Object tracking is the process of estimating in time N the location of one or more moving element through an agent (camera, sensor, or other perceptive device). An important application in object tracking is the analysis of animal behavior to estimate their health. Traditionally, experts in the field have performed this task. However, this approach requires a high level of knowledge in the area and sufficient employees to ensure monitoring quality. Another alternative is the application of sensors (inertial and thermal), which provides precise information to the user, such as location and temperature, among other data. Nevertheless, this type of analysis results in high infrastructure costs and constant maintenance. Another option to overcome these problems is to analyze an RGB image to obtain information from animal tracking. This alternative eliminates the reliance on experts and different sensors, yet it adds the challenge of interpreting image ambiguity correctly. Taking into consideration the aforementioned, this article proposes a methodology to analyze lamb behavior from an approach based on a predictive model and deep learning, using a single RGB camera. This method consists of two stages. First, an architecture for lamb tracking was designed and implemented using CNN. Second, a predictive model was designed for the recognition of animal behavior. The results obtained in this research indicate that the proposed methodology is feasible and promising. In this sense, according to the experimental results on the used dataset, the accuracy was 99.85% for detecting lamb activities with YOLO${}_{V4}$, and for the proposed predictive model, a mean accuracy was 83.52% for detecting abnormal states. These results suggest that the proposed methodology can be useful in precision agriculture in order to take preventive actions and to diagnose possible diseases or health problems.

The growing availability of large volumes of data has generated new assets for various industries. This poses a key challenge for higher education: measuring, defining, and structuring these assets by identifying relevant lines of research. These lines should serve as a guide in the training of new professionals, responding to the growing demand for technological skills within Industry 4.0. The present study aims to establish solid criteria for identifying areas of research applicable to this context. To achieve this, the Sample, Explore, Modify, Model, and Evaluate model has been used, which covers all stages of the data mining process, from initial collection to final evaluation of the models. Through a bibliometric analysis based on four key characteristics, essential fields of knowledge for the development of lines of research were identified by analyzing 1,300 high-impact scientific articles. As a result, the application of the Near Zero algorithm automated the classification of search criteria. This approach not only facilitates the identification of emerging areas but also opens up new opportunities in diverse industrial sectors, highlighting the relevance of academia to technological advancement as part of digital transformations.