Small datasets are frequent in some scientific fields. Such datasets are usually created due to the difficulty or cost of producing laboratory and experimental data. On the other hand, researchers are interested in using machine learning methods to analyze this scale of data. For this reason, in some cases, low-performance, overfitting models are developed for small-scale data. As a result, it appears necessary to develop methods for dealing with this type of data. In this research, we provide a new and innovative framework for regression problems with a small sample size. The base of our proposed method is the K-nearest neighbors (KNN) algorithm. For feature selection, instance selection, and hyperparameter tuning, we use the bat optimization algorithm (BA). Generative Adversarial Networks (GANs) are employed to generate synthetic data, effectively addressing the challenges associated with data sparsity. Concurrently, Deep Neural Networks (DNNs), as a deep learning approach, are utilized for feature extraction from both synthetic and real datasets. This hybrid framework integrates KNN, DNN, and GAN as foundational components and is optimized in multiple aspects (features, instances, and hyperparameters) using BA. The outcomes exhibit an enhancement of up to 5% in the coefficient of determination ($R^2$ score) using the proposed method compared to the standard KNN method optimized through grid search.

This research serves a dual purpose: it aims to map the current landscape of knowledge in a field rich with technological challenges while simultaneously showcasing innovative techniques that demonstrate the transformative power of computer vision in airport applications. To achieve this, the study offers a thorough analysis of various object recognition and tracking methods, exploring their implications both in general contexts and specifically within airport environments. As airport operations become increasingly complex, the integration of advanced technologies such as computer vision and AI models is essential. This review examines a variety of object detection and tracking techniques, including deep learning models, traditional algorithms, and hybrid approaches, highlighting their effectiveness in addressing specific challenges to airport environments and detailing their performance under real-world conditions such as adverse weather, occlusion, low visibility, and data privacy concerns. A key insight is the superior capability of deep learning techniques, which, through multiscale feature extraction and region proposal strategies, provide enhanced detection accuracy and robustness in dynamic airport settings. In addition, emerging trends such as federated learning and hybrid sensor fusion are explored, emphasizing their potential to improve scalability and situational awareness by integrating data from various sources, including radar and optical sensors. The analysis also examines precise aircraft detection to optimize ground operations, real-time runway surveillance to ensure safe takeoffs and landings, and advanced passenger monitoring systems that improve security by identifying potential threats. These findings underscore the pivotal role of computer vision in improving operational efficiency and safety while laying the foundation for the future development of intelligent, integrated airport management systems.

Unmanned aircraft are increasingly recognized for their potential to enhance healthcare logistics, offering rapid and reliable transport solutions. Among the many envisioned use cases, emergency medical deliveries stand out as particularly promising due to their immediate societal value. This study investigates the potential of drones operating under U-space to support hospital-to-hospital emergency deliveries in Madrid. Using the GEMMA tool, we modeled and simulated operations with two drone types along direct routes between four hospitals, resulting in six hospital pairs. Drone travel times were estimated and compared against road transport times obtained from the Google Routes API, incorporating one week of traffic data to capture daily and weekend variability. The results show substantial advantages of aerial transport, with time savings ranging from 2 to 26 min, equivalent to 35–58% compared to road transport. Drones consistently ensured deliveries within 15 min, outperforming regular cars (39%) and ambulances or motorcycles in highly congested periods. Sensitivity analysis confirms their reliability in scenarios with strict time constraints, especially under 15 min. These findings demonstrate that drones reduce travel times and improve predictability, providing a robust evidence base for policymakers and regulators to advance U-space integration in healthcare logistics.