Publications (276)
ARTICLE
SpreadSentinel: A Forward-Chaining Approach to Early and Adaptive DDoS Mitigation
Delwende D. Arthur Sawadogo, Abdoul Rahim Koné, Zakaria Sawadogo, Didier Bassole, Roland Kalmogo, et Gouayon Koala
The early detection of Distributed Denial-of-Service (DDoS) attacks in dynamic and imbalanced network environments remains a critical and unresolved challenge. While temporal deep learning architectures such LSTM, GRU, TCN, and RNN have demonstrated effectiveness in capturing sequential dependencies in network traffic, their real-time utilizat(...)
software security, Deep learning, Forward chaining, Class imbalance handling
ARTICLE
Recommendation Generation Justified for Information Access Assistance Service (IAAS): Study of Architectural Approaches and Post-Hoc Algorithm
Kabore, K. , Kyelem, Y. and Ouedraogo, T.
Recommendation systems only provide more specific recommendations to users. They do not consider giving a justification for the recommendation. However, the justification for the recommendation allows the user to make the decision whether or not to accept the recommendation. It also improves user satisfaction and the relevance of the recommend(...)
IAAS, Justification of Recommendations, Weight of Comments, Relevance of Recommendations, Justification of Recommendation Architecture for IAAS
ARTICLE
Recommendation of Documentary Units in a Progressively Intelligent City
Kiswendsida Kisito Kaboré, Désiré Guel, Yaya Traoré, Pegdwinde Justin Kouraogo, Didier Bassolé, Yacouba Kyelem, Tonguim Ferdinand Guinko, Oumarou Sié
The large cities and capitals of developing countries are becoming larger and more populated. These cities are modernizing day by day in order to increase the standard of living of their constantly growing population. To do this, they are equipping themselves with the latest generation of intelligent infrastructures that can make a city very a(...)
Smart City, Recommendation, IAAS, Documentary Unit
ARTICLE
Ouagadougou SUMO traffic scenarios for urban mobility.
Emile NANA, Ferdinand Tonguim GUINKO
In our work, we are attempting to predict urban traffic congestion in order to address issues related to urban mobility. We believe that congestion situations have upstream causes that significantly impact urban traffic flow. Thus, to resolve the problem of traffic congestion, there are intermediate steps that allow us to understand the traffi(...)
urban traffic simulation , road traffic prediction , road traffic engineering , SUMO , ontology , road traffic planning
PRéPUBLICATION
Numerical Assessment of Hydrodynamic Trends on Dam Stability, and Groundwater Prediction in the Bagré Dam, Burkina Faso: Implications for Climate Change
Triumph Prosper Orowale1 Seyni Salack2 Youssouf Koussoube1 Moussa Guira3 Issoufou Yangouliba2 Rajesh Gudihalli Munivenkatappa
In the context of climate change and the subsequent pressure from geological structures, there is a pressing need to analyse hydroclimatic trend events on dam stability and establish a clear correlation between reservoir surface water and groundwater levels in the Bagré Dam. Hydrodynamic variables (evaporation, inflow, irrigation, spillage, wa(...)
Climate Change Hydrodynamic Trends Groundwater Reservoir Water Level Bagré Dam
ARTICLE
When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair
Wenqiang Luo, Jacky Wai Keung, Boyang Yang, He Ye, Claire Le Goues, Tegawendé F. Bissyandé, Haoye Tian, Bach Le
Software systems have been evolving rapidly and inevitably introducing bugs at an increasing rate, leading to significant maintenance costs. While large language models (LLMs) have demonstrated remarkable potential in enhancing software development and maintenance practices, particularly in automated program repair (APR), they rely heavily on(...)
Fine-Tuning, LLMs
ARTICLE
A Robust Crop Recommendation System Leveraging Soil and Climate Parameters
Desire Guel and Jimna Kongo
We present a benchmarking study of classical machine learning (ML) methods for crop recommendation from soil and climate parameters with an emphasis on methodological transparency, interpretability and deployability in low-resource contexts. We evaluate K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), Gaussian Na(...)
Crop Recommendation, Precision Agriculture, Ensemble Learning, Interpretability
ARTICLE
A Survey on Deep Learning Techniques for Malaria Detection: Datasets Architectures and Future Perspectives
Desire Guel, Kiswendsida Kisito Kabore, Flavien Herve Somda
Malaria remains a significant global health challenge that affects more than 200 million people each year and disproportionately burdens regions with limited resources. Precise and timely diagnosis is critical for effective treatment and control. Traditional diagnostic approaches, including microscopy and rapid diagnostic tests (RDTs), encount(...)
Malaria Detection, Deep Learning (DL), Convolutional Neural Networks (CNNs), Medical Imaging, Automated Diagnostics
ARTICLE
Which approach of evolution for a service of document units recommendation?
Kabore Kiswendsida Kisito, Désiré Guel, Justin Kouraogo, Bertin Kaboré
Cloud computing is a major current trend that involves virtually distributing processing and data across configurable execution environments. Developing and deploying software for the cloud presents a new scientific challenge in terms of expressing and accounting for variability. Indeed, cloud computing relies on the principles of heterogeneit(...)
Application's migrations, Big Data, Cloud computing, Data migration, IAAS, IA2S, Recommendation system
ARTICLE
<scp>MORepair</scp> : Teaching LLMs to Repair Code via Multi-Objective Fine-Tuning
Boyang Yang, Haoye Tian, Jiadong Ren, Hongyu Zhang, Jacques Klein, Tegawendé F. Bissyandé, Claire Le Goues, Shunfu Jin
Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning Large language models (LLMs) to unlock state-of-the-art performance. Fine-tuning approaches proposed in the literature for LLMs on program repair tasks generally overlook the need to reason about the(...)
LLMs
ARTICLE
PriCod: Prioritizing Test Inputs for Compressed Deep Neural Networks
Yinghua Li, Xueqi Dang, Jacques Klein, Yves Le Traon, Tegawendé F. Bissyandé
The widespread adoption of Deep Neural Networks (DNNs) has brought remarkable advances in machine learning. However, the computational and memory demands of complex DNNs hinder their deployment in resource-constrained environments. To address this challenge, compressed DNN models have emerged, offering a compromise between efficiency and accur(...)
Prioritizing
ARTICLE
Towards an Ontology and Knowledge Graph-Based Recommendation Approach Enhanced with Large Language Models for Civil Service Recruitment in Burkina Faso
Wend-Panga Régis Cédric BÉRÉ, Yaya Traoré, P. Justin Kouraogo, Daouda Ouedraogo
Recruitment processes in the civil service often face challenges related to the management of competencies, complex regulatory rules, and the lack of explainability in selection decisions. To address these issues, we propose a hybrid recommendation approach that integrates a modular core ontology, a dynamic knowledge graph, and large language(...)
Knowledge engineering , Large language models , Systems architecture , Knowledge graphs , Ontologies , Resource management , Personnel , Sustainable development , Recommender systems , Recruitment
COMMUNICATION
Towards An Ecore-Based, Uncertainty-Aware Metamodel for Auditable Geopolitical Decision Support
Somda Flavien Hervé, Guel Désiré , Kangoye Sékou
We present GeoDepend-ML, a temporal, multiplex Ecore metamodel for representing geopolitical interdependence and influence with auditability. The model treats actors (states, blocs, enterprises, organizations), assets (resources, technologies, infrastructures, routes), and artifacts (e.g., treaties, licenses, sanctions) as first-class elements(...)
Multiplexing, Uncertainty, Sensitivity analysis, Semantics, Lithography, Licenses, Security, Risk analysis, Reliability, Sustainable development, Ecore, EMF, international relations, knowledge graphs, multiplex networks, decision support, provenance, uncertainty
ARTICLE
Mapping the COVID-19 pandemic in Burkina Faso: spatial patterns, socioeconomic factors, and public health implications
Abdoul Azize Millogo, Aboubacar Karabinta, Emmanuel Kiendrebeogo, Bry Sylla, Abdoulaye DIABATÉ, Lassane Yameogo
The first case of COVID-19 in Burkina Faso was reported in March 2020. As of June 8, 2025, Burkina Faso reported 22,114 confirmed cases and 400 deaths. However, few studies have investigated the spatiotemporal dynamics of pandemics within the national boundaries. This study provides a retrospective spatial analysis of COVID-19 transmission in(...)
Public health, Pandemic, Spatial analysis, Geographic information system, Population, Health geography, Spatial epidemiology, Geographically Weighted Regression, Poverty, Socioeconomic status
ARTICLE
Resolving Conditional Implicit Calls to Improve Static and Dynamic Analysis in Android Apps
Jordan Samhi, René Just, Michael D. Ernst, Tegawendé F. Bissyandé, Jacques Klein
An implicit call is a mechanism that triggers the execution of a method m without a direct call to m in the code being analyzed. For instance, in Android apps the Thread.start() method implicitly executes the Thread.run() method. These implicit calls can be conditionally triggered by programmer-specified constraints that are evaluated at runti(...)
Resolving