The management of chronic diseases is a major global challenge, especially regarding continuous monitoring and personalized care. In this context, bioengineering and telemedicine have emerged as key tools for remote patient monitoring, facilitating the efficient management of these conditions. This systematic review analyzes the impact and application of digital technologies in the monitoring of patients with chronic diseases. An analysis of 145 articles was conducted, of which 63 were selected for their relevance. The main findings highlight that digital health platforms, IoT devices, and video consultation platforms are the most widely used technologies in remote monitoring. The most monitored chronic diseases include chronic diseases in general, diabetes, and COPD. In terms of functionality, the applications focus mainly on telemonitoring and management, allowing for continuous and personalized patient follow-up. The results of this systematic review are useful for future research aimed at optimizing remote monitoring, improving the quality of care, and promoting smarter, more efficient, and sustainable health systems.

Artificial intelligence has become a key tool for today’s society, allowing early diagnosis, predictive analysis, and support in clinical decision-making. For this purpose, a systematic review of the literature was carried out in specialized databases, from which 84 articles were identified and, after applying the inclusion and exclusion criteria, 29 studies were selected as the final sample. The results show that AI has been applied in various diseases such as preeclampsia, cancer, and cardiovascular disorders, in addition to pointing out risks linked to ethics, privacy, and diagnostic reliability. In conclusion, AI represents a promising resource to make healthcare systems more efficient and equitable, provided that its implementation is responsible, ethical and human-centered.

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This study aimed to develop an intelligent virtual assistant for anemia management using Retrieval-Augmented Generation (RAG) integrated with large language models (LLMs). Through a comparative experimental methodology, four models — GPT-4 Turbo, LLaMA-3-70B, Mistral 7B, and Gemini 1.5 Pro — were evaluated before and after RAG implementation. The results showed notable improvements, with LLaMA-3-70B reaching 89

Alzheimer's is a progressive and degenerative disease affecting millions worldwide, incapacitating them physically and cognitively. This study aims to perform a comparative analysis of Machine Learning models to determine the model with the best performance in predicting Alzheimer's disease. The models used were Random Forest (RF), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Logistic Regression (LR). Two datasets called OASIS were used to train the models, the first one had a total of 436 records and 12 variables, while the second one stored 373 records and 15 variables. The article's content is divided into six main sections: introduction, literature review, methodological approach, results, discussions, and conclusions. After processing and pooling the datasets, RF, SVM, and LR proved the best predictors, achieving 96% accuracy, precision, sensitivity, and F1 score. This study highlights the efficacy of RF, SVM, and LR in predicting Alzheimer's disease, offering a significant advance toward understanding and management of this disease, which supports the relevance of implementing these models in future research and clinical applications.

Artificial intelligence (AI) is transforming agriculture by offering innovative solutions to persistent challenges. This systematic literature review explores the most studied AI applications in agriculture, emphasizing crop management, agronomic decision-making, early detection of diseases and pests, and climate change adaptation. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 700 publications were retrieved from databases such as Scopus, ScienceDirect, and IEEE Xplore, with 104 relevant articles selected after applying strict inclusion and exclusion criteria. The findings underscore the importance of machine learning and image processing in tailoring agronomic practices to specific plot conditions and microclimates. These tools enable early identification and control of plant diseases and pests, reducing crop losses and dependence on chemicals. Nonetheless, challenges remain, particularly regarding accessibility for smallholder farmers, high implementation costs, and limited data infrastructure. While AI offers significant potential to enhance agricultural productivity, sustainability, and resilience, addressing these limitations is crucial. A balanced, inclusive approach is essential to ensure AI’s benefits are widely distributed and contribute to long-term food security and environmental sustainability.

Autism is a disorder that poses significant challenges in various areas such as health, education, social interaction, and how the world perceives them. The implementation of artificial intelligence in daily life and different fields offers an innovative approach to addressing these challenges, facilitating early detection, support in learning, and social interaction for individuals with this condition. The systematic literature review focuses on studying 50 out of 144 articles obtained from various databases such as EBSCO Host, IEEE Xplore, ScienceDirect, Scopus, ProQuest, and Web of Science. These articles were systematically organized using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, providing information about machine learning as the most utilized discipline, the types of infrastructure it relies on, and the countries that are at the forefront of this topic. This review will serve as a reference for stakeholders regarding the advancements and contributions of artificial intelligence for individuals with autism.

Lung cancer is one of the diseases with the highest incidence and mortality in the world. Machine learning (ML) models can play an important role in the early detection of this disease. This study aims to identify the ML algorithm that has the best performance in predicting lung cancer. The algorithms that were contrasted were logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), gaussian Naive Bayes (GNB), multinomial Naive Bayes (MNB), support vector classifier (SVC), random forest (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and gradient boosting (GB). The dataset used was provided by Kaggle, with a total of 309 records and 16 attributes. The study was developed in several phases, such as the description of the ML models and the analysis of the dataset. In addition, the contrast of the models was performed under the metrics of specificity, sensitivity, F1 count, accuracy, and precision. The results showed that the SVC, RF, MLP, and GB models obtained the best performance metrics, achieving 98% accuracy, 98% precision, and 98% sensitivity.

The use of dermatological images and convolutional neural networks (CNNs) to predict skin diseases is one of the most promising applications of data science to improve the diagnosis and treatment of skin diseases. The aim of this work was to achieve maximum accuracy and efficiency in skin disease prediction using dermatological images and CNN models. Based on dermatological images, the ability of five CNN models to predict skin diseases was evaluated. The ResNet50, Inception V3, VGG-19, DenseNet201, and EfficientNet models were evaluated using the Kaggle HAM10000 (human against machine with 10000 training images) dataset. The metrics used were accuracy, recall, and F1 score. As a result, the study found that skin disease classification has variable performance. VGG-19 and DenseNet201 showed high values for accuracy, recall, and F1 score, with accuracy close to 98%. These models demonstrated an effective ability to identify and classify different types of skin diseases. In contrast, ResNet50 and Inception V3 obtained mixed results, while EfficientNet showed variable results in predicting skin diseases from dermatological images. Finally, the importance of choosing the right CNN model to predict skin diseases from dermatological images can be highlighted. VGG-19 and DenseNet201 performed well in classifying various skin diseases, which could be useful for developing dermatological diagnostic support systems.

This paper analyzes the application of artificial intelligence (AI) techniques in lean construction (LC) and their potential to enhance project management (PM) for improved cost and schedule efficiency. The PRISMA methodology is used to select relevant articles in four steps. Furthermore, a bibliometric analysis of keywords and their occurrences is conducted. The study emphasizes the different methods of utilizing lean tools and AI techniques to attain optimal results in the construction industry. By combining a variety of tools and techniques, it is possible to create an environment that fosters improved project outcomes while minimizing risks and inefficiencies. According to the articles reviewed, the LC methodology and its tools are becoming increasingly relevant in general practice (GP). Machine learning (ML) techniques, particularly artificial neural networks (ANN), have been extensively researched as a tool to enhance construction projects by minimizing delays, fostering collaboration, cutting costs, saving time, and boosting productivity. Combining LC with ML can enhance profitability and align with lean principles, leading to successful outcomes for construction projects.

Access to quality education remains a significant global challenge today. In line with the goals outlined by the World Health Organization (WHO) in its 2030 agenda, ensuring access to education is a fundamental objective. Consequently, it is imperative to undertake an investigation into the influence of technological innovation on educational practices. This study examines the impact of incorporating the 5G network into educational settings to improve learning experiences. The analysis covered 134 articles, 62 of which were deemed relevant, classifying the research as ongoing projects or pilot studies for future exploration. The main digital tools identified were artificial intelligence, the Internet of Things, virtual reality, and machine learning. The use of the 5G network appears to have a more significant impact on higher education and universities. Research in this field is mainly concentrated in Europe, America, and Asia. In addition, it is clear that the adoption of 5G technology is influencing pedagogical methods, emphasising immersive learning, e-learning platforms, and flipped classrooms. This study argues for further research into the integration of technology in education, advocating a careful examination of the implementation of 5G infrastructure and its potential to improve access to high-quality education.

In recent years, computer attacks on the server infrastructure in organizations have been increasing, and the pandemic of covid-19 and remote work have been the main causes for this massive wave of large-scale attacks, small businesses are especially vulnerable because to optimizing resources they leave aside the cyber security in their network infrastructure. The present research is a systematic review that compiles 58 articles where policies, techniques, and infrastructure for the prevention of threats in enterprise servers have been implemented and raised, these articles have been collected from major databases such as IEEE Xplore, SAGE, Science Direct, Scopus, and IOP Publishing. The results show that one of the most effective methods in preventing communications between institutional servers is public key infrastructure/SSL-TLS encryption. Most research claims that it is the most effective method as it provides a central certifier and manages the certificates for the servers allowing each of the modules or attachments within the infrastructure to identify and validate other members and to proceed with the encryption of network traffic, Finally, a security implementation model is proposed.

Monkeypox is a disease of zoonotic origin that reflects the connection between human, animal, and environmental health. It is transmitted primarily through direct contact with body fluids, skin lesions, or contaminated surfaces. Being of interest after the global outbreak of 2022, which recorded nearly 70,000 cases in more than 100 countries. This study presents a prototype mobile application that incorporates tools such as artificial intelligence (AI) for early detection and monitoring of the disease. The mobile-D methodology was used. The results were validated by experts and users, reaching an average of 85% in user satisfaction and 81% in expert evaluation, indicating a high level of acceptance. This application facilitates real-time data collection, improving communication between health authorities and the population, which is crucial for disease control. With the use of AI and data analysis, the application seeks to optimize case management and support health systems in at-risk areas, representing a significant advance in the response to monkeypox.

Business failure prediction has become crucially important for today's firms, enabling them to reduce financial risks and make informed decisions. This study uses a dataset of 6819 companies and 96 financial and macroeconomic variables to present a comparative analysis of machine learning (ML) models for predicting corporate bankruptcies. Behind this research is to improve the accuracy of bankruptcy prediction, which can help companies make more informed decisions and reduce financial risks. This study aims to evaluate the effectiveness of 16 ML algorithms in terms of accuracy, sensitivity, and other relevant metrics. The work uses methodologies that include data collection and cleaning, exploratory data analysis, model preprocessing and training, and model performance evaluation. Data preprocessing and hyperparameter optimization techniques were used to improve model performance. The evaluated algorithms include Classifiers such as Stacking Classifier (SCC), Randomized Search Classifier (RCV), Historical Gradient Boosting Classifier (HGBC), MLP Classifier (MLPC), K-Neighbors Classifier (KNC), Decision Tree Classifier (DTC), XGBRF Classifier (XGBRFC), Support Vector Classifier (SVC), Logistic Regression Classifier (LR), Linear SVC Classifier (LSVC). With an accuracy of 97.63%, recall of 97.63%, and F1-score of 97.63%, the results show that the SCC algorithm was the best. Other models, such as RCV and DTC, also showed good results, with accuracies above 97%. However, models such as PAC and BNB had lower performance and accuracy below 90%. Finally, this study compares the results of ML models in predicting business failures and highlights their effectiveness. The SCC algorithm is considered the most suitable model for this task, as it suggests that it can help economic actors make more informed decisions and reduce financial risks in the context of firms.

Parkinson's is a neurodegenerative disease that generally affects people over 60 years of age. The disease destroys neurons and increases the accumulation of α-synuclein in many parts of the brain stem, although at present its causes remain unknown. It is therefore a priority to identify a method that can detect the disease, and this is where machine learning models become important. This study aims to perform a comparative analysis of machine learning models focused on the early detection of Parkinson's disease. Logistic regression (LR), support vector machines (SVM), decision trees (DT), extra trees classifiers (ETC), K-nearest neighbors (KNN), random forests (RF), adaptive boosting (AdaBoost) and gradient boosting (GB) algorithms are described and developed to identify the one that offers the best performance. In the training stage, we used the Oxford University dataset for Parkinson's disease detection, which has a total of 23 attributes and 195 records on patient voice recordings. The article is structured into six sections, such as introduction, related work, methodology, results, discussions, and conclusions. The metrics of accuracy, sensitivity, F1 count, and precision were used to measure the models' performance. The results position the KNN model as the best predictor with 95% accuracy, precision, sensitivity, and F1 score.

Tuberculosis is a severe and life-threatening illness that affects numerous individuals worldwide every day. The key objective of this study was to create a system that could enhance the monitoring and management of tuberculosis patients. To achieve this goal, the Mobile D methodology was utilized because of its effectiveness in project management. This methodology emphasizes test-driven development, continuous integration, and optimization to enhance software processes. The outcome of this research was a prototype of a mobile application specifically designed for individuals with tuberculosis. Professionals and people affected by the disease assessed the quality of the prototype. They evaluated its effectiveness, user-friendliness, design, and functionality and gave ratings of 4.77 and 4.69 on a Likert scale, respectively. These figures indicate that the prototype meets high-quality criteria. In conclusion, this research successfully created an efficient prototype that enhances the monitoring and control of tuberculosis patients. The prototype includes features such as real-time consultations for immediate interaction between physicians and patients, clinical history visualization, and medication reminders, all of which improve the user’s experience.

Customer retention, a critical business priority, has become a growing concern, especially in the telecommunications industry. This study addresses the need to anticipate and understand customer churn through the application of Deep Learning models. The central focus of the research was the development and evaluation of a short-term memory model (LSTM) specifically designed to predict customer leakage. The choice of LSTM as the mainstay of the research is based on its proven ability to model long-term dependencies in sequences, its resilience to recurrent challenges in neural networks, and its success in various sequence prediction tasks. The model implementation, configured sequentially with Keras, comprised of an initial LSTM layer of 64 units, followed by a 20% removal layer to mitigate overfitting. The second LSTM layer, with 32 units, was supplemented with another elimination layer. Model training was conducted using a dataset consisting of 20 attributes and 4250 records. The model evaluation was based on crucial measures such as precision, accuracy, sensitivity and F1 count, revealing exceptional results with 95% performance on all metrics. This study, therefore, highlights the effectiveness of the LSTM model in predicting customer churn, providing companies with a valuable tool to improve retention and mitigate associated losses.

The use of augmented reality (AR) with GeoGebra allows for the contextualization of mathematical operations in real-world situations. In this approach, the teacher presents questions or problems that students solve using visualization and experimentation software. The objective of this work is to evaluate the impact of integrating the GeoGebra 3D calculator with AR. For the development of this study, the quasi-experimental method was employed, involving the comparison of results between two groups: the experimental group (EG) and the control group (CG). We worked with a population of 78 students. The study conducted confirms the use of the GeoGebra calculator in 3D with AR. AR effectively enhances mathematical learning. Seventy percent of the students in the EG achieved an outstanding level of performance, while 30% reached an expected level. In addition, a positive attitude towards mathematics was observed in 100% of the students. These results demonstrate that using the GeoGebra calculator in 3D with AR has a positive impact on mathematics learning. While in CG, 10% achieved the expected level of performance, 85% were in progress, and 5% were at the initial stage. Finally, it was concluded that the GeoGebra calculator in 3D with AR is very useful. It helps enhance the teaching and learning (TL) of mathematics and motivates students, making the development of class sessions more dynamic.

Vector-borne diseases (VBDs) are major threats to human health. They are estimated to cause more than 700,000 deaths each year. This presents serious health problems for CBD. In recent years, the incidence of VBDs has increased globally, affecting one billion people approximately and accounting for 17% of all infectious diseases. Globally, disease rates have risen at an alarming rate, with more than 3.9 billion people at risk of infection. Therefore, it is essential to find approaches to detect these diseases; this is where machine learning (ML) models come into play. The purpose of this study was to predict VBDs using tabular epidemiological data. For this purpose, a set of ML models was used, such as support vector classifier (SVC), extreme gradient boosting (XGBoost), LightGBM, CatBoost, random forest (RF), and balanced random forest (BRF). A dataset consisting of 65 features and 1262 records was used during the training stage. The results highlighted the successful integration of the different models, such as SVC, XGBoost, LightGBM, CatBoost, BRF, and RF, with weights of 0.49959 +/- 0.27112, 0.58496 +/- 0.22619, 0.48482 +/- 0.29971, 0.54992 +/- 0.27982, 0.24924 +/- 0.22654, and 0.45592 +/- 0.25849. In addition, the BRF model stood out for having the lowest log loss, evaluated through the ensemble log-loss metric, with an average of 0.24924 and a standard deviation of 0.22654.

The pandemic has made us quickly migrate to virtual environments, for this reason, we must look for quality education mechanisms, thus continuing with the same level of traditional teaching. This research aims to determine the influence of an educational video game in improving the learning process, for which a study was carried out on first-cycle university students. The level of the research is explanatory, of the applied type, with a quantitative approach and quasi-experimental design. We worked with two groups already formed in which 65 students participated. The following results were obtained: an increase in the grade of motivation of 12.9%, an improvement in the acquired knowledge indicator of 46.34%, and an improvement in the obtained qualification indicator of 12.77%. Therefore, it is concluded that an educational video game influences the motivation, acquisition, and application of learning.