A Hybrid Model for Optimizing the Municipal Public Budget

Participatory management establishes a dynamic of democratization of the public administration, since it associates planning and widespread participation, through political definitions and adjustments and changes. Its purpose is to discuss and define the population, in a democratic way, the distribution of investment resources. Major challenges put pressure on governments at the federal, state, and municipal levels, requiring them to be more creative and effective ways of achieving results, from the employment of increasingly scarce resources and an increasingly demanding and participatory population. Considering that the process of managing public resources is defined in the budget, it is necessary that it contains all the elements that facilitate and allow a correct application of these, among the universe of interests presented by the population through the participatory management process. Moreover, we propose a model for the management of optimization of the Public Budget, whose objective is to provide the administrator with the instruments necessary to optimize the application of available public resources. The methodology in the analysis of the feasibility, and the application of a multicriteria structured model and mathematical programming applied to the public budget, having as a case study a macro view of the budget for Municipal City Halls

A Novel Hybrid Methodology Applied Optimization Energy Consumption in Homogeneous Wireless Sensor Networks

A wireless sensor network’s lifetime is influenced directly by the sensors power management that composes the network. The models applied to the problem aims to optimize the energy usage managing the sensors activation in time intervals, activating only the minimum number of sensors respecting the coverage and connectivity restrictions. However, this problem’s class has a significant computational complexity and many applications. It is necessary to implement methodologies to find the optimal solution, increasing the network’s size, becoming closer to the real ones. This research’s objective is to present a method based on a Partition Heuristic aggregating the Generate and Solve method, improving the results, and increasing the network’s instances size, while maintaining the flexibility and reliability when applied to the homogeneous wireless sensors networks with coverage and connectivity restrictions

Application of Data Mining Algorithms for Dementia in People with HIV/AIDS

Dementia interferes with the individual’s motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracyinfo:eu-repo/semantics/publishedVersio

Dealing with Nonregular Shapes Packing

This paper addresses the irregular strip packing problem, a particular two-dimensional cutting and packing problem in which convex/nonconvex shapes (polygons) have to be packed onto a single rectangular object. We propose an approach that prescribes the integration of a metaheuristic engine (i.e., genetic algorithm) and a placement rule (i.e., greedy bottom-left). Moreover, a shrinking algorithm is encapsulated into the metaheuristic engine to improve good quality solutions. To accomplish this task, we propose a no-fit polygon based heuristic that shifts polygons closer to each other. Computational experiments performed on standard benchmark problems, as well as practical case studies developed in the ambit of a large textile industry, are also reported and discussed here in order to testify the potentialities of proposed approach

Hybrid model for early identification post-Covid-19 sequelae

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.info:eu-repo/semantics/publishedVersio

Towards Machine Learning Algorithms in Predicting the Clinical Evolution of Patients Diagnosed with COVID-19

Predictive modelling strategies can optimise the clinical diagnostic process by identifying patterns among various symptoms and risk factors, such as those presented in cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus (COVID-19). In this context, the present research proposes a comparative analysis using benchmarking techniques to evaluate and validate the performance of some classification algorithms applied to the same dataset, which contains information collected from patients diagnosed with COVID-19, registered in the Influenza Epidemiological Surveillance System (SIVEP). With this approach, 30,000 cases were analysed during the training and testing phase of the prediction models. This work proposes a comparative approach of machine learning algorithms (ML), working on the knowledge discovery task to predict clinical evolution in patients diagnosed with COVID-19. Our experiments show, through appropriate metrics, that the clinical evolution classification process of patients diagnosed with COVID-19 using the Multilayer Perceptron algorithm performs well against other ML algorithms. Its use has significant consequences for vital prognosis and agility in measures used in the first consultations in hospitals

Towards Machine Learning Algorithms in Predicting the Clinical Evolution of Patients Diagnosed with COVID-19

Predictive modelling strategies can optimise the clinical diagnostic process by identifying patterns among various symptoms and risk factors, such as those presented in cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus (COVID-19). In this context, the present research proposes a comparative analysis using benchmarking techniques to evaluate and validate the performance of some classification algorithms applied to the same dataset, which contains information collected from patients diagnosed with COVID-19, registered in the Influenza Epidemiological Surveillance System (SIVEP). With this approach, 30,000 cases were analysed during the training and testing phase of the prediction models. This work proposes a comparative approach of machine learning algorithms (ML), working on the knowledge discovery task to predict clinical evolution in patients diagnosed with COVID-19. Our experiments show, through appropriate metrics, that the clinical evolution classification process of patients diagnosed with COVID-19 using the Multilayer Perceptron algorithm performs well against other ML algorithms. Its use has significant consequences for vital prognosis and agility in measures used in the first consultations in hospitals