The practical increase of interest in intelligent technologies has caused a rapid development of all activities in terms of sensors and automatic mechanisms for smart operations. The implementations concentrate on technologies which avoid unnecessary actions on user side while examining health conditions. One of important aspects is the constant inspection of the skin health due to possible diseases such as melanomas that can develop under excessive influence of the sunlight. Smart homes can be equipped with a variety of motion sensors and cameras which can be used to detect and identify possible disease development. In this work, we present a smart home system which is using in-built sensors and proposed artificial intelligence methods to diagnose the skin health condition of the residents of the house. The proposed solution has been tested and discussed due to potential use in practice.

The increasing exploration of alternative methods for solving optimization problems causes that parallelization and modification of the existing algorithms are necessary. Obtaining the right solution using the meta-heuristic algorithm may require long operating time or a large number of iterations or individuals in a population. The higher the number, the longer the operation time. In order to minimize not only the time, but also the value of the parameters we suggest three proposition to increase the efficiency of classical methods. The first one is to use the method of searching through the neighborhood in order to minimize the solution space exploration. Moreover, task distribution between threads and CPU cores can affect the speed of the algorithm and therefore make it work more efficiently. The second proposition involves manipulating the solutions space to minimize the number of calculations. In addition, the third proposition is the combination of the previous two. All propositions has been described, tested and analyzed due to the use of various test functions. Experimental research results show that the proposed methodology for parallelization and manipulation of solution space is efficient (increasing the accuracy of solutions and reducing performance time) and it is possible to apply it also to other optimization methods.

Augmented reality (AR) is becoming increasingly popular due to its numerous applications. This is especially evident in games, medicine, education, and other areas that support our everyday activities. Moreover, this kind of computer system not only improves our vision and our perception of the world that surrounds us, but also adds additional elements, modifies existing ones, and gives additional guidance. In this article, we focus on interpreting a reality-based real-time environment evaluation for informing the user about impending obstacles. The proposed solution is based on a hybrid architecture that is capable of estimating as much incoming information as possible. The proposed solution has been tested and discussed with respect to the advantages and disadvantages of different possibilities using this type of vision.

Rapid development and conducted experiments in the field of the introduction the fifth generation of the mobile network standard allow for the flourishing of the Internet of Things. This is one of the most important reasons to design and test systems that can be implemented to increase the quality of our lives. In this paper, we propose a system model for managing tasks in smart homes using multi-agent solutions. The proposed solution organizes work and distributes tasks to individual family members. An additional advantage is the introduction of gamification, not only between household members, but also between families. The solution was tested to simulate the entire solution as well as the individual components that make up the system. The proposal is described with regard to the possibility of implementing smart homes in future projects.

The blockchain technique is becoming more and more popular due to its advantages such as stability and dispersed nature. This is an idea based on blockchain activity paradigms. Another important field is machine learning, which is increasingly used in practice. Unfortunately, the training or overtraining artificial neural networks is very time-consuming and requires high computing power. In this paper, we proposed using a blockchain technique to train neural networks. This type of activity is important due to the possible search for initial weights in the network, which affect faster training, due to gradient decrease. We performed the tests with much heavier calculations to indicate that such an action is possible. However, this type of solution can also be used for less demanding calculations, i.e., only a few iterations of training and finding a better configuration of initial weights.