6 citations · 14 across the 16 of their papers we have counts for
18 papers
Exposing AI Inference as a Telco Network API over the 5G User Plane
Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, Joberto S. B. Martins +2
Supporting edge Artificial Intelligence (AI) inference as a network service is challenging because current mobile systems can transport tensors but do not expose or understand the…
An Intelligent eUPF for Time-Sensitive Path Selection in B5G Edge Networks
Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, Tereza Cristina Carvalho +1
In Beyond 5G (B5G) networks, intelligent, flexible traffic management is essential to meet the stringent speed and reliability requirements of new applications. This paper presents…
Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic
Yasmin Souza Lima, Rodrigo Moreira, Larissa F. Rodrigues Moreira +2
Unsupervised anomaly detection is widely used to detect Distributed Denial-of-Service (DDoS) attacks in cloud-native 5G networks, yet most studies assume a fixed traffic representa…
Asynchronous Probability Ensembling for Federated Disaster Detection
Emanuel Teixeira Martins, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira +3
Quick and accurate emergency handling in Disaster Decision Support Systems (DDSS) is often hampered by network latency and suboptimal application accuracy. While Federated Learning…
Data Augmentation and Convolutional Network Architecture Influence on Distributed Learning
Victor Forattini Jansen, Emanuel Teixeira Martins, Yasmin Souza Lima +3
Convolutional Neural Networks (CNNs) have proven to be highly effective in solving a broad spectrum of computer vision tasks, such as classification, identification, and segmentati…
NeuroScaler: Towards Energy-Optimal Autoscaling for Container-Based Services
Alisson O. Chaves, Rodrigo Moreira, Larissa F. Rodrigues Moreira +7
Future networks must meet stringent requirements while operating within tight energy and carbon constraints. Current autoscaling mechanisms remain workload-centric and infrastructu…
Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification
Thamara Leandra de Deus Melo, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira +1
Efficient brain tumor diagnosis is crucial for early treatment; however, it is challenging because of lesion variability and image complexity. We evaluated convolutional neural net…
Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images
Elisa Gonçalves Ribeiro, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira +1
Deep learning for cancer histopathology training conflicts with privacy constraints in clinical settings. Federated Learning (FL) mitigates this by keeping data local; however, its…
Energy-Aware Ensemble Learning for Coffee Leaf Disease Classification
Larissa Ferreira Rodrigues Moreira, Rodrigo Moreira, Leonardo Gabriel Ferreira Rodrigues
Coffee yields are contingent on the timely and accurate diagnosis of diseases; however, assessing leaf diseases in the field presents significant challenges. Although Artificial In…
Noisy Neighbor Influence in the Data Plane of Beyond 5G Networks
Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, Tereza C. Carvalho +1
Virtualization and containerization enhance the modularity and scalability of mobile network architectures, facilitating customized user services and improving management and orche…
Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment
Rodrigo Moreira, Larissa F. Rodrigues Moreira, Flávio de Oliveira Silva
The deployment of large-scale software-based 5G core functions presents significant challenges due to their reliance on optimized and intelligent resource provisioning for their se…
Optimizing Edge Gaming Slices through an Enhanced User Plane Function and Analytics in Beyond-5G Networks
Bruno Marques da Silva, Larissa Ferreira Rodrigues Moreira, Flávio de Oliveira Silva +1
The latest generation of games and pervasive communication technologies poses challenges in service management and Service-Level Agreement compliance for mobile users. State-of-the…
Programming in Brazilian Higher Education and High School: A Systematic Literature Review
Sofia C. Latini Gonçalves, Rodrigo Moreira, Larissa F. Rodrigues Moreira +2
Programming, which is both economically significant and mentally stimulating, has been found to benefit the aging brain and to enhance cognitive function at various educational lev…
VINEVI: A Virtualized Network Vision Architecture for Smart Monitoring of Heterogeneous Applications and Infrastructures
Rodrigo Moreira, Hugo G. V. O. da Cunha, Larissa F. Rodrigues Moreira +1
Monitoring heterogeneous infrastructures and applications is essential to cope with user requirements properly, but it still lacks enhancements. The well-known state-of-the-art met…
Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks
Victor Júnio Alcântara Cardoso, Rodrigo Moreira, João Fernando Mari +1
Sickle cell anemia, which is characterized by abnormal erythrocyte morphology, can be detected using microscopic images. Computational techniques in medicine enhance the diagnosis…
Towards Cognitive Service Delivery on B5G through AIaaS Architecture
Larissa F. Rodrigues Moreira, Rodrigo Moreira, Flávio de Oliveira Silva +1
Artificial Intelligence (AI) is pivotal in advancing mobile network systems by facilitating smart capabilities and automation. The transition from 4G to 5G has substantial implicat…
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds
Daniel Pereira Monteiro, Lucas Nardelli de Freitas Botelho Saar, Larissa Ferreira Rodrigues Moreira +1
Novel applications demand high throughput, low latency, and high reliability connectivity and still pose significant challenges to slicing orchestration architectures. The literatu…
Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction
Thalita Mendonça Antico, Larissa F. Rodrigues Moreira, Rodrigo Moreira
The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accura…