Conference Agenda

This proposal aims to evaluate the efficiency of classification and determination of the maturation conditions of Swiss-type cheese through a prototype system using NIR spectroscopy and artificial intelligence. The goal is to create an economical and scalable system to improve the production capabilities of cheese companies. The work is divided into three phases: (a) the development of logical sequences for classification and quality determination; (b) the development of the prototype system at the hardware and software level that will allow data collection in a cheese company, obtain NIR profiles, and control actuators in a classification system; and (c) the comparison of results and classification. The construction and operation of the portable NIR system for cheese maturation evaluation were achieved. It was observed that classifying the cheeses based on maturation days is feasible, with the most determining wavelength found between 1000 nm and 1450 nm, which is related to the presence of fatty acids in the samples. Therefore, low-cost equipment like the one designed and built can be used in industry, and since this technology does not require a complex sample process, it can be widely used in the cheese industry.

This paper presents the design and implementation of an automated monitoring system for hydroponic crops, based on smart sensors and IoT connectivity. An ESP32 is used as a processing unit, collecting data on pH, electrical conductivity (EC), dissolved oxygen (DO), temperature and ORP. The information is transmitted in real time via the MQTT protocol to a cloud server and visualized through the MyMQTT application, ensuring continuous access to the system data. The process includes the calibration of sensors with standard solutions and specific tolerances, which guarantees accurate and stable measurements. The results showed that the implementation of the system not only optimizes monitoring accuracy, but also reduces water and nutrient consumption over a 35-day period, minimizing operating costs. The implemented architecture allows remote monitoring and parameter adjustment in real time, improving crop efficiency and promoting a sustainable and automated urban agriculture model.

The accelerated growth of the Industrial Internet of Things (IIoT) has driven the need for advanced and secure anomaly detection solutions, especially in industrial environments where cybersecurity is critical. This study provides a Systematic Literature Review (SLR) guided by the PRISMA 2020 method, with the objective of identifying the impact of IIoT on cybersecurity within Industry 4.0. Thirty-two studies published between 2020 and 2024 in academic databases such as Scopus and Web of Science were reviewed. The results reveal that emerging technologies such as Blockchain, Machine Learning and Deep Learning are playing a central role in data protection and intrusion detection in IIoT systems. Blockchain has proven to be effective in ensuring data integrity and improving operational efficiency. This review highlights the importance of adopting robust cybersecurity solutions to mitigate risks and strengthen resilience in Industry 4.0 and suggests key areas for future research in this field.

Currently, availability is one of the most relevant indicators in the fishing sector. This is because it allows for increased productivity and efficiency, which are affected by unplanned downtime. Additionally, there is a significant amount of research proposing the use of TPM as a solution tool. Furthermore, successful cases recommend the use of SMED for high setup times issues. Technological advancements have also made their way into the food industries, as seen in the case of Industry 4.0. Based on this, the purpose of this article is to improve production and minimize operational costs due to urgent repairs and production losses. The case study is carried out in a fishing company where low machinery availability is one of the main problems. Therefore, the proposed solution model suggests implementing the following TPM pillars along with SMED: autonomous maintenance and planned maintenance. Additionally, the use of Industry 4.0 devices is proposed. Likewise, there is a goal to increase availability by 10% in critical machines in the drying and pressing processes. Furthermore, a 27% reduction in setup time is targeted. In this way, it is believed that the study will contribute to the literature on the implementation of TPM, SMED, and Industry 4.0 tools to increase machinery availability in a fish processing company in Peru.

Many homes now feature smart technology and numerous devices connected to the Internet, exposing them to cyberattacks. Therefore, implementing protection mechanisms to identify, predict, and mitigate these threats to smart home devices is crucial. This research proposes two machine learning models—K-Nearest Neighbors and Decision Tree—to predict malicious activity in smart home connections and classify whether an attack is occurring. The study presents both models along with an in-depth analysis of their performance, assessing how they function on unseen data and their effectiveness on the dataset. The findings highlight the strengths and weaknesses of each model, providing valuable insights into their applicability in real-world scenarios. By offering a comparative evaluation, this research contributes to the ongoing efforts in enhancing the security of smart homes and underscores the importance of adopting advanced machine learning techniques for intrusion detection systems (IDS). This study aims to lay the groundwork for future developments in smart home cybersecurity solutions.

Abstract – Industry 4.0 has transformed the logistics sector in Latin America, especially in Brazil, Mexico and Colombia, through the integration of advanced technologies such as the Internet of Things and artificial intelligence. This has improved efficiency and competitiveness, although there are challenges such as lack of infrastructure. Digitalization and automation have increased productivity, and it is suggested to invest in technology, train staff and encourage collaboration in the supply chain to maximize these benefits and move towards sustainable development in the region. This qualitative study, based on the PRISMA methodology, analyzes how the relationship between Industry 4.0 and productivity in the logistics sector is key to sustainable development in Latin America.

Keywords – Industry 4.0, productivity, logistics companies, Latin America, interconnectivity