1 citations · 1 across the 3 of their papers we have counts for
9 papers
A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images
João Pedro Matos-Carvalho, Laio Oriel Seman, Stefano Frizzo Stefenon +2
The inspection of electrical power line insulators is essential for ensuring grid reliability and preventing failures caused by damaged or degraded insulation components. In recent…
Multispectral Indices for Wildfire Management
Afonso Oliveira, João P. Matos-Carvalho, Filipe Moutinho +1
The increasing frequency and severity of wildfires necessitates advanced methods for effective surveillance and management, as traditional ground-based techniques often struggle to…
Can Large Language Models Implement Agent-Based Models? An ODD-based Replication Study
Nuno Fachada, Daniel Fernandes, Carlos M. Fernandes +1
Large language models (LLMs) can now synthesize non-trivial executable code from textual descriptions, raising an important question: can LLMs reliably implement agent-based models…
CNN-TFT explained by SHAP with multi-head attention weights for time series forecasting
Stefano F. Stefenon, João P. Matos-Carvalho, Valderi R. Q. Leithardt +1
Convolutional neural networks (CNNs) and transformer architectures offer strengths for modeling temporal data: CNNs excel at capturing local patterns and translational invariances,…
GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries
Nuno Fachada, Daniel Fernandes, Carlos M. Fernandes +2
Large Language Models (LLMs) have advanced rapidly as tools for automating code generation in scientific research, yet their ability to interpret and use unfamiliar Python APIs for…
DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks
Daniel Fernandes, João P. Matos-Carvalho, Carlos M. Fernandes +1
This paper investigates the performance of 16 Large Language Models (LLMs) in automating LoRaWAN-related engineering tasks involving optimal placement of drones and received power…