AIDI · Conhecimento
Produção científica associada aos trabalhos AIDI nos projetos NEXUS e ILLIANCE.
Este catálogo agrega os registos bibliográficos transferidos dos projetos NEXUS e ILLIANCE, mantendo o título e a referência científica de cada trabalho. As dissertações e teses estão numa secção própria.
O registo legado ILLIANCE indicava 17 entradas, incluindo um cartão técnico duplicado e sem metadados bibliográficos. Aqui são apresentados os 16 registos bibliográficos distintos e utilizáveis, juntamente com os 35 registos não-tese do NEXUS.
Aliu, D., Gomes, N., & Rocha, E. (2026). A Composed and Derivative-Augmented Framework for Forecasting Intermittent Demand in Spare Parts Logistics.
Sousa, J. (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.
Martins, J., Costa, D., & Rocha, E. (2026). Belief Propagation for Early Sequential Feature Selection in Manufacturing. ENBIS-26 abstract.
Pereira, D., Costa, D., Carvalho, A., & Rocha, E. M. (2025/2026). Edge-Deployed Computer Vision for Glove-Aware Assembly Validation in Industrial Environments.
Project 3 team. (2026). Gossip Learning architecture for decentralized industrial analytics.
Nicolau, C. (2026). Processes Reconstruction for Workflow Analysis and Bottleneck Detection. DII26 — Data-Driven Innovation in the Industry, Aveiro, 3–7 June 2026.
Ribeiro, L., Kazmi, S., Brochado, Â. F. & Rocha, E. (2026). A Hybrid Analytics Framework for Seaport Equipment Efficiency Monitoring: Integrating MEA, Visual Analytics, and LLM-Driven Decision Support.
Raza, A., Rocha, E. M., Brochado, A. F., & Mohsin, M. (2026). A mathematical framework for assessing disruptions in maritime logistics operations. Procedia Computer Science, 277, 3710–3720.
Bukhari, S., Brochado, Â. F., & Rocha, E. (2026). A Meta-Learning-Based Dynamic Ensemble Framework for Time-Series Forecasting under Expanding Window Evaluation.
Kazmi, S., Rocha, E., & Brochado, Â. F. (2026). A nonlinear multi-directional efficiency framework for modeling operational and environmental inefficiencies in sustainable port logistics. Research in Transportation Business & Management, 67, 101722.
Lima, A., Rocha, E., Macedo, P., & Madaleno, M. (2026). Accurate Estimates of Drayage Dwell Times. In Springer Proceedings in Mathematics & Statistics (pp. 783–793). Springer Nature Switzerland.
Kazmi, S., Rocha, E. M., & Brochado, Â. F. (2026). AMNN-Based Municipal Efficiency: Constrained Deep Learning for Regional Policy.
Iglesias, A., Brochado, Â. F., & Rocha, E. (2026). An Adaptive Non-Linear Model for Real-Time Logistics Synchronization.
Sousa, L., Monteiro, M., Pereira, I., & Rocha, E. (2026). Clustering and machine-learning anomaly detection technique of count time series.
Khan, M., & Rocha, E. (2026). Convergence and Correctness of Belief Propagation for Nonlinear Programming via Sequential Linearization.
Lima, A., Rocha, E., Madaleno, M., & Macedo, P. (2026). Discovering latent structures in container logistics operations: a distributional clustering approach with high-density region.
Lima, A., Rocha, E., Madaleno, M., & Macedo, P. (2026). Distribution-Focused Clustering for Revealing Patterns in Container Logistics. In Advances in Production Management Systems. Cyber-Physical-Human Production Systems: Human-AI Collaboration and Beyond, IFIP AICT 764. Springer.
Kazmi, S., Rocha, E. M., & Brochado, Â. F. (2026). Fuzzy Bootstrapped Multi-directional Efficiency Analysis with Dominance-Based Ranking.
Brochado, Â. F., Mohsin, M., Rocha, E. M., & Raza, A. (2026). Mathematical Approaches to Supply Chain Disruption: A Review of Metrics, Techniques, and Applications Across Logistics Modes.
Mohsin, M., Rocha, E. M., Raza, A., & Brochado, Â. F. (2026). Mathematical Modeling of Maritime Logistics Disruptions with Logistic Growth and Nonlinear Incidence Dynamics.
Sousa, J., & Rocha, E. (2026). Meta-Ranking Algorithms for Sequential Model Selection in Real-World Time Series Forecasting.
Khan, M., & Rocha, E. (2026). Optimization of Logistics Operations based on the Convergence and Correctness of Belief Propagation for Nonlinear Programming via Sequential Linearization.
Iglesias, A., Brochado, Â., & Rocha, E. (2026). Optimizing Fleet Deployment: Real-Time Truck Scheduling Using Non-Linear Dynamics.
Kazmi, S., Rocha, E. M., & Brochado, Â. F. (2026). Predictive Modeling of MEA Scores Using Constrained Deep Learning: A Comparative Study with Machine Learning Baselines.
Zafar, M., Rocha, E., & Brochado, Â. F. (2026). Predictive Modeling of Port Operational Performance Using ML Models: A Data-Driven Analysis of Vessel Turnaround Time, Container Dwell Time, and Daily Throughput Volume.
Khan, M., Brochado, Â. F., & Rocha, E. (2026). Risk analysis in maritime logistics amid global crises: a systematic review of mathematical models, empirical validation, and policy implications.
Brochado, Â. F., Esteves, L., Lourenço Marques, J., & Rocha, E. M. (2026). The Computability Gap in Sustainable Supply Chain Metrics: A Systematic Review and Taxonomy.
Martins, J. D., Rocha, E., & Costa, D. (2025). A Data-Driven Framework for Early Sequential Feature Selection using Belief Propagation.
Costa, D., Costa, V. V., & Rocha, E. M. (2025). A new parametric information-gain criterion for tree-based machine learning algorithms. PeerJ Computer Science.
Costa, V., Costa, D., Veloso, B., & Rocha, E. M. (2025). A Parametric Information-Gain Framework for Streaming Fault Prediction in Assembly Lines.
Costa, V., Costa, D., Veloso, B., & Rocha, E. M. (2025). A Parametric Information-gain to Improve Online Tree-based Machine Learning Models. SSRN preprint.
Costa, D., Costa, V., & Rocha, E. M. (2025). Complexity-Aware Conformal Classification for Timely Industrial Fault Prediction.
Costa, V., Costa, D., et al. (2025). EDATA: Automated Exploratory Data Analysis with Conformal Prediction for Industrial Applications. Procedia Computer Science, 253, 2615–2624.
Costa, V., Costa, D., & Rocha, E. M. (2025). Enhancing Conformal Prediction Efficiency via Multiscale Impurity Analysis: A Complexity-Aware Non-Conformity Function. SSRN preprint.
Costa, D., Pereira, D., Brochado, Â. F., & Rocha, E. M. (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.
Brochado, Â. F., Rocha, E. M., & Pimentel, C. (2025). A big data-driven system for smart continuous improvement. Discover Applied Sciences.
Rocha, E. M., Lima, A., Macedo, P., & Madaleno, M. (2025). Accurate Estimates of Transshipment Container Dwell Times at Maritime Ports. In DGTMP24 Book of Abstracts, p. 117, Évora, Portugal.
Kazmi, S. M., Rocha, E. M., & Brochado, Â. F. (2025). Breaking the Linear Barrier: A Deep Learning Approach to Nonlinear Efficiency in Ports. In 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS), pp. 727–732. IEEE.
Addo, E., Brochado, Â. F., & Rocha, E. M. (2025). Diagnostic and Prescriptive Insights through XGBoost, SHAP and Differential Evolution: Application to Last-Mile Delivery in Portugal. In 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS), pp. 558–563. IEEE.
Costa, V., Costa, D., Brochado, Â. F. & Rocha, E. M. (2025). Evaluating Logistics Data for Machine Learning Applicability: A Decision Support Tool for Non-Experts. In DGTMP24 Book of Abstracts, p. 117, Évora, Portugal.
Costa, V., Costa, D., Brochado, Â. F. & Rocha, E. M. (2025). Evaluating Logistics Data for Machine Learning Applicability: A Decision Support Tool for Non-Experts. In DGTMP24 Book of Abstracts, p. 173, Évora, Portugal.
Kazmi, S. M., Rocha, E. M., & Brochado, Â. F. (2025). Evaluation of seaport equipment performance using multi-directional efficiency analysis for sustainable logistics operations: A case from Portugal. Research in Transportation Business & Management, 62, 101442.
Rocha, E. M., & Brochado, Â. F. (2025). General Prescriptive System for Smart KPI Management: Application in Container Terminal Operations. In 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS), pp. 595–600. IEEE.
Brochado, Â. F., Esteves, L., & Rocha, E. M. (2025). Smart Impact Management of KPIs on Maritime Ports. In DGTMP24 Book of Abstracts, p. 117, Évora, Portugal.
Brochado, Â. F., Esteves, L., & Rocha, E. M. (2025). Smart Impact Management of KPIs on Maritime Ports. In DGTMP24 Book of Abstracts, p. 294, Évora, Portugal.
Kazmi, S., Rocha, E. M. & Brochado, Â. F. (2025). Towards Equipment Operations Optimization through a Novel Efficiency Analysis Model. In DGTMP24 Book of Abstracts, p. 117, Évora, Portugal.
Kazmi, S., Rocha, E. M. & Brochado, Â. F. (2025). Towards Equipment Operations Optimization through a Novel Efficiency Analysis Model. In DGTMP24 Book of Abstracts, p. 217, Évora, Portugal.
Brochado, Â. F., Rocha, E. M., & Costa, D. (2024). A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System. Sustainability, 16, 742.
Costa, D., Rocha, E. M., & Ferreira, N. (2024). Estimating data complexity and drift through a multiscale generalized impurity approach. Journal of Computational Mathematics and Data Science, 12.
Brochado, Â. F., Rocha, E. M., & Costa, D. (2024). A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System. Sustainability, 16(2), 742.
Brochado, Â. F., Rocha, E., Addo, E., & Silva, S. (2024). Performance Evaluation and Explainability of Last-Mile Delivery. Procedia Computer Science, 232, 2478–2487.