Algoritmi | User | Luís Miguel da Rocha de Matos

Luís Miguel da Rocha de Matos

Luís Miguel da Rocha de Matos

At Algoritmi

Research Collaborator with PhD

Member of the IST R&D Group

Member of the IDS R&D Lab

Academic Degree

PhD

Current Position

Invited Assistant Professor at Escola de Engenharia da Universidade do Minho

Personal Webpage

https://lmm.dsi.uminho.pt/

Personal Email

luis.matos@dsi.uminho.pt

Orcid

0000-0001-5827-9129

Researcher ID

N-8043-2015

FCT Public Key

J64911226d44

Ciência ID

4B15-8E29-1D57

Google Scholar

https://scholar.google.pt/citations?user=o3_qOXkAAAAJ&hl=pt-PT

About Me



A Machine Learning Approach for Spare Parts Lifetime Estimation

Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,

2022 | conference-paper

Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio

Neural Computing and Applications

2022 | journal-article

Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection

Computers

2022 | journal-article

A Comparison of Anomaly Detection Methods for Industrial Screw Tightening

Computational Science and Its Applications - ICCSA 2021 - 21st International Conference, Cagliari, Italy, September 13-16, 2021, Proceedings, Part II

2021 | conference-paper

A Comparison of Machine Learning Approaches for Predicting In-Car Display Production Quality

Intelligent Data Engineering and Automated Learning – IDEAL 2021

2021 | book-chapter

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

2021 | book-chapter

Using deep autoencoders for in-vehicle audio anomaly detection

2021 | conference-paper

Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds

2020 | preprint

A Categorical Clustering of Publishers for Mobile Performance Marketing

Advances in Intelligent Systems and Computing

2019 | book

Using Deep Learning for Mobile Marketing User Conversion Prediction

Proceedings of the International Joint Conference on Neural Networks

2019 | conference-paper

Using Deep Learning for Ordinal Classification of Mobile Marketing User Conversion

Intelligent Data Engineering and Automated Learning – IDEAL 2019

2019 | book-chapter

A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction

2018 International Conference on Intelligent Systems (IS)

2018 | conference-paper

Forecasting store foot traffic using facial recognition, time series and support vector machines

Advances in Intelligent Systems and Computing

2017 | book

Forecasting Human Entrances at a Commercial Store using facial recognition data

2015 | dissertation-thesis

A Machine Learning Approach for Spare Parts Lifetime Estimation

Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,

2022 | conference-paper

Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio

Neural Computing and Applications

2022 | journal-article

Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection

Computers

2022 | journal-article

A Comparison of Anomaly Detection Methods for Industrial Screw Tightening

Computational Science and Its Applications - ICCSA 2021 - 21st International Conference, Cagliari, Italy, September 13-16, 2021, Proceedings, Part II

2021 | conference-paper

A Comparison of Machine Learning Approaches for Predicting In-Car Display Production Quality

Intelligent Data Engineering and Automated Learning – IDEAL 2021

2021 | book-chapter

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

2021 | book-chapter

Using deep autoencoders for in-vehicle audio anomaly detection

2021 | conference-paper

Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds

2020 | preprint

A Categorical Clustering of Publishers for Mobile Performance Marketing

Advances in Intelligent Systems and Computing

2019 | book

Using Deep Learning for Mobile Marketing User Conversion Prediction

Proceedings of the International Joint Conference on Neural Networks

2019 | conference-paper

Using Deep Learning for Ordinal Classification of Mobile Marketing User Conversion

Intelligent Data Engineering and Automated Learning – IDEAL 2019

2019 | book-chapter

A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction

2018 International Conference on Intelligent Systems (IS)

2018 | conference-paper

Forecasting store foot traffic using facial recognition, time series and support vector machines

Advances in Intelligent Systems and Computing

2017 | book

Forecasting Human Entrances at a Commercial Store using facial recognition data

2015 | dissertation-thesis


This user account status is Approved