Conference Agenda

Private parking lots face significant challenges due to the increasing number of vehicles, requiring advanced technological solutions to optimize space use and improve user experience. The artificial vision system for spot detection is proposed as an innovative solution using machine learning techniques. The VDI 2206 methodology was adopted due to its suitability in the design of mechatronic systems, integrating various areas of knowledge for effective system control. The development of the system was divided into five key stages. In the first stage, "Requirements", quality images were collected to define architectures and select electronic and software components. In the second stage, the hyperparameters of the VGG16 model and the SVM classifier were adjusted, designing flowcharts and programming. The third stage included coding the detection model, developing live video streaming modules, and improving image post-processing. The fourth stage focused on the assembly and configuration of the hardware and camera, optimizing the code for accurate real-time detection. Finally, in the fifth stage, the data were validated, and the model was evaluated, reaching a precision of 0.90 and an accuracy of 0.92 in image processing in one second, ensuring the coherence and effectiveness of the process and achieving effective detection of parking spaces.

The article explored how augmented reality (AR) transformed the customer experience in the retail sector and its contribution to increased sales. According to recent studies, it was highlighted that AR offered consumers the possibility to virtually interact with products before making a purchase, which not only increased their confidence in decisions but also streamlined the purchasing process. This technology resulted in an outstanding improvement in consumer satisfaction by providing an immersive, personalized and engaging shopping experience. In addition, AR reinforces customer loyalty by making interaction with brands more dynamic, which increases the likelihood of future purchases. By reducing uncertainty about products, it also reduces the number of returns, benefiting both consumers and companies. However, its adoption presents certain challenges, such as technological costs and integration into existing systems. Despite these obstacles, AR is emerging as a key tool for companies to stand out in a highly competitive market. Ultimately, AR is changing the way stores connect with their customers, and its potential to redefine commerce is increasingly evident, becoming an essential strategy for the future of retail.

In modern agriculture, identifying diseases or pests in fruit plants is one of the biggest challenges. It is true that there are various methods for making diagnoses, mostly with the help of specialists who traverse the fields checking for any signs of disease in the plants. Given the aforementioned as stated in article [1], many of these techniques are slow and very costly, and even with errors, since diseases are not detected until they have a significant degree of damage. In the present systematic literature review, recent studies that approach the diagnosis of plant health through deep learning in mobile applications were analyzed, with the intention of identifying emerging trends and assessing the efficiency of the employed technologies. The methodology employed consists of the comprehensive search and analysis of each scientific article from academic literature sources, for instance, Web of Science and Scopus. In the selection of these articles, keywords related to fruit plants, mobile applications, deep learning, health, and diagnosis were used, following the established inclusion and exclusion criteria, considering that they are RSL articles or conference papers and open access. Subsequently, these articles were analyzed, comparing different approaches using various deep learning models such as RestNet50, VGG16, and other pre-trained models that achieve a high level of accuracy in image capture through mobile devices. This RSL analyzed the use of innovative tools such as Deep learning that systematize knowledge in this area, optimizing crop production and reducing significant losses, which is crucial for future research.

The primary goal of this study work will focus on carrying out a complete analysis on how the implementation of AI and the incorporation of automation allow not only the improvement in business processes, but also the strengthening and continuous optimization of competitive strategies, which will be key during the process of creating this study's systematic review. The methodology used involves an in- depth analysis of scientific studies from the "Scopus" database. Fifty-five of the 172 pertinent papers that were found were chosen for this review, utilizing strict inclusion and exclusion standards, as well as full-text access. The analysis of the following study has three main characteristics where it is evidenced: [1] The automation and use of AI to business process optimization. [2] The identification of tools and applications that contribute significantly to strategic decision-making and improving operational productivity. [8] For business competitiveness, these technologies are used, which have a great impact on both advantages and ethical and technical challenges. It is evident that automation in business processes together with AI favors operational efficiency, maximizing resources and improving capacity for market adaptation.Furthermore, it is evident that corporate accountability must be upheld when utilizing these technologies. This evaluation will be beneficial for new and future research on process optimization using automation and artificial intelligence, and will allow business managers to offer fundamental strategies. The aim is to ensure that the results obtained offer a greater understanding of these technologies and are applicable in the workplace.

The main objective of the research is to present the role that artificial intelligence applications play in the quantitative analysis required for the development of predictive models and the support they provide in this process. The aim is to understand how these applications can influence the field of business management and continuous improvement within organizations. To this end, a practical example of the use of an artificial intelligence application for the development of a predictive model is shown, comparing it with the development of the same predictive model under a conventional method, with the main results being that artificial intelligence applications facilitate and speed up the data analysis process for correct business decision making.

This study presents an improvement model to increase the level of service in a wholesale pet food company, which faces a technical gap of 13% with respect to the sector in this indicator, a gap mainly attributed to stock breakage caused by inadequate demand planning and inefficient inventory management. As a solution to this problem, a demand forecasting model is developed based on k-means and RFM clustering techniques, leading into categorizing customers according to their purchase level and geographic location. Identifying 4 customer categories and 31 key products. In addition, an ABC analysis is applied together with Lean 5S and Kanban techniques to reorganize the warehouse, achieving a 23.26% reduction in operating times through a pilot test. To avoid stock-outs, EOQ and ROP parameters are introduced to standardize the purchasing process and thus achieve a timely supply of inventory, resulting in an increase in sales equivalent to 1100 bags of feed. The simulation in Arena validates that the set of these techniques together increase the service level by 13.18% and reduce the average inventory by 22.70%. In this way, the project achieves revenue maximization by increasing the units sold and optimizes storage costs. These improvements have a positive economic impact equivalent to USD 72,750 and consolidate a significant improvement in the company's operating efficiency.