AIDI · Knowledge
Scientific outputs associated with AIDI work in the NEXUS and ILLIANCE projects.
This catalogue aggregates the bibliographic records transferred from the NEXUS and ILLIANCE projects, preserving each work’s title and scientific citation. Dissertations and theses are presented in a dedicated section.
The legacy ILLIANCE catalogue reported 17 entries, including one duplicated technical card without bibliographic metadata. This website presents the 16 distinct usable bibliographic records, together with the 35 non-thesis NEXUS records.
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.