Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning.

In an increasingly technology-driven world, the security of Internet-of-Things systems has become a top priority. This article presents a study on the implementation of security solutions in an innovative manufacturing plant using IoT and machine learning. The research was based on collecting historical data from telemetry sensors, IoT cameras, and control devices in a smart manufacturing plant. The data provided the basis for training machine learning models, which were used for real-time anomaly detection. After training the machine learning models, we achieved a 13% improvement in the anomaly detection rate and a 3% decrease in the false positive rate. These results significantly impacted plant efficiency and safety, with faster and more effective responses seen to unusual events. The results showed that there was a significant impact on the efficiency and safety of the smart manufacturing plant. Improved anomaly detection enabled faster and more effective responses to unusual events, decreasing critical incidents and improving overall security. Additionally, algorithm optimization and IoT infrastructure improved operational efficiency by reducing unscheduled downtime and increasing resource utilization. This study highlights the effectiveness of machine learning-based security solutions by comparing the results with those of previous research on IoT security and anomaly detection in industrial environments. The adaptability of these solutions makes them applicable in various industrial and commercial environments.

Tamper Detection in Industrial Sensors: An Approach Based on Anomaly Detection.

The Industrial Revolution 4.0 has catapulted the integration of advanced technologies in industrial operations, where interconnected systems rely heavily on sensor information. However, this dependency has revealed an essential vulnerability: Sabotaging these sensors can lead to costly and dangerous interruptions in the production chain. To address this threat, we introduce an innovative methodological approach focused on developing an anomaly detection algorithm specifically designed to track manipulations in industrial sensors. Through a series of meticulous tests in an industrial environment, we validate the robustness and accuracy of our proposal. What distinguishes this study is its unique adaptability to various sensor conditions, achieving high detection accuracy and prompt response. Our algorithm demonstrates superiority in accuracy and sensitivity compared to previously established methodologies. Beyond detection, we incorporate a proactive alert and response system, guaranteeing timely action against detected anomalies. This work offers a tangible solution to a growing challenge. It lays the foundation for strengthening security in industrial systems of the digital age, harmonizing efficiency with protection in the Industry 4.0 landscape.

Method for the Development of Accessible Mobile Serious Games for Children with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) covers a range of neurodevelopmental disorders that begin in early childhood and affects developmental activities. This condition can negatively influence the gaining of knowledge, skills, and abilities, such as communication. Over time, different techniques and methods have been put into practice to teach and communicate with children with ASD. With the rapid advancement in the field of technology, specifically in smartphones, researchers have generated creative applications, such as mobile serious games, to help children with ASD. However, usability and accessibility have not been often taken into account in the development of this type of applications. For that reason, in this work we considered that both, usability and especially accessibility are a very important differentiators for the quality and efficiency of mobile serious games. Our approach has two important contributions, the incorporation of accessibility as a fundamental requirement in the development of a mobile serious game and the proposal of a method for the development of this type of applications for children with ASD, a method that can be used by other developers.

Exploring the technological acceptance of a mobile learning tool used in the teaching of an indigenous language.

Language is the primordial element for cultural transfer in indigenous communities; if it is not practiced, there is a risk of losing it and with it, a large part of the history of a community. Ecuador is a multicultural and multiethnic country with 18 indigenous peoples. Currently, in this country, some native languages are at risk of disappearing due to factors such as racial discrimination, underestimation of the language, and, above all, the lack of interest and motivation of the new generations to learn this language. Information technologies have made it possible to create mobile applications such as games, dictionaries, and translators that promote the learning of the Kichwa language. However, the acceptance of technology has not been evaluated, nor the intention to involve mobile devices in the process of teaching this language. Subsequently the objective of this work is to explore the acceptance of technology and the use of mobile devices to motivate the learning of the Kichwa language. For this purpose, the mobile application "Otavalo Rimay" was used with several students of a Kichwa language learning center. The methodology used to verify the hypothesis of this work was Design Sciences Research (DSR) together with the theory of acceptance and use of technology (UTAUT). The instrument used for this evaluation was a survey carried out after the use of the mobile application. The statistical analysis of the results obtained indicates characteristics such as the utility and perceived ease of use, positively influence students to motivate the use of mobile devices in learning a language. The results also show the great technological acceptance by students for learning and confirm that currently, mobile learning is accepted for use in education.