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About

About

Fátima Rodrigues is currently an Associate Professor at ISEP, the Polytechnic Institute of Porto, and a researcher in the INESC TEC. Her main skills and expertise are related to business analytics, data science, decision support systems, neural networks, and machine learning. She is the co-author of more than 25 indexed (e.g., ISI, Scopus) publications in international peer-reviewed journals. She has participated in more than seven R&D projects and has supervised four PhD thesis, 35 MSc thesis and 65 BSc final graduation projects in the area of Intelligent Data Analysis. She has been a regular reviewer of ISI JCR journals such as IEEE Trans. Neural Networks and Learning Systems, Information Sciences, Decision Support Systems, and Data and Knowledge Engineering. Moreover, she has been Program Committee/Reviewer of several international conferences/workshops.

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Details

Details

  • Name

    Fátima Rodrigues
  • Role

    Senior Researcher
  • Since

    17th January 2024
Publications

2024

A Systematic Review on Responsible Multimodal Sentiment Analysis in Marketing Applications

Authors
César, I; Pereira, I; Rodrigues, F; Miguéis, VL; Nicola, S; Madureira, A; Reis, JL; Dos Santos, JPM; De Oliveira, DA;

Publication
IEEE ACCESS

Abstract
The intrinsic challenges of contemporary marketing encourage discovering new approaches to engage and retain customers effectively. As the main channels of interactions between customers and brands pivot between the physical and the digital world, analyzing the outcome behavioral patterns must be achieved dynamically with the stimulus performed in both poles. This systematic review investigates the collaborative impact of adopting multidisciplinary fields of Affective Computing to evaluate current marketing strategies, upholding the process of using multimodal information from consumers to perform and integrate Sentiment Analysis tasks. The adjusted representation of modalities such as textual, visual, audio, or even psychological indicators enables prospecting a more precise assessment of the advantages and disadvantages of the proposed technique, glimpsing future applications of Multimodal Artificial Intelligence in Marketing. Embracing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as the research method to be applied, this article warrants a rigorous and sequential identification and interpretation of the synergies between the latest studies about affective computing and marketing. Furthermore, the robustness of the procedure is deepened in knowledge-gathering concerning the current state of Affective Computing in the Marketing area, their technical practices, ethical and legal considerations, and the potential upcoming applications, anticipating insights for the ongoing work of marketers and researchers.

2024

Deep learning for predicting respiratory rate from physiological signals

Authors
Rodrigues, F; Pereira, J; Torres, A; Madureira, A;

Publication
Procedia Computer Science

Abstract
This paper presents a comprehensive study on the application of machine learning techniques in the prediction of respiratory rate via time-series-based statistical and machine learning methods using several physiological signals. Two different models, ARIMA and LSTM, were developed. The LSTM model showed a stronger capacity for learning and capturing complicated patterns in the data compared to the ARIMA model. The findings imply that LSTM models, by incorporating many variables, have the ability to provide predictions that are more accurate, particularly in situations where respiratory rate values vary significantly. © 2024 The Authors. Published by ELSEVIER B.V.

2024

Exploring multimodal learning applications in marketing: A critical perspective

Authors
César, I; Pereira, I; Rodrigues, F; Miguéis, V; Nicola, S; Madureira, A;

Publication
International Journal of Hybrid Intelligent Systems

Abstract
This review discusses the integration of intelligent technologies into customer interactions in organizations and highlights the benefits of using artificial intelligence systems based on a multimodal approach. Multimodal learning in marketing is explored, focusing on understanding trends and preferences by analyzing behavior patterns expressed in different modalities. The study suggests that research in multimodality is scarce but reveals that it is as a promising field for overcoming decision-making complexity and developing innovative marketing strategies. The article introduces a methodology for accurately representing multimodal elements and discusses the theoretical foundations and practical impact of multimodal learning. It also examines the use of embeddings, fusion techniques, and explores model performance evaluation. The review acknowledges the limitations of current multimodal approaches in marketing and encourages more guidelines for future research. Overall, this work emphasizes the importance of integrating intelligent technology in marketing to personalize customer experiences and improve decision-making processes.

2024

An automated approach for binary classification on imbalanced data

Authors
Vieira, PM; Rodrigues, F;

Publication
KNOWLEDGE AND INFORMATION SYSTEMS

Abstract
Imbalanced data are present in various business sectors and must be handled with the proper resampling methods and classification algorithms. To handle imbalanced data, there are numerous resampling and learning method combinations; nonetheless, their effective use necessitates specialised knowledge. In this paper, several approaches, ranging from more accessible to more advanced in the domain of data resampling techniques, will be considered to handle imbalanced data. The application developed delivers recommendations of the most suitable combinations of techniques for a specific dataset by extracting and comparing dataset meta-feature values recorded in a knowledge base. It facilitates effortless classification and automates part of the machine learning pipeline with comparable or better results than state-of-the-art solutions and with a much smaller execution time.

2023

A Deep Learning Approach to Monitoring Workers’ Stress at Office

Authors
Rodrigues, F; Marchetti, J;

Publication
Lecture Notes in Networks and Systems

Abstract
Identifying stress in people is not a trivial or straightforward task, as several factors are involved in detecting the presence or absence of stress. The problem of detect stress has attracted much attention in the last decade and is mainly addressed with physiological signals and in a controlled ambience with specific tasks. However, the widespread use of video cameras permitted the creation of a new non-invasive data collection techniques. The goal of this work is to provide an alternative way to detect stress in the workplace without the need of specific laboratory conditions. For that, a stress detection model based on images analysed with deep learning neural networks was developed. The trained model achieved a F1 = 79.9% on a binary dataset, of stress/non-stress, with an imbalanced ratio of 0.49. This model can be used in a non-invasive application to detect stress and provide recommendations to the collaborators in the workplace in order to help them to control their stress condition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.