Our Publications
Showing 17 of 17 publications
A Composed and Derivative-Augmented Framework for Forecasting Intermittent Demand in Spare Parts Logistics
Aliu, D., Gomes, N., & Rocha, E. (2026). A Composed and Derivative-Augmented Framework for Forecasting Intermittent Demand in Spare Parts Logistics. Manuscript submitted for publication in the DII26 Conference Proceedings.
A Data-Driven Framework for Early Sequential Feature Selection using Belief Propagation
J.D. Martins; E. Rocha; D. Costa. (2025) “A Data-Driven Framework for Early Sequential Feature Selection using Belief Propagation”. International scientific conference (DII26 / accepted submission). Status: accepted.
A Meta-Ranking Framework for Adaptive Model Selection in Time Series Forecasting
João Sousa. (2026) “A Meta-Ranking Framework for Adaptive Model Selection in Time Series Forecasting”. DII26 — Data-Driven Innovation in the Industry, Aveiro, 3–7 June 2026. Status: conference submission reported in the project document.
A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System
Â.F. Brochado; E.M. Rocha; D. Costa. (2024) “A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System”. Sustainability, 16, 742. Status: published.
A new parametric information-gain criterion for tree-based machine learning algorithms
D. Costa; V.V. Costa; E.M. Rocha. (2025) “A new parametric information-gain criterion for tree-based machine learning algorithms”. PeerJ Computer Science. Status: published.
A Parametric Information-Gain Framework for Streaming Fault Prediction in Assembly Lines
V. Costa; D. Costa; B. Veloso; E.M. Rocha. (2025) “A Parametric Information-Gain Framework for Streaming Fault Prediction in Assembly Lines”. International scientific conference (DII26 / accepted submission). Status: accepted.
A Parametric Information-gain to Improve Online Tree-based Machine Learning Models
V. Costa; D. Costa; B. Veloso; E.M. Rocha. (2025) “A Parametric Information-gain to Improve Online Tree-based Machine Learning Models”. SSRN preprint; submitted to journal. Status: under evaluation.
Belief Propagation for Early Sequential Feature Selection in Manufacturing
Joana Martins; D. Costa; E. Rocha. (2026) “Belief Propagation for Early Sequential Feature Selection in Manufacturing”. ENBIS-26 abstract, submitted on 6 April 2026 and accepted. Status: accepted abstract.
Belief Propagation for Early Sequential Feature Selection in Manufacturing
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Complexity-Aware Conformal Classification for Timely Industrial Fault Prediction
D. Costa; V. Costa; E.M. Rocha. (2025) “Complexity-Aware Conformal Classification for Timely Industrial Fault Prediction”. International scientific conference (DII26 / accepted submission). Status: accepted.
EDATA: Automated Exploratory Data Analysis with Conformal Prediction for Industrial Applications
V. Costa; D. Costa; et al. (2025) “EDATA: Automated Exploratory Data Analysis with Conformal Prediction for Industrial Applications”. Procedia Computer Science 253, 2615–2624; 6th International Conference on Industry 4.0 and Smart Manufacturing. Status: published.
Edge-Deployed Computer Vision for Glove-Aware Assembly Validation in Industrial Environments
D. Pereira; D. Costa; A. Carvalho; E.M. Rocha. (2025/2026) “Edge-Deployed Computer Vision for Glove-Aware Assembly Validation in Industrial Environments”. International scientific conference submission. Status: accepted/submitted.
Enhancing Conformal Prediction Efficiency via Multiscale Impurity Analysis: A Complexity-Aware Non-Conformity Function
V. Costa; D. Costa; E.M. Rocha. (2025) “Enhancing Conformal Prediction Efficiency via Multiscale Impurity Analysis: A Complexity-Aware Non-Conformity Function”. SSRN preprint; submitted to journal. Status: under evaluation.
Estimating data complexity and drift through a multiscale generalized impurity approach
D. Costa; E.M. Rocha; N. Ferreira. (2024) “Estimating data complexity and drift through a multiscale generalized impurity approach”. Journal of Computational Mathematics and Data Science, vol. 12. Status: published.
Gossip Learning architecture for decentralized industrial analytics
Project 3 team. (2026) “Gossip Learning architecture for decentralized industrial analytics”. International scientific conference submission referenced in S25.12. Status: in preparation/accepted.
Low-cost and non-intrusive human digital twin component for task and navigation tracking through pose estimation
D. Costa; D. Pereira; Â.F. Brochado; E.M. Rocha. (2025) “Low-cost and non-intrusive human digital twin component for task and navigation tracking through pose estimation”. CIRP Journal of Manufacturing Science and Technology. Status: published.
Processes Reconstruction for Workflow Analysis and Bottleneck Detection
Cristiano Nicolau. (2026) “Processes Reconstruction for Workflow Analysis and Bottleneck Detection”. DII26 — Data-Driven Innovation in the Industry, Aveiro, 3–7 June 2026. Status: conference submission reported in the project document.