Algoritmi | User | Pedro José Silva Pereira
Pedro José Silva Pereira
Pedro José Silva Pereira
At Algoritmi
Academic Degree
PhD
Current Position
Invited Assistant Professor at Escola de Engenharia da Universidade do Minho
Personal Webpage
https://pjp.dsi.uminho.pt/Personal Email
id6927@alunos.uminho.ptOrcid
0000-0002-6169-8778Researcher ID
D-4336-2017Ciência ID
861C-FF53-609BGoogle Scholar
TUDKBfEAAAAJh-index
0Publications
0Editorial
0Citations
0Q1 / Q2
0About Me 
Pedro Pereira is a senior researcher in the field of Artificial Intelligence at the CCG/ZGDV Institute and holds the position of Invited Assistant Professor at the Department of Information Systems at the University of Minho, where he has been teaching since 2018. He successfully completed his Ph.D. in Technologies and Information Systems at the same university in 2021, specializing in Machine Learning and Evolutionary Computation. Engaged in various Research and Development (R&D) projects since 2017, he has coauthored over a dozen scientific publications presented at international conferences and journals, in addition to contributing to the development of the Python module evoltree. Currently, he also co-supervises master’s students in their thesis work and their involvement in R&D projects.
Publications (18)
A Benchmark of Automated Multivariate Time Series Forecasting Tools for Smart Cities
2025 | conference-paper
A Data Drift Approach to Update Deployed Energy Prediction Machine Learning Models
2025 | conference-paper
AI4CITY - An Automated Machine Learning Platform for Smart Cities
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
2023 | conference-paper
A Comparison of Automated Machine Learning Tools for Predicting Energy Building Consumption in Smart Cities
Progress in Artificial Intelligence
2023 | conference-paper
Predicting Multiple Domain Queue Waiting Time via Machine Learning
Computational Science and Its Applications – ICCSA 2023
2023 | conference-paper
A Comparison of Automated Time Series Forecasting Tools for Smart Cities
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2022 | book
Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio
Neural Computing and Applications
2022 | journal-article
A Comparison of Machine Learning Methods for Extremely Unbalanced Industrial Quality Data
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2021 | book
An Intelligent Decision Support System for Production Planning in Garments Industry
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2021 | book
Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection
IFIP Advances in Information and Communication Technology
2021 | book
Multi-objective Grammatical Evolution of Decision Trees for Mobile Marketing user conversion prediction
Expert Systems with Applications
2021 | journal-article
Using deep autoencoders for in-vehicle audio anomaly detection
Procedia Computer Science
2021 | conference-paper
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection In Machine Condition Sounds
arXiv
2020 | other
Multi-step time series prediction intervals using neuroevolution
Neural Computing and Applications
2020 | journal-article
A Categorical Clustering of Publishers for Mobile Performance Marketing
Advances in Intelligent Systems and Computing
2019 | book
Using Neuroevolution for Predicting Mobile Marketing Conversion
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2019 | book
Forecasting store foot traffic using facial recognition, time series and support vector machines
Advances in Intelligent Systems and Computing
2017 | book
Multi-objective learning of neural network time series prediction intervals
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2017 | book
Fundings (2)
PROMOS - Previsão e otimização de campanhas publicitárias para dispositivos móveis em modelos de subscrição
N/A
2016-12 to 2019-11 | NORTE-01-0247-FEDER-017497
Factory of the Future: Smart Manufacturing
N/A
2019-12 to 2021-12 | POCI-01-0247-FEDER-039479
History
| Init | End | Change | Value | |
|---|---|---|---|---|
| 1970-01-01 | 1288-01-17 14:43:34 | |||
| 17/01/2023 20:17 | ||||
| https://scholar.google.pt/citations?hl=pt-PT&user=TUDKBfEAAAAJ | ||||
| 1376-01-17 14:43:34 | ||||
| Member Type | Assistant Researcher | |||
| 1288-03-02 10:57:02 | ||||
| 02/03/2023 20:21 | ||||
| PhD Student | ||||
| 1376-03-02 10:57:02 | ||||
| Internal Member Type | ||||
Pedro Pereira is a senior researcher in the field of Artificial Intelligence at the CCG/ZGDV Institute and holds the position of Invited Assistant Professor at the Department of Information Systems at the University of Minho, where he has been teaching since 2018. He successfully completed his Ph.D. in Technologies and Information Systems at the same university in 2021, specializing in Machine Learning and Evolutionary Computation. Engaged in various Research and Development (R&D) projects since 2017, he has coauthored over a dozen scientific publications presented at international conferences and journals, in addition to contributing to the development of the Python module evoltree. Currently, he also co-supervises master's students in their thesis work and their involvement in R&D projects. |
A Benchmark of Automated Multivariate Time Series Forecasting Tools for Smart Cities
2025 | conference-paper
A Data Drift Approach to Update Deployed Energy Prediction Machine Learning Models
2025 | conference-paper
AI4CITY - An Automated Machine Learning Platform for Smart Cities
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
2023 | conference-paper
A Comparison of Automated Machine Learning Tools for Predicting Energy Building Consumption in Smart Cities
Progress in Artificial Intelligence
2023 | conference-paper
Predicting Multiple Domain Queue Waiting Time via Machine Learning
Computational Science and Its Applications – ICCSA 2023
2023 | conference-paper
A Comparison of Automated Time Series Forecasting Tools for Smart Cities
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2022 | book
Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio
Neural Computing and Applications
2022 | journal-article
A Comparison of Machine Learning Methods for Extremely Unbalanced Industrial Quality Data
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2021 | book
An Intelligent Decision Support System for Production Planning in Garments Industry
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2021 | book
Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection
IFIP Advances in Information and Communication Technology
2021 | book
Multi-objective Grammatical Evolution of Decision Trees for Mobile Marketing user conversion prediction
Expert Systems with Applications
2021 | journal-article
Using deep autoencoders for in-vehicle audio anomaly detection
Procedia Computer Science
2021 | conference-paper
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection In Machine Condition Sounds
arXiv
2020 | other
Multi-step time series prediction intervals using neuroevolution
Neural Computing and Applications
2020 | journal-article
A Categorical Clustering of Publishers for Mobile Performance Marketing
Advances in Intelligent Systems and Computing
2019 | book
Using Neuroevolution for Predicting Mobile Marketing Conversion
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2019 | book
Forecasting store foot traffic using facial recognition, time series and support vector machines
Advances in Intelligent Systems and Computing
2017 | book
Multi-objective learning of neural network time series prediction intervals
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2017 | book
PROMOS - Previsão e otimização de campanhas publicitárias para dispositivos móveis em modelos de subscrição
N/A
2016-12 to 2019-11 | NORTE-01-0247-FEDER-017497
Factory of the Future: Smart Manufacturing
N/A
2019-12 to 2021-12 | POCI-01-0247-FEDER-039479
| Start | End | Term | Value | |
|---|---|---|---|---|
| 1288-01-17 14:43:34 | ||||
| 17/01/2023 20:17 | ||||
| https://scholar.google.pt/citations?hl=pt-PT&user=TUDKBfEAAAAJ | ||||
| 1376-01-17 14:43:34 | ||||
| Member Type | Assistant Researcher | |||
| 1288-03-02 10:57:02 | ||||
| 02/03/2023 20:21 | ||||
| PhD Student | ||||
| 1376-03-02 10:57:02 | ||||
| Internal Member Type | ||||
Pedro Pereira is a senior researcher in the field of Artificial Intelligence at the CCG/ZGDV Institute and holds the position of Invited Assistant Professor at the Department of Information Systems at the University of Minho, where he has been teaching since 2018. He successfully completed his Ph.D. in Technologies and Information Systems at the same university in 2021, specializing in Machine Learning and Evolutionary Computation. Engaged in various Research and Development (R&D) projects since 2017, he has coauthored over a dozen scientific publications presented at international conferences and journals, in addition to contributing to the development of the Python module evoltree. Currently, he also co-supervises master's students in their thesis work and their involvement in R&D projects. |
