Elif Varol Altay

The performance of Multi-Layer Perceptron (MLP) models depends on the effective optimization of weight and bias parameters during the training process. Gradient-based learning methods may not always produce stable and high-performance results due to factors such as initial sensitivity, the need for derivative information, and stagnation at local minima in complex, high-dimensional search spaces. In this study, four current metaheuristic algorithms for optimizing the weight and bias parameters of MLPs were evaluated comparatively: Hippopotamus Optimization Algorithm (HO), Puma Optimizer, Starfish Optimization Algorithm (SFOA), and War Strategy Optimization Algorithm (WSO). The algorithms were tested on the Diabetes, Diagnosis, Mammographic Mass, and Wisconsin Breast Cancer datasets. The same MLP architecture and experimental conditions were used for each dataset; the data were evaluated with an 80% training and 20% testing split. Each method was run in 10 independent runs, with a population size of 30 and a number of iterations of 100, and candidate solutions were limited to the range [-10, 10]. Performance was measured by accuracy, precision, and F1-score; the overall ranking of the methods was examined using the Friedman test. The findings show that metaheuristic performance is sensitive to the dataset. In general, Puma-based training produced more competitive results in accuracy and F1-scores in most datasets, while HO excelled in precision and stability in some scenarios. Although SFOA and WSO produced competitive results in certain datasets, they did not exhibit consistent superiority across all metrics. The results demonstrate that the selection of meta-heuristic methods for MLP training should consider dataset characteristics and target metrics and provide a practical comparison framework for medical decision support applications. In addition, mean and standard deviation values are reported for each dataset, and the strengths of the methods are discussed based on the best run results.

Biomedicine datasets often contain noisy, imbalanced, and high-dimensional features, which makes the choice of neural network training strategy a decisive factor for clinical decision support. While artificial neural networks (ANNs) have been widely applied to medical diagnosis tasks, comparative analyses across a broad spectrum of training algorithms remain limited. This study addresses this gap by evaluating twelve distinct ANN training algorithms on sixteen publicly available biomedicine datasets. The performance of each method was assessed according to training duration, loss metrics, and classification accuracy. During the testing phase, essential metrics such as F-measure, G-mean, accuracy, sensitivity, specificity, and precision were evaluated. A Friedman test was performed to statistically verify the performance discrepancies among the algorithms. The findings indicated that the selection of the training algorithm significantly influences classification efficacy. The results reveal that Levenberg-Marquardt and Bayesian Regularization consistently achieve high predictive performance on complex datasets, whereas the Variable Learning Rate method demonstrates robust and scalable performance across heterogeneous data conditions. Importantly, the findings indicate that no universal “best” algorithm exists; optimal selection depends on dataset characteristics such as size, dimensionality, and noise distribution. By mapping algorithm strengths and weaknesses across diverse diagnostic scenarios, this work provides actionable guidelines for researchers and practitioners aiming to design reliable and efficient ANN-based decision support systems in healthcare. Compared to baseline gradient descent variants, advanced training algorithms such as Levenberg-Marquardt and Bayesian Regularization achieved up to 25% higher accuracy (e.g., on the Skin_Nonskin dataset) and 20% higher F-measure (e.g., on the Saheart dataset) across multiple biomedicine datasets.

To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R² value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems.

The K nearest neighbor (KNN) technique is well recognized and extensively used in the field of machine learning classification algorithms. In this study, the performance of distance weighting functions, one of the most important factors affecting the performance of KNN, was compared. These functions are equal, inverse, and squared-inverse. The performance of the functions was examined in five different medical data sets. To evaluate the performances, a confusion matrix and five different metrics commonly used to evaluate classification problems were used. Out of the five data sets used in the research, the inverse KNN method yielded effective outcomes in four of them, while both the equal and squared-diverse methods achieved success in three data sets.

The Slime mould algorithm (SMA) is a relatively new metaheuristic technique that was just presented. While the performance of the newly proposed algorithms gives satisfactory results in optimization problems, combining a recently proposed algorithm with the components of different algorithms improves the performance of SMAs. In recent years, leader SMA (LSMA) and equilibrium optimizer SMA (ESMA) methods, in which SMA is combined with different algorithms, have been proposed. The advantages of the two proposed methods over SMA in different problems are shown. In this study, in order to eliminate the disadvantages of SMA, such as slow convergence rate and local optimum, the performances of the CEC2020 test functions were investigated together with the LSMA and ESMA methods proposed in recent years. The results obtained are statistically analyzed and given in detail in the study.