The mankind's home has evolved as humanity itself and through history, humanity has observed the safety and comfort of their homes. The adaptation of homes to the modern times, is now involved in a technological environment and constant innovation, especially in the control of appliances, safety, pleasure, the monitoring of electrical consumption, etc. These factors have allowed the integration of homes with IoT environments in what is known as smart home. In this work, an IoT smart sensor of electrical consumption in smart homes is presented which is capable to analyze the power consumption using mobile devices through a wireless connection. The smart meter was designed using a cyber-physical system based on the ESP32 micro-controller in which an embedded Web application is executed that shows the electrical consumption of electrical devices. The aim of this technological IoT smart device is to help to detect the phantom consumption of electrical energy in a smart home environment in order to promote the energy saving. The results obtained show that this kind of IoT technology contributes to decrease the economic expense for home owners and also allows to observe and analyze the electrical energy consumption of different electrical devices using mobile devices.

In its broadest sense, the term artificial intelligence indicates the ability of an artifact to perform the same types of functions that characterize human thought. The goal of AI is to use algorithms, heuristics and methodologies based on the ways in which the human brain solves problems. Artificial neural networks recreate the structure of the human brain imitating the learning process. The Artificial neural networks theory has provided an alternative to classical computing for those problems in which traditional methods have delivered results that are not very convincing or not very convenient such as in the case of the neutron spectrometry and dosimetry problem for radiation protection purposes, using the Bonner spheres spectrometer as measurement system, mainly because many problems are encountered when trying to determine the neutron energy spectrum of a measured data. The most delicate part of the spectrometry based on this system is the unfolding process, for which several neutron spectrum unfolding codes have being developed. However, these codes require an initial guess spectrum in order to initiate the unfolding process. Their poor availability and their not easy management for the end user are other associated problems. Artificial Intelligence technology, is an alternative technique that is gaining popularity among researchers in neutron spectrometry research area, since it offers better results compared with the traditional solution methods. In this work, "Synapse", a neutron spectrum unfolding code based on Generalized Regression Artificial Neural Networks technology is presented. The Synapse code is capable to unfold the neutron spectrum and to calculate 15 dosimetric quantities using the count rates, coming from a BSS as the only entrance information. The results obtained show that the Synapse code, based on GRANN technology, is a promising and innovative technological alternative for solving the neutron spectrometry and dosimetry problems.