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, Fabian Roman 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, Shariq Butt-Aziz 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.
Semi-supervised ensemble learning for human activity recognition in casas Kyoto dataset Ariza-Colpas Paola Patricia, Pacheco-Cuentas Rosberg, Shariq Butt-Aziz, Piñeres-Melo Marlon Alberto, Morales-Ortega Roberto-Cesar, Urina-Triana Miguel, Sumera Naz Heliyon, 2024 -The automatic identification of human physical activities, commonly referred to as Human Activity Recognition (HAR), has garnered significant interest and application across various sectors, including entertainment, sports, and notably health. Within the realm of health, a myriad of applications exists, contingent upon the nature of experimentation, the activities under scrutiny, and the methodology employed for data and information acquisition. This diversity opens doors to multifaceted applications, including support for the well-being and safeguarding of elderly individuals afflicted with neurodegenerative diseases, especially in the context of smart homes. Within the existing literature, a multitude of datasets from both indoor and outdoor environments have surfaced, significantly contributing to the activity identification processes. One prominent dataset, the CASAS project developed by Washington State University (WSU) University, encompasses experiments conducted in indoor settings. This dataset facilitates the identification of a range of activities, such as cleaning, cooking, eating, washing hands, and even making phone calls. This article introduces a model founded on the principles of Semi-supervised Ensemble Learning, enabling the harnessing of the potential inherent in distance-based clustering analysis. This technique aids in the identification of distinct clusters, each encapsulating unique activity characteristics. These clusters serve as pivotal inputs for the subsequent classification process, which leverages supervised techniques. The outcomes of this approach exhibit great promise, as evidenced by the quality metrics' analysis, showcasing favorable results compared to the existing state-of-the-art methods. This integrated framework not only contributes to the field of HAR but also holds immense potential for enhancing the capabilities of smart homes and related applications.
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, Sumera Naz, Leidys del Carmen Contreras-Chinchilla, Maribel Romero-Mestre, Ronald Alexander Vacca Ascanio 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.
The Importance of Robust Communication in Large-Scale Agile Development Shariq Aziz Butt, Sumera Naz, Piñeres-Espitia Gabriel, Paola Ariza-Colpas Patriciac, Marlon Alberto Piñeres-Melo Procedia Computer Science, 2024 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.
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, Roman- Fabian 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, Fabian Roman Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2024
Machine Learning and AI Approaches for Analyzing Diabetic and Hypertensive Retinopathy in Ocular Images: A Literature Review Miguel Alberto Urina-Triana, Marlon Alberto Piñeres-Melo, Mirary Mantilla-Morrón, Shariq Butt-Aziz, Luisa Galeano-Muñoz, Sumera Naz, Paola Patricia Ariza-Colpas IEEE Access, 2024 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.
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
Exploring the History and Culture of Main Square Los Tupes with Augmented Reality in San Diego, Cesar Paola-Patricia Ariza-Colpas, Marlon-Alberto Piñeres-Melo, Roberto-Cesar Morales-Ortega, Andres-Felipe Rodriguez-Bonilla, Shariq Butt-Aziz, Leidys del Carmen Contreras Chinchilla, Maribel Romero Mestre, Ronald Alexander Vacca Ascanio, Alvaro Oñate-Bowen Lecture Notes on Data Engineering and Communications Technologies, 2023
Prediction based cost estimation technique in agile development Shariq Aziz Butt, Tuncay Ercan, Muhammad Binsawad, Paola-Patricia Ariza-Colpas, Jorge Diaz-Martinez, Gabriel Piñeres-Espitia, Emiro De-La-Hoz-Franco, Marlon Alberto Pineres Melo, Roberto Morales Ortega, Juan-David De-La-Hoz-Hernández Advances in Engineering Software, 2023
Multilayer Perceptron Applied to the IOT Systems for Identification of Saline Wedge in the Magdalena Estuary - Colombia Paola Patricia Ariza-Colpas, Cristian Eduardo Ayala-Mantilla, Marlon-Alberto Piñeres-Melo, Diego Villate-Daza, Roberto Cesar Morales-Ortega, Emiro De-la-Hoz-Franco, Hernando Sanchez-Moreno, Shariq Butt Aziz, Carlos Collazos-Morales Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2021
RDF query and protocols language using for description and representation of web ontologies Journal of Theoretical and Applied Information Technology, 2020
Internet of Things applied to Aquifer Monitoring Systems: A survey Ariza-Colpas Paola Patricia, Sanchez-Moreno Hernando Alberto, Pineres-Melo Marlon Alberto, Morales-Ortega Roberto Cesar, Ayala-Mantilla Cristian Eduardo, Villate-Daza Diego Andrés, De-la Hoz-Franco Emiro, Collazos-Morales Carlos Andrés Procedia Computer Science, 2020
Monitoring system of environmental variables for a strawberry crop using IoT tools Amaya Diaz Juan Carlos, Luzmila Rojas Estrada, Cardenas-Ruiz Cesar Augusto, Ariza-Colpas Paola Patricia, Piñeres-Melo Marlon Alberto, Ramayo González Ramón Enrique, Morales-Ortega Roberto César, Ovallos-Gazabon David Alfredo, Collazos-Morales Carlos Andrés Procedia Computer Science, 2020
Glyph Reader App: Multisensory Stimulation Through ICT to Intervene Literacy Disorders in the Classroom Paola Ariza-Colpas, Alexandra Leon-Jacobus, Sandra De-la-Hoz, Marlon Piñeres-Melo, Hilda Guerrero-Cuentas, Mercedes Consuegra-Bernal, Jorge Díaz-Martinez, Roberto Cesar Morales-Ortega, Carlos Andrés Collazos Morales Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 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, Caicedo-Ortiz Jose, Piñeres-Melo Marlon Alberto, Mendoza-Palechor Fabio Enrique, Roca-Vides Margarita 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, Katherinne Salas-Navarro 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, Guillermo Hernandez-Sanchez, Hugo Martínez-Osorio Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
Teleagro: Software Architecture of Georeferencing and Detection of Heat of Cattle Paola Ariza-Colpas, Roberto Morales-Ortega, Marlon Alberto Piñeres-Melo, Farid Melendez-Pertuz, Guillermo Serrano-Torné, Guillermo Hernandez-Sanchez, Hugo Martínez-Osorio, Carlos Collazos-Morales Communications in Computer and Information Science, 2019
