La tesis doctoral se enfoca en el análisis del sistema de gestión de salud y las habilidades directivas en la calidad de los servicios de una red de salud en Junín durante el año 2023. El estudio es de tipo básico, de nivel explicativo, con un enfoque cuantitativo y diseño transversal. Se apoya en el Modelo de Aceptación de Tecnología (TAM) y la teoría Motivación-Oportunidad-Habilidad (MOA) para medir las variables. La población estudiada incluye a los colaboradores de la red de salud de Junín, seleccionando una muestra de 200 mediante muestreo aleatorio simple. La técnica de recolección de datos empleada fue la encuesta, y el instrumento utilizado fueron cuestionarios validados. La investigación concluye que la gestión de salud y las habilidades directivas tienen un impacto significativo en la calidad de los servicios, sugiriendo la necesidad de mejoras en estas áreas p...

The Internet of Underwater Things (IoUT) exhibits promising advancement with underwater acoustic wireless network communication (UWSN). Conventionally, IoUT has been utilized for the offshore monitoring and exploration of the environment within the underwater region. The data exchange between the IoUT has been performed with the 5G enabled-communication to establish the connection with the futuristic underwater monitoring. However, the acoustic waves in underwater communication are subjected to longer propagation delay andhigher transmission energy. To overcome those issues autonomous underwater vehicle (AUV) is implemented for the data collection and routing based on cluster formation. This paper developed a memeticalgorithm-basedAUV monitoring system for the underwaterenvironment. The proposed Autonomous 5G Memetic (A5GMEMETIC) model performs the data collection and transmission to inc...

The development of Internet of Things (IoT) applications for creating behavioural and physiological monitoring methods, such as an IoT-based student healthcare monitoring system, has been accelerated by advances in sensor technology. Today, there are an increasing number of students living alone whoare dispersed across large geographic areas, therefore it is important to monitor their health and function. This research propose novel technique in high performance modelling for health monitoring system by 5G network based machine learning analysis. Here the input is collected as EEG brain waves which are monitored and collected through 5G networks. This input EEG waves has been processed and obtained as fragments and noise removal is carried out. The processed EEG wave fragments has been extracted using K-adaptive reinforcement learning. this extracted features has been classified using na...