1 citations · 2 across the 7 of their papers we have counts for
10 papers
An LLM-Based Agent for Legal Compliance Assessment: Architecture, Memory, and Evaluation in a Canadian and Quebec Context
Soukaina Alaoui Ismaili, Jean-Sebastien Dessureault
Checking whether a text complies with a body of law is slow when done by hand and fragile when done with keyword rules. This paper presents ComplianceAgent, the legal compliance su…
From Conceptual EneMonIE to an Executable Backend: A Modular Architecture for Energy Information Extractionand Grounded Question Answering
Janik Lesage, Khadim Lô, and Jean-Sébastien Dessureault
The original EneMonIE proposal describes a Web-based semantic system for continuously monitoring energy trends through targeted information extraction from heterogeneous online med…
COMPASS: The explainable agentic framework for Sovereignty, Sustainability, Compliance, and Ethics
Jean-Sébastien Dessureault, Alain-Thierry Iliho Manzi, Soukaina Alaoui Ismaili +3
The rapid proliferation of large language model (LLM)-based agentic systems raises critical concerns regarding digital sovereignty, environmental sustainability, regulatory complia…
District Vitality Index Using Machine Learning Methods for Urban Planners
Sylvain Marcoux, Jean-Sébastien Dessureault
City leaders face critical decisions regarding budget allocation and investment priorities. How can they identify which city districts require revitalization? To address this chall…
AI2: The next leap toward native language based and explainable machine learning framework
Jean-Sébastien Dessureault, Daniel Massicotte
The machine learning frameworks flourished in the last decades, allowing artificial intelligence to get out of academic circles to be applied to enterprise domains. This field has…
ck-means, a novel unsupervised learning method that combines fuzzy and crispy clustering methods to extract intersecting data
Jean-Sébastien Dessureault, Daniel Massicotte
Clustering data is a popular feature in the field of unsupervised machine learning. Most algorithms aim to find the best method to extract consistent clusters of data, but very few…
Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation
Jean-Sébastien Dessureault, Daniel Massicotte
Evaluating the performance of an algorithm is crucial. Evaluating the performance of data imputation and data augmentation can be similar since both generated data can be compared…
DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction
Jean-Sébastien Dessureault, Daniel Massicotte
This paper discusses the critical decision process of extracting or selecting the features in a supervised learning context. It is often confusing to find a suitable method to redu…
Feature selection or extraction decision process for clustering using PCA and FRSD
Jean-Sebastien Dessureault, Daniel Massicotte
This paper concerns the critical decision process of extracting or selecting the features before applying a clustering algorithm. It is not obvious to evaluate the importance of th…
Unsupervised Machine learning methods for city vitality index
Jean-Sébastien Dessureault, Jonathan Simard, Daniel Massicotte
This paper concerns the challenge to evaluate and predict a district vitality index (VI) over the years. There is no standard method to do it, and it is even more complicated to do…