Latin American Journal of Computing
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African National Artificial Intelligence Strategies: A review, analysis and research agenda
Get PDFSome countries have developed their national artificial intelligence strategies (NAISs) while others have formed task forces to develop them. This study reviewed elements and concepts required to develop NAIS, related science, technology and innovation (STI) strategies, policies and manifestos. Some of these elements and concepts apply to both developing and developed countries while some others are specific to one of them. STI elements and concepts apply to artificial intelligence strategies since AI technology is a specialization of STI technologies. The concepts and elements identified by this study can aid strategy creators by providing important insights for creating NAISs. For instance, catch-up strategies based on learning from a country with similar past technology, catch-up successes, and others who have created NAISs are a low-cost way for developing and implementing NAISs.Some countries have developed their national artificial intelligence strategies (NAISs) while others have formed task forces to develop them. This study reviewed elements and concepts required to develop NAIS, related science, technology and innovation (STI) strategies, policies and manifestos. Some of these elements and concepts apply to both developing and developed countries while some others are specific to one of them. STI elements and concepts apply to artificial intelligence strategies since AI technology is a specialization of STI technologies. The concepts and elements identified by this study can aid strategy creators by providing important insights for creating NAISs. For instance, catch-up strategies based on learning from a country with similar past technology, catch-up successes, and others who have created NAISs are a low-cost way for developing and implementing NAISs
Evaluación y mitigación de inyecciones SQL en aplicaciones web: desarrollo de un prototipo
Get PDFIn the current context of increasing digital vulnerability, SQL injections continue to pose a critical threat to web application security. To address this issue, SecureSQLTester was developed—a prototype aimed at detecting and mitigating SQL injection attacks, designed to be accessible to developers and small businesses. The proposal was based on a systematic review of existing techniques, integrating both classical and advanced protection approaches. The prototype was developed using the agile Scrum methodology, which enabled progressive improvements through iterative work cycles. Usability tests were conducted with software engineering students, who evaluated the tool in simulated scenarios. The results show that SecureSQLTester accurately identifies SQL vulnerabilities in the evaluated applications. However, opportunities for improvement were identified in the user interface, as well as the need to enhance parameter customization according to the usage context. Overall, the findings support the potential of the prototype as an effective and low-cost tool to strengthen cybersecurity in small- and medium-scale development environments and to promote the adoption of best practices throughout the software lifecycle.En el contexto actual de creciente vulnerabilidad digital, las inyecciones SQL continúan representando una amenaza crítica para la seguridad de aplicaciones web. Ante esta problemática, se desarrolló SecureSQLTester, un prototipo orientado a la detección y mitigación de ataques de inyección SQL, diseñado para ser accesible a desarrolladores y pequeñas empresas. La propuesta se fundamentó en una revisión sistemática de técnicas existentes, integrando enfoques clásicos y avanzados de protección. El desarrollo del prototipo se realizó aplicando la metodología ágil Scrum, lo que permitió realizar mejoras progresivas a través de ciclos de trabajo iterativos. Se realizaron pruebas de usabilidad con estudiantes de ingeniería de software, quienes evaluaron la herramienta en escenarios simulados. Los resultados muestran que SecureSQLTester identifica con precisión vulnerabilidades SQL en aplicaciones evaluadas. No obstante, se identificaron oportunidades de mejora en la interfaz de usuario, así como la necesidad de ampliar la personalización de parámetros según el contexto de uso. En conjunto, los hallazgos respaldan el potencial del prototipo como herramienta efectiva y de bajo costo para fortalecer la ciberseguridad en entornos de desarrollo de pequeña y mediana escala, y promover la adopción de buenas prácticas en el ciclo de vida del software
Data Visualization Model for Multi-party Analysis and Strategic Decision-Making in International Trade
Get PDFThis paper presents a detailed analysis of Ecuador’s non-oil exports over ten years. The study was performed using the SPEM methodology and data-cleaning processes. The results highlight a notable coherence in analyzing the most relevant export items and the main trading partners, providing essential information for strategic decision-making. Furthermore, recommendations related to the technical conditions necessary to achieve precise and accurate communication through data visualization were considered, and adequate answers to the questions generated in the business knowledge stage contributed to the users’ knowledge.Furthermore, the study suggests incorporating import data to enhance the analysis and provide a foundation for future research in this area.This paper presents a detailed analysis of Ecuador’s non-oil exports over ten years. The study was performed using the SPEM methodology and data-cleaning processes. The results highlight a notable coherence in analyzing the most relevant export items and the main trading partners, providing essential information for strategic decision-making. Furthermore, recommendations related to the technical conditions necessary to achieve precise and accurate communication through data visualization were considered, and adequate answers to the questions generated in the business knowledge stage contributed to the users’ knowledge.Furthermore, the study suggests incorporating import data to enhance the analysis and provide a foundation for future research in this area
A Blockchain-based Identity Management Solution for Secure Personal Data Sharing in Africa: A Systematic Literature Review
Get PDFAfrica’s digital transformation has amplified systemic vulnerabilities in personal data governance, particularly due to reliance on centralized identity systems ill-equipped to evolve cyber threats. For instance, the 2016 Cambridge Analytica scandal exposed not only global data misuse but also catalyzed African nations like Nigeria and Kenya to audit their electoral data practices, revealing similar risks. Centralized databases are frequently the backbone of conventional identity management systems, which unfortunately leaves them vulnerable to security violations and unwanted entry resulting in attackers taking advantage of these vulnerabilities and causing security incidents like identity theft or the exposure of confidential information. Self-Sovereign Identity (SSI) empowers individuals to take control of their personal identity and understand how their data is utilized. In this context, blockchain technology plays a pivotal role by supporting decentralized systems for identity management and access control. This literature review explores five key dimensions of blockchain-based identity and access control management, including security / privacy, scalability, interoperability, regulatory compliance, and user control through a systematic analysis of 62 African case studies and a framework synthesized from that review. The study identifies critical gaps in scalability (40% of studies) and regulatory alignment (50%), offering actionable insights for decentralized identity frameworks in emerging economies. Prior reviews lack Africa-specific insights; this SLR addresses this gap by synthesizing 62 African case studies, offering the first comprehensive analysis of blockchain-based IDMS implementations in the region.Africa’s digital transformation has amplified systemic vulnerabilities in personal data governance, particularly due to reliance on centralized identity systems ill-equipped to evolve cyber threats. For instance, the 2016 Cambridge Analytica scandal exposed not only global data misuse but also catalyzed African nations like Nigeria and Kenya to audit their electoral data practices, revealing similar risks. Centralized databases are frequently the backbone of conventional identity management systems, which unfortunately leaves them vulnerable to security violations and unwanted entry resulting in attackers taking advantage of these vulnerabilities and causing security incidents like identity theft or the exposure of confidential information. Self-Sovereign Identity (SSI) empowers individuals to take control of their personal identity and understand how their data is utilized. In this context, blockchain technology plays a pivotal role by supporting decentralized systems for identity management and access control. This literature review explores five key dimensions of blockchain-based identity and access control management, including security / privacy, scalability, interoperability, regulatory compliance, and user control through a systematic analysis of 62 African case studies and a framework synthesized from that review. The study identifies critical gaps in scalability (40% of studies) and regulatory alignment (50%), offering actionable insights for decentralized identity frameworks in emerging economies. Prior reviews lack Africa-specific insights; this SLR addresses this gap by synthesizing 62 African case studies, offering the first comprehensive analysis of blockchain-based IDMS implementations in the region
A web-based tool for the sizing of grid-connected photovoltaic (PV) systems in Ecuador
Get PDFThe transition to cleaner and more sustainable energy sources involves the use of solar photovoltaic energy. This energy source has the potential to re-duce greenhouse gas emissions and dependence on fossil fuels. The research project focused on the development of a web-based tool for sizing photovoltaic systems in Ecuador. This tool considers several factors, including technical, theoretical, economic and environmental aspects. The tool allows sizing based on electricity consumption and power requirements. Further-more, the tool provides technical information, CO2 reduction data and eco-nomic perspectives based on the operation of the electricity system in Ecuador. The comparative validation with installed systems and similar web tools demonstrated the reliability and robustness of the developed tool.The transition to cleaner and more sustainable energy sources involves the use of solar photovoltaic energy. This energy source has the potential to re-duce greenhouse gas emissions and dependence on fossil fuels. The research project focused on the development of a web-based tool for sizing photovoltaic systems in Ecuador. This tool considers several factors, including technical, theoretical, economic and environmental aspects. The tool allows sizing based on electricity consumption and power requirements. Further-more, the tool provides technical information, CO2 reduction data and eco-nomic perspectives based on the operation of the electricity system in Ecuador. The comparative validation with installed systems and similar web tools demonstrated the reliability and robustness of the developed tool
Editorial
Get PDFWelcome to Volume 12, Issue 2 of the Latin-American Journal of Computing (LAJC)
It is an honor to present this new issue, which brings a collection of eight research articles that tackle today’s pressing challenges in computing with insight, innovation, and care. This issue reflects a shared commitment to advancing technical and scientific knowledge for the benefit of the Latin American region.
The contributions featured in this edition spans educational tools, sustainability efforts, and institutional change. Among them are studies that analyze the adoption of digital technologies in public universities, and the design of digital tools for estimating household greenhouse gas emissions. This issue also includes applied proposals such as educational chatbots for secondary schools, intelligent systems for analyzing black holes through digital signal processing techniques, and desktop applications to support the efficient management of blood banks. From an institutional perspective, one article explores digital transformation in universities as a mechanism to improve academic administration, while another analyzes how cloud computing is transforming higher education. Lastly, a systematic review addresses the use of blockchain technology for digital identity management in Africa, highlighting its potential in low-infrastructure contexts.
These studies demonstrate technical advancement across diverse areas in computing science and emphasize the importance of collaborative and contextualized research.
We extend our gratitude to the authors for sharing their research, to the reviewers for their constructive comments, and to the editorial team for their continuous commitment to quality and scientific dissemination.
We hope this issue inspires new ideas, collaborations, and new directions in computing science research.
Gabriela SuntaxiEditor-in-ChiefLatin-American Journal of Computing – LAJCEscuela Politécnica Nacional, Ecuador
Bienvenidos al Volumen 12, Número 2 de la Revista Latinoamericana de Computación (LAJC).
Es un honor presentar este nuevo número, que incluye una colección de ocho artículos de investigación que abordan los desafíos actuales de la informática con conocimiento, innovación y cuidado. Este número refleja un compromiso compartido con el avance del conocimiento técnico y científico en beneficio de América Latina.
Las contribuciones presentadas en esta edición abarcan herramientas educativas, iniciativas de sostenibilidad y cambio institucional. Entre ellas, se encuentran estudios que analizan la adopción de tecnologías digitales en universidades públicas y el diseño de herramientas digitales para estimar las emisiones de gases de efecto invernadero en los hogares. Este número también incluye propuestas aplicadas como chatbots educativos para escuelas secundarias, sistemas inteligentes para analizar agujeros negros mediante técnicas de procesamiento digital de señales y aplicaciones de escritorio para apoyar la gestión eficiente de bancos de sangre. Desde una perspectiva institucional, un artículo explora la transformación digital en las universidades como mecanismo para mejorar la administración académica, mientras que otro analiza cómo la computación en la nube está transformando la educación superior. Por último, una revisión sistemática aborda el uso de la tecnología blockchain para la gestión de la identidad digital en África, destacando su potencial en contextos de baja infraestructura. Estos estudios demuestran avances técnicos en diversas áreas de la informática y enfatizan la importancia de la investigación colaborativa y contextualizada.
Agradecemos a los autores por compartir su investigación, a los revisores por sus comentarios constructivos y al equipo editorial por su continuo compromiso con la calidad y la difusión científica.
Esperamos que este número inspire nuevas ideas, colaboraciones y nuevas direcciones en la investigación en ciencias de la computación.
Gabriela SuntaxiEditor-in-ChiefLatin-American Journal of Computing – LAJCEscuela Politécnica Nacional, Ecuador
 
Safeguarding Mobile Users from Violation by Third-party Apps
Get PDFInsecure third-party mobile applications (apps) can have a detrimental impact on mobile users in terms of information security and data privacy. Insufficient protection for third-party mobile apps platforms may result in harmful installations. The purpose of this paper was to make recommendation on guidelines for safeguarding mobile users from violations by third-party apps. In this regard, empirical data was collected through questionnaires developed to determine the necessary themes that led to the development of the recommendations. The findings showed that a large percentage of participants are not aware of basic security methods to safeguard themselves. Secondly, there is a need for increased confidence in data integrity protocols, and the necessity to ability for emphasizing strong availability controls and backup strategies for mobile users’ continuous access to services. Since the findings align to the Confidentiality, Integrity, and Availability (CIA) triad framework, then the recommendations were made as an implementation strategy of the CIA triad for safeguarding mobile users against violation by the third-party apps. Mobile users will benefit immensely on the recommendations as empower them as the first line of defense against cybercrimes.Insecure third-party mobile applications (apps) can have a detrimental impact on mobile users in terms of information security and data privacy. Insufficient protection for third-party mobile apps platforms may result in harmful installations. The purpose of this paper was to make recommendation on guidelines for safeguarding mobile users from violations by third-party apps. In this regard, empirical data was collected through questionnaires developed to determine the necessary themes that led to the development of the recommendations. The findings showed that a large percentage of participants are not aware of basic security methods to safeguard themselves. Secondly, there is a need for increased confidence in data integrity protocols, and the necessity to ability for emphasizing strong availability controls and backup strategies for mobile users’ continuous access to services. Since the findings align to the Confidentiality, Integrity, and Availability (CIA) triad framework, then the recommendations were made as an implementation strategy of the CIA triad for safeguarding mobile users against violation by the third-party apps. Mobile users will benefit immensely on the recommendations as empower them as the first line of defense against cybercrimes
Numerical Modeling For Fracture Mechanics Problems Using The Open-source Fenics Platform
Get PDFFracture mechanics is the mechanical approach to fracture processes, which emerged due to limitations in the application of traditional concepts of Mechanics of Materials to predict the behavior of cracked materials. Analytical problem solutions with this approach may be unattainable, which allows the use of numerical modeling, such as the finite element method. However, the use of more advanced software that solves engineering problems numerically is limited by its high cost. FEniCS is an open source computational platform that solves partial differential equations by the finite element method. Thus, from a tutorial for this computational platform, this work proposes to reproduce a classic problem of linear elastic fracture mechanics, based on the validation of a comparison of a linear elastic problem with the commercial software ANSYS ®. With the help of the provided tutorial, an code was built to model a three-point bending test. Implemented with the aid of Gmsh and Paraview, it was possible to obtain satisfactory results, and to show that FeniCS is a powerful and accessible tool for solving fracture mechanics problems. La mecánica de la fractura es el enfoque mecánico de los procesos de fractura, que surgió debido a las limitaciones en la aplicación de los conceptos tradicionales de la Mecánica de Materiales para predecir el comportamiento de los materiales fisurados. Las soluciones analíticas de los problemas con este enfoque pueden ser inalcanzables, lo que permite el uso de la modelización numérica, como el método de los elementos finitos. Sin embargo, el uso de software más avanzado que resuelve numéricamente problemas de ingeniería está limitado por su elevado coste. FEniCS es una plataforma computacional de código abierto que resuelve ecuaciones diferenciales parciales por el método de los elementos finitos. Así, a partir de un tutorial para esta plataforma computacional, este trabajo propone reproducir un problema clásico de mecánica de fractura elástica lineal, basado en la validación de una comparación de un problema elástico lineal con el software comercial ANSYS ®. Con la ayuda del tutorial proporcionado, se construyó un código para modelar un ensayo de flexión en tres puntos. Implementado con la ayuda de Gmsh y Paraview, fue posible obtener resultados satisfactorios, y demostrar que FeniCS es una herramienta potente y accesible para resolver problemas de mecánica de fractura
Sistema de Diagnostico del Alzheimer basado en imágenes de resonancia magnética mediante el algoritmo VGG16
Get PDFEarly diagnosis of Alzheimer\u27s disease is essential to provide timely treatment to patients. In this regard, a system for diagnosing Alzheimer\u27s disease based on magnetic resonance imaging and utilizing a convolutional neural network algorithm called VGG16, has been developed. Magnetic resonance images of patients with and without Alzheimer\u27s disease were collected and processed. These images were used to train the algorithm, which learned to identify and associate patterns with the disease. Subsequently, tests were performed with a set of unseen images to evaluate the diagnostic ability of the system. Through the analysis of magnetic resonance images, the VGG16 algorithm has shown a capacity of over 82% to correctly recognize these signs. These results validate the effectiveness of the artificial intelligence-based approach for diagnosing Alzheimer\u27s disease.El diagnóstico temprano del Alzheimer es fundamental para brindar un tratamiento oportuno a los pacientes. En este sentido se ha desarrollado un sistema de diagnóstico del Alzheimer basado en imágenes de resonancia magnética que utiliza un algoritmo de redes neuronales convolucionales denominado VGG16. Se recopilaron y procesaron imágenes de resonancia magnética de pacientes con y sin Alzheimer. Estas imágenes se utilizaron para entrenar al algoritmo, el cual aprendió a identificar y asociar patrones con la enfermedad. Posteriormente, se realizaron pruebas con un conjunto de imágenes no vistas para evaluar la capacidad de diagnóstico del sistema. Mediante el análisis de las imágenes de resonancia magnética, el algoritmo VGG16 ha demostrado una capacidad superior al 82% para reconocer correctamente dichos signos. Estos resultados validan la efectividad del enfoque basado en inteligencia artificial para el diagnóstico del Alzheimer
Electricity Energy Demand Prediction Using Computational Intelligence Techniques
Get PDFEnergy is an important pillar for the economic development of a country. The demand for electricity is something that continues to grow, one of the contributing factors is the emergence of various technological equipment and the consequent use by the population. There are several resources that can be exploited to generate electricity, with hydroelectric power stations being one of the most used resources. As electrical energy cannot be stored, there is a need to estimate its consumption, looking for a way to meet this energy demand. In this context, this study seeks to apply machine learning techniques, using the Grey Wolf Optimization (GWO) meta-heuristic to optimize regression models, to predict the demand for electricity in Brazil, and it aims to estimate how much energy should be produced. For the predictions, the period between the years 2017 to 2022 was used, totaling around 2,190 samples. The methodology involves pre-processing, crossvalidation, parameters optimization and regression. The results show that Random Forest performed well in the experiments carried out, presenting a coefficient of determination (R2) of 0.8751, Root Mean Squared Error (RMSE) of 0.0554 and Mean Absolute Error (MAE) of 0.0348 in the best model.Energy is an important pillar for the economic development of a country. The demand for electricity is something that continues to grow, one of the contributing factors is the emergence of various technological equipment and the consequent use by the population. There are several resources that can be exploited to generate electricity, with hydroelectric power stations being one of the most used resources. As electrical energy cannot be stored, there is a need to estimate its consumption, looking for a way to meet this energy demand. In this context, this study seeks to apply machine learning techniques, using the Grey Wolf Optimization (GWO) meta-heuristic to optimize regression models, to predict the demand for electricity in Brazil, and it aims to estimate how much energy should be produced. For the predictions, the period between the years 2017 to 2022 was used, totaling around 2,190 samples. The methodology involves pre-processing, crossvalidation, parameters optimization and regression. The results show that Random Forest performed well in the experiments carried out, presenting a coefficient of determination (R2) of 0.8751, Root Mean Squared Error (RMSE) of 0.0554 and Mean Absolute Error (MAE) of 0.0348 in the best model