Given an undirected connected graph G = (V, E), a subset of vertices S is a maximum 2-packing set if the number of edges in the shortest path between any pair...

This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment.

In this paper, we address the problem of impulsive noise reduction in color images through an evolutionary approach. We designed a new hybrid genetic algorithm, called GARIN, which takes as input a noisy image and generates as output a reduced noise version of the same image. As part of its evolutionary process, GARIN integrates the execution of robust and adaptive filters from literature with the aim to combine and improve their results. Unlike most genetic algorithms designed for color image denoising, our algorithm evolves a set of images instead of the set of parameters of the filters. Experimental results show that, compared with other filters of literature, the algorithm GARIN efficiently removes impulsive noise in color images while, at the same time, it preserves their main features.

Background/Objectives: Strawberry crops are vulnerable to fungal diseases that severely affect yield and quality. Deep learning has shown strong potential for plant disease recognition; however, most architectures rely on tens of millions of parameters, limiting their use in low-resource agricultural settings. This study presents Light-MobileBerryNet, a lightweight and interpretable model designed to achieve accurate strawberry disease classification while remaining computationally efficient for potential use on mobile and edge devices. Methods: The model, inspired by MobileNetV3-Small, integrates inverted residual blocks, depthwise separable convolutions, squeeze-and-excitation modules, and Swish activation to enhance efficiency. A publicly available dataset was processed using CLAHE and data augmentation, and split into training, validation, and test subsets under consistent conditions. Performance was benchmarked against state-of-the-art CNNs. Results: Light-MobileBerryNet achieved 96.6% accuracy, precision, recall, and F1-score, with a Matthews correlation coefficient of 0.96, while requiring fewer than one million parameters (~2 MB). Grad-CAM confirmed that predictions focused on biologically relevant lesion regions. Conclusions: Light-MobileBerryNet approaches state-of-the-art performance with a fraction of the computational cost, providing a practical and interpretable solution for precision agriculture.

A ridesharing system is a transport mode where two or more users share the same vehicle and divide the trip’s expenses based on similar routes and itineraries. Popular ridesharing systems, such as Uber, Flinc, and Lyft, define a matching among users based only on the coincidence of routes. However, these systems do not guarantee a stable matching (i.e., a matching in which no user prefers another different from the assigned one). In this work, a new ridesharing system model is proposed, including three types of trips: identical, inclusive, and partial. This model is used to introduce a new algorithm to address the stable matching problem for ridesharing systems. Finally, a set of experimental simulations of the proposed algorithm is conducted. Experimental results show that the proposed algorithm always produces a stable matching.