Wind power plays an increasingly vital role in sustainable energy production, yet the harsh environments in which turbines operate often lead to mechanical or structural degradation. Detecting such faults early is essential to reducing maintenance expenses and extending operational lifetime. In this work, we propose a deep learning-based image classification framework designed to assess turbine condition directly from drone-acquired imagery. Unlike object detection pipelines, which require locating specific damage regions, the proposed strategy focuses on recognizing global visual cues that indicate the overall turbine state. A comprehensive comparison is performed among several lightweight and transformer-based architectures, including MobileNetV3, ResNet, EfficientNet, ConvNeXt, ShuffleNet, ViT, DeiT, and DINOv2, to identify the most suitable model for real-time deployment. The MobileNetV3-Large network achieved the best trade-off between performance and efficiency, reaching 98.9% accuracy while maintaining a compact size of 5.4 million parameters. These results highlight the capability of compact CNNs to deliver accurate and efficient turbine monitoring, paving the way for autonomous, drone-based inspection solutions at the edge.
Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could p…
The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detect…
The variations in the atmospheric refractivity in the lower atmosphere create a natural phenomenon known as atmospheric ducts. The atmospheric ducts allow radio signals to travel long distances. This can adversely affect telecommunication systems, as cells with similar frequencie…
Wireless sensor networks (WSNs) have become a critical component of modern Internet of Things (IoT) infrastructures; however, their constrained resources and distributed deployment expose them to various cyber threats. In this work, we present a machine learning-driven intrusion…
Wireless Sensor Networks (WSNs) consist of distributed sensor nodes that collect and transmit environmental data, often in resource-constrained and unsecured environments. These characteristics make WSNs highly vulnerable to various security threats. To address this, the objectiv…
The rapid expansion of the Internet of Things (IoT) and industrial Internet of Things (IIoT) ecosystems has introduced new security challenges, particularly the need for robust intrusion detection systems (IDSs) capable of adapting to increasingly sophisticated cyberattacks. In t…