Evolution of Artificial Neural Networks

The history of artificial neural networks originates from the distant 1943, when Warren McCulloch and Walter Pitts formalized the notion of a neural network in a fundamental article on the logical calculation of ideas and nervous activity. This was the starting point in the history of artificial neural networks. Later, a huge number of different architectures of neural networks, learning methods and optimization algorithms were proposed. There was a time when neural networks were left in oblivion, but after the 1980s they were talked about again when John Hopfield introduced his famous full-mesh network. Today, artificial neural networks are indispensable tools in a huge number of tasks, such as tasks of forecasting and analyzing time series, the task of recognizing images and even emotions, classification tasks, natural language recognition, neural networks are used in industry, defense and medicine; many enumerate areas of their application. All this tells us that artificial neural networks have become part of modern life

Нейронные сети в семантическом анализе

This paper presents research of the possibilities of application deep neural networks in semantic analysis. This paper presents the current situation in this area and the prospects for application an artificial intelligence in se-mantic analysis and trend and tendencies of this science area. For better un-derstanding future tendencies of researches in semantical area we present detailed review of the studies in semantic analysis with using artificial intelligence, studies about a human brain. В статье описываются глубинные архитектуры искусственных нейронных сетей и возможности их применения в семантическом анализе. Рассматривается история разработки глубинных нейронных сетей. Исследуются современные тенденции в задачах семантического анализа, а также представляется кратких обзор исследований в данной области

Time Series Prediction based on Hybrid Neural Networks

In this paper, we suggest to use hybrid approach to time series forecasting problem. In first part of paper, we create a literature review of time series forecasting methods based on hybrid neural networks and neuro-fuzzy approaches. Hybrid neural networks especially effective for specific types of applications such as forecasting or classification problem, in contrast to traditional monolithic neural networks. These classes of problems include problems with different characteristics in different modules. The main part of paper create a detailed overview of hybrid networks benefits, its architectures and performance under traditional neural networks. Hybrid neural networks models for time series forecasting are discussed in the paper. Experiments with modular neural networks are given

Anal Incontinence after Anterior Rectum Resection in Cancer Patients

Aim. To study the functioning of the anal sphincter after performing anterior rectum resection in cancer patients.Materials and methods. The anal sphincter function was studied in 144 patients with colorectal cancer, equally in 72 men and 72 women. The study was carried out at the Rostov Cancer Research Institute. All patients underwent R0 anterior rectum resection with the total mesorectumectomy and the formation of a preventive ileostomy. An assessment of the retention function was performed by sphincterometry using a manometric complex with a nonperfusion water-filling sensor according to the generally accepted method.Results. Disturbances in the normal functioning of the sphincter were noted in 24 (16.7 %) patients. The use of sphincterometry allowed disturbances in the functioning of the internal and external anal sphincters in men and women to be identified, along with differences between the groups. The results indicate a higher incidence of anal incontinence (AI) in women compared to men (t ≥ 2, the differences are significant). In addition, patients with a lower localisation of the anastomosis demonstrated a higher incidence of AI and more pronounced AI manifestations.Conclusions. The obtained data on the functioning of the anal sphincter after anterior rectum resection in cancer patients indicate gender differences in the incidence of late complications after surgery, as well as the importance of taking into account the localisation of the tumour for selecting an adequate amount of surgical intervention

Neural networks in semantic analysis

В работе проводится исследование по возможностям применения искусственных нейронных сетей в семантическом анализе. Рассматривается современное состояние дел в данной отрасли, перспективы использования искусственного интеллекта в области семантического анализа, направления и тенденции развития науки в данном направлении. Приводится обзор некоторых работ в области семантического анализа с применением технологий искусственного интеллекта и нейронных сетей.In this paper we study the possibilities of application of artificial neural networks in the semantic analysis. The current state of affairs in the industry, the prospects for the use of artificial intelligence in the field of semantic analysis, trends and tendencies of development of science in this direction. A review of some of the works in the field of semantic analysis with the use of artificial intelligence and neural networks