 99.50 percent.

In addition to the fact that kisspeptin and its receptor GPR54 are well known to be abundantly expressed in the hypothalamus with suggestive roles in the initiation of puberty and similar reproductive system properties, there is also proof showing that kisspeptin might have influences on hippocampal functions. In our previous study, it was shown that kisspeptin increased free intracellular Ca2+ values ([Ca2+]i) through protein kinase C (PKC) activation in GT1-7 cells. For this reason, we examined the influences of kisspeptin on [Ca2+]i in hippocampal neurons to determine if kisspeptin shows its effects on hippocampus through the same mechanism. Hippocampal neurons were excised from the brains of fetuses on 17th embryonic day from maternal rats. The influences of kisspeptin on [Ca2+]i in hippocampal neurons were examined through in vitro calcium imaging system. The responses of [Ca2+]i to kisspeptin were quantified by the changes in 340nm/380nm ratio. Kisspeptin-10 caused [Ca2+]i transients in hippocampal neurons. The change in [Ca2+]i by 100 nM kisspeptin was prevented by pre-treating the cells in PKC inhibitor chelerythrine chloride. According to the results, kisspeptin activates intracellular calcium signaling in hippocampal neurons via the pathway that depends on PKC. The results of this study suggest that kisspeptin may have a role in hippocampal neuron functions.

Financial markets exhibit complex nonlinear dynamics driven by interactions between firm-specific characteristics and macroeconomic conditions, requiring robust mathematical models capable of capturing structural changes and temporal heterogeneity. This study proposes an explainable machine learning framework that combines regime-switching analysis and predictive modeling to investigate the long-term resilience of firms listed in the BIST 100 Index. Structural breaks in monthly return and volatility series are first detected using the Pruned Exact Linear Time (PELT) algorithm, enabling the classification of firm trajectories into resilient and fragile market regimes. A Regime Persistence Score is then introduced to quantify the proportion of time each firm remains in the resilient state. This score serves as the response variable in a Random Forest model that evaluates the influence of firm-specific financial indicators and macroeconomic variables, including exchange rates, producer price inflation, and commercial loan interest rates. In addition, a forward-looking classification model estimates the probability that firms will transition into a fragile regime within the subsequent six months. The proposed framework achieves an AUC of 0.706 and demonstrates stable predictive performance under alternative regime definitions, penalty parameters, cost functions, and cross-validation strategies. Explainability analysis derived from Shapley Additive Explanations (SHAP) data shows book-to-market ratio and sensitivities to inflation and interest rate changes as the most important components of enduring resilience. The proposed methodology provides an interpretable mathematical framework for regime detection, nonlinear time-series modeling, and decision support in sustainable financial systems, offering practical value for risk assessment and resilience-oriented portfolio management.

Accurate prediction of pest density and population levels in almond production is of great importance for crop yield, the prevention of quality losses, and the effectiveness of integrated pest management (IPM) strategies. Complex feature interactions in agricultural field data can affect both the predictive performance and interpretability of machine learning models. In this study, an explainable hybrid machine learning framework combining Mutated Particle Swarm Optimization (MPSO), XGBoost classification, and rule extraction was developed to classify pest insect density levels. The proposed approach was evaluated using two complementary validation settings. First, it was compared with 16 classification algorithms, including ensemble, deep learning, and rule-based methods, under stratified 5-fold cross-validation, achieving a mean accuracy of 79.63% and a mean Weighted F1-Score of 79.79%. In addition, nested stratified 5-fold cross-validation was employed to separate MPSO-based hyperparameter optimization from outer-fold performance evaluation. Under this more rigorous evaluation, the proposed approach achieved a mean accuracy of 79.27 ± 1.66% and a mean Weighted F1-Score of 79.29 ± 1.38%. Furthermore, interpretable IF–THEN decision rules were derived from the trained XGBoost decision structures to provide a transparent representation of the feature–threshold combinations associated with the classification decisions.