Examinando por Autor "Ariza-Colpas, Paola Patricia"
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Ítem Internet of Things applied to Aquifer Monitoring Systems: A survey(Elsevier, 2020) 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 AndrésThe interaction between the oceanic and continental basins has been of general interest among the scientific community of multiple disciplines, from the physical perspective of how the interaction of fresh and salt waters mutually modulate their hydrodynamic behavior, and how this in turn determines the transport of sediments, nutrients and other tracers, in addition to inducing changes in the morphodynamics of the river and / or coastal-oceanic zone. Due to the importance of technology for the prevention of different environmental phenomena, this article aims to show the systematic review of the literature about different applications that allow software and hardware interaction to support decision making in the sense of aquifers.Ítem Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning(Elsevier, 2021) García-Restrepo, Johanna; Ariza-Colpas, Paola Patricia; Oñate-Bowen, Alvaro Agustín; Suarez-Brieva, Eydy del Carmen; Urina-Triana, Miguel; De-la-Hoz-Franco, Emiro; Díaz-Martínez, Jorge Luis; Butt, Shariq Aziz; Molina_Estren, DiegoAI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and therefore enrich the quality of life while preserving their privacy. An SH system is usually equipped with a collection of software interrelated with hardware components to monitor the living space by capturing the behavior of the resident and their occupations. By doing so, the system can report risks, situations, and act on behalf of the resident to their satisfaction. This research article shows the experimentation carried out with the human activity recognition dataset, CASAS Kyoto, through preprocessing and cleaning processes of the data, showing the Vía Regression classifier as an excellent option to process this type of data with an accuracy 99.7% effective