Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. These approaches, however, suffer from at least one of the following problems: (i) they treat each CT scan slice independently and (ii) the methods are trained and tested with sets of images from the same dataset. Treating the slices independently means that the same patient may appear in the training and test sets at the same time which may produce misleading results. It also raises the question of whether the scans from the same patient should be evaluated as a group or not. Moreover, using a single dataset raises concerns about the generalization of the methods. Different datasets tend to present images of varying quality which may come from different types of CT machines reflecting the conditions of the countries and cities from where they come from. In order to address these two problems, in this work, we propose an Efficient Deep Learning Technique for the screening of COVID-19 with a voting-based approach. In this approach, the images from a given patient are classified as group in a voting system. The approach is tested in the two biggest datasets of COVID-19 CT analysis with a patient-based split. A cross dataset study is also presented to assess the robustness of the models in a more realistic scenario in which data comes from different distributions. The cross-dataset analysis has shown that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario. These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

Background: The classification of electrocardiogram (ECG) signals is essential for early arrhythmia detection. However, many studies fail to follow standardization protocols, leading to inconsistencies in performance evaluation and real-world applicability. Additionally, hardware constraints crucial for deployment in pacemakers, Holter monitors, and wearable ECG patches are often overlooked, limiting practical use in resource-constrained environments. Objective: This review systematically analyzes ECG classification research published between 2017 and 2024, focusing on studies that meet the E3C (Embedded, Clinical, and Comparative Criteria). Methods: These criteria include adherence to the inter-patient paradigm, compliance with AAMI recommendations, and evaluation of model feasibility for embedded deployment. We conduct a comparative analysis of accuracy, inference time, energy consumption, and memory usage, identifying state-of-the-art methods that align with the E3C. Results: Our findings show that while many models achieve high accuracy, few address key constraints like patient-independent data partitioning and hardware limitations. Conclusions: Finally, we propose standardized performance reporting to enable fair comparisons and enhance real-world applicability. By addressing these gaps, this study aims to guide research toward more robust and clinically viable ECG classification models.

Tuning the Proportional Integral Derivative, or PID, controller in cyber-physical systems is a major challenge as it requires advanced mathematical skills. Several authors in the literature have shown that optimization algorithms are efficient for auto-adjust PID controller constants, especially when there is no mathematical modeling. However, the literature lacks works that show the efficiency of the Variable Neighborhood Search (VNS) algorithm to auto-adjust the PID. In this work, we investigate the efficiency of the Variable Neighborhood Algorithm to fine-tune a PID controller of a real cyber physical-system: a birotor flying drone. The approach consists of applying a numerical neighborhood structure to optimize the three constants of the PID, according to a proposed fitness function. Experiments reveal the feasibility of fine-tuning the PID controller and the birotor balancing with the Variable Neighborhood Algorithm with reduced time. We compared the VNS-approach against one based on genetic algorithms, and on average, the VNS-approach achieves better results with lower computational and memory costs. Results suggest that the approach may be used in real or commercial systems, helping to fine-tune the controller to new environment changes or even last-minute project modifications.