Publications (274)
COMMUNICATION
ChatGPT Does Not Understand Moore: On the Challenges of Integrating African Languages in Large Language Models
Aminata Sabané, Tegawendé F. Bissyandé
The advent of Large Language Models (LLMs) likely constitutes a turning point in the history of mankind. LLMs have the potential to revolutionize the way we interact with computers and each other. While current state of the art LLMs mostly support western languages, such as English, it is paramount that we consider the opportunity of integrati(...)
LLM, low-resource languages, natural language processing, Moore
ARTICLE
Accessibilité aux technologies de l'information et de la communication des étudiants en licence de l'UFR Sciences de la Santé (SDS) de l'Université Joseph KI-ZERBO de Ouagadougou
Relwendé Aristide YAMEOGO, ZONGO Armelle Francine, DOUMONBOU A. Isabelle. Rachelle, Joël BAMOUNI, SOUBIABIGA Romaric, Patrice ZABSONRE , Adama SANOU, Nicolas MEDA
L’objectif de ce travail est d’évaluer l’accès aux technologies numériques chez les étudiants de l’UFR/SDS de l’Université Joseph Ki-Zerbo. Il s’est agi d’une étude transversale descriptive menée à l’aide d’un questionnaire auto-administré, qui a été distribué aux étudiants inscrits en licence dans les filières de médecine, pharmacie et chirur(...)
Accès à la technologie , Compétences numériques , Burkina Faso
ARTICLE
Ameliorating Energy Efficiency in Wireless Sensor Networks: Integrating the Syracuse Algorithm with K-MEAN for Enhanced Base Station Mobile Management
OUATTARA Yacouba and PODA Pasteur
In designing sensor networks for hostile environments, energy conservation at each sensor node is a critical challenge. This study proposes a novel deployment
of two mobile base stations utilizing Syracuse's modified algorithm, alongside the original algorithm, to predict breakpoints. The approach aims to extend the
sensor network's lifespan(...)
Syracuse, Wireless Network, Mobile Network, SHEM, algorithm
ARTICLE
AI-BASED APPROACH FOR EARLY DIAGNOSIS SUPPORT IN HEMORRHAGIC STROKE
SAWADOGO Athanase, TAPSOBA Lydie Simone, KAFANDO Rodrique, Tegawende François d’Assise BISYANDE
A hemorrhagic stroke is a life-threatening medical condition that happens when a blood vessel in your brain ruptures and
bleeds. It constitutes a burden on health services and the victim's family. The current definitive diagnosis of stroke is based
on brain scanning. However, the clinical diagnosis of hemorrhagic stroke is complex and depend(...)
hemorrhagic stroke, machine learning, clinical data
ARTICLE
Integrating the Syracuse Algorithm with K-MEAN: A Comprehensive Approach to Energy Optimization in Wireless Sensor Networks
Yacouba OUATTARA
In deploying a sensor network in a challenging environment, it is crucial to consider energy consumption to ensure an extended network lifespan. Since the inception of sensor networks, researchers have proposed various energy-saving solutions outlined in the introduction. In our study, we introduce a novel approach for cluster formation and po(...)
Energy, K-MEAN, Syracuse, WSN, SHEM.
ARTICLE
The Personalization of Justified Recommendations Using the Users Profile Interest and Reviews
Kyelem Yacouba, Tounwendyam Frederic Ouedraogo, Kiswendsida Kisito Kaboré
This paper is about the adaptive and personalized justification of the recommenders collaborative filtering system using notices. A method to justify recommendations based on item reviews and the user profile interest is suggested. The reviews with a positive sentiment have been first kept through the sentiment analysis expressed on the review(...)
Personalization, Justified Recommendations, Users Profile, Interest, Reviews
ARTICLE
Optimizing Real-time Video Analytics for Resource-Constrained Environments
Rodrique Kafando, Aminata Sabane, Tégawendé F. Bissyandé
Developing countries face a growing demand for video analytics, yet often lack sufficient computational resources. This paper addresses this challenge by proposing and evaluating optimization techniques for efficient video stream processing on resource-constrained devices, including edge systems. We introduce and evaluate several techniques, i(...)
RSTP stream, Real-time Video Analytics, Computational Optimization, Post-Training Optimization
ARTICLE
Enhancing 5G-NR mm Wave: Phase Noise Models Evaluation with MMSE for CPE Compensation
GUEL Desire, SOMDA Flavien Herve, ZERBO Boureima, SIE Oumarou
The rapid development of 5G New Radio (NR) and millimeter-wave (mmWave) communication systems highlights the critical importance of maintaining accurate phase synchronization to ensure reliable and efficient communication. This study focuses on evaluating phase noise models and implementing Minimum Mean Square Error (MMSE) algorithms for Commo(...)
5G New Radio (NR), mmWave, CPE Compensation, Phase Noise Models, Minimum Mean Square Error (MMSE), EVM (Error Vector Magnitude), BLER (BLock Error Rate), SNR (Signal-to-Noise Ratio), NRPDSCH (Physical Downlink Shared Channel), PT-RS (Phase Tracking Reference Signals)
ARTICLE
Enhancing Connectivity and Energy Efficiency in Mobile Wireless Sensor Networks with SHEM
Yacouba Ouattara, Christophe Lang
The wireless sensor network can be useful in many domains such as military field, environmental control, medicine and healthcare. We, specially, focus on networks with mobiles nodes. Such networks require continuous dynamic reconfiguration to maintain effective communication between the nodes. Maintaining communication links and connectivity i(...)
algorithmic, energy, mobility, topology, connectivity, distributed
ARTICLE
5G NR PRACH Detection with Convolutional Neural Networks (CNN): Overcoming Cell Interference Challenges
GUEL Desire, KABORE Arsene, BASSOLE Didier
In this paper, we present a novel approach to interference detection in 5G New Radio (5G-NR) networks using Convolutional Neural Networks (CNN). Interference in 5G networks challenges high-quality service due to dense user equipment deployment and increased wireless environment complexity. Our CNN-based model is designed to detect Physical Ran(...)
5G-NR, PRACH, Réseaux de Neurones Convolutifs (CNN), Interference Detection
ARTICLE
5G NR PRACH Detection with Convolutional Neural Networks (CNN): Overcoming Cell Interferences Challenges
Desire Guel, Arsene Kabore, Didier Bassole
In this paper, we present a novel approach to interference detection in 5G New Radio (5G-NR) networks using Convolutional Neural Networks (CNN). Interference in 5G networks challenges high-quality service due to dense user equipment deployment and increased wireless environment complexity. Our CNN-based model is designed to detect Physical Ran(...)
5G-NR, PRACH, CNN, Interference Detection
ARTICLE
Deep Learning and Web Applications Vulnerabilities Detection: An Approach Based on Large Language Models
Sidwendluian Romaric Nana, Didier Bassole, Desire Guel and Oumarou Sie
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
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