Teacher
Luis Miguel Da Rocha De Matos

Resume

Luís Miguel da Rocha de Matos is an esteemed scholar and practitioner in Information Systems and Technologies, with a comprehensive academic trajectory that began in high school with a focus on Information Management. He holds an Integrated Master’s degree and a Ph.D. from the University of Minho, where he specialized in Machine Learning, Artificial Intelligence. His doctoral research concentrated on the development of an intelligent decision support system tailored to the dynamic mobile market, a significant contribution that laid the groundwork for his academic and research career. Luís Miguel Matos has also been involved in several high-impact scientific projects, including Factory of the Future, TexBoost, STVgoDigital, EasyRide, and PROMOS, and has received recognition for his work, such as the Best Paper Award at the ICCSA 2021 Conference. Luís Miguel Matos has also made substantial contributions as a peer reviewer for top-tier journals like Pattern Recognition and IEEE Access, and as a member of scientific committees and session chairs for conferences such as ARTIIS and ISTIIS. His academic engagement extends to his role as an examiner in Masters Dissertations and his active participation in educational outreach and public engagements, including keynote speeches and media appearances. Luís Miguel Matos is also the creator of the Python module Cane - Categorical Attribute traNsformation Environment, which has gained widespread use with more than 100,000 downloads since 2020. His current research interests encompass Business Analytics, Decision Support Systems, Data Mining, Data Science, Neural Networks, Anomaly Detection, and eXplainable AI (XAI).

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