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About

About

Davide Carneiro is a Coordinator Professor at the School of Management and Technology, of the Polytechnic Institute of Porto. He is also an integrated researcher at INESC TEC . He holds a PhD from a joint Doctoral Programme in Computer Science of three top Portuguese Universities (MAP-i Programme – Minho, Aveiro and Porto). He develops scientific research in the field of Artificial Intelligence, touching topics such as Machine Learning (including distributed and streaming Machine Learning), Meta-Learning and AI Ethics. The application areas of his research include Healthcare and Wellbeing, Online Conflict Resolution and Fraud Detection.

In the past, Davide has coordinated or participated in several national and international funded research projects in these fields. He was the scientific coordinator of the NEURAT project (NORTE-01-0247-FEDER-039900) and is the institutional coordinator of the EU-funded EJUST ODR Scheme project (JUST-2021-EJUSTICE, 101046468). He is also the Principal Investigator of the FCT-funded projects CEDEs (EXPL/CCI- COM/0706/2021) and xAIDMLS (CPCA-IAC/AV/475278/2022). He is also currently participating in the EU-funded FACILITATE-AI and PRIVATEER projects.

He is the author of more than 150 publications in his fields of interest, including one authored book, four edited books, and over one 140 book chapters, journal papers and conference and workshop papers.

He is also the co-founder and CRO of AnyBrain, a Portuguese startup in the field of Human Computer Interaction. The company develops software for fatigue detection in office environments (https://performetric.net/), performance assessment in eSports (https://performetric.gg/), and user identification and cheat detection (https://anybrain.gg/).

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Details

Details

  • Name

    Davide Rua Carneiro
  • Role

    Senior Researcher
  • Since

    01st August 2022
005
Publications

2024

Supervised and unsupervised techniques in textile quality inspections

Authors
Ferreira, HM; Carneiro, DR; Guimaraes, MA; Oliveira, FV;

Publication
5TH INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING, ISM 2023

Abstract
Quality inspection is a critical step in ensuring the quality and efficiency of textile production processes. With the increasing complexity and scale of modern textile manufacturing systems, the need for accurate and efficient quality inspection and defect detection techniques has become paramount. This paper compares supervised and unsupervised Machine Learning techniques for defect detection in the context of industrial textile production, in terms of their respective advantages and disadvantages, and their implementation and computational costs. We explore the use of an autoencoder for the detection of defects in textiles. The goal of this preliminary work is to find out if unsupervised methods can successfully train models with good performance without the need for defect labelled data. (c) 2023 The Authors. Published by Elsevier B.V.

2024

Application of Meta Learning in Quality Assessment of Wearable Electrocardiogram Recordings

Authors
Huerta, A; Martínez Rodrigo, A; Guimarâes, M; Carneiro, D; Rieta, J; Alcaraz, R;

Publication
IFMBE Proceedings

Abstract
The high rates of mortality provoked by cardiovascular disorders (CVDs) have been rated by the OMS in the top among non-communicable diseases, killing about 18 million people annually. It is crucial to detect arrhythmias or cardiovascular events in an early way. For that purpose, novel portable acquisition devices have allowed long-term electrocardiographic (ECG) recording, being the most common way to discover arrhythmias of a random nature such as atrial fibrillation (AF). Nonetheless, the acquisition environment can distort or even destroy the ECG recordings, hindering the proper diagnosis of CVDs. Thus, it is necessary to assess the ECG signal quality in an automatic way. The proposed approach exploits the feature and meta-feature extraction of 5-s ECG segments with the ability of machine learning classifiers to discern between high- and low-quality ECG segments. Three different approaches were tested, reaching values of accuracy close to 83% using the original feature set and improving up to 90% when all the available meta-features were utilized. Moreover, within the high-quality group, the segments belonging to the AF class outperformed around 7% until a rate over 85% when the meta-features set was used. The extraction of meta-features improves the accuracy even when a subset of meta-features is selected from the whole set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

2024

Fabric Defect Detection and Localization

Authors
Oliveira, F; Carneiro, D; Ferreira, H; Guimaraes, M;

Publication
ADVANCES IN ARTIFICIAL INTELLIGENCE IN MANUFACTURING, ESAIM 2023

Abstract
Quality inspection is crucial in the textile industry as it ensures that the final products meet the required standards. It helps detect and address defects, such as fabric flaws and stitching irregularities, enhancing customer satisfaction, and optimizing production efficiency by identifying areas of improvement, reducing waste, and minimizing rework. In the competitive textile market, it is vital for maintaining customer loyalty, brand reputation, and sustained success. Nonetheless, and despite the importance of quality inspection, it is becoming increasingly harder to hire and train people for such tedious and repetitive tasks. In this context, there is an increased interest in automated quality control techniques that can be used in the industrial domain. In this paper we describe a computer vision model for localizing and classifying different types of defects in textiles. The model developed achieved an mAP@0.5 of 0.96 on the validation dataset. While this model was trained with a publicly available dataset, we will soon use the same architecture with images collected from Jacquard looms in the context of a funded research project. This paper thus represents an initial validation of the model for the purposes of fabric defect detection.

2023

Real-Time Algorithm Recommendation Using Meta-Learning

Authors
Palumbo, G; Guimaraes, M; Carneiro, D; Novais, P; Alves, V;

Publication
AMBIENT INTELLIGENCE-SOFTWARE AND APPLICATIONS-13TH INTERNATIONAL SYMPOSIUM ON AMBIENT INTELLIGENCE

Abstract
As the field of Machine Learning evolves, the number of available learning algorithms and their parameters continues to grow. On the one hand, this is positive as it allows for the finding of potentially more accurate models. On the other hand, however, it also makes the process of finding the right model more complex, given the number of possible configurations. Traditionally, data scientists rely on trial-and-error or brute force procedures, which are costly, or on their own intuition or expertise, which is hard to acquire. In this paper we propose an approach for algorithm recommendation based on meta-learning. The approach can be used in real-time to predict the best n algorithms (based on a selected performance metric) and their configuration, for a given ML problem. We evaluate it through cross-validation, and by comparing it against an Auto ML approach, in terms of accuracy and time. Results show that the proposed approach recommends algorithms that are similar to those of traditional approaches, in terms of performance, in just a fraction of the time.

2023

Algorithm Recommendation and Performance Prediction Using Meta-Learning

Authors
Palumbo, G; Carneiro, D; Guimares, M; Alves, V; Novais, P;

Publication
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS

Abstract
In the last years, the number of machine learning algorithms and their parameters has increased significantly. On the one hand, this increases the chances of finding better models. On the other hand, it increases the complexity of the task of training a model, as the search space expands significantly. As the size of datasets also grows, traditional approaches based on extensive search start to become prohibitively expensive in terms of computational resources and time, especially in data streaming scenarios. This paper describes an approach based on meta-learning that tackles two main challenges. The first is to predict key performance indicators of machine learning models. The second is to recommend the best algorithm/configuration for training a model for a given machine learning problem. When compared to a state-of-the-art method (AutoML), the proposed approach is up to 130x faster and only 4% worse in terms of average model quality. Hence, it is especially suited for scenarios in which models need to be updated regularly, such as in streaming scenarios with big data, in which some accuracy can be traded for a much shorter model training time.