Conference Agenda

This systematic literature review (SLR) evaluates the impact of functional programming (FP) on the detection of mutable states, focusing on the integration with imperative systems and APIs. The PICO methodology and PRISMA protocols were used to elaborate this SLR. In this sense, four questions were posed to guide the search and, through the inclusion and exclusion criteria, this work focused on 49 articles directly related to the topic. The findings show that functional languages such as Haskell and SML offer an open window to memory management, concurrency optimization, and improved resilience in distributed systems. Likewise, tools such as monads and lazy evaluation have a positive impact on strengthening the reliability of critical systems in the face of technical challenges. It is concluded that functional programming and mutable state management are effective, but there is a deficiency in the interaction between imperative and functional systems, especially in large-scale industrial applications. It is suggested that the integration of imperative and functional systems be investigated to improve scalability, modularization and reliability in industrial environments

In this new technological era, there are many threats through a simple internet search, and we are daily exposed to them. A lot of people don’t know the risks and leads them to be a potential victim causing them serious consequences. The phishing is one of the most common threats that scams people all over the world. Due to that, this investigation wants to examinate the literatures existing about the based machine learning solutions for the phishing attacks. After the recompilation, that ended on 464 articles original from Scopus. But now the investigation has 30 open access articles that were carefully selected with PRISMA methodology using the inclusion and exclusion rules to guarantee that these ones are closely related to the investigated subject. The results showed that the solutions have 90% or superior precision in most of the cases. With this information, it concluded that the machine learning techniques are very effective and a good choice to affront the problem. However, there are still some aspects that has to be considered before putting it on practice.

The increasing reliance on web applications in organizations requires effective protection of sensitive data to maintain user trust. However, the diversity of methodologies to evaluate the security of these applications makes it difficult to select the most effective ones, exposing them to vulnerabilities such as SQL injection and Cross-Site Scripting attacks. This study aimed to analyze how static and dynamic analysis methodologies, together with automated and manual tools, contribute to identifying and mitigating these vulnerabilities. Through a systematic review of the literature, structured under the PICO technique, searches were carried out in databases such as Scopus, obtaining 1,279 initial documents. Through a PRISMA flowchart and considering the inclusion and exclusion criteria, 53 final studies were selected for analysis. The results highlight the need to develop standardized criteria that facilitate the choice of more effective methodologies to guarantee the security of web applications. However, a lack of consensus on optimal approaches was identified, representing a significant challenge for security professionals. In conclusion, although there are promising tools and methods, the diversity and absence of standardization limit their practical implementation, evidencing the importance of new research to close these gaps and move towards safer web environments.

In recent years, artificial intelligence has revolutionized various fields of medicine, highlighting its impact on the early detection of neurodegenerative diseases (ND). This study analyzes Machine Learning (ML) and Deep Learning (DL) models applied in the detection of neurodegenerative diseases. For this purpose, a systematic literature review (SLR) was performed following the PICO method in the search of information from SCOPUS, Web of Science and the PRISMA statement for the final screening, evaluating metrics such as accuracy, sensitivity and area under the curve (AUC). Likewise, the Bibliometrix of R study was used to evaluate in depth the studies collected from the 2 databases analyzed. Among the most prominent studies, the most frequent approaches include support vector machines (SVM) in ML, with 6 main investigations, and convolutional neural networks (CNN) in DL, with 11 outstanding studies. In addition, one SVM model achieved 100% accuracy, while CNN-InceptionV4 stands out in DL with 99% accuracy. DL models, such as GCN and advanced combinations such as CNN-GCN, have proven to be more robust in handling complex data, while ML approaches present advantages in terms of lower computational demand. In conclusion, DL- and ML-based models represent a promising tool for early detection of ND. However, their adoption in clinical practice requires further optimizations to overcome technical barriers and ensure their applicability in real-world scenarios.

The integration of artificial intelligence (AI) in disease diagnosis is transforming the field of medicine, offering precise and efficient tools to detect critical health conditions. However, the diversity of algorithms and their applications raises questions about which of these are the most effective. Therefore, this systematic review aims to identify the most accurate AI models for detecting various diseases. A systematic review was conducted considering Scopus and Web of Science databases as main information sources, analyzing 416 studies that used AI algorithms within the medical field. Additionally, rigorous inclusion and exclusion criteria were applied, screening 26 articles that prioritize quantitative results relevant to clinical diagnosis. The most prominent model is Random Forest, with a frequency of use in 12 investigations and an average accuracy of 0.91. Likewise, the XGBoost, CNN, and SVM models were used in 9 investigations each and obtained accuracy results of 0.87, 0.88, and 0.96 respectively. These performances were particularly notable in applications related to dermatological, cardiological, and oncological diseases. The results position Random Forest as an efficient tool for medical diagnosis, although its practical implementation faces some technological and budgetary challenges. It is recommended to explore hybrid methodologies that combine advanced algorithms with more traditional approaches and conduct longitudinal studies to evaluate their impact in different clinical settings.

La industria automotriz se encuentra en un momento de profunda transformación impulsada por los avances de la inteligencia artificial (IA). Los vehículos ya no son solo medios de transporte, sino que se están convirtiendo en compañeros inteligentes que integran la IA