Publications (277)
ARTICLE
Towards a Chatbot for Medical Diagnosis Based on Patient Symptoms
Kisito KABORE, Omar SAWADOGO, Yaya TRAORE, Julie THIOMBIANO
The advent of artificial intelligence has positively transformed many
areas of our lives, including the medical field. In this article, we propose the
development of a medical diagnosis chatbot based on patients' symptoms, using
artificial intelligence as an innovative solution. The aim of this tool is to provide
doctors with a preliminary(...)
AI, Machine Learning, Chatbot, Medical diagnosis
ARTICLE
Categorizing Approaches to Justify Recommendations
Yacouba Kyelem, T. Frédéric Ouédraogo, K. Kisito Kaboré
Recommendation justification enables users to understand the reasons and motivation behind
the recommendation of an item in a recommender system. It makes the recommendation model
much more transparent, and improves user satisfaction. It is because of the important role
assigned to the justification of recommendations that the present work(...)
Recommender systems, Justification of recommendation, Model-based justification, Post-hoc justification
ARTICLE
Transcription-Based on Pitches an Ontology for the System Knowledge Representation
Pengwendé Zongo, Yamba Dabone & Tounwendyam Frédéric Ouedraogo
This article aims to present an ontological approach for the domain knowledge representation of an intelligent tutoring system for learning transcription based on pitches in the Mooré language. This system that we built allows us to learn the transcription of polysemous words in the Mooré language. In our intelligent tutoring system, the knowl(...)
Ontology, Intelligent tutoring system, Tone language, Moor´
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