A large number of researchers spend a lot of time searching for the most efficient neural network to solve a given problem. The procedure of configuration, training, testing, and comparison for expected performance is applied to each experimental neural network. The configuration parameters—training methods, transfer functions, number of hidden layers, number of neurons, number of epochs, and tolerable error—have multiple possible values. Setting guidelines for appropriate parameter values would shorten the time required to create an efficient neural network, facilitate researchers, and provide a tool to improve the performance of automated neural network search methods. The task considered in this paper is related to the determination of upper bounds for the number of hidden layers and the number of neurons in them for approximating artificial neural networks trained with algorithms using the Jacobi matrix in the error function. The derived formulas for the upper limits of the number of hidden layers and the number of neurons in them are proved theoretically, and the presented experiments confirm their validity. They show that the search for an efficient neural network can focus below certain upper bounds, and above them, it becomes pointless. The formulas provide researchers with a useful auxiliary tool in the search for efficient neural networks with optimal topology. They are applicable to neural networks trained with methods such as Levenberg–Marquardt, Gauss–Newton, Bayesian regularization, scaled conjugate gradient, BFGS quasi-Newton, etc., which use the Jacobi matrix.

This paper describes a model of modular system for Rapid Application Development of Web-based Information Systems (RADWIS). The existing modular systems on technology, framework and platform level does not fully solve the problems of functionality reuse, rapid application development and balance between the complexity, size and functionality. The proposed modular system addresses these problems in a new way. The current work fills the gap between the modular systems on the framework and platform level. The model uses flexible, reusable modules, which can be built with different technologies. They are installable and shareable with the standard dependency manager of the technology and can communicate using web services. The modules use NoSQL approaches in SQL databases. A workflow engine module, based on the Petri Nets theory, allows a graphical and formal mathematical solution for a wide variety of problems.

The goal of the present study is to find a method for improving the predictive capabilities of feedforward neural networks in cases where values distant from the input–output sample interval are predicted. This paper proposes an iterative prediction algorithm based on two assumptions. One is that predictions near the statistical sample have much lower error than those distant from the sample. The second is that a neural network can generate additional training samples and use them to train itself in order to get closer to a distant prediction point. This paper presents the results of multiple experiments with different univariate and multivariate functions and compares the predictions made by neural networks before and after their training with the proposed iterative algorithm. The results show that, having passed through the stages of the algorithm, artificial neural networks significantly improve their interpolation performance in long-term forecasting. The present study demonstrates that neural networks are capable of creating additional samples for their own training, thus increasing their approximating efficiency.

Making forecasts for the development of a given process over time, which depends on many factors, is in some cases a difficult task. The choice of appropriate methods—mathematical, statistical, or artificial intelligence methods—is also not obvious, given their great variety. This paper presented a model of a forecasting system by comparing the errors in the use of time series on the one hand, and artificial neural networks on the other. The model aims at multifactor predictions based on forecast data on significant factors, which were obtained by automated testing of different methods and selection of the methods with the highest accuracy. Successful experiments were conducted to forecast energy consumption in Bulgaria, including for household consumption; industry consumption, the public sector and services; and total final energy consumption.

This paper introduces an innovative integrated system leveraging sensors, artificial intelligence (AI), and monitoring technologies to revolutionize care in nursing homes. Despite the availability of various digital tools, the absence of a cohesive system that synergizes these technologies represents a significant gap in current nursing home care. Our approach unifies these technologies into a single, comprehensive environment, addressing the fragmentation in existing solutions and targeting the holistic improvement of care delivery. By automating data collection and analysis, facilitating seamless information sharing, and simplifying user interaction with the system, we propose a model that significantly enhances operational efficiency, care quality, and user experience. The development and implementation of this integrated system promise a shift towards more reliable, efficient, and user-friendly care in nursing homes, setting a new standard for digital healthcare environments.