The triage system an emergency department must prioritize the patients' needs above all else. Additionally, it enables improved care organization based on client type, institutional mission, and available resources. In order to conduct effective triage, the Orientation and Reception Nurse (ORN) should employ a reliable, valid, and reproducible tool that considers the specificities of both pediatric and adult patients in prehospital medicine, as well as the national healthcare. The aim is to assess the validity and reliability of existing triage systems in emergency care, with the ultimate objective of developing an emergency triage system tailored to Burkina. To achieve this, our focus will be on identifying reliable and valid triage systems, which will serve as a basis for proposing or adapting a suitable triage system for Burkina Faso.
Despite government and industry efforts, internet connectivity in South African public schools located in rural and township areas remains unsatisfactory. What seems to be lacking is a 'virtuous' model for an efficient and practical solution that takes into account all important aspects relating to public schools and maps them into an appropriate network infrastructure. This paper presents a model, re-conceptualising a previous model for school and community connectivity called 'Broadband Island', developed and tested within the ICT4D long-term project known as Siyakhula Living Lab. In this model, schools belonging to an educational Circuit are connected via an overlay built on top of the network provided by a local Wireless Internet Service Provider (WISP). In this model, schools benefit from having access to the Internet efficiently (via statistical multiplexing of the Internet link), through interacting with each other at high speed without crossing the public Internet, as well as sharing common educational resources hosted by the WISP. The WISP, in turn, will benefit from having a solid and well-sized customer in the form of the provincial Department of Education. The community will also benefit because the revenue generated by the local WISP will remain in the community, helping further local economic development. Finally, the reliance on local operators, as opposed to a single, nationwide implementer, mitigates the risk of a single point of failure.
With the current era of technology, protecting data and infrastructure has become more of a concern as sensitive information is being stored on Digital platforms. The various new technologies being developed make it harder to secure electronic data as malicious actors keep utilizing the latest tips and tools to perform attacks. These latest technologies also display gaps within digital forensics as there are not a lot of tools that can assist in the investigation of cyber incidents and properly preserve digital evidence after an incident has been detected. For example, as more individuals and organizations migrate their data and infrastructure to cloud platforms, new skills and forensic tools are required to extract evidence from the cloud. This study presents various electronic security measures and case studies of security breaches where the use of Digital Forensics assisted with the investigation and the subsequent results assisted the organization affected or educated other institutions to be aware of techniques used by malicious actors. Keeping abreast with the latest tools and techniques will ensure that effective security measures are implemented to prevent security breaches. The continuous adaptation in technologies will assist in ensuring that investigators are able to perform forensically sound.
Wireless mesh networks (WMNs) are considered one of the most promising approaches to power networks using non-physical connection media that require high bandwidth and coverage. Because of its qualities in terms of bandwidth and coverage, they face quality of service problems such as throughput due in some cases to poor channel allocation. The channel allocation issue in a WMN is similar to a graph edge coloring problem which is an NP-complete problem. In order to solve this problem, various approaches have been proposed, some based on graph theory, some on conflict graph theory, and others using message exchange for synchronization on communication channel between nodes. In this paper, we present K-MEDAL, an approach to channel allocation in WMNs based on K-medoids algorithm. For our simulation, we used a testbed composed of 33 static nodes randomly arranged over an area of 1000Km2 in the NS-2 simulator. The K-medoids algorithm allowed us to build small network clusters to reduce the complexity of the channel allocation problem. Compared to other solutions found in the literature, the K-MEDAL approach shows out a 2 to 3 times increase in both the throughput distributed over the active links of a cluster and the aggregate throughput per cluster.
Atmospheric free-space optical (FSO) transmission is one of the various types of wireless communication that are being developed today. This is an important alternative to consider for next-generation broadband to support high bandwidth. In this study we demonstrate the feasibility of using an FSO system in a 5G architecture operating in Côte d’Ivoire weather conditions, mainly in the following Korhogo, Man, Bouaké, Bondoukou, Man and Abidjan. Such an architecture requires high quality interconnection between the different parts of the network. To conduct this study, we collected meteorological data from January 2018 to December 2022 for each of these towns on the ’weather history’ site. To characterize our propagation channel, we used the Gamma-gamma model. Attenuation levels caused by meteorological factors such as rain, fog, humidity and temperature were evaluated. Rain proved to be the most attenuating factor for FSO signals in Côte d’Ivoire, with attenuation levels of up to 5 dB/Km. The performance of these systems was analyzed in terms of signal-to-noise ratio and bit error rate. The results show that the rainy season is the least favorable time to deploy an FSO link, and that Man is the least favorable environment for such deployment. The Korhogo environment and the dry season are the most favorable for deploying an FSO connection. Similarly, for setting up an FSO connection, it is preferable to favor connection distances of less than 4.5 km.
The digital divide remains a major concern for rural areas in developing countries in general and for sub-Saharan Africa in particular. It is particularly characterized by the unavailability of energy resources on which the functioning and operation of network connectivity infrastructure depends. The constraints of availability of electrical energy, coupled with the difficulty of access, make it difficult to implement relevant and sustainable digital solutions in these hard-to-reach areas. We propose in this article, a model for solar photovoltaic power generation that allows for autonomous and continuous operation of a wireless access point (WAP) in areas where access to electrical power is difficult. Our approach first consists of designing the system and mathematically modeling the photovoltaic solar panel and the BUCK series chopper, as well as the P&O type MPPT control for the solar panel' maximum power point tracking. Finally, we simulate the operation of the system for appreciating its behavior in a possible real situation. For this final point, before the simulation process, we realized the system's global diagram consisted of the solar generator, adaptation stage (provided with the MPPT control of type P&O) and battery park. This simulation allowed us to study the behavior of the system in normal conditions but also in particular conditions reflecting the environment in which it will be deployed. The results of the simulation are satisfactory and allowed us to validate the model we proposed. We have successfully designed a solar photovoltaic power supply for the WiABox 2507 that takes into account all its energy constraints.
In this paper we propose a data mining technique for the discovery of intrusions in big data. To achieve our objective, we first reviewed the different data mining works and tools to our knowledge for the extraction of data from big data. Secondly, we chose a honeypot (honeyD) from a set (of honeypots) based on well-defined criteria. Thirdly, we combined this honeypot (honeyD) with different classification algorithms (decision trees and clustering such as k-means, DBSCAN to identify possible intrusions into the databases) in a functional architecture in which, we have presented and explained the role of each of its components. The implementation of our proposal shows that the combination of the honeypot with these different clustering algorithms gives convincing results which make it possible to detect possible intrusions in the data big databases.
The construction of ontologies is a classification process in which concepts in a the domain and the relationships between these concepts need to be identified. The classification of concepts to construct an ontology is a difficult problem. The holistic objective is to propose a system that is capable of automatically classifying concepts of a company to construct ontologies. To achieve this, researchers proposed some solutions among which Norms2Onto, Text2onto and APOET (Automatic Product Ontology Extraction from Textual) but majority of them are either domain specific or construct ontologies with generic data which makes the resulting ontology not precise. This paper proposes a system named CAOGen (Company Automatic ontology Generator) that applies CRISPDM (Cross Industry Standard Process for Data Mining) methodology to construct ontologies by using data mining techniques to automatically classify concepts of a specific company to produce its ontology while using wordnet and wikipeadia to augment them. The validation of this work is done through the construction of an ontology of catalogue of service for an Enterprise in Cameroon named yowyob (yowyob.com). After evaluation of the ontology, the system had an accuracy of 0.994, a precision of 0.992, a recall of 1.0 and a F1 score of 0.996 for semantic search and an accuracy of 0.888, a precision of 0.990, a recall of 0.849 and a F1 score of 0.914 for recommendation.
The analysis, concept, and implementation of a computer program capable of detecting ripe mangoes are at the core of this study. Traditional methods are often faced with calibration errors. Recently, deep learning has shown promising performance in visually guided agricultural applications. Faced with these constraints, it is necessary to establish an automatic system for robust and efficient detection of mangoes in orchards. In this study, a fast implementation system of a mango detector, distinguishing between ripe and unripe mangoes based on deep learning using the YOLOv5 algorithm, was developed. From a simple photo, the algorithm detects and counts the number of mangoes on a tree. This artificial intelligence system (deep neural network) was trained on a dataset of over 500 annotated mango images. Experimental results show that the algorithm achieves 98
PLAVIDA, PLatform for Audio and VIdeo Data Annotation is a platform designed to facilitate audio and video data annotation. To perform sound classification tasks with Machine Learning algorithms, we need annotated data on these sounds. It is on the basis of this annotated data that these algorithms will learn to make classifications. However, the community lacks labelled audio data on African languages. PLAVIDA will allow researchers the opportunity to create a multimedia labelled databases which can be used as input in Artificial Intelligence models. This could boost research around audio classification in several African languages. We have used python and Android IONIC/Angular technology to develop this tool. The innovation in PLAVIDA, is the possibility given to illiterate people to be able to interact with, when we want to labelle sound or video in African local languages. The tool can be then used both by literate and illiterate people. The type of labelling we are faced on concern the emotional perception people can have when listening or watching a media. It incorporates an annotation logic based primarily on the maximum rate of the same emotional perception over all. In the case where there is no majority vote, the user profile criterion is used. The data annotated using this application can be exported in XML, CSV or JSON format. These types of format are the data formats used to create Artificial Intelligence models.
Name Entity Recognition (NER) is an important core component of Natural Language Processing (NLP) systems for identifying entities like person names, locations, and organizations. Many NER models have been proposed in the literature those whose architecture are based on deep neural networks are the most efficient. Burkina Faso, a West African country, is a French-speaking country with its culture and its specificities in the use of the French language. Our work consists in building a user-friendly NER model that reliably identifies 8 entity types from Burkina Faso media information where the French language is the most spoken. To achieve this goal, we firstly build a dataset that has been labeled along these entity types. Then, in order to choose the best architecture, we assessed 6 multilanguage NER models namely Spacy (with its three pre-trained models), Flair, Stanza, and Camembert NER. This paper presents the performance evaluation of existing French NER models when applying to media news data of Burkina Faso. They have been assessed along 3 common entity types Person (PER), LOCation (LOC), and ORGanization (ORG). Results show that Stanza and Flair outperform all models under study with a percentage greater than 70
Cotton is the most important agricultural product in Burkina Faso, and it is farmed by 25
Design of energy resources, transmission, distribution, and consumption in network architecture is becoming a challenging energy optimization issue. The demand for power analysis becomes a key pillar in sustainable renewable energy and adaptation to climate change. State-of-the-art technologies can play a vital role in realizing the new architectural design for smart grids and cities. The 4th generation mobile network is considered a critical technology and enabler. The 3GPP standard body is set to a target of 35
Diabetes is considered the most deadly and chronic disease that causes an increase in glucose. Polygenic disease is one in which the exocrine gland does not produce the hypoglycemic agent and according to the International Federation of Polygenic Diseases 382 million people live with polygenic disease in the world. By 2035, this number will double to 592 million. Diabetes mellitus or simply the disease can be a disease due to increased blood glucose levels. Many difficulties can arise if diabetes is not treated and not identified by the doctor. Thus, artificial intelligence (AI), which has become the new term we hear every day in recent years, generally defines the ability of a machine to act on its own and which is not explicitly programmed to reproduce actions or functions that are generally those of human beings. Today, we find it in our computing machines, social networks, transportation and in the medical sector etc. Therefore, machine learning is one of the disciplines of artificial intelligence that seeks to find a way to create computer programs that automatically improve with experience. In this work, we will focus on the use of machine learning algorithms for the prediction of diabetes, which is a dysfunction of the blood sugar regulation system, in order to reduce the risks of complications of this chronic disease on the health of the patient. To achieve this goal, we used machine learning algorithms such as Random Forest RF, Logistic Regression RL, K-nearest neighbors KNN and Neural Networks ANN and the data were extracted from Kaggle which is a web platform owned by Google that operates as a community for data scientists and developers. The performance of the classifiers was compared based on the accuracy rate.
Cybersecurity attacks are classified in the top 10 global risks by the World Economic Forum in 2023. The common cyber-incidents that affect businesses, governments, and individuals across the globe are data breaches. Recent reports indicate that these incidents are on the rise, with an estimation of over 12 billion personal records breached, impacting on individual's privacy and organization's reputation. Comprehensive insight on data breaches in Africa is not readily available as reporting of these incidents by victims is not mandatory in many African countries, and available trends tend to be limited and scattered. This paper aims to provide a contextual understanding of data breach trends in Africa using social media data (current) and research literature (past). The data from social media, including news websites, were collected over a 3-month period tracking data breaches in the African content, whilst the research literature focused on prominent data breaches in African countries between 2020 and 2023. The research results indicate the data breaches trend that is on the rise in Africa with Nigeria showing higher engagements on social media, whilst South Africa being the data breach haven with a large exposure of personal data online. The impact of data breaches on customers and businesses in Africa is determined as being negative and unabated. This paper recommends practical and evidence-based security controls to minimize data breaches.
Rapid developments in cellular communications are accompanied by rising privacy and security worries. Many people refrain from participating in activities like social networking, shopping, transactions, and conducting a lot of business because network security and user privacy are major concerns in our daily lives. However, there is now a greater need for a private, highly secure business. This was accompanied by an increase in the requirements for it due to the growing hazards and programmers in our daily activities. The Fifth Generations (5G) groups are rapidly developing, as evidenced by the fact that the number of supporters is increasing by several times per second around the world in light of the ongoing revelations. According to statistics, 80
In healthcare facilities, data is collected and stored for traceability and subsequent use. The objective of this work is to develop an application for data collection and analysis in maxillofacial surgery. After a preliminary study on the needs. We used the UML language (Unified Modeling Language) to model the system. The 2TUP (Two Tracks Unified Process) process was used for analysis and design. The database created was implemented in Microsoft Access; Power BI was used for statistical analysis and data visualization. We have developed an Access data collection application and a Power BI application allowing easy and reliable visualization of the activity balance. Five hundred and fifty-eight (558) scanned patient records. Average age 30.44. Sex ratio 3.20 in favor of men. 419 patients consulted in emergency. Traumatic pathologies were more frequent 69.89
In Burkina Faso, most existing hospital information systems are limited to managing administrative and financial aspects. Computerized tools for managing medical data (clinical, paraclinical, therapeutic, etc.) are almost non-existent. Our goal was to setup a basic HIS in which will be integrated as first module, the very one that is lacking in Burkina Faso's hospitals, a computerized patient records module. The design of this module as well as the hospital information system within which it will be deployed was based on a study of the businesses identified in Ouahigouya regional university hospital center and the interactions of patients with this hospital center. The pediatrics department has been taken as a model for the design of the module. This basic hospital information system with its embedded computerized patient records module has been deployed into the pediatrics department. The designed module allows almost complete digitization of commonly used paper supports (consultation record, medical record, treatment and monitoring sheet) with the resulting advantages (time saving, ease of access to data and statistical reports draw up). This was a very new experience in a reference healthcare center, which can respond to the identified problem.
Technological advancements have created new issues in IT security. Cyber-physical infrastructures, which mix physical elements with interconnected IT systems, have emerged as a major trend in industries such as transportation, energy, health, and public safety. However, the integration of the physical and digital worlds has generated new security threats, including social engineering attacks. Cybercriminals employ social engineering to trick users and obtain personal information or access privileges to computer systems. Social engineering attacks are frequently carried out using communication channels such as social networks, emails, and phone conversations. This study intends to investigate how social engineering attacks can be carried out in a cyber-physical-human environment. We will investigate the impact of cyber-physical-human infrastructures, cybercriminals' attack strategies, the effects of these attacks, and measures of prevention. The significance of this research stems from the fact that cyber-physical infrastructures are increasingly being employed in crucial scenarios where a breach in security could have fatal implications. It is therefore critical to understand the dangers associated with these infrastructures and to put adequate safeguards in place to protect them against social engineering attacks.
This paper is a theoretical review following a systematic literature review aimed at providing insightful information and advisory regarding potential cyber threats to national elections in Africa in the digital age. It therefore, focuses on potential cyber threats to the general elections process in Africa. It highlights the importance of cybersecurity in relation to the digital and traditional electoral process. The paper delves into different types of cyber ills, citing examples from past instances worldwide including Africa, emphasizing the need for robust cybersecurity measures. It also discusses the possible impacts of cyber threats on the electoral process and its stakeholders. This paper offers mitigation techniques to ensure a safe and secure national general election, particularly in the cyberspace.
