https://repositorio.uema.br/jspui/handle/123456789/1885 Wed, 29 Jul 2026 04:07:07 GMT 2026-07-29T04:07:07Z http://repositorio.uema.br:80/jspui/retrieve/81630c2c-ff88-4675-815a-b0db45be13a1/CCT UEMA.png https://repositorio.uema.br/jspui/handle/123456789/1885 https://repositorio.uema.br/jspui/handle/123456789/6316 Título: Alucinações em modelos de IA Generativa: limitações e desafios na compreensão da linguagem humana Abstact: Chatbots are artificial intelligence systems that simulate human interactions and have been widely used in diverse areas, from customer service to content creation. However, despite advances in technology, chatbots still present limitations and challenges, such as the tendency to make mistakes and the difficulty in dealing with complex questions and nuances of human language. These errors are known as hallucinations and can be caused by several factors, such as a lack of common sense, biased data and limitations in the models' ability to deal with non-existent or unrepresented information in the data. Chatbot hallucinations can have significant impacts in several areas, such as education, customer service and content creation. For example, a chatbot that cannot understand the nuances of human language may provide inappropriate or incorrect responses, which can lead to a negative user experience. Additionally, chatbots can perpetuate social bias and discrimination if they are trained on biased data. To deal with these limitations in the educational context, it was proposed the development of a chatbot for teaching programming in Python that uses advanced natural language processing techniques to reduce hallucinations when learning the language. Fri, 31 May 2024 00:00:00 GMT https://repositorio.uema.br/jspui/handle/123456789/6316 2024-05-31T00:00:00Z https://repositorio.uema.br/jspui/handle/123456789/6283 Título: Estudo teórico das propriedades eletrônicas, termodinâmicas e ópticas na estrutura de zeólita (rub-11) com as dopagens de metais de transição Abstact: The molecular selectivity of zeolites gives these materials the function of molecular sieves, a central property in research focused on the space sector. The ability to őlter molecules based on nanometric dimensions is what drives the development of new structural and chemical applications in this őeld. Furthermore, zeolitic material possesses speciőc requirements for environments in this segment, since the material has a certain resistance to extreme environments, thermal stability, and eiciency in adsorption and air puriőcation processes in enclosed environments, such as aircraft cabins or space stations, serving to control contaminants such as ��3, �2�, and ��4, given its already consolidated use. Therefore, this work aims to investigate possible enhancements of the properties of RUB-11 zeolite through metallic doping by silicon atom substitution and branching at points previously calculated by population analysis, using the DFT computational method with PBE-GGA functional. Initially, the structure of RUB-11 zeolite exhibited characteristics of an insulating material; however, the results of doping with transition metals show considerable alterations in the characteristics of the original structure, making the zeolitic material more reactive. Furthermore, the viability is conőrmed by the thermodynamic properties (Enthalpy, Heat Capacity at Constant Pressure, Entropy, and Gibbs Free Energy) and the reactivity through the band gap energy, agreeing with the optical results, with only an expected variation due to the methods used. Adsorption studies resulted in adsorption for all gases proposed in this research; however, ammonia and sulfur dioxide obtained the best results. Finally, the stability analysis of the materials demonstrated that the nature of the transition elements with oxygen atoms are determining factors for the stability of the variations in the doping process Fri, 13 Mar 2026 00:00:00 GMT https://repositorio.uema.br/jspui/handle/123456789/6283 2026-03-13T00:00:00Z https://repositorio.uema.br/jspui/handle/123456789/6279 Título: Estimativas de arrecadação do ICMS do Estado do Maranhão usando algoritmos de machine learning Abstact: Tax collection forecasting is a cornerstone of fiscal planning and efficient public management. The Tax on Circulation of Goods and Services (ICMS) constitutes the main source of revenue for Brazilian states, and its accurate projection is crucial for allocating resources to strategic areas. However, the complexity of its dynamics, influenced by non-linear macroeconomic variables, and the lack of studies applied to the reality of the state of Maranhão pose a challenge for public administrators. This work aims to address this gap by investigating how machine learning techniques can improve the accuracy of forecasting monthly ICMS revenue in Maranhão. The overall objective is to develop and validate advanced computational models using a historical series of economic and social data from January 1997 to April 2024. This quantitative and applied research adopted the CRISP-DM framework. Data were collected from public sources such as SEFAZ-MA, IBGE, and the Central Bank. Initially, nineteen independent variables were considered, and a Multiple Linear Regression model was used to select the most relevant ones, such as GDP, diesel consumption, and electricity consumption indicators. Four machine learning algorithms were implemented, compared, and validated: Random Forest, Decision Tree, Linear Regression, and XGBoost. Performance evaluation was performed using the RMSE, MAE, MAPE, SMAPE, and R² metrics, using the k-fold cross-validation technique (with k=10) and a data split of 80% for training and 20% for testing. This study contributes a practical and validated model that can be integrated into the state's budget planning process, promoting more transparent, efficient, and data-driven fiscal management Wed, 01 Jan 2025 00:00:00 GMT https://repositorio.uema.br/jspui/handle/123456789/6279 2025-01-01T00:00:00Z https://repositorio.uema.br/jspui/handle/123456789/6273 Título: Método computacional para auxiliar o diagnóstico precoce da Granulomatose de Wegener Abstact: This paper presents a proteomic pattern recognition system aimed at assisting in the early diagnosis of Wegener's Granulomatosis (WG), a rare idiopathic vasculitis that is difficult to detect and carries a high mortality rate for untreated individuals. The proposed method involves extracting features from proteomic signals and classifying them as belonging to individuals with or without WG. To achieve this, Independent Component Analysis is used for feature extraction, the Minimum Redundancy Maximum Relevance algorithm is employed to reduce the number of features and computational costs, and a Support Vector Machine is used for classification. The method's performance was evaluated using a dataset of 335 proteomic signals, comprising 75 active cases, 101 negative cases, and 159 cases in remission. The best result was obtained using a twenty-feature vector, yielding accuracy, specificity, and sensitivity of 98.24%, 99.73%, and 99.50%, respectively. These results demonstrate that the proposed system is efficient for diagnosing WG and outperforms the current methodology, which is based on clinical, serological, and radiological examinations proposed by the American College of Rheumatology. Fri, 22 Jul 2016 00:00:00 GMT https://repositorio.uema.br/jspui/handle/123456789/6273 2016-07-22T00:00:00Z