O programa tem como área de concentração a Ciência da Computação, e será desenvolvido segundo 2 linhas de pesquisa: Sistemas de Computação e Computação Aplicada. https://tedebc.ufma.br/jspui/handle/tede/3739 2026-06-05T05:23:10Z 2026-06-05T05:23:10Z SILVA, Victor Hugo Barros https://tedebc.ufma.br/jspui/handle/tede/6964 2026-05-13T19:15:40Z 2026-02-26T00:00:00Z

Título: Aprendizado por reforço aplicado ao problema de alocação de berços graneleiros com controle de estoque Autor: SILVA, Victor Hugo Barros Primeiro orientador: OLIVEIRA, Alexandre César Muniz de Abstract: International maritime transport has established itself as the cornerstone of the global economy, accounting for approximately 80% of the volume and more than 70% of the total value of world trade in goods. Given this magnitude, optimizing port operations efficiency has become strategic for supply chain resilience. However, the recent period (2020–2025) was marked by a succession of disruptive events, including geopolitical crises along critical routes and climatic instability, which posed unprecedented challenges to port logistics. In this context, the Berth Allocation Problem (BAP), although well-established in the literature, demands new approaches that offer greater robustness to uncertainty. Classical optimization methodologies often exhibit scalability limitations and make simplifying assumptions that neglect the high dimensionality and operational volatility of real terminals. This thesis proposes a formulation of the BAP for dry bulk cargo, integrated with inventory control, and modeled under the Deep Reinforcement Learning (DRL) paradigm. The investigation initially employs the DQN (Deep Q-Network) algorithm. Subsequently, it evolves toward an architecture incorporating LSTM (Long Short-Term Memory) cells to capture the temporal dependencies inherent to complex sequential decision-making problems. The experimental results demonstrate that the proposed approach not only generates high-quality solutions in dynamic scenarios but also effectively mitigates inventory failures, surpassing the limitations of traditional heuristics. Instituição: Universidade Federal do Maranhão Tipo do documento: Tese

2026-02-26T00:00:00Z VIANA, Francisco dos Santos https://tedebc.ufma.br/jspui/handle/tede/6958 2026-05-13T17:19:53Z 2026-03-27T00:00:00Z

Título: Inserção colaborativa de camadas em redes stacked autoencoder Autor: VIANA, Francisco dos Santos Primeiro orientador: ALMEIDA NETO, Areolino de Abstract: This work proposes an innovative method for the structured growth of deep neural networks of the stacked autoencoder type, aiming to overcome limitations of conventional network expansion approaches. Deep neural networks have proven highly effective in classification, pattern recognition, and automatic feature extraction tasks; however, defining appropriate topologies remains a challenge. In traditional methods, inserting new hidden layers during training often leads to an increase in output error and degradation of previously acquired knowledge, making the expansion process time-consuming and dependent on complex manual adjustments. The method developed in this research performs the parallel addition of a new hidden layer and a new output layer alongside the existing output layer. Unlike conventional approaches, the added layers create a new data flow toward the network output, forming an auxiliary processing branch in which the new layers learn without degrading previously acquired knowledge. As a result, the proposed insertion strategy collaborates with the existing layers, characterizing the method as collaborative. After insertion, an integration step is carried out in which the auxiliary branch and the original output layer are combined to consolidate the learning of the new layers. This ensures that the global network error exhibits a decreasing behavior while maintaining learning stability. At the end of the process, the previous output layer becomes part of the new hidden layer, forming a network with a single output layer. The proposed method was evaluated on different classification datasets, demonstrating its ability to preserve previously consolidated knowledge, consistently reduce output error, and maintain stability during network expansion. The results indicate that the method provides a robust alternative for the automatic expansion of adaptive neural networks, improving training time efficiency, reducing the need for manual adjustments, and promoting greater flexibility and reliability in classification and pattern recognition applications. Instituição: Universidade Federal do Maranhão Tipo do documento: Tese

2026-03-27T00:00:00Z ARAÚJO, Alexandre de Carvalho https://tedebc.ufma.br/jspui/handle/tede/6873 2026-03-26T18:22:07Z 2025-09-29T00:00:00Z

Título: Segmentação automática do pâncreas e massas pancreáticas em tomografias computadorizadas usando arquiteturas encoder-decoder Autor: ARAÚJO, Alexandre de Carvalho Primeiro orientador: ALMEIDA, João Dallyson Sousa de Abstract: Cancer, the second leading cause of mortality worldwide, accounted for approximately 10 million deaths in 2020. Although relatively rare compared to other malignancies, pancreatic cancer presents one of the poorest prognoses, with a mortality rate of approximately 98%, ranking among the highest across all cancer types. Early-stage detection is the most critical determinant of patient prognosis. However, early diagnosis is particularly challenging due to the difficulty in identifying small lesions in medical imaging modalities such as abdominal ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). These imaging techniques constitute the primary tools for early detection, and the accurate identification of small pancreatic lesions at early stages significantly improves clinical outcomes. Therefore, there is a pressing need for technologies that can augment and enhance image-based diagnostic workflows. In this work, two deep learning-based methods were developed: one for automatic pancreas segmentation and another for pancreatic mass segmentation in CT scans. The proposed pancreas segmentation approach integrates the EfficientNetB7 backbone within a U-Net architecture, achieving promising results, with a mean Dice similarity coefficient (DSC) of 85.39 ± 2.39% on the NIH dataset and 85.96 ± 2.08% on the MSD dataset. For pancreatic mass segmentation, an ensemble of five encoder-decoder architectures was employed: U-Net, FPN, LinkNet, EDU-Net, and ETDPU-Net. The first three utilize EfficientNetB7 as their encoder backbone. The latter two—EDU-Net and ETDPU-Net—were designed as part of this thesis and incorporate deformable convolutions and Squeeze-and-Excitation (SE) attention mechanisms to enhance feature representation. The outputs of all five models are aggregated using a majority voting ensemble strategy to surpass the performance of individual models. The proposed mass segmentation framework achieved a DSC of 65.28% ± 5.57% on the MSD dataset, validating the efficacy and robustness of the method. Instituição: Universidade Federal do Maranhão Tipo do documento: Tese; Trabalho sob Sigilo. Motivo: Resultados apresentados na Tese ainda não foram publicados. Data Provável de Liberação: 2 anos.

2025-09-29T00:00:00Z CRUZ, Allan Kássio Beckman Soares da https://tedebc.ufma.br/jspui/handle/tede/6664 2025-12-10T11:48:54Z 2025-10-15T00:00:00Z

Título: Geração de documentos de design de jogos educacionais a partir de templates orientados ao Pensamento Computacional Autor: CRUZ, Allan Kássio Beckman Soares da Primeiro orientador: SOARES NETO, Carlos de Salles Abstract: This thesis investigates how to support the authoring of educational games oriented to Computational Thinking by tightly linking pedagogical objectives, game mechanics, and assessability. It proposes the GDD2PC model, which makes explicit the correlation between Computational Thinking pillars (abstraction, algorithmic thinking, decomposition, and pattern recognition) and families of mechanics, and derives an operational Game Design Document template tailored to teaching practice, including a concise onepage format. Both were instantiated in the Hefesto Game Lab tool, designed to reduce authoring effort, standardize document quality, and preserve flexibility across educational contexts. The research followed a Design Science Research process: requirements elicitation, model and template construction, tool implementation, and user-oriented evaluation. Technology acceptance was assessed with the TAM, complemented by pedagogical alignment and applicability. Results show mean perceived usefulness around 4.0, perceived ease of use near 4.1, and behavioral intention at 3.8 on a 1–5 Likert scale, with high instrument reliability (high Cronbach’s alpha). Evidence indicates that the approach reduces authoring ambiguity, speeds up iteration cycles, and strengthens alignment between Computational Thinking pillars and design decisions, contributing to a more efficient and replicable teaching practice. Instituição: Universidade Federal do Maranhão Tipo do documento: Tese

2025-10-15T00:00:00Z