Publications – CIRICS

Slide

Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société

18 entries « ‹ 1 of 2 › »

Sadfi, Y.; Amamou, H.; Davoust, A.; Avila, A. R.

Comparative Analysis of Machine Learning, LLMs, and RAG for Fake News Detection Proceedings Article

In: IEEE Conf. Artif. Intell., CAI, pp. 2128–2133, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156039-3 (ISBN), (Journal Abbreviation: IEEE Conf. Artif. Intell., CAI).

Abstract | Links | BibTeX | Tags: Classification (of information), Comparative analyzes, Comprehensive assessment, decision making, Decision-making process, Fake detection, Generalisation, High-accuracy, Language model, Learning algorithms, Learning systems, Machine learning, Machine learning algorithms, Machine-learning, Network platforms, Performance

@inproceedings{sadfiComparativeAnalysisMachine2026,

title = {Comparative Analysis of Machine Learning, LLMs, and RAG for Fake News Detection},

author = {Y. Sadfi and H. Amamou and A. Davoust and A. R. Avila},

url = {https://www.scopus.com/pages/publications/105042025800?origin=resultslist},

doi = {10.1109/CAI68641.2026.11536343},

isbn = {979-833156039-3 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {IEEE Conf. Artif. Intell., CAI},

pages = {2128–2133},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The rapid spread of misinformation on social network platforms poses a serious threat to the decision-making processes in democratic societies. To mitigate the problem and cope with new misinformation scenarios several datasets have been proposed, followed by the emergence of new methods for detecting fake news. While several comparative analyses have been proposed to benchmark these solutions, the ever-growing development of new methods requires up-to-date analysis. For instance, recent approaches based on retrieval-augmented generation (RAG) have yet to be assessed and compared to previous methods used for fake news detection. In this study, we aim to fill this gap by presenting a comprehensive assessment of classical machine learning (ML) algorithms, Transformer-based models, such as BERT, large language models (LLMs), and RAG-based classifiers. We adopt four traditional fake news datasets, including a Portuguese one. Results indicate that classical ML and BERT deliver strong performance on in-domain tasks, demonstrating efficiency and high accuracy. RAG-augmented LLMs, on the other hand, offer enhanced generalization and robustness in cross-domain and cross-dataset settings, but are outperformed by traditional approaches for in-domain evaluation. © 2026 IEEE.},

note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},

keywords = {Classification (of information), Comparative analyzes, Comprehensive assessment, decision making, Decision-making process, Fake detection, Generalisation, High-accuracy, Language model, Learning algorithms, Learning systems, Machine learning, Machine learning algorithms, Machine-learning, Network platforms, Performance},

pubstate = {published},

tppubtype = {inproceedings}

}

Cheddadi, A. El; Moudoud, H.; Gagnon, S.

The role of artificial intelligence and machine learning in safeguarding IoMT security and privacy Journal Article

In: Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability, pp. 1–17, 2025, ISSN: 978-100364032-5 (ISBN); 978-104107046-7 (ISBN).

Links | BibTeX | Tags: Artificial intelligence learning, Data privacy, Learning systems, Machine learning, Machine-learning, Security and privacy

Cheddadi, A. El; Moudoud, H.; Gagnon, S.

The role of artificial intelligence and machine learning in safeguarding IoMT security and privacy Journal Article

In: Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability, pp. 1–17, 2025, ISSN: 978-100364032-5 (ISBN); 978-104107046-7 (ISBN).

Links | BibTeX | Tags: Artificial intelligence learning, Data privacy, Learning systems, Machine learning, Machine-learning, Security and privacy

Zhu, Y.; Falk, T.

WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model Journal Article

In: IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 9, pp. 6353–6365, 2025, ISSN: 21682194 (ISSN).

Abstract | Links | BibTeX | Tags: Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform

@article{zhuWavRxDiseaseAgnosticGeneralizable2025,

title = {WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model},

author = {Y. Zhu and T. Falk},

url = {https://www.scopus.com/pages/publications/85203439930?origin=resultslist},

doi = {10.1109/JBHI.2024.3454550},

issn = {21682194 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {29},

number = {9},

pages = {6353–6365},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability. © 2013 IEEE.},

keywords = {Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform},

pubstate = {published},

tppubtype = {article}

}

Cheddadi, A. El; Moudoud, H.; Gagnon, S.

The role of artificial intelligence and machine learning in safeguarding IoMT security and privacy Journal Article

In: Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability, pp. 1–17, 2025, ISSN: 978-100364032-5 (ISBN); 978-104107046-7 (ISBN).

Links | BibTeX | Tags: Artificial intelligence learning, Data privacy, Learning systems, Machine learning, Machine-learning, Security and privacy

Joudeh, I. O.; Cretu, A. -M.; Bouchard, S.

Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features † Journal Article

In: Sensors, vol. 24, no. 13, 2024, ISSN: 14248220 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).

Abstract | Links | BibTeX | Tags: adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features

@article{joudeh_predicting_2024,

title = {Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features †},

author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198382238&doi=10.3390%2fs24134398&partnerID=40&md5=cefa8b2e2c044d02f99662af350007db},

doi = {10.3390/s24134398},

issn = {14248220 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {13},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features},

pubstate = {published},

tppubtype = {article}

}

The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.

Doney, E.; Dion-Albert, L.; Coulombe-Rozon, F.; Osborne, N.; Bernatchez, R.; Paton, S. E. J.; Kaufmann, F. N.; Agomma, R. O.; Solano, J. L.; Gaumond, R.; Dudek, K. A.; Szyszkowicz, J. K.; Aardema, F.; Bentaleb, L. A.; Beauchamp, J.; Bendahmane, H.; Benoit, E.; Bergeron, L.; Bertone, A.; Bertrand, N.; Berube, F. -A.; Blanchet, P.; Boissonneault, J.; Bolduc, C. J.; Bonin, J. -P.; Borgeat, F.; Boyer, R.; Breault, C.; Breton, J. -J.; Briand, C.; Brodeur, J.; Brule, K.; Brunet, L.; Carriere, S.; Chartrand, C.; Chenard-Soucy, R.; Chevrette, T.; Cloutier, E.; Cloutier, R.; Cormier, H.; Cote, G.; Cyr, J.; David, P.; Benedictis, L. De; Delisle, M. -C.; Deschenes, P.; Desjardins, C. D.; Desmarais, G.; Dubreucq, J. -L.; Dumont, M.; Dumais, A.; Ethier, G.; Feltrin, C.; Felx, A.; Findlay, H.; Fortier, L.; Fortin, D.; Fortin, L.; Francois, N.; Gagne, V.; Gagnon, M. -P.; Gignac-Hens, M. -C.; Giguere, C. -E.; Godbout, R.; Grou, C.; Guay, S.; Guillem, F.; Hachimi-Idrissi, N.; Herry, C.; Hodgins, S.; Homayoun, S.; Jemel, B.; Joyal, C.; Kouassi, E.; Labelle, R.; Lafortune, D.; Lahaie, M.; Lahlafi, S.; Lalonde, P.; Landry, P.; Lapaige, V.; Larocque, G.; Larue, C.; Lavoie, M.; Leclerc, J. -J.; Lecomte, T.; Lecours, C.; Leduc, L.; Lelan, M. -F.; Lemieux, A.; Lesage, A.; Letarte, A.; Lepage, J.; Levesque, A.; Lipp, O.; Luck, D.; Lupien, S.; Lusignan, F. -A.; Lusignan, R.; Luyet, A. J.; Lynhiavu, A.; Melun, J. -P.; Morin, C.; Nicole, L.; Noel, F.; Normandeau, L.; O'Connor, K.; Ouellette, C.; Parent, V.; Parizeau, M. -H.; Pelletier, J. -F.; Pelletier, J.; Pelletier, M.; Plusquellec, P.; Poirier, D.; Potvin, S.; Prevost, G.; Prevost, M. -J.; Racicot, P.; Racine-Gagne, M. -F.; Renaud, P.; Ricard, N.; Rivet, S.; Rolland, M.; Sasseville, M.; Safadi, G.; Smith, S.; Smolla, N.; Stip, E.; Teitelbaum, J.; Thibault, A.; Thibault, L.; Thibault, S.; Thomas, F.; Todorov, C.; Tourjman, V.; Tranulis, C.; Trudeau, S.; Trudel, G.; Vacri, N.; Valiquette, L.; Vanier, C.; Villeneuve, K.; Villeneuve, M.; Vincent, P.; Wolfe, M.; Xiong, L.; Zizzi, A.; Lebel, M.; Doyen, A.; Durand, A.; Lavoie-Cardinal, F.; Audet, M. -C.; Menard, C.; on behalf of Signature Consortium, Cecile Lepage

Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions Journal Article

In: Biological Psychiatry Global Open Science, vol. 4, no. 1, pp. 213–228, 2024, ISSN: 26671743 (ISSN), (Publisher: Elsevier Inc.).

Abstract | Links | BibTeX | Tags: animal experiment, animal model, animal tissue, article, chronic social defeat, chronic stress, chronic variable stress, claudin 3, Claudins, controlled study, corticosterone, corticosterone blood level, Cytokines, enzyme linked immunosorbent assay, female, human, immunohistochemistry, intestine flora, jejunum, lipopolysaccharide, lipopolysaccharide binding protein, Machine learning, major depression, male, mouse, nonhuman, pathogenesis, protein expression, real time polymerase chain reaction, sex difference, sex differences, social stress, tight junction, Variable stress

@article{doney_chronic_2024,

title = {Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions},

author = {E. Doney and L. Dion-Albert and F. Coulombe-Rozon and N. Osborne and R. Bernatchez and S. E. J. Paton and F. N. Kaufmann and R. O. Agomma and J. L. Solano and R. Gaumond and K. A. Dudek and J. K. Szyszkowicz and F. Aardema and L. A. Bentaleb and J. Beauchamp and H. Bendahmane and E. Benoit and L. Bergeron and A. Bertone and N. Bertrand and F. -A. Berube and P. Blanchet and J. Boissonneault and C. J. Bolduc and J. -P. Bonin and F. Borgeat and R. Boyer and C. Breault and J. -J. Breton and C. Briand and J. Brodeur and K. Brule and L. Brunet and S. Carriere and C. Chartrand and R. Chenard-Soucy and T. Chevrette and E. Cloutier and R. Cloutier and H. Cormier and G. Cote and J. Cyr and P. David and L. De Benedictis and M. -C. Delisle and P. Deschenes and C. D. Desjardins and G. Desmarais and J. -L. Dubreucq and M. Dumont and A. Dumais and G. Ethier and C. Feltrin and A. Felx and H. Findlay and L. Fortier and D. Fortin and L. Fortin and N. Francois and V. Gagne and M. -P. Gagnon and M. -C. Gignac-Hens and C. -E. Giguere and R. Godbout and C. Grou and S. Guay and F. Guillem and N. Hachimi-Idrissi and C. Herry and S. Hodgins and S. Homayoun and B. Jemel and C. Joyal and E. Kouassi and R. Labelle and D. Lafortune and M. Lahaie and S. Lahlafi and P. Lalonde and P. Landry and V. Lapaige and G. Larocque and C. Larue and M. Lavoie and J. -J. Leclerc and T. Lecomte and C. Lecours and L. Leduc and M. -F. Lelan and A. Lemieux and A. Lesage and A. Letarte and J. Lepage and A. Levesque and O. Lipp and D. Luck and S. Lupien and F. -A. Lusignan and R. Lusignan and A. J. Luyet and A. Lynhiavu and J. -P. Melun and C. Morin and L. Nicole and F. Noel and L. Normandeau and K. O'Connor and C. Ouellette and V. Parent and M. -H. Parizeau and J. -F. Pelletier and J. Pelletier and M. Pelletier and P. Plusquellec and D. Poirier and S. Potvin and G. Prevost and M. -J. Prevost and P. Racicot and M. -F. Racine-Gagne and P. Renaud and N. Ricard and S. Rivet and M. Rolland and M. Sasseville and G. Safadi and S. Smith and N. Smolla and E. Stip and J. Teitelbaum and A. Thibault and L. Thibault and S. Thibault and F. Thomas and C. Todorov and V. Tourjman and C. Tranulis and S. Trudeau and G. Trudel and N. Vacri and L. Valiquette and C. Vanier and K. Villeneuve and M. Villeneuve and P. Vincent and M. Wolfe and L. Xiong and A. Zizzi and M. Lebel and A. Doyen and A. Durand and F. Lavoie-Cardinal and M. -C. Audet and C. Menard and Cecile Lepage on behalf of Signature Consortium},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162172419&doi=10.1016%2fj.bpsgos.2023.04.007&partnerID=40&md5=aae4250eee5bd708599126cf76c3f5d7},

doi = {10.1016/j.bpsgos.2023.04.007},

issn = {26671743 (ISSN)},

year = {2024},

date = {2024-01-01},

journal = {Biological Psychiatry Global Open Science},

volume = {4},

number = {1},

pages = {213–228},

publisher = {Elsevier Inc.},

abstract = {Background: Major depressive disorder (MDD) is the leading cause of disability worldwide. Of individuals with MDD, 30% to 50% are unresponsive to common antidepressants, highlighting untapped causal biological mechanisms. Dysfunction in the microbiota-gut-brain axis has been implicated in MDD pathogenesis. Exposure to chronic stress disrupts blood-brain barrier integrity; still, little is known about intestinal barrier function in these conditions, particularly for the small intestine, where absorption of most foods and drugs takes place. Methods: We investigated how chronic social or variable stress, two mouse models of depression, impact the jejunum intestinal barrier in males and females. Mice were subjected to stress paradigms followed by analysis of gene expression profiles of intestinal barrier–related targets, fecal microbial composition, and blood-based markers. Results: Altered microbial populations and changes in gene expression of jejunum tight junctions were observed depending on the type and duration of stress, with sex-specific effects. We used machine learning to characterize in detail morphological tight junction properties, identifying a cluster of ruffled junctions in stressed animals. Junctional ruffling is associated with inflammation, so we evaluated whether lipopolysaccharide injection recapitulates stress-induced changes in the jejunum and observed profound sex differences. Finally, lipopolysaccharide-binding protein, a marker of gut barrier leakiness, was associated with stress vulnerability in mice, and translational value was confirmed on blood samples from women with MDD. Conclusions: Our results provide evidence that chronic stress disrupts intestinal barrier homeostasis in conjunction with the manifestation of depressive-like behaviors in a sex-specific manner in mice and, possibly, in human depression. © 2023 The Authors},

note = {Publisher: Elsevier Inc.},

keywords = {animal experiment, animal model, animal tissue, article, chronic social defeat, chronic stress, chronic variable stress, claudin 3, Claudins, controlled study, corticosterone, corticosterone blood level, Cytokines, enzyme linked immunosorbent assay, female, human, immunohistochemistry, intestine flora, jejunum, lipopolysaccharide, lipopolysaccharide binding protein, Machine learning, major depression, male, mouse, nonhuman, pathogenesis, protein expression, real time polymerase chain reaction, sex difference, sex differences, social stress, tight junction, Variable stress},

pubstate = {published},

tppubtype = {article}

}

Joudeh, I. O.; Cretu, A. -M.; Bouchard, S.

Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features † Journal Article

In: Sensors, vol. 24, no. 13, 2024, ISSN: 14248220 (ISSN).

@article{joudehPredictingArousalValence2024,

title = {Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features †},

author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198382238&doi=10.3390%2fs24134398&partnerID=40&md5=cefa8b2e2c044d02f99662af350007db},

doi = {10.3390/s24134398},

issn = {14248220 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {13},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.},

keywords = {adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features},

pubstate = {published},

tppubtype = {article}

}

Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions Journal Article

In: Biological Psychiatry Global Open Science, vol. 4, no. 1, pp. 213–228, 2024, ISSN: 26671743 (ISSN).

@article{doneyChronicStressExposure2024,

title = {Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162172419&doi=10.1016%2fj.bpsgos.2023.04.007&partnerID=40&md5=aae4250eee5bd708599126cf76c3f5d7},

doi = {10.1016/j.bpsgos.2023.04.007},

issn = {26671743 (ISSN)},

year = {2024},

date = {2024-01-01},

journal = {Biological Psychiatry Global Open Science},

volume = {4},

number = {1},

pages = {213–228},

publisher = {Elsevier Inc.},

pubstate = {published},

tppubtype = {article}

}

10.

Damadi, M. S.; Davoust, A.

Fairness in Socio-Technical Systems: A Case Study of Wikipedia Journal Article

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14199 LNCS, pp. 84–100, 2023, ISSN: 03029743, (ISBN: 9783031421402).

Abstract | Links | BibTeX | Tags: Algorithmics, Bias, Case-studies, Causal relationships, Cultural bias, Fairness, Gender bias, Machine learning, Machine-learning, Parallel processing systems, Sociotechnical systems, Wikipedia

18 entries « ‹ 1 of 2 › »

Share this page