Mateus Mendes

Adjunct Professor

Polytechnic Institute of Bragança

Portugal

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Mateus Mendes is an Adjunct Professor at the Polytechnic Institute of Coimbra, Portugal. His research interests focus on machine learning applications in fault detection, AI-driven predictive maintenance, and optimization of operational processes across various industries. He has published recent articles analyzing plastic injection machines, forest management techniques, and mobile augmented reality for environmental impact assessment.

Articles (20)

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Professor

Instituto Politécnico de Coimbra Escola Superior de Educação de Coimbra

PORTUGAL

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Mateus Mendes

Research Interests

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Articles (20)

AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis

Optimizing Operational Processes in Bicycle Testing: A Case Study

Analysis of the State and Fault Detection of a Plastic Injection Machine—A Machine Learning-Based Approach

Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks

Fault Detection and Prediction for a Wood Chip Screw Conveyor

Optimising Forest Management Using Multi-Objective Genetic Algorithms

Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity

Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra

Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD

Comparative analysis of four classification algorithms for fault detection of heat pumps

Improved GRU prediction of paper pulp press variables using different pre-processing methods

Augmented Reality in Maintenance—History and Perspectives

Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images

Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

Counting People and Bicycles in Real Time Using YOLO on Jetson Nano

Offline Handwritten Signature Verification Using Deep Neural Networks

Forecasting Steel Production in the World—Assessments Based on Shallow and Deep Neural Networks

Cork Oak Production Estimation Using a Mask R-CNN

Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning

Collaborators (7)

Antonio J. Marques Cardoso

José Torres Farinha

Manuel Marques Crisóstomo

Nuno Lavado

Inácio de Sousa Adelino da Fonseca

Balduíno Mateus

Raúl Salas González

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