Conference Agenda

In the industrial sector, many organizations tend to look for optimal technological solutions for their processes; however, over time some of these solutions become obsolete. In this sense, companies have chosen to improve the business systems they have in their possession, through web services or systems hosted in the cloud. Therefore, the research aimed to improve the quotation management process with the implementation of a web service using the SAP Business One DI API. The research is of an applied type, with a quantitative approach, experimental design with a pre-experimental degree and explanatory level. The sample was 10 sales employees of an industrial organization, being non-probabilistic for convenience. The agile development methodology SCRUM was used, with C# and Java as programming languages. As for the database, HANA was used for compatibility with SAP. Likewise, the SAP DI API was used for secure connections and methods to the ERP. The results achieved after the implementation of the web service were the following: The increase in the number of quotations prepared by 36.62%, the reduction in the time taken to prepare quotations by 49.11%, the reduction in the costs of quotation management by 65.63% and the improvement in the satisfaction of quotation management with the implementation of the web service, where 80% of employees are very satisfied.

This research focuses on the problem of occupational accidents due to the improper use of personal protective equipment (PPE) in the construction industry. The implementation of a detection model based on Artificial Intelligence is necessary to increase the efficiency and accuracy of monitoring these implements, as well as to reduce the incidence rate. During the development and training process of the model, 5,886 high-definition (HD) JPG images were collected in a dataset on the RoboFlow platform. For processing, these images were scaled to 640 x 640 pixels and were related to working environments in this sector, as well as different climatic conditions, spaces, and focuses. A comparison of the models was carried out. YOLOv8 presented a mAP of 93.38%, in contrast to SSD, which reached 34.22%, and Faster R-CNN, which achieved 41.99% on average during the first 50 training epochs. Subsequently, for the final review of the training, 400 epochs of the YOLOv8 model were completed, resulting in a mAP of 93.22%, a recall of 89.67%, and an accuracy of 91.18%. With tuning and training finalized, the model was used for the development of a web system, which was subsequently hosted on a cloud server to facilitate access. This tool promotes compliance with safety regulations during the execution of daily tasks in this sector

Urban bus transport in Metropolitan Lima is a problem that the authorities have not been able to control, causing citizens to lose time, exposing them to danger and a terrible experience. Therefore, it is essential to develop a solution and design it to improve the service that companies offer to the user. The study seeks to design the solution through facial recognition in order to improve the user experience using YOLOv8-Deep Learning on public transport buses operating in Metropolitan Lima.

For this purpose, the area, type of bus, the ideal way to reduce processing resources and to make it as interactive as possible for the drivers of the units were analysed. The detection area was defined and a pre-trained Yolov8 model with counting was used to compare this value with the maximum capacity entered as the key to activate or deactivate the bus boarding and alighting access doors with other indicators.

The test results were acceptable, obtaining, with optimal lighting, about 90% accuracy, managing to control and count the number of passengers in the bus passageway, however, there were limitations related mainly to the lighting that, if it exceeded 60% of absence of light, regardless of the resolution of the cameras, the accuracy had a drop to less than 20%. The project will improve the quality of service and avoid the bad driving tactics employed, which will reduce accidents, reduce hours of congestion and provide a continuous flow of traffic in a chaotic city like Metropolitan Lima.

The phenomenon of hate speech, widely present on digital platforms, poses unique challenges in the Spanish language due toits linguistic ric hness and cultural diversity—characteristics that complicate the automatic identification of such content. This issue is further exacerbated by the language's ability to disguise hate messages through sarcasm, irony, or specific cultural references. This research focuses on the extraction of phraseological features and TF-IDF n- grams, utilizing traditional statistical classification models, neural networks, and ensemble methods to enhance the performance of classification models collectively. The OffendEs dataset, specifically labeled for hate speech tasks in Spanish, was used. Results demonstrate that ensemble models achieve higher levels of accuracy, striking a good balance between classes and showcasing their ability to handle the linguistic complexity of Spanish. In particular, the Voting Classifier achieved a macro F1 score of 0.742261. Our results were compared with predictions made using specific pre-trained models for hate speech detection, such as Piuba and Pysentimiento, demonstrating that our approach outperforms these models. These findings highlight the effectiveness of our methodology and its contribution to the development of more accurate tools for the automatic detection of hate speech in Spanish.

Drunk driving remains a leading cause of traffic crashes worldwide. To better understand impaired driving behaviors, this study utilized a driving simulator to compare two scenarios: one where participants consumed alcohol and another where they drove without alcohol’s effects. A virtual environment replicating the city of Palmira, Colombia, was created using geospatial data provided by GIS and procedural modeling techniques to enhance realism, including the customization of characteristic buildings. Autonomous non-player characters (NPCs) were integrated using NavMesh agents, and external controllers such as a steering wheel and pedals were employed to replicate real driving conditions. The simulation evaluated driving behaviors, focusing on the blurred vision and delayed steering responses associated with alcohol consumption. Thirty students participated in the study, driving a predefined 3-kilometer route. Participants were divided into two groups: those who consumed alcohol and those who did not. A difference of 1 minute and 25 seconds in driving time was observed between the two groups, with alcohol consumption contributing to slower response times and impaired visibility. A post-simulation questionnaire assessed participants’ perceptions of the simulation. The environment’s realism received an average score of 4.8/5, while the simulation’s immersion was rated at 3.7/5. This experiment highlights the potential of virtual simulations in studying impaired driving behaviors.

In this paper, a sign language recognition system for the Ecuadorian Sign Language vowels (A, E, I, O, U) using Random Forest (RF) and YOLOv8 models is proposed. For this purpose, a new dataset with a total of 500 RGB images in natural light for single-hand gestures was created. RF model used the normalized hand landmark coordinates obtained by using Mediapipe while for real-time gesture detection, YOLOv8 took images with higher resolutions. Hypothesis testing results also showed that the RF model had better accuracy, precision, recall, and computational complexity with the accuracy, precision and Recall scores all 100 % and were preferred for real-time applications. YOLOv8 performance was high with a precision of 100% revealing the model as suitable for tasks related to images. Final real-time inference tests validated our claims of scalability and efficiency of RF as it was able to classify gestures within an average of 0.0055 seconds of inference time. This paper underscores the importance of machine learning models in enhancing inclusion as well as closing communication barriers for the hearing-impaired population.