User profiles for Luís Miguel Matos
 | Luís Miguel MatosAssistant Professor, ALGORITMI, Department Information Systems, University of Minho Verified email at dsi.uminho.pt Cited by 264 |
Isolation forests and deep autoencoders for industrial screw tightening anomaly detection
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques
are becoming more critical due to the generation of massive amounts of production data. …
are becoming more critical due to the generation of massive amounts of production data. …
Deep dense and convolutional autoencoders for unsupervised anomaly detection in machine condition sounds
This technical report describes two methods that were developed for Task 2 of the DCASE
2020 challenge. The challenge involves an unsupervised learning to detect anomalous …
2020 challenge. The challenge involves an unsupervised learning to detect anomalous …
Using deep autoencoders for in-vehicle audio anomaly detection
Current developments on self-driving cars have increased the interest on autonomous shared
taxicabs. While most self-driving technologies focus on the outside environment, there is …
taxicabs. While most self-driving technologies focus on the outside environment, there is …
Using deep learning for mobile marketing user conversion prediction
Mobile performance marketing is a growing industry due to the massive adoption of smartphones
and tablets. In this paper, we explore Deep Multilayer Perceptrons (MLP) to predict the …
and tablets. In this paper, we explore Deep Multilayer Perceptrons (MLP) to predict the …
[HTML][HTML] Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing
Categorical Attribute traNsformation Environment (CANE) is a simpler but powerful data
categorical preprocessing Python package. The package is valuable since there is currently a …
categorical preprocessing Python package. The package is valuable since there is currently a …
Forecasting store foot traffic using facial recognition, time series and support vector machines
In this paper, we explore data collected in a pilot project that used a digital camera and facial
recognition to detect foot traffic to a sports store. Using a time series approach, we model …
recognition to detect foot traffic to a sports store. Using a time series approach, we model …
Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio
The growing usage of digital microphones has generated an increased interest in the topic
of Acoustic Anomaly Detection (AAD). Indeed, there are several real-world AAD application …
of Acoustic Anomaly Detection (AAD). Indeed, there are several real-world AAD application …
[HTML][HTML] Proactive prevention of work-related musculoskeletal disorders using a motion capture system and time series machine learning
In this paper, we propose a proactive method to prevent Work-related MusculoSkeletal
Disorders (WMSDs) in manufacturing industries. The integrated method includes a Motion …
Disorders (WMSDs) in manufacturing industries. The integrated method includes a Motion …
A deep learning approach to prevent problematic movements of industrial workers based on inertial sensors
Nowadays, manufacturing industries still face difficulties applying traditional Work-related
MusculoSkeletal Disorders (WMSDs) risk assessment methods due to the high effort required …
MusculoSkeletal Disorders (WMSDs) risk assessment methods due to the high effort required …
Predicting yarn breaks in textile fabrics: A machine learning approach
In this paper, we propose a Machine Learning (ML) approach to predict faults that may
occur during the production of fabrics and that often cause production downtime delays. We …
occur during the production of fabrics and that often cause production downtime delays. We …