Paulo Cortez

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Associate Professor

Universidade do Porto Faculdade de Engenharia

Portugal

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Paulo Cortez is an Associate Professor at Universidade do Minho Escola de Engenharia in Portugal. His research focuses on data-driven approaches in various fields, including predictive analytics for quality issues, anomaly detection in manufacturing, and decision support systems utilizing explainable artificial intelligence. He has published numerous articles exploring applications of machine learning and data visualization in industries such as automotive, business, and sustainable development.

Recent Grants

Grant: Close

ProChild CoLAB Against Poverty and Social Exclusion Collab

Open Date: 2019-01-01

Close Date: 2022-01-01

Grant: Close

Digital Transformation CoLab - Experiencing the Future - DTx

Open Date: 2018-01-01

Close Date: 2022-01-01

Grant: Close

TexBoost - Less Commodities more Specialities

Open Date: 2017-07-01

Close Date: 2020-06-01

Grant: Close

PROMOS - Prediction and optimization of advertising campaigns for Mobile devices via a Subscription model

Open Date: 2016-01-01

Close Date: 2019-01-01

Grant: Close

INTCARE II - Intelligent Decision Support System for Intensive Care

Open Date: 2013-06-01

Close Date: 2015-05-01

Paulo Cortez

Research Interests

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About

Recent Grants

Articles (22)

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

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

Screw Process Anomaly Visualization (SPAV): A Python module for local and global machine learning visualizations for screw tightening anomaly detection

A context-aware decision support system for selecting explainable artificial intelligence methods in business organizations

Data science approaches for sustainable development

A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods

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

Using Machine Learning to Predict Wine Quality and Prices: A Demonstrative Case Using a Large Tabular Database

International revenue share fraud prediction on the 5G edge using federated learning

A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics

An Evolutionary Neural Network Approach for Slopes Stability Assessment

Discovering ethnic minority business research directions using text mining and topic modelling

A decision support system based on a multivariate supervised regression strategy for estimating supply lead times

Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities

AutoOC: A Python module for automated multi-objective One-Class Classification[Formula presented]

AutoOC: Automated multi-objective design of deep autoencoders and one-class classifiers using grammatical evolution

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

Author placement in Computer Science: a study based on the careers of ACM Fellows

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

RanCoord — A random geographic coordinates generator for transport and logistics research and development activities[Formula presented]

Collaborators (11)

António Costa

Jose Guilherme Cruz Moreira

David Toll

Luís Miguel Matos

Pedro José Pereira

Antonio Gomes Correia

João N. C. Gonçalves

Sérgio Moro

Helena Rodrigues

ELIZABETH FERNANDES

João M. Fernandes

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