Algoritmi | User | Pedro José Costa de Oliveira
Pedro José Costa de Oliveira
Pedro José Costa de Oliveira
At Algoritmi
Academic Degree
MSc
Current Position
Other at Escola de Engenharia da Universidade do Minho
Personal Webpage
Personal Email
pedro.jose.oliveira@algoritmi.uminho.ptOrcid
0000-0001-7143-5413Researcher ID
Ciência ID
7A1C-0C2F-E5BDGoogle Scholar
h-index
4Publications
21Editorial
0Citations
90Q1 / Q2
0About Me 
Pedro Oliveira is a PhD student in Informatics, at the School of Engineering of the University of Minho, in Braga, Portugal. He is also a researcher at the ALGORITMI Center, namely in the Synthetic Intelligence Lab (ISLAB) research group. He holds a Master's degree in Informatics Engineering from the same university. His research interests include Recurrent Neural Networks, Predictive Models, Time Series Problems, Anomaly Detection, and Decision Support Systems.
Publications (21)
A Deep Learning-Based Model to Predict Nitrogen Dioxide in Urban Environments
2025 | book-chapter
Efficiency of the Transformer Model in Time Series Forecasting: A Case Study in Wastewater Treatment Plants
2025 | book-chapter
Employing Explainable AI Techniques for Air Pollution: An Ante-Hoc and Post-Hoc Approach in Dioxide Nitrogen Forecasting
2025 | book-chapter
A Self-Organizing Map Clustering Approach to Support Territorial Zoning
2024 | book-chapter
Assessment of LSTM and GRU Models to Predict the Electricity Production from Biogas in a Wastewater Treatment Plant
Lecture Notes in Networks and Systems
2024 | journal-article
Geothermal Heat Exchanger’s Temperature Input Sensor Prediction Based on Deep Learning Modelling Technique
2024 | book-chapter
Next-Generation Surveillance: Exploring the Intersection of Artificial Intelligence and Security
Lecture Notes in Networks and Systems
2024 | journal-article
Conception and evaluation of anomaly detection models for monitoring analytical parameters in wastewater treatment plants
AI Communications
2023 | journal-article
A Review of Computational Modeling in Wastewater Treatment Processes
ACS ES&T Water
2023 | journal-article
A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications
Sensors
2023 | journal-article
Cost-Sensitive Learning and Threshold-Moving Approach to Improve Industrial Lots Release Process on Imbalanced Datasets
2023 | book-chapter
Image Classification Understanding with Model Inspector Tool
Lecture Notes in Computer Science
2023 | journal-article
Using Deep Learning Models to Predict the Electrical Conductivity of the Influent in a Wastewater Treatment Plant
Intelligent Data Engineering and Automated Learning - IDEAL: International Conference, Proceedings. Lecture Notes in Computer Science (14404)
2023 | journal-article
A Tree-Based Approach to Forecast the Total Nitrogen in Wastewater Treatment Plants
2022 | book-chapter
Applying Anomaly Detection Models in Wastewater Management: A Case Study of Nitrates Concentration in the Effluent
2022 | book-chapter
Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities
Electronics
2021 | journal-article
Evaluating Unidimensional Convolutional Neural Networks to Forecast the Influent pH of Wastewater Treatment Plants
2021 | book-chapter
Multi-step ultraviolet index forecasting using long short-term memory networks
Advances in Intelligent Systems and Computing
2021 | book
Unsupervised Learning Approach for pH Anomaly Detection in Wastewater Treatment Plants
2021 | book-chapter
Using Machine Learning to Forecast Air and Water Quality
International Conference on Agents and Artificial Intelligence (ICAART)
2021 | conference-paper
A Deep Learning Approach to Forecast the Influent Flow in Wastewater Treatment Plants
2020 | book-chapter
Fundings (2)
Predictive and Analytic Models for the Optimization of Multi-municipal Water Systems
Fundação para a Ciência e Tecnologia
2020-04 to 2022-12 | DSAIPA/AI/0099/2019
Anomaly Detection Models to improve the Sustainability of Water Companies
MIT Portugal
2023-01 to - | PRT/BD/154311/2022
History
| Init | End | Change | Value |
|---|
A Deep Learning-Based Model to Predict Nitrogen Dioxide in Urban Environments
2025 | book-chapter
Efficiency of the Transformer Model in Time Series Forecasting: A Case Study in Wastewater Treatment Plants
2025 | book-chapter
Employing Explainable AI Techniques for Air Pollution: An Ante-Hoc and Post-Hoc Approach in Dioxide Nitrogen Forecasting
2025 | book-chapter
A Self-Organizing Map Clustering Approach to Support Territorial Zoning
2024 | book-chapter
Assessment of LSTM and GRU Models to Predict the Electricity Production from Biogas in a Wastewater Treatment Plant
Lecture Notes in Networks and Systems
2024 | journal-article
Geothermal Heat Exchanger’s Temperature Input Sensor Prediction Based on Deep Learning Modelling Technique
2024 | book-chapter
Next-Generation Surveillance: Exploring the Intersection of Artificial Intelligence and Security
Lecture Notes in Networks and Systems
2024 | journal-article
Conception and evaluation of anomaly detection models for monitoring analytical parameters in wastewater treatment plants
AI Communications
2023 | journal-article
A Review of Computational Modeling in Wastewater Treatment Processes
ACS ES&T Water
2023 | journal-article
A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications
Sensors
2023 | journal-article
Cost-Sensitive Learning and Threshold-Moving Approach to Improve Industrial Lots Release Process on Imbalanced Datasets
2023 | book-chapter
Image Classification Understanding with Model Inspector Tool
Lecture Notes in Computer Science
2023 | journal-article
Using Deep Learning Models to Predict the Electrical Conductivity of the Influent in a Wastewater Treatment Plant
Intelligent Data Engineering and Automated Learning - IDEAL: International Conference, Proceedings. Lecture Notes in Computer Science (14404)
2023 | journal-article
A Tree-Based Approach to Forecast the Total Nitrogen in Wastewater Treatment Plants
2022 | book-chapter
Applying Anomaly Detection Models in Wastewater Management: A Case Study of Nitrates Concentration in the Effluent
2022 | book-chapter
Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities
Electronics
2021 | journal-article
Evaluating Unidimensional Convolutional Neural Networks to Forecast the Influent pH of Wastewater Treatment Plants
2021 | book-chapter
Multi-step ultraviolet index forecasting using long short-term memory networks
Advances in Intelligent Systems and Computing
2021 | book
Unsupervised Learning Approach for pH Anomaly Detection in Wastewater Treatment Plants
2021 | book-chapter
Using Machine Learning to Forecast Air and Water Quality
International Conference on Agents and Artificial Intelligence (ICAART)
2021 | conference-paper
A Deep Learning Approach to Forecast the Influent Flow in Wastewater Treatment Plants
2020 | book-chapter
Predictive and Analytic Models for the Optimization of Multi-municipal Water Systems
Fundação para a Ciência e Tecnologia
2020-04 to 2022-12 | DSAIPA/AI/0099/2019
Anomaly Detection Models to improve the Sustainability of Water Companies
MIT Portugal
2023-01 to - | PRT/BD/154311/2022
| Start | End | Term | Value | |
|---|---|---|---|---|
