Objetivo: Este artículo presenta un estudio cuyo objetivo principal fue evaluar el impacto de la implementación de tecnología de Realidad Aumentada (AR) en la Plaza Principal Alfonso Ávila Quintero, con la hipótesis de que la AR puede revitalizar el turismo y la cultura en Agustín Codazzi, Cesar. Metodología o método: Para la implementación, se diseñaron experiencias de AR específicas para la plaza, integrando aspectos históricos y culturales locales. Se recopilaron datos que permitieron identificar cuáles serían las características de los desarrollos a realizar, para que contaran con la articulación del componente autóctono de la región. Resultados: Los resultados mostraron un aumento significativo en la mejora en la percepción y el interés cultural de los visitantes hacia Agustín Codazzi. Estos resultados son novedosos en el contexto del uso de AR en espacios públicos para fines turísticos y culturales. Conclusiones: Se concluye que la implementación de AR en la Plaza Alfonso Ávila Quintero ha tenido un impacto positivo en el turismo y la cultura local. Se sugiere que futuras investigaciones exploren la sostenibilidad a largo plazo de estas tecnologías en entornos similares y su posible aplicación en otras regiones.

Home care and telemedicine are crucial for physical and mental health. Although there is a lot of information on these topics, it is scattered across various sources, making it difficult to identify key contributions and authors. This study conducts a scientometric analysis to consolidate the most relevant information. The methodology is divided into two parts: first, a scientometric mapping that analyzes scientific production by country, journal, and author; second, the identification of prominent contributions using the Tree of Science (ToS) tool. The goal is to identify trends and support decision-making in the health sector by providing guidelines based on the most relevant research.

Cultural and heritage tourism is an important source of income for many regions around the world, including Colombia. However, it often faces a series of challenges that hinder its development and success. In the Cesar department of Colombia, the lack of updated information and effective management of tourism infrastructure have limited the potential of cultural and heritage tourism in the region. Tourists often do not have access to detailed information about places of interest, such as the history behind them and available activities. This makes travel planning difficult and reduces the quality of the tourist experience. To address these issues, the implementation of a cloud-based service architecture has been proposed to boost cultural and heritage tourism in the Cesar department. This architecture focuses on enhancing the tourist experience by providing updated and personalized information about tourist sites, allowing tour reservations, and facilitating the management of tourism infrastructure. The application called Enamorate del Cesar allows centralized and real-time management of tourist sites, which improves the quality of the tourist experience and reduceswaiting times. Additionally, the architecture provides access to detailed and personalized information about tourist sites, which facilitates travel planning and improves tourist satisfaction.

The use of augmented reality applied to museums to preserve and communicate cultural heritage sustainably is a topic of increasing relevance today. Museums play an essential role in preserving and disseminating culture and history, and augmented reality has emerged as a powerful technological tool to enrich the visitor experience and ensure the sustainable preservation of cultural heritage. The fundamental objective of this literature review is to explore and understand the key contributions that are being made in the field of augmented reality applied to museums, with a focus on sustainability. The literature related to this topic is dispersed in various sources of information, which motivates the need to carry out a detailed and systematic analysis incorporating sustainability aspects. To carry out this analysis, the metaphor of the “tree of science” is used. This metaphor provides a structured approach that is applied in two complementary ways. Firstly, it focuses on collecting and analyzing scientometric statistics that cover data on countries, authors, academic institutions, and research centers involved in developing augmented reality applications for museums with sustainable methodologies. This quantitative perspective offers a global view of the contributions and their geographical scope including their sustainability impact. Secondly, an evolutionary analysis based on the “tree of science” is carried out. This historical approach examines the origin and evolution of contributions in the field of augmented reality applied to museums, from its first manifestations to the most recent innovations, with an emphasis on sustainable practices. This historical approach is essential to understanding the trajectory and development of augmented reality applications in the museum context and their role in promoting sustainable cultural heritage preservation. This review aims to provide a complete and contextualized view of the use of augmented reality in museums for the sustainable preservation and communication of cultural heritage. Through a multidimensional approach encompassing scientometric statistics and historical analysis, we seek to shed light on this technology’s most significant contributions and evolution in the museum sector, with a particular focus on sustainability.

The accurate diagnosis of lung cancer using predictive modeling presents significant challenges, primarily due to the imbalanced nature of clinical datasets where certain outcomes are underrepresented. This study addresses the critical impact of class imbalance on the predictive accuracy of machine learning models applied to lung cancer diagnosis. We evaluated several popular classification algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forests, and Deep Learning models, across original and various enhanced datasets. Our methodology involved preprocessing the data to handle missing values and applying several techniques to balance the classes effectively. These techniques included manual oversampling, undersampling, and synthetic oversampling methods. The manual oversampling allowed us to duplicate instances of the minority classes, while undersampling reduced the instances of the majority class. Synthetic oversampling, using a method like the Adaptive Synthetic Sampling (ADASYN), generated new synthetic instances for the minority classes. This combined approach allowed us to enhance the representation of minority classes and improve the generalizability of our models.The performance of each model was assessed using accuracy metrics and Receiver Operating Characteristic (ROC) curves across both dataset conditions. Results indicated that SVM, Random Forest, and Deep Learning models, when trained on balanced data, demonstrated significant improvements in accuracy and ROC-AUC scores compared to training on the original imbalanced dataset. Specifically, the Random Forest and Deep Learning models showed a notable increase in performance, highlighting the effectiveness of ensemble and deep learning methods in dealing with class imbalances. This study confirms that addressing class imbalance through a combination of manual and synthetic oversampling techniques can substantially improve the accuracy of predictive models in lung cancer diagnosis. These findings advocate for the integration of these techniques in preprocessing steps for clinical data analysis, potentially leading to more reliable and equitable healthcare outcomes.

The COVID-19 pandemic continues to constitute a public health emergency of international importance, although the state of emergency declaration has indeed been terminated worldwide, many people continue to be infected and present different symptoms associated with the illness. Undoubtedly, solutions based on divergent technologies such as machine learning have made great contributions to the understanding, identification, and treatment of the disease. Due to the sudden appearance of this virus, many works have been carried out by the scientific community to support the detection and treatment processes, which has generated numerous publications, making it difficult to identify the status of current research and future contributions that can continue to be generated around this problem that is still valid among us. To address this problem, this article shows the result of a scientometric analysis, which allows the identification of the various contributions that have been generated from the line of automatic learning for the monitoring and treatment of symptoms associated with this pathology. The methodology for the development of this analysis was carried out through the implementation of two phases: in the first phase, a scientometric analysis was carried out, where the countries, authors, and magazines with the greatest production associated with this subject can be identified, later in the second phase, the contributions based on the use of the Tree of Knowledge metaphor are identified. The main concepts identified in this review are related to symptoms, implemented algorithms, and the impact of applications. These results provide relevant information for researchers in the field in the search for new solutions or the application of existing ones for the treatment of still-existing symptoms of COVID-19.

This article compares various classification techniques in their ability to predict risk levels and recurrence in patients with thyroid disorders. Focusing on a detailed dataset incorporating clinical and pathological features, four prominent classification methods were implemented and evaluated: Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM). The findings revealed that Random Forests achieved the highest accuracy in predicting the risk level (88.70

This study presents a comparative assessment of various machine learning models for predicting mortality in heart failure patients. Through a rigorous analytical approach, we have scrutinized models ranging from logistic regression and support vector machines (SVM) to advanced ensemble algorithms like Random Forest and XGBoost. Our analysis delves into the accuracy, sensitivity, and specificity of each model, utilizing real clinical data to validate our predictions. The results indicate that while traditional models such as logistic regression maintain robust performance, it is the ensemble algorithms that stand out for their superior predictive capability, evidenced by areas under the curve (AUC) close to 0.90. The findings underscore the transformative potential of machine learning techniques in the prognosis and management of heart failure, providing crucial insights for early intervention and improving clinical outcomes in high-risk patients.

The agile methodology stands out as a prevalent model for efficient software development, particularly favored for its adaptability and suitability in small-scale projects across various software industries. Nevertheless, its widespread adoption has brought to light certain communication challenges, particularly when applied to large-scale distributed teams. Agile, it appears, may not be the optimal choice for extensive teams engaged in global software development efforts. This study delves into the intricacies of issues faced by teams employing agile in the context of large-scale distributed development, particularly focusing on communication-related challenges and their repercussions. Our approach involved in-depth interviews with diverse developers and teams hailing from various sectors within the software industry. Moreover, we conducted an extensive quantitative analysis, surveying 50 developers representing different distributed teams. The outcomes of our investigation unearthed several communication-related deficiencies that significantly impact the development process. To arrive at these insights, we employed two robust statistical analysis methods: descriptive analysis and regression analysis. The implications of our findings have led us to propose innovative software solutions, bearing distinctive features engineered to mitigate the communication issues often encountered in large-scale software development. These solutions have the potential to enhance the efficiency and effectiveness of agile practices when applied in extensive and globally dispersed development endeavors.

The field of healthcare holds significant global importance due to its profound impacts on both individual well-being and the broader healthcare system. It plays a pivotal role in the economic landscape, with far-reaching effects at the local, national, and global levels. Moreover, healthcare stands as a vital source of employment, supporting countless individuals across the world. It is a sector characterized by persistent challenges that have been met with innovation and technological advancements. In this literature review, our goal is to explore the key contributions in the healthcare domain, specifically in the diagnosis of diabetic and hypertensive retinopathy using advanced technologies such as Machine Learning and Artificial Intelligence (AI). The use of these technologies is instrumental in enhancing diagnostic accuracy and patient care. The wealth of research in this field is dispersed across various scholarly databases, presenting an opportunity for an extensive and focused investigation. By combining scientometric analysis with the metaphorical "tree of science," we can gain two valuable perspectives on this domain. The first perspective delves into scientometric statistics, shedding light on countries, authors, academic institutions, and research centers that are at the forefront of developing innovative solutions for diagnosing retinopathy using AI and Machine Learning. The second perspective employs an evolutionary analysis, exploring the origins of seminal research contributions and how they have evolved over time. This literature review underscores the ongoing relevance of leveraging Machine Learning and AI in healthcare, particularly in the diagnosis of retinopathy. Furthermore, the COVID-19 pandemic has accelerated the development of technologies that enable remote diagnosis and care, revolutionizing the healthcare landscape. As we navigate the intricate web of healthcare innovation, this literature review aims to provide a comprehensive understanding of the current state of research and its trajectory in the realm of diabetic and hypertensive retinopathy diagnosis through advanced technologies.

This article shows the implementation of a prediction model of the payment behavior of the renewal concept of companies registered in the commercial registry of the Barranquilla Chamber of Commerce using machine learning techniques in a multilevel classification scenario, where it will offer the organization a tool that allows it to know in advance the behavior of the payment of the renewal of a company in such a way that it is able to design strategies to increase the success indicators in terms of the number of registration renewals, mercantile, and of the income collected for this concept.

Technology has emerged as an essential tool that has revolutionized the conditions for travelers to fully immerse themselves in the culture, gastronomy, and recreation of the places they explore. This literature review aims to understand the crucial contributions currently shaping the implementation of augmented reality as an enriching technological support for user experiences in tourism and the conservation of natural heritage. While the literature on this topic is scattered across specialized databases, this review provides a unique opportunity for a deeper and more cohesive analysis. Employing the metaphor of the tree of science, we have developed two valuable approaches to the data collected during our bibliographic exploration. On the one hand, we have examined scientometric statistics related to the countries, authors, universities, and research and technological development centers that are at the forefront of creating innovative augmented reality-based applications to promote tourism and conservation. On the other hand, we have conducted an evolutionary analysis based on the tree of science to trace the origins of the most significant contributions and understand how they have evolved over time in this dynamic and ever-developing field.

Tourism is a sector of high relevance worldwide, due to the multiple impacts it generates in local, regional, national, continental, and global economies, and it is a key generator of employment and provides sustenance to an innumerable number of people around the world. There have been many challenges at a global level to improve the user experience in a particular tourist place, where technology has played a highly relevant role in strengthening the conditions for tourists to achieve immersion in the culture, gastronomy, and recreation. The objective of this literature review is precisely to know and understand the key contributions that are currently being developed around the implementation of augmented reality as tourist technological support for user experiences. The literature on this topic is quite dispersed in specialized databases; therefore, it constitutes an opportunity to carry out a more detailed exploration of the topic. To address the different developments that have been carried out on tourism and augmented reality, an analysis was carried out based on the fusion of scientometric analysis and the metaphor of the Tree of Science, in which two relevant visions about the data were generated. The first focused on the different scientometric statistics regarding countries, authors, universities, or research or technological development centers that currently generate new applications based on augmented reality for tourism. The second focused on an evolutionary analysis based on the Tree of Science, analyzing the origins of the basic contributions of research and how it has evolved over time. This review indicates that the topic is currently valid and that it has been strengthened even more with the post-pandemic process, where many technological developments have been strengthened that allow people to enjoy tourist and cultural sites even without leaving home.

Augmented reality (AR) has gained popularity as a tool for exploring cultural heritage sites. This study investigates the use of AR technology to enhance the visitor experience at Plaza Principal Los Tupes in San Diego, Cesar. As an important cultural heritage site, the study aims to explore the potential of AR technology in providing visitors with an immersive and interactive experience of the site's history and culture. An AR application was developed, incorporating historical and cultural information about Plaza Principal Los Tupes, to offer visitors an interactive and educational experience. A user study was conducted to evaluate the effectiveness of the AR application in enhancing visitor experience. The results reveal that AR technology significantly enhances the visitor experience at cultural heritage sites, allowing for a deeper understanding of historical and cultural significance. The study demonstrates the potential of AR technology in increasing visitor engagement, accessibility, preservation of cultural heritage, and overall visitor satisfaction and revenue. However, challenges associated with AR technology usage are identified, emphasizing the need for further research to overcome these challenges and fully realize the potential of AR technology in the cultural heritage sector.

The tourism sector is one of the sectors that have been most affected by the Covid-19 pandemic, due to the reduction in its income by more than half in 2020 compared to the previous year, according to the UNWTO. In Colombia, the panorama does not differ, with fall in sales close to 70

Agile has been invented to improve and overcome the deficiencies of efficient software development. At present, the agile model is used in software development vastly due to its support to both developers and clients resourcefully. Agile methodology increases the interaction between the developer and client to make the software product defect-free. The agile model is getting to be a well-known life cycle model because of its particular features and most owing is to allow changes at any level of the project from the product owner. However, on other hand, this novel feature is a disadvantage of the agile model due to frequent change requests from the client has increased the cost and time. To overcome cost and time estimation issues different cost estimation techniques are being used in agile development but no one is pertinent for accurate estimation. Therefore, this study has proposed a cost estimation technique. The proposed estimation technique is predictions-based and has different categorizations of projects based on user stories complexities and the developer's expertise. We have applied the suggested technique to ongoing projects to find the results and effectiveness. We have used two projects with different sizes and user stories. Both projects have different modules and developers with different expertise. We have used the proposed estimation technique on projects and done a survey session with the teams. This survey session's main objective is to reveal the statistical findings of the proposed solution. We have designed the 12 hypotheses for statistical analysis.

In the department of Atlántico-Colombia, inter-municipal transport companies operate that mobilize 325,000 people daily. The nature of inter-municipal transport makes it very difficult for companies and vehicle owners to have real control of the income of each bus because, unlike urban transport, the value of the ticket depends on the place of getting on and off each bus. One of the main motivations of this research is to help solve the problems associated with the management of drivers who usually hire assistants who manually and visually control each passenger’s entry and exit points and, according to this criterion, calculate the amount to be paid. Charge individually. Since there is no certainty of the actual monetary income from the buses, companies and owners charge drivers a fixed daily value (fee). The objective of the platform described in this article is to manage the analysis of economic resources generated in public transport activity. This form of work affects the formality of the transport sector and generates a loss of competitiveness in the department Atlántico – Colombia.

Background In order to remain active and productive, older adults with poor health require a combination of advanced methods of visual monitoring, optimization, pattern recognition, and learning, which provide safe and comfortable environments and serve as a tool to facilitate the work of family members and workers, both at home and in geriatric homes. Therefore, there is a need to develop technologies to provide these adults autonomy in indoor environments. Objective This study aimed to generate a prediction model of daily living activities through classification techniques and selection of characteristics in order to contribute to the development in this area of knowledge, especially in the field of health. Moreover, the study aimed to accurately monitor the activities of the elderly or people with disabilities. Technological developments allow predictive analysis of daily life activities, contributing to the identification of patterns in advance in order to improve the quality of life of the elderly. Methods The vanKasteren, CASAS Kyoto, and CASAS Aruba datasets were used to validate a predictive model capable of supporting the identification of activities in indoor environments. These datasets have some variation in terms of occupation and the number of daily living activities to be identified. Results Twelve classifiers were implemented, among which the following stand out: Classification via Regression, OneR, Attribute Selected, J48, Random SubSpace, RandomForest, RandomCommittee, Bagging, Random Tree, JRip, LMT, and REP Tree. The classifiers that show better results when identifying daily life activities are analyzed in the light of precision and recall quality metrics. For this specific experimentation, the Classification via Regression and OneR classifiers obtain the best results. Conclusion The efficiency of the predictive model based on classification is concluded, showing the results of the two classifiers, i.e., Classification via Regression and OneR, with quality metrics higher than 90% even when the datasets vary in occupation and number of activities.

One of the technical aspects that contribute to improving the quality of life for older adults is the automation of physical spaces using sensors and actuators, which facilitates the performance of their daily activities. The interaction between individuals and their environment enables the detection of abnormal patterns that may arise from a decline in their cognitive abilities. In this study, we evaluate the CASAS Kyoto dataset from WSU University, which provides information on the daily living activities of individuals within an indoor environment. We developed a model to predict activities such as Cleaning, Cooking, Eating, Washing hands, and Phone Call. A novel approach is proposed, which involves preprocessing and segmenting the dataset using sliding windows. Furthermore, we conducted experiments with various classifiers to determine the optimal choice for the model. The final model utilizes the regression classification technique and is trained on a reduced dataset containing only 5 features. It achieves outstanding results, with a Recall of 99.80% and a ROC area of 100%.

The Department of Cesar is a highly important tourist attraction in Colombia where most of its visitors go on vacation, recreation, and leisure. Despite being positioned as a high tourist attraction, it has been possible to identify that there is a lack of public-private articulation, as well as the participation of the academy in the development of projects with a greater regional impact, weaknesses in the working capital of the sector, little entrepreneurship, and innovation around the generation of new tourism products. That is why it is necessary to strengthen and expand the coverage of national or regional programs, projects, and initiatives on these routes, corridors, and infrastructure projects proposed, the development of information systems that allow assertive decision-making, as well as the pertinent regulation by the sustainability framework. One of the sectors that require support and strengthening is the tourism sector which requires support to strengthen and boost the economy. The purpose of this article is to show the development of an application based on gamification that allows the cultural strengthening of the region, which allowed appropriation processes to be generated in the community that is the object of intervention of the department and economic revitalization.