Machine Learning Applied to the Analysis of Prolonged COVID Symptoms: An Analytical Review Paola Patricia Ariza-Colpas, Marlon Alberto Piñeres-Melo, Miguel Alberto Urina-Triana, Ernesto Barceló-Martinez, Camilo Barceló-Castellanos, et al. Informatics, 2024 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.
Home Monitoring Tools to Support Tracking Patients with Cardio–Cerebrovascular Diseases: Scientometric Review Elisabeth Restrepo-Parra, Paola Patricia Ariza-Colpas, Laura Valentina Torres-Bonilla, Marlon Alberto Piñeres-Melo, Miguel Alberto Urina-Triana, et al. Iot, 2024 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.
Sustainability in Hybrid Technologies for Heritage Preservation: A Scientometric Study Paola Patricia Ariza-Colpas, Marlon Alberto Piñeres-Melo, Roberto-Cesar Morales-Ortega, Andrés Felipe Rodríguez-Bonilla, Shariq Butt-Aziz, et al. Sustainability Switzerland, 2024 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.
Improving the Accuracy of Predictive Models in Imbalanced Lung Cancer Data Ariza-Colpas Paola Patricia, Piñeres-Melo Marlon Alberto, Barceló-Martínez Er-nesto, Blanco-Anillo Sharith Alejandra, Barceló-Castellanos Camilo, et al. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2024
Heart Failure Mortality Prediction: A Comparative Study of Predictive Modeling Approaches Paola Patricia Ariza-Colpas, Marlon Alberto Piñeres-Melo, Ernesto Barceló-Martínez, Nelson Camilo Morales-Quintero, Camilo Barceló-Castellanos, et al. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2024
RTLA-HAR: A model proposal based on Reinforcement and Transfer Learning for the Adaptation of learning in Human Activity Recognition. International Journal of Artificial Intelligence, 2023
RDF query and protocols language using for description and representation of web ontologies Journal of Theoretical and Applied Information Technology, 2020
Method Based on Data Mining Techniques for Breast Cancer Recurrence Analysis Morales-Ortega Roberto Cesar, Lozano-Bernal German, Ariza-Colpas Paola Patricia, Arrieta-Rodriguez Eugenia, Ospino-Mendoza Elisa Clementina, et al. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020
SSwWS: Structural model of information architecture Marlon Alberto Piñeres-Melo, Paola Patricia Ariza-Colpas, Wilson Nieto-Bernal, Roberto Morales-Ortega Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
Parkinson disease analysis using supervised and unsupervised techniques Paola Ariza-Colpas, Roberto Morales-Ortega, Marlon Piñeres-Melo, Emiro De la Hoz-Franco, Isabel Echeverri-Ocampo, et al. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
Teleagro: IOT applications for the georeferencing and detection of zeal in cattle Paola Ariza-Colpas, Roberto Morales-Ortega, Marlon Alberto Piñeres-Melo, Farid Melendez-Pertuz, Guillermo Serrano-Torné, et al. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
