Conference Agenda

The evaluation of optical distortion in laminated windshields is a critical aspect of automotive safety and manufacturing quality control. Traditional inspection methods rely on manual visual assessment, which introduces subjectivity, variability, and human error. This research presents the design and implementation of an automated measurement system based on artificial vision and image processing to accurately detect and quantify optical distortion in windshields. The proposed system integrates OpenCV and Python to process high-resolution images captured under controlled conditions. A dataset of 100 windshield samples was analyzed, achieving an accuracy of 94.0%, precision of 94.3%, and recall of 94.0%, demonstrating superior performance compared to manual inspection. The methodology includes image acquisition, edge detection using the Canny algorithm, and circular shape recognition via Hough Transform. The experimental results confirm that artificial vision provides a faster, more reliable, and cost-effective alternative to conventional inspection methods. This work contributes to the automation of optical quality control, reducing production defects and improving windshield safety standards. Future work will explore deep learning techniques to enhance detection robustness under variable lighting conditions.

This paper details the development of an advanced robotic arm designed to package chocolates efficiently in a small-scale process. The arm features a Prismatic-Revolute-Revolute) configuration and is equipped with a Universal Vacuum Grip to handle chocolates precisely. Its construction utilizes PLA 3D printed parts for the mechanical structure and flexible TPU belts to ensure optimal movement control. Electronically, the system is powered by NEMA 17 motors, controlled by an Arduino board and a CNC interface for precision motor management. The programming, conducted in Python, integrates artificial intelligence through computer vision techniques to enhance the accuracy and adaptability of the packaging process. A graphical user interface was developed, allowing intuitive control and sequence management of the robotic arm's operations. The incorporation of artificial intelligence enabled the robotic arm to identify successfully and sort chocolates of varying types, greatly enhancing packaging efficiency. The results demonstrate the arm's capability to consistently place chocolates in trays, thereby augmenting productivity, reducing human error, and maintaining packaging quality. This project underscores the significance and potential of integrating automation and artificial intelligence in the food packaging industry.

This study examines the impact of the Enterprise Architecture course in the Systems Engineering program at the Universidad Nacional de San Agustín (UNSA), through a pre- and post-course evaluation based on quantitative and qualitative methodologies employed. A quasi-experimental methodology was adopted, collecting data through entry and exit surveys, in addition to the evaluation of the final grades of the course without individual linkage. The findings indicate a marked improvement in the understanding of corporate architecture, analytical and technical competencies, the ability to collaborate in teams and the practical implementation of knowledge. Additionally, areas of opportunity associated with the evaluation structure and transparency in project documentation were detected. Qualitative analysis facilitated the categorization of open-ended responses, highlighting the relevance of project-based pedagogy and active learning.

The present study, entitled Impact of the implementation of facial recognition for the security of homes in the city of Cajamarca, aims to evaluate how the implementation of a facial recognition system affects security in homes in this city. To do so, a structured survey was used as the main data collection tool. Questionnaires were applied at two times: Pre-test and Post-test, in order to measure the impact of the system before and after its implementation. The results obtained suggest an improvement in the perception of security in homes where the technology was implemented, although several limitations were also identified that could have affected the interpretation of the data.

Artificial intelligence (AI) is currently used in various techniques for disease prediction, including cardiovascular disease (CVD). Therefore, this review aims to demonstrate the most effective AI technology methods for the prediction of CVD risk factors and diagnoses during early detection in humans, compared to traditional methods such as electrocardiogram (ECG) signal reporters, intensive care unit (ICU) review, and echocardiographic examinations. PICO and PRISMA methodologies were used for the search and selection of relevant documents. Along these lines, 397 documents were identified, including articles and systematic reviews in the following databases: Scopus and IEEE. According to the inclusion and exclusion criteria, 26 open access articles were selected. Where, the use of AI technological methods allowed analyzing and capturing predictive values on people with or without CVD problems in an effective way, achieving a range greater than 0.99. t should be noted that the representations were made with spreadsheet tools and the data manipulation, analysis and graph generation with Python libraries. Finally, it is concluded that the most effective AI technological methods for CVD prediction are based on machine learning (ML) techniques for predicting measurement values such as sensitivity, specificity, precision, F1 score and area under the curve (AUROC), with measurement indices between 0.97 and 0.99, compared to deep learning (DL) techniques, whose indices are between 0.88 and 0.90

This study explores the role of artificial intelligence (AI) in enhancing customer service in nanostores, addressing key aspects of AI adoption. First, it evaluates how AI-driven automation improves customer service efficiency by streamlining processes like inventory management and customer interactions, leading to faster service and reduced operational costs (Technology Acceptance Model). Second, it examines how AI-powered personalization enhances customer experience by offering tailored product recommendations and promotional offers, resulting in increased satisfaction and loyalty (Service-Dominant Logic). Third, it highlights how AI integration provides nanostores with a competitive advantage by enabling them to differentiate through superior service and operational efficiency (Resource-Based View). Fourth, the study shows how AI-driven insights support data-driven decision-making, optimizing inventory control and pricing strategies to improve overall performance (Data-Driven Decision-Making Theory). Lastly, it identifies the unique challenges nanostores face in adopting AI, including resource constraints, technological capabilities, and resistance to change, which affect successful implementation (Theory of Planned Behavior). A systematic literature review (SLR) was conducted using databases like Scopus, WoS, IEEE Xplore, and Google Scholar, focusing on recent and relevant contributions. The findings suggest that while AI adoption brings clear benefits, such as enhanced customer service, efficiency, and competitive advantage, successful implementation requires addressing barriers like limited resources and resistance to change. This research offers valuable insights for researchers and practitioners, providing a novel framework for AI adoption in small-scale retail contexts.