The Ministry of Economy and Finance (MEF) is responsible for the development and maintenance of Paraguay's Integrated State Financial Administration System (SIARE), which provides state financial services for the Ministry and other governmental entities. Currently, the MEF count with a system built on client-server architecture and monolithic sub-systems, which presents challenges related to scalability, agility in the implementation of new functionalities and high coupling between modules. This article presents the technological adaptation and integration of the microservices architecture in SIARE. The solution comprises an ecosystem of components. Each of these components adheres to a layering scheme, which responds to a logical grouping of the internal components of each one of them. This architectural approach allows them to be grouped by responsibilities from the point of view of information, behavior and security. This architectural approach favors the decoupling of components, which increases the flexibility and maintainability of the system. The development practice employed throughout the project life cycle was the agile scrum methodology. The functionalities are outlined in Use Cases and the main technologies used to build the applications were: Spring Boot, Angular, Oracle, PostgreSql, Redis and Kafka. Besides, for artifact orchestration, Jenkins and WSO2 API Manager were deployed to manage the REST API services. In addition, for application observability, the ELK tool was selected as a monitoring tool. As a result of the performance tests we could observe that the architecture is stable to support large amount of workload, supporting at least 5000 simultaneous requests per second.

Diabetic retinopathy is an eye complication of a widespread disease named diabetes mellitus. The most widely used method for diagnosing diabetic retinopathy is the analysis of retinal fundus images obtained by retinography. Deep Learning-based methods have shown promising results as a diagnostic tool for diabetic retinopathy, achieving, in some cases, performance close to the human inspection of images. However, the performance of these methods relies heavily on fine-tuning the algorithm hyperparameters and big data sets. In this work, we propose training a Deep Learning network with evolutionary algorithms to classify three stages of Diabetic Retinopathy: i) no sign of diabetic retinopathy, ii) Non-proliferative dia-betic retinopathy, and iii) proliferative diabetic retinopathy. We propose a neuroevolution methodology for selecting the most efficient Deep Learning model. The results of the neuroevolution methodology were improved by including Simulated Annealing strategies, Population Reinitialization, and ensembles. With high accuracy, sensitivity, specificity, and kappa index rates of 0.889, 0.889, 0.951, and 0.822, respectively, in the best case found, the experiments show that our neuroevolution methodology for selecting the Deep Learning model hyperparameters is a competitive alternative for training deep neural networks to classify three stages of diabetic retinopathy even with a small data set.

Image segmentation is one of the first steps in most image processing procedures. The segmentation aims to obtain a more meaningful or simplified image representation by grouping pixels with common characteristics, which allows regions or features of interest to be uniquely identified. The result of the segmentation has a significant impact on the subsequent steps. Segmentation is part of several superior applications such as artificial vision, medical, topographic, and astronomical image analysis. No single or universal segmentation process gets optimal performance for all image types. Hence, determining a function that fits specific image types or applications becomes a detailed, complex, and not trivial task requiring much time and effort. In this paper, we propose using Multi-Objective Evolutionary Algorithms (MOEAs) as a training tool that combines operations that represent the techniques and strategies commonly used for generating image segmentation. As a result, sequences of operations are suitable for specific applications or image types. The objective functions used to guide the evolutionary process are sensitivity maximization (TPR) and specificity maximization (TNR), the basic components of ROC analysis. Sensitivity and specificity are commonly used as classification metrics to evaluate the quality of a proposed segmentation compared to an ideal segmentation. We used sensitivity and specificity as objective functions rather than accuracy because, as stated in [1], the dependence on prevalence makes accuracy less effective than a simultaneous consideration of sensitivity and specificity. Experiments were conducted on multiple images that share common characteristics obtained from image databases, specifically: i) benign and malignant melanoma images, ii) ophthalmoscopic retinal images, and iii) binary cell form images, where the segmentation generated by the proposed algorithm was compared with ideal segmentation. The results are quite promising and show that using MOEAs to generate sequences of segmentation operations valid for specific applications is feasible.

Special devices capable of transforming continuous images into digital formats generate digital images. Image digitization enables treating, visualizing and storing images in a computer system. However, the high-intensity light captured by a device of this type can cause unwanted bright areas in the generated images. Since these bright areas can lead to image processing errors, manifested as a false appearance, their deletion and subsequent reconstruction can produce a more faithful image. A conventional approach, based on mathematical morphology, consists of reducing or eliminating unwanted image brightness by an erosion process. The image obtained through this method undergoes a reconstruction process based on successive geodesic dilations. In this paper, a new brightness reduction method is proposed. This method identifies bright areas by applying a defined brightness limit value to remove pixels with brightness values above it, but leaving the remaining pixels unmodified. Then, the image is reconstructed by the usual image reconstruction approach based on mathematical morphology. Compared to the conventional approach, the proposed method in this work enhances brightness, generating an image more faithful to the actual object.

The acquisition of microscopic images of minerals with good contrast is critical for the identification and analysis of their properties. However, in many cases, the microscopic images of minerals obtained are unclear due to the image environment, imperfect adjustment of the microscopy operators or improper collection of samples. In this paper, we present an algorithm to enhance the microscopic images of minerals by multiscale Top-Hat transform using contrast adjustment weights. First, the multiple dark and bright features of the mineral image are extracted using the top-hat transform. Secondly, bright scale differences and dark scale differences obtained in the previous step are calculated. Third, all the intensities of the multiple dark and bright features from the previous steps are summed separately. Finally, the bright features adjusted for a contrast weight are then added to the image and dark features adjusted for the same weight are subtracted from the image. Experimental results on various kinds of microscopic mineral images verified the effective performance of this proposed enhancing the contrast, improving the detail and spatial information about the images

Infrared images (IR) help us to detect hidden targets in the environment, according to the radiation they emit. These work well on the day, at night and in weather conditions such as rain or fog. At the same time, visible images (VIS) provide us with good details of the scenes, which are better perceived by the human eye. Therefore, the fusion of an infrared image and a visible image of the same scene is very useful. In this paper, we propose a fusion method of visible and infrared images using a multiscale morphological approach. First, the base image is generated through the fusion of the two source images. Second, multiple bright and dark features are extracted from each source image by the top-hat transform. Third, the multiple bright and dark scales of the source images are fused. Finally, the fusion of the visible and infrared images is obtained by adding to the base image the maximum values obtained in the previous step. The results show that the proposed method is competitive compared to state-of-the-art methods in terms of contrast, brightness, texture and spatial information.

Extracting useful image features and preserving details effectively is a crucial part of fusion of images. Infrared images can distinguish objects from their background based on the difference in radiation. In the other hand, visible images can provide textured details consistent with the human visual system. The fusion of these two types of images can combine the advantages of thermal radiation information in infrared images and detailed texture information in visible images. In this work, we propose an algorithm of fusion of infrared and visible images using the multiscale top-hat transformation. The extraction of bright and dark regions from the images is done using two structuring elements. This algorithm provides significantly better results in contrast, brightness and texture than other state-of-theart algorithms.