Luís Miguel Matos

Has grant

Invited Professor

University of Minho

Portugal

Research Interests

Explore related searches

Professors in Portugal Professors in Artificial Intelligence Artificial Intelligence professors in Portugal

About

Dr. Luís Miguel Matos is an Invited Professor at Universidade do Minho, Portugal. His research primarily focuses on predictive analytics, data-driven approaches to anomaly detection, and machine learning applications in manufacturing and decision support systems. Recent articles include studies on production disruptions, quality issues in automotive display manufacturing, and decision support for mobile performance marketing.

Recent Grants

Grant: Close

Connected Manufacturing - Digital Transformation

Open Date: 2022-01-02

Close Date:

Grant: Close

Factory of the Future: Smart Manufacturing

Open Date: 2020-01-01

Close Date: 2021-12-23

Grant: Close

Easy Ride: Experience is everything

Open Date: 2020-01-01

Close Date: 2021-12-31

Grant: Close

PROMOS - Previsão e otimização de campanhas publicitárias para dispositivos móveis em modelos de subscrição.

Open Date: 2016-12-02

Close Date: 2019-11-30

Articles (16)

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Collaborators (3)

Jose Guilherme Cruz Moreira

PORTUGAL

View details

Pedro José Pereira

Invited Professor

Universidade do Porto Faculdade de Engenharia

PORTUGAL

View details

Paulo Cortez

Associate Professor

Universidade do Porto Faculdade de Engenharia

PORTUGAL

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Luís Miguel Matos

Research Interests

Explore related searches

About

Recent Grants

Articles (16)

A data-driven approach to anomaly prediction in automotive display manufacturing

A predictive analytics framework for early detection of production halts and quality issues

Proactive prevention of work-related musculoskeletal disorders using a motion capture system and time series machine learning

Ahead of Time Prediction of Decorated Particleboard Production Disruptions and Defects Using Single and Multi-Target AutoML

RTSIMU: Real-Time Simulation tool for IMU sensors[Formula presented]

A Deep Learning-Based Decision Support System for Mobile Performance Marketing

Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing[Formula presented]

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

Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection

A Deep Learning-Based Decision Support System for Mobile Performance Marketing

An Empirical Study on Anomaly Detection Algorithms for Extremely Imbalanced Datasets

Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach

A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting

An Intelligent Decision Support System for Road Freight Transport

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

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Collaborators (3)

Jose Guilherme Cruz Moreira

Pedro José Pereira

Paulo Cortez

Make your next move.