Algoritmi | User | Pedro José Silva Pereira

Pedro José Silva Pereira

Pedro José Silva Pereira

At Algoritmi

Researcher 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://pjp.dsi.uminho.pt/

Personal Email

id6927@alunos.uminho.pt

Orcid

0000-0002-6169-8778

Researcher ID

D-4336-2017

FCT Public Key

J679275t6tht

Ciência ID

861C-FF53-609B

Google Scholar

TUDKBfEAAAAJ

h-index

Publications

Editorial

0

Citations

Q1 / Q2

0

About Me



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

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.

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


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.

This user account status is Approved