Publications (277)
ARTICLE
Deep Learning and Web Applications Vulnerabilities Detection: An Approach Based on Large Language Models
NANA Sidwendluian Romaric, BASSOLE Didier, GUEL Desire, SIE Oumarou
Web applications are part of the daily life of Internet users, who find services in all sectors of activity. Web applications have become the target of malicious users. They exploit web application vulnerabilities to gain access to unauthorized resources and sensitive data, with consequences for users and businesses alike. The growing complexi(...)
Deep learning, Web application, Vulnerability, Detection, Large language model
ARTICLE
A comparison of AI methods for Groundwater Level Prediction in Burkina Faso
Abdoul Aziz Bonkoungou, Souleymane Zio, Aminata Sabane, Rodrique Kafando, Abdoul Kader Kabore, Tégawendé F Bissyandé
Groundwater serves as a valuable resource to supplement surface water, and its extensive utilization underscores the importance of precise groundwater level predictions. Burkina Faso confronts a critical challenge in the domain of sustainable groundwater resource management, underscoring the need for accurate forecasts of groundwater levels to(...)
Mots clés non renseignés
ARTICLE
Detecting Illicit Data Leaks on Android Smartphones Using an Artificial Intelligence Models
Serge Lionel Nikiema, Aminata Sabane, Abdoul-Kader Kabore, Rodrique Kafando & Tégawendé F. Bissyande
In today’s digital landscape, hackers and espionage agents are increasingly targeting Android, the world’s most prevalent mobile operating system. We introduce DeepDetector - a system based on artificial intelligence to recognize data thefts in Android. This model is based upon a large dataset comprising of clean and tainted network traffic tr(...)
Mots clés non renseignés
COMMUNICATION
5G-NR PRACH Detection Performance Optimization in Context of Intra/Inter-Cell Interference
Désiré Guel, Pegdwindé Justin Kouraogo, Boureima Zerbo, Elie Jephte Yaro
The ever-evolving landscape of wireless communication technologies has led to the development of 5G-NR (5G New Radio) networks [1] promising higher data rates and lower latency. However, with these advancements come challenges in managing intra-cell and inter-cell interference, particularly during the random-access procedure. This article aims(...)
5G-NR PRACH, Intra/Inter-Cell Interference, Quality of Service (QoS)
COMMUNICATION
A Metamodel for Enhancing Program Increment (PI) Planning: Towards a Framework for Modeling and Impact Analysis
Flavien Hervé SOMDA, Désiré GUEL, Kiswendsida Kisito KABORE
This article introduces a novel approach to addressing challenges in Program Increment (PI) Planning within Agile methodologies and large-scale software development. Our research develops a metamodel and framework to formalize the PI Planning domain, enabling systematic modeling and effective impact analyses. PI Planning is crucial in Agile so(...)
Program Increment (PI) Planning
ARTICLE
5G-NR PRACH Detection Performance Optimization in Context of Intra/Inter-Cell Interference
Désiré Guel, Pegdwindé Justin Kouraogo, Boureima Zerbo, Elie Jephte Yaro
The ever-evolving landscape of wireless communication technologies has led to the
development of 5G-NR (5G New Radio) networks [1] promising higher data rates and lower latency. However, with these advancements come challenges in managing intra-cell and inter-cell
interference, particularly during the random-access procedure. This article ai(...)
5G-NR PRACH, Intra/Inter-Cell Interference, Quality of Service (QoS)
ARTICLE
Convolutional Neural Networks Deep Learning Based for Malaria Detection and Diagnosis
Kabore, Josue, Guinko, Ferdinand T, Kiswendsida Kisito, Ouedraogo
Malaria is a disease that occurs worldwide, especially in tropical regions where a high prevalence is observed. Difficulties are encountered especially in developing countries where resources in terms of equipment and trained personnel are limited. Until today, microscopic analysis is the standard method for diagnosing Plasmodium falciparum, w(...)
Malaria, Deep Learning, Object detection, YOLOv5
ARTICLE
Is it better to bring digital health tools together? Where Burkina Faso is going with a minimal digital ecosystem (MDE)
Joël Arthur Kiendrébéogo 1,2,3,4*, Charlemagne Tapsoba5,6, Orokia Sory2, Issa Kaboré7, Yamba Kafando7, Simon Tiendrébéogo2,8, David Zombré9, Rémi Kaboré2,10, Noellie Konsebo6,11, Nacanabo Relwendé2,8, Jean Serge Dimitri Ouattara12, Guillaume Foutry13, Sara Hyde14, Dylan Green15, Michael Chaitkin16, André Lin Ouédraogo17 and S. Pierre Yaméogo
Digital health technologies are proliferating in low-income countries. However, they are not always optimally integrated and focused on health system priorities. To improve the performance of primary health care and accelerate progress toward universal health coverage, Burkina Faso aims to bring together eight digital health tools in two healt(...)
digital ecosystem; digital health; digital health solutions; digital health tools; primary healthcare performance; Burkina Faso
ARTICLE
FRAGILE BASE-CLASS PROBLEM, PROBLEM?
Aminata Sabané, Yann-Gaël Guéhéneuc, Venera Arnaoudova et Giuliano Antoniol
The fragile base-class problem (FBCP) has been described in the literature as a consequence of “misusing” inheritance and composition in object-oriented programming when (re)using frameworks. Many research works have focused on preventing the FBCP by proposing alternative mechanisms for reuse, but, to the best of our knowledge, there is no pre(...)
Mots clés non renseignés
ARTICLE
Quantifying Ruin Metrics in a Diffusion-Driven Erlang (2) Risk Model with Dependency Modeled using the Spearman Copula.
François Xavier Ouedraogo, Delwendé Abdoul-Kabir Kafando, Frédéric Béré, Pierre Clovis Nitiéma
This paper focuses on the perturbation of an Erlang (2) risk model by a diffusion process, challenging the assumption of independence between claim amounts and inter claim durations. To account for a tail dependency structure, we introduce the Spearman copula, enabling the evaluation of Gerber-Shiu functions and ruin probabilities associated w(...)
gerber-shiu functions, dependence, copula, integro-differential equation, laplace transform, ruin probability.
COMMUNICATION
ICT emerging technologies context and possibilities of applications in GIST
KOURAOGO Pegdwindé Justin
In this work we give some overviews on how emerging technologies can improve the use and design of Geospatial Information Systems and Technologies (GIST). We present the issues in three points of views, namely hardware systems points of view, software systems point of view and data management systems points of view. We present opportunity of i(...)
Emerging Technologies, GIST, hardware, software, data
ARTICLE
Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives
Hamidou Harouna Omar, Kouraogo Justin P., Kabre Windmi Jonathan, Tapsoba Stanislas David Wendkouni, Sie Oumarou
This article explores the growing importance of the QoE (Quality of Experience) metric as a fundamental criterion in the optimization of heterogeneous multimedia networks. We explore the bene2ts of using QoE, such as improving user experience, efficient resource management, and adaptation to network conditions. However, this paradigm presents(...)
QoS metric, Heterogeneous Network, Multimédia, Optimization
ARTICLE
Convolutional Neural Networks Deep Learning Based for Malaria Detection and Diagnosis
Kabore K. Kisito; Ouédraogo Josué; Guinko T. Ferdinand
Malaria is a disease that occurs worldwide, especially in tropical regions where a high prevalence is observed. Difficulties are encountered especially in developing countries where resources in terms of equipment and trained personnel are limited. Until today, microscopic analysis is the standard method for diagnosing Plasmodium falciparum, w(...)
Malaria, deep learning, Object detection, YOLOv5
ARTICLE
Characterization of Malicious URLs Using Machine Learning and Feature Engineering
Nana, S.R., Bassolé, D., Dimitri Ouattara, J.S., Sié, O.
In this paper, we use Machine Learning models for malicious URL detection and classification by Feature Engineering techniques. These models were implemented with scikit-learn using Random Forest, Support Vector Machine and XGBoost classifier algorithms. Our models were trained, tested, and then optimized with a dataset of 641,125 URLs (benign(...)
Malicious URL, Characterization, Feature Engineering, Detection, Classification
ARTICLE
Towards a More Generic and Elastic Metadata Management Model in a Data Lake Environment
Safiatou S Sore, T Frederic T Frédric Ouedraogo, Moustapha M Bikienga, Yaya Y Traore
The evolution of the vast amount of heterogeneous data sources is leading to the emergence of several new concepts. One of the best-known concepts that is emerging as a new and trending topic in the big data space is the data lake. This is a central repository that stores heterogeneous data sources in their native format, without any predefine(...)
Mots clés non renseignés