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Documents dont le département est "Département de génie informatique et génie logiciel"

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Nombre de documents: 23

A

Abdalwhab, A. B. M., Beltrame, G., & St-Onge, D. (2026). Scalable Multi-Agent Reinforcement Learning Framework for Multi-Machine Tending. IEEE Robotics and Automation Letters, 11(3), 3135-3142. Lien externe

Al-Mallah, R., & Quintero, A. (2026). Toward Improved ICU Care - Phlebotomy Frequency Using Deep Learning. Inspire Intelligence Journal, 1(1), 9 pages. Lien externe

B

Bouakkaz, S., Suárez, L., Boulahia Cuppens, N., & Cuppens, F. (décembre 2025). Adaptive Layered Trustworthiness Assessment Model (ALTAM) for5G Services Deployment [Communication écrite]. 19th International Conference on Network and System Security (NSS 2025), Wuhan, China. Publié dans Lecture notes in computer science. Lien externe

C

Chitsazian, Z., Sedighian Kashi, S., & Nikanjam, A. (2026). Detecting concept drift in just-in-time software defect prediction using model interpretation. International Journal of Systems Assurance Engineering and Management, 22 pages. Lien externe

D

Dorostkar, F., Dagenais, M., Tyagi, A., & Bridgers, V. (2026). ThreadMonitor: Low‐Overhead Data Race Detection Using Intel Processor Trace. Concurrency and Computation: Practice and Experience, 38(2), 14 pages. Lien externe

E

Erfan, F., Bellaïche, M., & Halabi, T. (mars 2025). Retaliation Game forMitigating Selfish Mining Attacks inBlockchain Networks [Communication écrite]. 13th EAI International Conference (GameNets 2025), Cambridge, UK. Publié dans Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Lien externe

F

Foundjem, A. T., Nganyewou Tidjon, L., Da Silva, L. M. P., & Khomh, F. (2026). Multi-Agent AI Framework for Threat Mitigation and Resilience in Machine Learning Systems. ACM Transactions on Software Engineering and Methodology. Lien externe

I

Imran, A., Varadharajan, V. S., Gomes Braga, R., Beltrame, G., & St-Onge, D. (2026). Swarm robotics localization: comparing methods from infrared to foundation models. Swarm Intelligence, 20(1), 23 pages. Lien externe

J

Jamshidi, S., Abdul Wahab, O., Herrero, R., Khomh, F., Bellaïche, M., Keivanpour, S., Shahabi, N., Nikanjam, A., & Wazed Nafi, K. (2026). Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway. IEEE Internet of Things Journal, 30 pages. Lien externe

K

Kirana, M. C., Keyela, P., Rostamian, F., Tashman, D., & Cherkaoui, S. (2026). ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities. IEEE Communications Surveys & Tutorials, 33 pages. Lien externe

L

Leblanc, T., Baghalizadeh Moghadam, N., Cuppens, F., & Boulahia Cuppens, N. (décembre 2025). Real-Time Anomaly Detection forEvent-Based Insider Threat Hunting [Communication écrite]. 19th International Conference on Network and System Security (NSS 2025), Wuhan, China. Publié dans Lecture notes in computer science. Lien externe

N

Computer Security – ESORICS 2025 [Communication écrite] (septembre 2025). 30th European Symposium on Research in Computer Security (ESORICS 2025), Toulouse, France. Publié dans Lecture Notes in Computer Science, 16054. Lien externe

P

Pesant, G., & Saikali, D. (janvier 2026). 339 - Constrained Molecule Generation Modelled Using the Grammar Constraint [Affiche]. 40th AAAI Conference on Artificial Intelligence (AAAI-26), Singapore, Singapore. Lien externe

Pesant, G., & Saikali, D. (janvier 2026). Constrained Molecule Generation Modelled Using the Grammar Constraint [Communication écrite]. 40th AAAI Conference on Artificial Intelligence (AAAI-26), Singapore, Singapore. Lien externe

Q

Qin, Q., Li, H., & Merlo, E. (2026). Unsupervised, robust, and lightweight detection of data pattern anomalies and outliers. Information and Software Technology, 192, 108035 (16 pages). Lien externe

R

Rodet, H., & Séoud, L. (2026). Body shape diversity in the training data and consequences on motion generation. Computer Vision and Image Understanding, 264, 104632 (17 pages). Lien externe

S

Saber, R., Carneiro, M., Montagnon, E., Tang, A., Turcotte, S., & Kadoury, S. (2026). Multi-omics fusion network for prediction of early recurrence in colorectal liver metastases. npj Precision Oncology. Lien externe

Saber, R., Tonneau, M., Salko, O., Liberman, M., Malo, J., Elkrief, A., Turcotte, S., Bouchard, N., Joubert, P., Kadoury, S., & Routy, B. (2026). Multi-Institutional CT Scan-Based Radiomics for Predicting Tumor PD-L1 Expression in Patients with Advanced and Limited Non-Small Cell Lung Cancer. Cancers, 18(4), 552. Lien externe

Škrlj, L., Kadoury, S., & Vrtovec, T. (septembre 2025). Transformer-Based Instance Detection in 3D Medical Images [Communication écrite]. 4th International Workshop on Applications of Medical Artificial Intelligence (AMAI 2025), Daejeon, South Korea. Publié dans Lecture notes in computer science. Lien externe

T

Tashman, D., & Cherkaoui, S. (2026). Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks. IEEE Transactions on Network and Service Management, 1 page. Lien externe

Y

Yoshiaki Okano, E., Aloise, D., & Nascimento, M. C. V. (2026). An analytics framework for graph-based anomaly detection in healthcare time series. Healthcare Analytics, 100447. Lien externe

Z

Zhang, X.R., Zhao, P., Jaskolka, J., Li, H., & Lu, R. (2026). SecMLOps: A comprehensive framework for integrating security throughout the machine learning operations lifecycle. Empirical Software Engineering, 31(3), 74 (48 pages). Lien externe

Zrelli, R., Amaral Misson, H., Kamkuimo, S., Ben Attia, M., Shabah, A., Gohring de Magalhaes, F., & Nicolescu, G. (2026). Integrating formal methods and automated tools for DO-178C compliance in UAV software. Information and Software Technology, 108068. Lien externe

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