Docente
Luis Miguel Da Rocha De Matos

Resume

Luís Miguel da Rocha de Matos is a specialist in Information Systems and Technologies, with an academic background in Information Systems and Management. He holds a Master's and a Ph.D. from the University of Minho, specializing in Machine Learning and Artificial Intelligence. He is affiliated with the ALGORITMI R&D center (IDS Group), where he has contributed to several high-impact scientific projects, such as Factory of the Future, TexBoost, and EasyRide. He was awarded the Best Paper Award at the ICCSA 2021 Conference. Since 2022, he has served as a peer reviewer for top-tier (Q1 and Q2) scientific journals in Machine Learning and has collaborated in scientific committees, chaired sessions, and presented workshops. Beyond academia, he actively engages with society through keynote speeches, media appearances, and educational initiatives. He is also the creator of the Python module Cane, designed for categorical attribute transformation, which has surpassed 160,000 downloads since June 2020.

Graus

• Mestrado integrado
  Mestrado Integrado em Engenharia e Gestão de Sistemas de Informação
• Doutoramento
  Tecnologia e Sistemas de Informação

Publicações

Artigo em revista

• 2024-12, Proactive prevention of work-related musculoskeletal disorders using a motion capture system and time series machine learning, Engineering Applications of Artificial Intelligence
• 2024, Ahead of Time Prediction of Decorated Particleboard Production Disruptions and Defects Using Single and Multi-Target AutoML, Procedia Computer Science
• 2023-06, RTSIMU: Real-Time Simulation tool for IMU sensors, Software Impacts
• 2022-08, A Deep Learning-Based Decision Support System for Mobile Performance Marketing, International Journal of Information Technology & Decision Making
• 2022-07, Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing, Software Impacts
• 2022-05-19, Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio, Neural Computing and Applications
• 2022-04-08, Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection, Computers
• 2022, Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach, Procedia Computer Science

Tese / Dissertação

• 2015, Forecasting Human Entrances at a Commercial Store using facial recognition data

Livro

• 2019, A Categorical Clustering of Publishers for Mobile Performance Marketing, Silva, S.; Cortez, P.; Mendes, R.; Pereira, P.J.; Matos, L.M.; Garcia, L.
• 2017, Forecasting store foot traffic using facial recognition, time series and support vector machines, Cortez, P.; Matos, L.M.; Pereira, P.J.; Santos, N.; Duque, D.

Capítulo de livro

• 2023, Machine Learning for Predicting Production Disruptions in the Wood-Based Panels Industry: A Demonstration Case
• 2022, An Intelligent Decision Support System for Road Freight Transport, Intelligent Data Engineering and Automated Learning – IDEAL 2022, Springer International Publishing
• 2022, An Empirical Study on Anomaly Detection Algorithms for Extremely Imbalanced Datasets
• 2022, A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting, Intelligent Data Engineering and Automated Learning – IDEAL 2022, Springer International Publishing
• 2021, Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection, Springer International Publishing
• 2021, A Comparison of Machine Learning Approaches for Predicting In-Car Display Production Quality, Intelligent Data Engineering and Automated Learning – IDEAL 2021, Springer International Publishing
• 2019, Using Deep Learning for Ordinal Classification of Mobile Marketing User Conversion, Intelligent Data Engineering and Automated Learning – IDEAL 2019, Springer International Publishing

Artigo em conferência

• Using deep autoencoders for in-vehicle audio anomaly detection
• 2022-07-18, A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors
• 2022, A Machine Learning Approach for Spare Parts Lifetime Estimation
• 2021, A Comparison of Anomaly Detection Methods for Industrial Screw Tightening
• 2019, Using Deep Learning for Mobile Marketing User Conversion Prediction
• 2018, A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction, 2018 International Conference on Intelligent Systems (IS)

Outra produção

• 2020-06-18, Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds