The Internet of Medical Things (IoMT) enables real-time monitoring and decision support systems in healthcare. However, due to their heterogeneous structure, limited resources, and high criticality, IoMT networks are vulnerable to cyberattacks. This situation increases the need for low-latency, high-accuracy, and generalizable attack detection systems. In this experimental study, the Multi-Scale Depthwise Channel Attention Network (MSCA-Net) model is proposed for multi-class attack detection in IoMT environments. The model consists of three core components: multi-scale depthwise separable convolutions to capture traffic patterns across different time scales, a squeeze-and-excitation-based channel attention mechanism that adaptively weights discriminative features, and a lightweight unidirectional LSTM layer that models temporal dependencies. This architecture enables effective representation learning with low parameter costs. The proposed model was evaluated on the WUSTL-EHMS-2020 and CICIoMT2024 datasets. On the CICIoMT2024 dataset, it achieved 99.75% accuracy and a weighted F1 score of 99.77% in a 6-class scenario. It has also demonstrated competitive results in 19-class fine-grained classification. Experimental comparisons show that MSCA-Net offers a better performance-to-cost trade-off compared to nine different baseline models. Furthermore, it demonstrates a speed advantage of up to two times in inference time. The results obtained at the conclusion of the experimental study demonstrate that the proposed approach effectively addresses the challenges of multi-scale feature extraction, class imbalance, and computational efficiency. Furthermore, the model appears to offer a viable solution for real-time attack detection in IoMT environments.

This study proposes an artificial intelligence-based method for the detection and semantic segmentation of C2, C3, and C4 cervical vertebrae in lateral cephalometric radiographs. The dataset comprises 2520 radiographs obtained from the Orthodontics Department of Van Yüzüncü Yıl University Faculty of Dentistry. In the initial stage, vertebral regions were identified using YOLOv8 and YOLOv11 object detection models, and these areas were meticulously annotated using QuPath software. The labelled data were then subjected to segmentation using advanced deep learning models such as Attention-UNet, Attention-ResUNet, SEEA-UNet, and ResAt-UNet. The study revealed that the object detection models achieved a high performance with an accuracy of 99.8%. Among the segmentation models, Attention-ResUNet demonstrated the best performance with an accuracy of 99.25%, while the ResAt-UNet model stood out with its balanced generalization capacity. The generated binary masks provided a reliable dataset for bone age estimation and skeletal maturity analysis. This study aims to reduce radiation exposure and streamline clinical workflows by eliminating the need for additional imaging. The findings indicate that AI-supported methods minimize errors caused by manual assessments and ensure standardization in skeletal analysis. It is anticipated that these methods could be widely utilized in orthodontic and pediatric medical applications in the future.

In this study, an automatic classification of cervical vertebra maturation (CVM) stages was performed using raw lateral cephalometric radiographs to assess growth and development. A total of 4285 radiographs from the Department of Orthodontics at Van Yüzüncü Yıl University Faculty of Dentistry were utilized. Following detailed evaluations by specialist physicians, 3750 images meeting diagnostic accuracy and clinical suitability criteria were included. The selected images were categorized into six classes (CVMS 1–6), forming a balanced dataset for classification with the NFNet, ConvNeXt V2, EfficientNet V2, and DeiT3 models. The NFNet model achieved the highest overall performance, with 96% training accuracy and 85.7% test accuracy. ConvNeXt V2, attaining 95% training accuracy and 86.9% test accuracy, emerged as the most balanced in terms of generalization. Although EfficientNet V2 reached 94% training accuracy, its 80.7% test accuracy indicated limited generalization. With 93% training accuracy and 77.6% test accuracy, DeiT3 demonstrated the lowest capacity. Both NFNet and ConvNeXt V2 stood out as strong classification candidates based on their high accuracy and balanced performance. While NFNet showed a 10.3% gap between training and test accuracy, indicating somewhat reduced generalization, ConvNeXt V2’s narrower 8.1% gap suggested greater stability. In conclusion, NFNet and ConvNeXt V2 are promising models for CVM classification. Future studies should employ larger datasets and conduct hyperparameter optimization to enhance these models’ performance and strengthen their clinical applicability.

Electricity meters, which are read manually, bring a great financial burden to companies in terms of workload, time and economy. In this study, examined the reading of electric meters automatically process, Turkey focused on cost-benefit analysis performed. With the ever-increasing electricity consumption, the number of electricity expenses and accordingly the number of subscribers increases. Along with the increasing number of electricity consumers, the number of subscribers and consequently electricity consumption increases. Automatic Meter Reading Systems is one of the recommended solution packages for Energy Management Systems. It is for python programming language and its frameworks as software infrastructure. Nginx is used as a web server, and Web Server Gateway Interface and Gunicorn frameworks are used for the connection between the application and the web server. Thanks to automatic meter reading system, which has an extremely fast reading structure, it is predicted that tens of thousands of electricity meters can be read over the internet in seconds. Nginx Load Balancer software technology is capable of transferring tens of thousands of indexes to the database at once. This technology is able to distribute data load and respond to data traffic. In addition, the effect of reading cost on Energy Management Systems is analyzed in meters that are read manually. Turkey's Elazig province, economic research analysis was conducted on a total of 334 thousand electricity subscribers. According to survey data, 47 million has electricity subscribers in Turkey. In addition, cost-benefit analysis of the meters that are foreseen to be read in Nginx load balancer software technology is performed.

Pneumonia is a dangerous disease that causes severe inflammation of the air sacs in the lungs. It is one of the infectious diseases with high morbidity and mortality in all age groups worldwide. Chest X-ray (CXR) is a diagnostic and imaging modality widely used in diagnosing pneumonia due to its low dose of ionizing radiation, low cost, and easy accessibility. Many deep learning methods have been proposed in various medical applications to assist clinicians in detecting and diagnosing pneumonia from CXR images. We have proposed a novel PneumoNet using a convolutional neural network (CNN) to detect pneumonia using CXR images accurately. Transformer-based deep learning methods, which have yielded high performance in natural language processing (NLP) problems, have recently attracted the attention of researchers. In this work, we have compared our results obtained using the CNN model with transformer-based architectures. These transformer architectures are vision transformer (ViT), gated multilayer perceptron (gMLP), MLP-mixer, and FNet. In this study, we have used the healthy and pneumonia CXR images from public and private databases to develop the model. Our developed PneumoNet model has yielded the highest accuracy of 96.50% and 94.29% for private and public databases, respectively, in detecting pneumonia accurately from healthy subjects.