https://repositorio.unifei.edu.br/jspui/handle/123456789/65 2026-10-16T05:58:34Z 2026-10-16T05:58:34Z https://repositorio.unifei.edu.br/jspui/handle/123456789/3173 2024-02-05T10:36:02Z 2022-02-17T00:00:00Z

Título: Aquisição e validação de sinal de ruído eletroquímico Abstract: Corrosion is a global problem, which implies costs in industrialized countries of up to 4.5% of GDP, with either economic, but also social and environmental impacts. In the case of Brazil, the waste of water supply networks due to leaks loss is quite significant and much of it is caused by network degradation, indicating that corrosion control should be promoted whenever possible. This study proposes a corrosion monitoring system, in system subject to the use of inhibitor, with the approach of passive technique for monitoring corrosion by electrochemical noise (EN), in which the classification of events in a corrosion sensor by EN is part of methodological study for structural integrity (or “health”) monitoring system (SHM). Due to very dynamic and stochastic nature of the signal, this study and analysis of EN measurements (ENM) considers numerical and graphic characteristics of two corrosion systems both in saline aqueous solution: carbon steel and stainless steel. These experiments are repeated for accumulating data, which allow the generation of several graphs in time and frequency domains, from which at least one characteristic is extracted, which has a good correlation with data from corrosion processes. Then, based on a supervised machine learning system, the training data allows the model to be calibrated. From the test data, the correctness rate of the model above 50% is verified. Tipo: Dissertação

2022-02-17T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4486 2026-10-13T14:22:46Z 2026-07-28T00:00:00Z

Título: Framework multicritério para seleção de modelos de linguagem em contextos educacionais: uma análise de desempenho, custo e eficiência energética Abstract: The adoption of artificial intelligence models in Virtual Learning Environments (VLEs) requires a balance among response quality, latency, computational cost, and energy consumption, especially in continuous and large-scale usage scenarios. This study proposes a multicriteria decision framework for selecting between Small Language Models (SLMs) and Large Language Models (LLMs) in educational contexts, modeling the choice as a Profile Matching problem based on the Euclidean distance between the scenario’s target profile and the normalized empirical performance of each model. Following the logic of Design Science Research (DSR), 21 models (13 base models and 8 fine-tuned models) were evaluated, ranging from 1 to 70 billion parameters, across 240 multiple-choice questions (120 original and 120 from ENEM), distributed among eight subjects and three difficulty levels. The results indicate that LLMs achieve higher accuracy, with a clear advantage on difficult questions, whereas untuned SLMs are substantially faster (approximately 8.56 times lower latency) and more energy-efficient (approximately 4.83 times less energy). Fine-tuning, under the conditions evaluated, increased operational costs without a consistent gain in accuracy. Across the ten simulated scenarios, the framework selected eight distinct models, demonstrating that the best choice depends on the context and that profile-based selection outperforms the maximization of isolated metrics. Tipo: Dissertação

2026-07-28T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4485 2026-10-13T13:30:56Z 2026-02-12T00:00:00Z

Título: Prototipação de um sistema de apoio ao diagnóstico de TEA baseado em imagens de ressonância magnética e aprendizado de máquina Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose diagnosis remains centered on behavioral criteria, which can introduce delays and inter-rater variability. Resting-state functional magnetic resonance imaging (rs-fMRI) enables investigation of brain functional connectivity and, combined with machine learning models, has been explored as an objective aid to diagnosis. However, choices of configuration parameters and preprocessing strategies strongly influence feature quality and, consequently, classifier performance and reproducibility. This work assesses the impact of different rs-fMRI preprocessing configurations on the performance of models for ASD classification. Modular software was developed to automate image preprocessing, feature extraction and selection, and the training and evaluation of multiple classifiers. Data from the ABIDE I and II datasets were used. Initially, a sex-balanced set with 72 participants (half with ASD) was assembled, to which 108 different preprocessing configurations were applied, varying spatial smoothing, mask threshold, noise removal, and dimensionality reduction parameters. Connectivity features were derived using full correlation (FC), partial correlation (PC), and their combination (FC+PC), followed by statistical tests and incremental feature selection with features added sequentially in ascending order of p-value. Thirteen classification algorithms were evaluated with leave-one-out validation, incrementally varying the number of features used, totaling 113,698 experiments. In this initial set, the best accuracy reached 95.8% and specificity 100%, with multilayer perceptron (MLP)-based classifiers and the combined use of FC+PC features standing out. Next, to simulate performance in a scenario closer to clinical practice, the preprocessing pipelines with the best initial results were applied to an independent set of 216 additional participants. Models were trained on the first-stage participants and tested on this new set, where accuracy dropped to 61.4% and specificity to 58.4%. These findings highlight the sensitivity of model performance to preprocessing choices and reinforce the need for strategies that prioritize generalization. Tipo: Dissertação

2026-02-12T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4484 2026-10-09T19:13:32Z 2026-07-27T00:00:00Z

Título: Pipeline de prospecção de leads: descoberta do cliente, qualificação com lead scoring (ICP) orientado a customer success com apoio de IA generativa e IAPAAS Abstract: Client prospecting and lead qualification in website development projects often rely on informal processes, resulting in ambiguities in requirement elicitation, customer understanding, and decision-making. In this context, this research investigates how Generative Artificial Intelligence can support Customer Discovery and lead qualification activities aligned with Customer Success principles. A Design Science Research (DSR) approach combined with a Systematic Literature Review was adopted to support the design and evaluation of a technological artifact. As a result, an AI-supported prospecting pipeline was proposed and implemented, comprising Generative AI conversational agents, iPaaS integrations, and lead qualification mechanisms responsible for lead qualification, needs diagnosis, proposal generation, sales support, and follow-up activities. The pipeline was integrated through iPaaS platforms and evaluated with specialists and graduate students representing potential users. The results indicated positive perceptions regarding the pipeline's ability to support needs identification, structure relevant information for commercial processes, and improve understanding of customer objectives. The findings suggest that the proposed approach has the potential to assist web development professionals and small digital service businesses in conducting more structured customer discovery and lead qualification processes. The main contribution of this research is the demonstration of how Generative AI agents can be applied to early-stage customer interactions by integrating Customer Discovery, Lead Scoring, and Customer Success concepts into a unified prospecting pipeline. Tipo: Dissertação

2026-07-27T00:00:00Z