This study conducts a comparative analysis of six Natural Language Processing (NLP) models for detecting mental disorders among university students: BERT, Support Vector Machine (SVM), Random Forest, FLAN-T5, Mental-Llama, and a Customized NLP Model (C-NLP). We utilized a dataset of 10,000 textual responses from college students in Lima and texts sourced from platforms like Reddit, which encompass a diverse array of emotions and mental states. The models were evaluated using statistical significance tests such as p-values and confidence intervals alongside traditional metrics including precision, accuracy, F1-score, and AUC-ROC. The results indicate that both BERT and the C-NLP achieved the highest accuracy of 92%. The custom model effectively adapted to Peruvian students' cultural and linguistic nuances, providing a balanced approach between performance and cultural relevance.

Dental cavities represent a significant global health challenge, particularly in low-and middle-income countries, where early detection and diagnosis can substantially improve clinical outcomes. This study presents the development of a mobile application that utilizes YOLOv7 to detect early carious lesions on intraoral images, intending to provide dental professionals with a tool for timely diagnosis and intervention. The research was carried out in three key phases: analysis of YOLOv7, system development, and validation. The application was trained in a real clinical environment in Peru in collaboration with two independent dentists and their patients in two private clinics. Intraoral images were collected and processed from 40 participants, ensuring complete adherence to the ethical and privacy standards required for clinical studies. The experimental results demonstrated that the application achieved an average accuracy of 94%, with both accuracy and Positive Predictive Value (PPV) exceeding 90% in most cases. The results demonstrated consistent diagnostic accuracy and efficiency, validating the application's performance. Patient surveys reflected high satisfaction, with average scores of 4.4 for usability, 4.2 for efficiency, and 4.6 for functionality. Similarly, dentists rated the usability, functionality, and efficiency of the application with average scores of 4.5. These findings highlight the potential of the application to improve clinical workflows and accuracy in detecting early carious lesions.

This work presents the development of a web-based monitoring and prediction system designed to optimize food supply in restaurants in Metropolitan Lima, addressing challenges such as efficient inventory management and food waste reduction. The solution employs six Machine Learning models (Random Forest, Gradient Boosting, Ridge Regression, Lasso Regression, Linear SVR, and Neural Network), evaluated using accuracy metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Among the models, Gradient Boosting demonstrated the best performance, with an MSE of 0.0032, RMSE of 0.057, and MAE of 0.027, outperforming the others in terms of accuracy, including Neural Network and Random Forest, which also offered competitive results. While the approach was developed in the specific context of Metropolitan Lima, the applied methods and obtained results can be adapted to other urban markets with similar dynamics, demonstrating broader applicability. This system not only promotes more efficient and sustainable inventory planning, but also contributes to the economic growth of restaurants by optimizing resources and improving their profitability in a highly competitive environment.

This study evaluates the performance of Convolutional Neural Networks (CNNs) for the early detection of childhood malnutrition. The architectures analyzed include ResNet-50, EfficientNet-B4, VGG16, and AlexNet, selected for their demonstrated effectiveness in image classification tasks. Images were categorized into three classes - normal, at risk of malnutrition, and severely malnourished - and underwent preprocessing steps such as resizing, flipping, zooming, and face detection using MTCNN. The evaluation employed metrics including accuracy, precision, recall, and F1-score, with ResNet-50 emerging as the most effective model, achieving an accuracy of 92%. Based on these findings, this study explores the potential application of ResNet-50 in mobile solutions to provide accessible and practical tools for malnutrition detection in resource-limited settings.

The management of nurses' orders to a Peruvian hospital pharmacy faces efficiency challenges, which affects time spent on patient care. This context highlights the need for a system that optimizes this process. In this sense, a web-based system is proposed for the automation of orders from the nurse's cell phone, which uses a customized model for automatic speech recognition. The development of the system was divided into three stages: first, data collection with 21 orders placed by three nurses and 978 audio recordings to validate the automatic speech recognition model; second, implementation of the system allowing agile order processing; and finally, evaluation of results in terms of time and accuracy. The findings indicate that the processing time ranged from 37.70 to 60.15 seconds, representing a 97%-time reduction in operational tasks. In addition, the Word Error Rate was 10.51%, significantly lower than the 25.05% of Whisper Large V2. These results demonstrate the system's potential for adoption in various hospital contexts.

This paper presents the design and implementation of an inventory management system that integrates Robotic Process Automation (RPA) and Internet of Things (IoT) technologies to enhance operational efficiency in small and medium-sized enterprises (SMEs) within Peru's automotive sector. The system addresses common challenges faced by SMEs, such as inaccurate inventories and inefficient stock management, through automated processes and real-time monitoring. By streamlining repetitive tasks and enabling continuous inventory updates, the solution reduces operating costs and improves record-keeping accuracy. Initial results show a 30% reduction in management time and a 25% decrease in operational costs, highlighting the transformative potential of RPA and IoT technologies in inventory management. The project offers a practical model that can be scaled and replicated across other sectors, contributing to the long-term competitiveness of SMEs.

Small and medium-sized enterprises (SMEs) face significant challenges from security breaches, which can jeopardize their operational sustainability. This study presents the BLOCKSAGE SME system, a model designed to enhance the security of sensitive data storage and transfer. The system integrates customizable cloud infrastructure, private blockchain networks, Zero Trust architecture, a scalable API, and IPFS encryption, ensuring data privacy and business continuity. Based on a comprehensive literature review of blockchain-based solutions for SMEs, a web-based file-sharing prototype was developed and tested to validate the framework. The system was then evaluated through expert judgment and feedback from SME leaders. The results showed a satisfaction score of 4.06 from cybersecurity and blockchain specialists and 4.2 from the target SME audience on a Likert scale, indicating the system’s feasibility and effectiveness. While the system provides robust security measures, adoption challenges were identified, including the early-stage maturity of blockchain technology and cultural and workforce-related barriers within the Peruvian SME ecosystem. In conclusion, the findings suggest that blockchain-based architectures hold strong potential for addressing security gaps in SMEs, but implementation faces current limitations in resources and knowledge. Future research should explore adapting the system as a Software-as-a-Service (SaaS) solution to improve scalability and accessibility, further supporting the sustainability of SMEs.

In the context of IT incident management, the prioritization and automation of tickets can be a challenge for companies that lack advanced technologies. However, these difficulties can be overcome today by applying machine learning algorithms and techniques that use historical data to train predictive models, which allows for more efficient and effective IT incident management. The article proposes the implementation of a predictive model that uses machine learning to prioritize IT incidents in these companies. The goal of this proposal is to allow small and medium-sized enterprises to prioritize their incidents automatically, using a model that has been previously trained with a supervised multi-label classification algorithm technique to achieve high accuracy. Experimental results show that the Mean Absolute Error (MAE) is 2.79 and a Mean Squared Error (MSE) of 8.21, using the metrics provided by the scikit-learn library. Additionally, the entropy loss approaches a value of 0, suggesting a precise ability of the model to predict real values. Additionally, an average accuracy level of 93.74% was achieved.

Small and medium-sized enterprises have always proven to be crucial to national economies, yet many of them fail for a variety of reasons. One of them is selecting an inadequate supplier that does not meet the needs that the company requires to properly perform its core business. Therefore, this project proposes to develop a web application to select the best supplier option for a company. The main objective is to assist in decision making and reduce costs with a web application that implements the Fuzzy Analytic Hierarchy Process (FAHP), based on a series of criteria and priorities chosen by the company itself. After testing the application, the result was a relative score assigned to each alternative, thus identifying the supplier with the highest value as the most optimal choice. Finally, through the measurement of the consistency ratio for each pair matrix developed, it was possible to verify that the solution achieved acceptable results that do not exceed the range of inconsistency.

According to a study conducted by UNICEF, the main causes of childhood obesity are the high consumption of processed foods and the low amount of physical activity performed by children, generating a higher risk of respiratory, metabolic and cardiovascular conditions. Based on research conducted in different studies, we found that there are not many technologies that monitor and improve the physical performance of children. This paper presents a technological system based on IoT and using wearables to improve the physical performance of children with high obesity rates type 1 and 2. This technological system was verified by conducting a study with 30 children between 4 and 8 years old, evaluating their physical activity and collecting the data obtained from the smartwatch. This study showed, according to the conclusions found, that it is a useful tool for the collection of data required by specialists and easy to use for children and their parents. In addition, it is a means to overcome the obesity problem.