This article presents a practical application of the Kalman filter by implementing a sensor array in a Bebop Parrot drone in order to detect objects within a determined rank to reduce the background noise in the obtained measurements. The effect of the relationship between the measurement taken by the sensors and the effectiveness of the background noise reduction using the Kalman filter was studied, thus reducing the variation of the sensor measurement. The objective of this implementation can help improve the teaching of the application of Kalman filter. Information readings are performed by the HC-SR04 ultrasonic sensors connected to the data acquisition board Arduino Yun, which interprets the electronic pulses to get the distance in meters and, at the same time, send them via serial port to a Raspberry Pi board; this board implements the Kalman filter sending the data via Wi-Fi to a computer connected to the network that allows visualizing the results of measurements and filter performance. The results obtained show the improvement of the data measurement by the distance sensors with the purpose of providing the drone with greater accuracy of data acquisition.

This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0.

The rapid growth of the Internet of Things (IoT) in industrial environments has increased efficiency but also heightened vulnerability to sophisticated cyber-attacks. Traditional cyber security approaches are insufficient to protect critical infrastructure, creating a need for dynamic, adaptive solutions. Evolutionary algorithms (EAs), owing to their ability to explore large search spaces and optimise parameters, offer a promising route to enhancing IoT security. This review highlights the integration of EAs with deep-learning techniques to improve intrusion detection and system resilience. Building on this background, we propose an adaptive cyber-security framework that leverages evolutionary optimisation and continual learning to detect, prevent and mitigate attacks in real time. The study emphasises the importance of validating hybrid models in real-world settings and of optimising computational efficiency. Future work should investigate autonomous response mechanisms and the scalability of solutions for large-scale Industrial IoT (IIoT) deployments, ensuring robust protection against emerging threats and aligning academic advances with industry needs.

This study introduces a predictive air quality monitoring system based on Random Forest machine learning models and low-cost embedded sensors. The system was designed and implemented in Guadalupe, Nuevo León, Mexico, to monitor carbon monoxide (CO), car-bon dioxide (CO2), and particulate matter (PM). Real-time data was collected using a Particle Photon 2 microcontroller with four different sensors. The data was processed using Python scripts, and the Random Forest model was trained to predict future pollutant values. Results demonstrated strong model performance, validated through statistical evaluation metrics and graphical comparisons. The proposed system shows promise for deployment in smart urban environments. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1234Dimensions.Open Alex.

Este artículo presenta una propuesta de un sistema inteligente para el mantenimiento predictivo de un horno industrial, el objetivo del sistema es el monitoreo en tiempo real, el ajuste automático de perfiles térmicos y la predicción de fallas mediante modelos de aprendizaje automático. Se hace uso de datos simulados generados con base en condiciones reales de operación. La solución integra tecnologías del Internet de las Cosas (IoT) y algoritmos de aprendizaje automático, hace énfasis en la importancia de la Industria 4.0 e Industria 5.0. El sistema desarrollado es modular, escalable. Se reportan métricas destacadas en un entorno simulado, como una precisión del 99.5%, sensibilidad del 100% y un área bajo la curva ROC de 0.9995. Esta tecnología busca acelerar la transformación digital del sector industrial mexicano, facilitando su adopción tanto en grandes corporativos como en pequeñas y medianas empresas [5].