Conference Agenda

The development of an innovative system that uses Machine Learning and IoT sensors to automatically activate emergency signals in critical situations, improving the speed and efficiency of the response. Using a Random Forest machine learning model, trained with data from temperature, gas, humidity, and flame sensors, the system achieved a 96.8% accuracy, with key metrics such as an AUC of 0.997 and an F1-score of 0.968. Integrated with an Arduino microcontroller, this system can autonomously activate alarms and lights, eliminating the need for human intervention in emergency situations. By detecting risks such as gas leaks, fires, or temperature spikes, the system responds almost instantly, which can be crucial for saving lives. This approach not only optimizes safety in vulnerable environments but also establishes a smarter and more efficient model for emergency management.

Joint hypermobility is a common condition affecting many people and carries an increased risk of injury and associated complications such as chronic pain and fatigue. The need for effective injury prevention strategies in this population is crucial to improve their quality of life. This study aimed to develop a smart glove incorporating sensors and artificial neural networks (ANN) to monitor and predict risky movements in people with joint hypermobility. A comprehensive approach was employed, starting with an extensive literature review on joint hypermobility and its implications. In the design and development of the predictive model, ANNs were implemented for data analysis and prediction. It was tested with a population of patients diagnosed with joint hypermobility. Key findings revealed a 92% accuracy rate in detecting risky movements, indicating its potential for practical application in daily activities that could affect joint use. The developed technology shows great promise in preventing injuries and improving the quality of life of people with joint hypermobility. This innovative approach can transform rehabilitation practices and promote personalized care in the treatment of this condition.

This study proposes a design of an efficient and safe electrical network for a textile company located in San Antonio de Chaclla in Lima -Peru. The importance of this study lies in the need to ensure the determination and optimization of the Demand Factor in the electrical design for a textile industry. The objective is to minimize interruptions and improve operational efficiency and cost savings. The design complies with national and international standards of safety and efficiency. The results highlight the technical and economic benefits of the design, which can serve as a replicable model for other companies in the sector at national and international level. The conclusions underline the importance of correct planning and use of the Demand Factor for a correct electrical design and greater efficiency of the electrical network. Recommendations for future research include exploring a correct application of regulations and recommendations in electrical sizing and its correct application in industrial electrical installations.

This article presents an automated monitoring system designed to enhance security and control in real-time banknote processing, aimed at identifying anomalies and suspicious activities. Data transmission is performed wirelessly through Xbee S2C modules with Zigbee technology, ensuring the integrity of information such as currency, denomination, serial number, quantity of pieces, rejects, and amounts in soles (Peruvian currency) and dollars. The system interface ensures real-time tracking, storage, and analysis of the recorded process. Furthermore, it predicts the lifespan of equipment and cash flow congestion in a banking agency using FeedForward neural networks. This system optimizes processes by analyzing usage patterns and processing times, providing an efficient and cost-effective solution for monitoring in banking agencies.

This study proposes the design of a SCADA system incorporating Machine Learning and linear regression to enhance predictive maintenance for the TR injection molding machine. The objective is to minimize failures and operational costs through the analysis of electrical and environmental parameters that influence the machine's performance. The system's effectiveness is evaluated via simulations, with an emphasis on failure prediction and maintenance optimization.

Abstract– The objective of this project is to implement the electrical system of the primary power source of the overhead crane, located in the leaching area of ​​the Cajamarquilla refinery, to improve the availability and safety in the use of the overhead crane. To do this, it began with the collection of information on the current condition of the overhead crane. It was identified that the recurring failure occurred in the electrical power supply system. To determine the areas susceptible to improvement, an evaluation of the current state of the equipment was carried out, and then planning was carried out and what type of system could replace the existing one was evaluated, since the current system is deficient, causing the equipment to become inoperative, delaying the scheduled work with the crane. At the same time, the current condition of the equipment does not guarantee safe working conditions, exposing the collaborators who use the equipment. Of the different electrical systems that are found for overhead cranes, 2 systems were identified that could replace the existing one: the C-Track System and the Extensible System. Finally, the 2 systems were economically evaluated in terms of cost and installation times.

Keywords-- Festoon system; cable trolleys; electrical system, Extendable System, C-Track System.