The Black-Winged Kite Algorithm (BKA) is a relatively new bio-inspired metaheuristic approach developed to tackle challenging optimization tasks by maintaining a balance between exploration and exploitation. In this context, an improved version of BKA is introduced to better handle complex optimization scenarios. Three modified variants are proposed: CBKA, which incorporates logistic chaos-based mapping to improve solution diversity; LBKA, which utilizes Lévy flight to reinforce global exploration capability; and CLBKA, which merges both mechanisms to enhance the balance between exploration and intensification. The algorithms are assessed on 23 standard benchmark problems spanning unimodal, multimodal, and fixed-dimension test sets. CLBKA achieved the global optimum in 20 out of 23 test functions and ranked first in the Friedman statistical test, with the lowest average rank of 2.9348 among eight algorithms. In addition to the Friedman test, the Wilcoxon signed-rank test was also employed to statistically validate the significance of the observed improvements. Experimental findings indicate that CLBKA consistently outperforms the original BKA and various other metaheuristic techniques in terms of convergence reliability, solution quality, and search stability. Moreover, all proposed algorithms were implemented on six practical engineering design problems, including the Gear Train, Welded Beam, Three-Bar Truss, Pressure Vessel, Tension/Compression Spring, and Cantilever Beam design cases, delivering notably better optimization outcomes. In each case, CLBKA consistently outperformed both its baseline and enhanced variants, as well as several state-of-the-art algorithms from the literature, in terms of solution accuracy, convergence speed, and robustness. The performance of all algorithms was statistically validated using the Friedman test, further confirming the significance and robustness of the proposed hybrid strategies. The results confirm that the proposed hybrid strategies significantly enhance the search efficiency of BKA, making CLBKA a reliable and versatile optimizer for a wide range of complex, constrained optimization tasks.

In this article, a metaheuristic optimization algorithm called the Black Kite Algorithm (BKA) is proposed, inspired by the nomadic and predatory behaviors. BKA integrates the Cauchy mutation strategy and the Leader strategy to enhance the algorithm's global search capability and convergence speed. This novel combination provides a good balance between exploring global solutions and utilizing local information. Clustering is a widely used technique in data analysis. Its fundamental purpose is to reveal structures and relationships in a dataset by grouping data points with similar characteristics. These groups can be utilized to understand patterns in the dataset, perform data exploration, and make predictions. Clustering algorithms typically work by measuring similarities between data points using a distance metric. Determining the number of clusters is a challenging task, even when clustering is done correctly. To address these challenges, several techniques have been proposed in the literature. Most of these methods require prior knowledge of the number of clusters to be addressed, which should be provided as an algorithm parameter. Real-world clustering problems arise when the number of clusters in the data collection set is unknown in advance. The Blackwinged Kite Optimization algorithm is a powerful search algorithm that has been proposed to solve optimization problems seen as clustering problems. With this developed method, datasets are divided into clusters based on distance, and the number of clusters is also accurately determined at a satisfactory level.

Nicotine is known not only for its addictive properties but also for its potential to alter the genetic structure of cancer cells. This study investigates the genetic alterations caused by chronic nicotine exposure in HCC38 breast cancer cells through a multi-layered computational analysis. Using K-means clustering and seven supervised machine learning algorithms, differentially expressed genes were grouped into three clusters and used as class labels in classification models. Logistic Regression achieved the highest performance with 98.76% accuracy and an F1 score of 0.9869. The Friedman test was applied to evaluate the statistical significance of performance differences among classifiers, and multi-class ROC curves were used to demonstrate their discriminative power. The findings indicate that nicotine exposure leads to genetic reprogramming and activates inflammation-related gene pathways in specific cellular subpopulations. These results highlight the utility of machine learning methods in uncovering biologically meaningful gene expression patterns in cancer research.

Profiling educators based on their attitudes toward technology and digital competence provides valuable guidance for developing targeted professional development strategies. This study grouped mathematics teacher educators according to their levels of perceived usefulness, perceived ease of use, intention to use technology, and technological proficiency. An open-access dataset was analyzed using K-Means, K-Means++, and Hierarchical Clustering algorithms, resulting in three distinct participant profiles. K-Means was specifically employed to enhance the stability and convergence of initial centroids in the clustering process. These profiles were then used as target labels in supervised classification tasks using five machine learning algorithms: Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors (KNN), and Naive Bayes. Among these algorithms, the SVM model achieved the highest accuracy of 92%. To assess the performance of the classification models, additional evaluation metrics such as precision, recall, F1-score, AUC, and the Friedman test were employed. The Friedman test showed that SVM consistently outperformed other models, confirming its superior classification capability. The findings underline the value of data-driven approaches in educational technology research and contribute to the optimization of teacher education programs by providing insights into teacher profiling based on technology acceptance and digital competence.

Background and Objective: Axial spondyloarthritis (axSpA) is a group of chronic inflammatory diseases that primarily affect the sacroiliac joints. Early diagnosis is crucial for preventing irreversible structural damage. Magnetic Resonance Imaging (MRI) is the gold standard for detecting early inflammatory changes such as sacroiliitis. However, conventional MRI interpretation is inherently subjective and susceptible to both intra- and inter-observer variability. Therefore, artificial intelligence (AI)-driven diagnostic solutions are increasingly being explored. Among them, the Gated Attention Multiple Instance Learning (MIL) framework holds strong potential in modeling heterogeneous inflammatory distributions, thanks to its slice-level attention mechanism. This study aims to evaluate the diagnostic performance of a deep learning model based on Gated Attention MIL for automated sacroiliitis detection. Furthermore, its results are compared with a baseline deep learning architecture (standard ResNet-18), and its consistency with radiologist annotations is analyzed. Materials and Methods: The dataset included 554 subjects, comprising 276 patients diagnosed with axSpA and 278 healthy controls. All MRI data were derived from axial T2-weighted fat-suppressed (T2_TSE_TRA_FS) sequences. Patient-wise data splitting was employed to construct training, validation, and independent test sets. The proposed model architecture integrates ResNet-18-based feature extraction, a gated attention mechanism for instance-level weighting, and bag-level classification. Additionally, Test-Time Augmentation (TTA) was implemented to enhance robustness during inference. Results: On the independent test set, the model achieved an accuracy of 85.88%, sensitivity of 92.86%, specificity of 79.07%, and an F1-score of 86.67%. Attention heatmaps generated by the MIL module showed strong spatial overlap with bone marrow edema regions annotated by expert radiologists. Implementation of TTA led to an approximate 10% improvement in overall classification accuracy. Conclusions: The Gated Attention MIL framework demonstrated high diagnostic performance for sacroiliitis detection, indicating its value as a reliable decision support tool for early axSpA diagnosis. Validation on larger, multi-center datasets is warranted to ensure generalizability and to support clinical integration in routine radiology workflows.