Background/Objectives: Brain tumors are among the most severe neurological disorders, and their variability in size, morphology, and anatomical location complicates early and accurate diagnosis. Although magnetic resonance imaging (MRI) is the most reliable non-invasive modality for tumor detection, manual interpretation remains time-consuming, subjective, and susceptible to human error. This study aims to develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification. Methods: The dataset includes 834 MRI images (583-training, 123-validation, 128-independent test). Because YOLOv11 detects tumor and non-tumor regions separately, the sample size doubled during region-based analysis, and all subsequent stages were conducted at the regions of interest (ROI) level. On the independent test set, YOLOv11 achieved 98.87% mAP@50, 98.54% precision, and 98.21% recall. The proposed framework combines automated tumor localization with image standardization using Gaussian noise reduction and bilinear interpolation. From the processed MR images, 39 entropy-based features were extracted. To enhance diagnostic performance and eliminate redundant information, the superb fairy-wren optimization algorithm (SFOA) was applied for feature selection and compared with particle swarm optimization (PSO), Harris hawk optimization (HHO), and puma optimization (PO). Final classification was primarily performed using k-nearest neighbors (kNN), while support vector machines (SVM) were used for comparative evaluation. Results: SFOA reduced the feature dimensionality from 39 to 5 features while achieving 99.20% classification accuracy on the independent test set. In comparison, PSO selected 10 features, HHO selected 6 features and PO selected 10 features, all achieving 98.45% accuracy. The best performance obtained with SVM was 98.45% accuracy (HHO-SVM), which remained lower than the 99.20% achieved by the proposed SFOA-kNN model. Conclusions: The results indicate that combining entropy-based feature extraction with SFOA-driven feature selection and kNN classification significantly enhances diagnostic accuracy while reducing computational complexity, highlighting the strong potential of the proposed framework for integration into computer-aided diagnosis systems to support clinical decision-making.

While there are many verification studies on signature images using deep learning algorithms in the literature, there is a lack of studies on the classification of signature images. Signatures are used as a means of identification for banking, security controls, symmetry, certificates, and contracts. In this study, the aim was to design network architectures that work very fast in areas that require only signature images. For this purpose, a new Si-CNN network architecture with existing layers was designed. Afterwards, a new loss function and layer (Si-CL), a novel architecture using Si-CL as classification layer in Si-CNN to increase the performance of this architecture, was designed. This architecture was called Si-CNN+NC (New Classification). Si-CNN and Si-CNN+NC were trained with two datasets. The first dataset which was used for training is the “C-Signatures” (Classification Signatures) dataset, which was created to test these networks. The second dataset is the “Cedar” dataset, which is a benchmark dataset. The number of classes and sample numbers in the two datasets are symmetrical with each other. To compare the performance of the trained networks, four of the most well-known pre-trained networks, GoogleNet, DenseNet201, Inceptionv3, and ResNet50, were also trained with the two datasets with transfer learning. The findings of the study showed that the proposed network models can learn features from two different handwritten signature images and achieve higher accuracy than other benchmark models. The test success of the trained networks showed that the Si-CNN+NC network outperforms the others, in terms of both accuracy and speed. Finally, Si-CNN and Si-CNN+NC networks were trained with the gold standard dataset MNIST and showed superior performance. Due to its superior performance, Si-CNN and Si-CNN+NC can be used by signature experts as an aid in a variety of applications, including criminal detection and forgery.

Early detection of potato leaf diseases is important for maintaining agricultural productivity and crop quality. This study comparatively evaluates different deep learning–based feature extraction methods and classifier models for potato leaf disease classification under both controlled and field conditions. Two separate datasets were used: the publicly available PlantVillage dataset and the Beunto dataset developed in this study. Each dataset contains 3000 images belonging to three classes: Potato_Early_blight, Potato_Late_blight, and Potato_healthy. Deep features extracted from pre-trained CNN models, namely VGG19, ResNet50, Xception, MobileNetV2, and DenseNet121, were evaluated using both traditional machine learning classifiers and DNN-based classifiers. Beyond a simple model comparison, the study offers a comparative framework for analysing the relationship between feature representation quality and classifier capacity across datasets with different image acquisition characteristics. Among all evaluated combinations, ResNet50 + DNN-3 achieved the best performance, with accuracy values of 98.56% on PlantVillage and 98.20% on Beunto. This comparison further revealed that classification performance was governed not only by the learning capacity of the classifier, but also by the discriminative quality of the extracted deep feature space. In particular, the consistent superiority of ResNet50-based features across different classifier settings indicates that backbone-level representation quality plays a primary role, whereas increasing classifier capacity mainly improves performance when the feature space is already sufficiently separable. Thus, the study provides scientific evidence that the interaction between feature representation and classifier complexity is not arbitrary, but structurally linked to the separability and transferability of disease-related visual patterns. Cross-dataset experiments further showed that the proposed framework retained meaningful generalization ability under different visual conditions. The results demonstrate that combining effective deep feature representations with properly configured classifiers provides a reliable benchmark for potato leaf disease classification and may support future research in agricultural image analysis.

Lung and colon cancers are among the most prevalent and fatal diseases worldwide, characterized by high morbidity and mortality rates. In recent years, several state-of-the-art convolutional neural network (CNN) architectures, including VGG19, ResNet50, InceptionV3, MobileNetV2, and EfficientNet-B4, have demonstrated remarkable performance in histopathological image analysis and cancer classification. Expanding on these developments, this study aims to classify lung and colon cancers based on microscopic pathology images. To this end, six different datasets (A–F) were derived from the publicly available LC25000 dataset. Various data augmentation techniques were applied, and image dimensions were standardized to ensure consistency. Multiple CNN-based models were developed and evaluated to distinguish between three classes of colon and lung cancer. Additionally, the effectiveness of transfer learning approaches was examined due to the limited number of available samples. Experimental results indicated that the EfficientNet-B4 model achieved the highest classification accuracy of 95.08%. However, despite the high accuracy, relatively lower recall, F1-score and precision values in certain cases suggest limitations in the model’s ability to consistently identify positive cases and capture all relevant examples within the dataset. These findings provide valuable insights into the strengths and weaknesses of modern CNN architectures for histopathological cancer classification and emphasize the importance of balancing accuracy with sensitivity and precision in clinical diagnostic applications.

Artificial intelligence is also used to transfer knowledge and culture about the past to future generations. Thus, the improvement of students' history education processes and the innovations that artificial intelligence can bring to history education can be discussed. Historical research is an important discipline that helps us understand the evolution of humanity by examining past events. Traditionally, historians access and interpret information by manually analyzing documents, archives and other sources. However, with the development of artificial intelligence technologies in recent years, new possibilities in historical research are emerging, and AI has great potential in historical research. In the future, AI technologies are expected to help us understand our historical knowledge in a deeper and more comprehensive way. In this study, as well as answering the question of what artificial intelligence is, the question of how artificial intelligence can be used in history education is also answered.

Artificial intelligence has made revolutionary advances in medical imaging in recent years. Various algorithms and techniques are used in this scientific field to significantly improve the accuracy and speed of medical diagnosis and classification processes. In this direction, approaches have been improved, from the past to the present, to extract meaningful features from dental images and classify them accurately. Especially, high asymmetry in morphological balance, play a critical role in distinguishing pathological patterns from normal anatomy. In this study, we propose a scenario for the classification of periapical lesions, supported by a combination of improved image processing techniques and regularization strategies integrated into the VGG16 transfer learning architecture, as the experience and time criteria required for manual interpretation of lesion detection confirm the need for a computer-aided system. In this study, which was conducted on the UFPE public dataset, an improvement in the performance of the VGG16 transfer learning architecture was achieved, with 18 different regularization methods proposed. These values indicate optimized training within the parameters of avoiding overfitting, stability, generalizability, and high accuracy. This optimization has the potential to use as a decision support system for diagnosis and treatment processes in various subfields of the medical world.