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Researcher profile

Larissa Ferreira Rodrigues Moreira

Larissa Ferreira Rodrigues Moreira contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionArtificial IntelligenceMachine LearningNetworking and Internet Architecture

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Published work

5 published item(s)

preprint2026arXiv

Energy-Aware Ensemble Learning for Coffee Leaf Disease Classification

Coffee yields are contingent on the timely and accurate diagnosis of diseases; however, assessing leaf diseases in the field presents significant challenges. Although Artificial Intelligence (AI) vision models achieve high accuracy, their adoption is hindered by the limitations of constrained devices and intermittent connectivity. This study aims to facilitate sustainable on-device diagnosis through knowledge distillation: high-capacity Convolutional Neural Networks (CNNs) trained in data centers transfer knowledge to compact CNNs through Ensemble Learning (EL). Furthermore, dense tiny pairs were integrated through simple and optimized ensembling to enhance accuracy while adhering to strict computational and energy constraints. On a curated coffee leaf dataset, distilled tiny ensembles achieved competitive with prior work with significantly reduced energy consumption and carbon footprint. This indicates that lightweight models, when properly distilled and ensembled, can provide practical diagnostic solutions for Internet of Things (IoT) applications.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification

Efficient brain tumor diagnosis is crucial for early treatment; however, it is challenging because of lesion variability and image complexity. We evaluated convolutional neural networks (CNNs) in a federated learning (FL) setting, comparing models trained on original versus preprocessed MRI images (resizing, grayscale conversion, normalization, filtering, and histogram equalization). Preprocessing alone yielded negligible gains; combined with test-time augmentation (TTA), it delivered consistent, statistically significant improvements in federated MRI classification (p<0.001). In practice, TTA should be the default inference strategy in FL-based medical imaging; when the computational budget permits, pairing TTA with light preprocessing provides additional reliable gains.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images

Deep learning for cancer histopathology training conflicts with privacy constraints in clinical settings. Federated Learning (FL) mitigates this by keeping data local; however, its performance depends on hyperparameter choices under non-independent and identically distributed (non-IID) client datasets. This paper examined whether hyperparameters optimized on one cancer imaging dataset generalized across non-IID federated scenarios. We considered binary histopathology tasks for ovarian and colorectal cancers. We perform centralized Bayesian hyperparameter optimization and transfer dataset-specific optima to the non-IID FL setup. The main contribution of this study is the introduction of a simple cross-dataset aggregation heuristic by combining configurations by averaging the learning rates and considering the modal optimizers and batch sizes. This combined configuration achieves a competitive classification performance.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Noisy Neighbor Influence in the Data Plane of Beyond 5G Networks

Virtualization and containerization enhance the modularity and scalability of mobile network architectures, facilitating customized user services and improving management and orchestration across the network. In the context of the 5th Generation Mobile Network (5G), these advancements contribute to reduced Operational Expenditures (OPEX) and enable sliced-based networking for novel applications and services. However, as beyond fifth-generation (B5G) networks aim to address the remaining challenges regarding network slice isolation, the shared underlying hardware can lead to data plane contention among slices, resulting in the Noisy Neighbor (NN) effect, which may compromise network slicing and Service-Level Agreements (SLAs). We propose a kernel-level instrumentation of the User Plane Function (UPF) to assess the impact of noisy slices on data plane processing. Our findings reveal that even prioritized slices are susceptible to degradation induced by NN, with observable effects on latency metrics pertinent to user experience.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PRISM: Perinuclear Ring-based Image Segmentation Method for Acute Lymphoblastic Leukemia Classification

Automated analysis of peripheral blood smears for Acute Lymphoblastic Leukemia (ALL) is hindered by low contrast and substantial variability in cytoplasmic appearance, which complicate conventional membrane-based segmentation. We found that many recent approaches rely on heavy neural architectures and extensive training, but still struggle to generalize across staining and acquisition variability. To address these limitations, we propose the Perinuclear Ring-based Image Segmentation Method (PRISM), which replaces explicit cytoplasmic delineation with adaptive concentric zones constructed around the nucleus. These perinuclear regions enable the extraction of robust cytoplasmic descriptors by integrating color information with texture statistics derived from grey-level co-occurrence patterns, without requiring accurate cell-boundary detection. A calibrated stacking ensemble of traditional classifiers leverages these descriptors to achieve a high performance, with an accuracy of 98.46% and a precision-recall AUC of 0.9937.

0 citations0 reviewsNo code linkedOpen access