scholarly journals Subjectness of digital communication in the context of technological evolution of the Internet: features and transformation scenarios

2021 ◽  
Author(s):  
S.V. Volodenkov ◽  
S.N. Fedorchenko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Current Practice ◽  
Digital Communication ◽  
Social Practices ◽  
Network Algorithms ◽  
Political Sphere ◽  
Communication Processes ◽  
Self Learning

The work aimed to study the peculiarities of the subjectness of the phenomenon of digital communication in the context of intensive digitalization of key spheres of life of modern society, as well as to identify the prospects and threats of introducing self-learning neural network algorithms and artificial intelligence technologies into communication processes unfolding in the social and political sphere. One of the study's key objectives was to identify scenarios of possible social changes in the context of society digitalization and the traditional social practices transformation in terms of the emergence of new digital subjects of mass public communication that form the pseudo structure of digital interaction between people. As a methodological optics, the work used the method of discourse analysis of scientific research devoted to the implementation and application of artificial intelligence technologies and self-learning neural networks in the processes of socio-political digitalization, as well as the method of critical analysis of current communication practice in the socio-political sphere. At the same time, when analyzing the current practice of digitalization in foreign countries, the case study method was used. In turn, to determine the scenarios for the transformation of traditional social space and social practices, the method of scenario techniques and scenario forecasting was applied. As a research result, it was concluded that the introduction of technological solutions based on artificial intelligence algorithms and self-learning neural networks into contemporary socio-political communication processes creates the potential for the problem of identifying the subjects of communicative acts in the socio-political sphere of the contemporary society life. Based on the results of the study, it is shown that artificial intelligence and self-learning neural network algorithms are increasingly being implemented in the current practice of contemporary digital communications, forming a high potential for information and communication impact on the mass consciousness of technological solutions that no longer require self-control from human operators. The work also concludes that in the current practice of social interactions in the digital space, a person faces a new phenomenon – interfaceization, within which self-communication stimulates the universalization and standardization of digital behavior, creating, disseminating, strengthening, and imposing special digital rituals. The article proves that digital rituals blur the line between digital avatars' activity based on artificial intelligence and the activity of real people, resulting in the potential for a person to lose their own subjectness in the digital universe.

scholarly journals Subjectness of Digital Communication in the Context of the Technological Evolution of the Contemporary Society: Threats, Challenges, and Risks

2021 ◽  
pp. 437-456
Author(s):  
Sergey Volodenkov ◽  
Sergey Fedorchenko
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Political Communication ◽  
Digital Communication ◽  
Digital Communications ◽  
Political Communications ◽  
Wide Range ◽  
Information And Communication ◽  
Communication Practices ◽  
Self Learning

The purpose of this article is to identify the risks, threats, and challenges associated with possible social changes in the processes of digitalization of society and transformations of traditional communication practices, which is associated with the emergence of new digital subjects of mass public communication that form the pseudo structure of digital interaction of people. The primary tasks of the work were to identify the potential of artificial intelligence technologies and neural networks in the field of social and political communications, as well as to analyze the features of “smart” communications in terms of their subjectness. As a methodological optics, the work used the method of discourse analysis of scientific research devoted to the implementation and application of artificial intelligence technologies and self-learning neural networks in the processes of social and political digitalization, as well as the method of critical analysis of current communication practices in the socio-political sphere. At the same time, when analyzing the current digitalization practices, the case study method was used. The authors substantiate the thesis that introducing technological solutions based on artificial intelligence algorithms and self-learning neural networks into contemporary processes of socio-political communication creates the potential for a wide range of challenges, threats, and risks, the key of which is the problem of identifying the actual subjects of digital communication acts. The article also discusses the problem of increasing the manipulative potential of “smart” communications, for which the authors used the concepts of cyber simulacrum and information capsule developed by them. The paper shows that artificial intelligence and self-learning neural network algorithms, being increasingly widely introduced into the current practice of contemporary digital communications, form a high potential for information and communication impact on the mass consciousness from technological solutions that no longer require control by operators – humans. As a result, conditions arise to form a hybrid socio-technical reality – a communication reality of a new type with mixed subjectness. The paper also concludes that in the current practices of social interactions in the digital space, a person faces a new phenomenon – interfaceization, within which self-communication stimulates the universalization and standardization of digital behavior, creating, disseminating, strengthening, and imposing special digital rituals. In the article, the authors suggest that digital rituals blur the line between the activity of digital avatars based on artificial intelligence and the activity of actual people, resulting in the potential for a person to lose his own subjectness in the digital communications space.

Dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms

2020 ◽  
Vol 39 (4) ◽  
pp. 5521-5534
Author(s):  
Ying Liu ◽  
Zhongqi Fan ◽  
Hongliang Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Management ◽  
Coal Mine ◽  
Evaluation System ◽  
Statistical Evaluation ◽  
Specific Situation ◽  
Network Algorithms ◽  
Management Capability ◽  
Coal Enterprises

By establishing the evaluation system of emergency management capability for coal mine enterprises, we can identify the problems and shortcomings in coal mine emergency management, improve and improve its emergency management capability for coal mine emergencies. In this paper, the authors analyze the dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms. Neural networks can form any form of topological structure through neurons, so they can directly simulate fuzzy reasoning in structure, that is to say, the equivalent structure of neural networks and fuzzy systems can be formed. This paper constructs the index system based on accident causes, and verifies the scientific rationality of the system. On this basis, according to the specific situation of coal mine emergency management, we design the evaluation criteria of coal mine emergency management capability evaluation index. Because coal mine accidents have the characteristics of complexity, variability and sudden dynamic, it is necessary to adjust and improve the accidents dynamically at any time. The model combines qualitative and quantitative indicators, and can make an overall evaluation of coal mine emergency management capability. It has the characteristics of clear results and strong fitting of simulation results.

Construction of first-class university course based on artificial intelligence and neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Yingli Duan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Vocational Training ◽  
Network Algorithms ◽  
Teaching Efficiency ◽  
Neural Network Algorithm ◽  
Solid Foundation ◽  
University Level ◽  
University Course

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school’s teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them.

scholarly journals Artificial Intelligence Techniques for Phishing Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 2330-2335
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Neural Systems ◽  
Element Determination ◽  
Artificial Intelligence Techniques ◽  
The Neural Network ◽  
Proper Number ◽  
Phishing Detection

The objective of this undertaking is to apply neural systems to phishing email recognition and assess the adequacy of this methodology. We structure the list of capabilities, process the phishing dataset, and execute the Neural Network frameworks. we analyze its exhibition against that of other real Artificial Intelligence Techniques – DT , K-nearest , NB and SVM machine.. The equivalent dataset and list of capabilities are utilized in the correlation. From the factual examination, we infer that Neural Networks with a proper number of concealed units can accomplish acceptable precision notwithstanding when the preparation models are rare. Additionally, our element determination is compelling in catching the qualities of phishing messages, as most AI calculations can yield sensible outcomes with it.

scholarly journals Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA

2013 ◽  
Vol 7 (2) ◽  
pp. 189
Author(s):  
Silviani E Rumagit ◽  
Azhari SN
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Statistical Method ◽  
Moving Average ◽  
Electric Energy ◽  
Backpropagation Neural Network ◽  
The Difference ◽  
The Government ◽  
Increasing Demand

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif groups

scholarly journals Comparison of the efficiency of neural network algorithms in recognition and classification problems

2020 ◽  
Vol 224 ◽  
pp. 01025
Author(s):  
Alexey Beskopylny ◽  
Alexandr Lyapin ◽  
Nikita Beskopylny ◽  
Elena Kadomtseva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computation Time ◽  
Classification Problems ◽  
Handwritten Digit Recognition ◽  
Network Algorithms ◽  
Digit Recognition ◽  
Labour Intensity ◽  
Handwritten Digit ◽  
Accuracy Of Results

The article is devoted to the problem of comparing the effectiveness of feedforward (FF) and convolutional neural networks (CNN) algorithms in the problems of handwritten digit recognition and classification. In recent years, the attention of many researchers to the FF and CNN algorithms has given rise to many hybrid models focused on solving specific problems. At the same time, the efficiency of each algorithm in terms of accuracy and labour intensity remains unclear. It is shown that in classical problems, FFs can have advantages over CNN in terms of labour intensity with the same accuracy of results. Using the handwritten digits data from the MNIST database as an example, it is shown that FF algorithms provide greater accuracy and require less computation time than CNN.

scholarly journals Demand Modelling in Telecommunications

10.14311/1121 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
M. Chvalina
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Term Forecast ◽  
Artificial Intelligence Methods ◽  
Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models. 

Introduction

2013 ◽  
pp. 1-5
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Evolutionary Computing ◽  
Historical Background ◽  
Intelligent Computing ◽  
Biometric Security ◽  
Introductory Overview ◽  
Over Time

This chapter presents an introductory overview of the application of computational intelligence in biometrics. Starting with the historical background on artificial intelligence, the chapter proceeds to the evolutionary computing and neural networks. Evolutionary computing is an ability of a computer system to learn and evolve over time in a manner similar to humans. The chapter discusses swarm intelligence, which is an example of evolutionary computing, as well as chaotic neural network, which is another aspect of intelligent computing. At the end, special concentration is given to a particular application of computational intelligence—biometric security.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

2019 ◽  
pp. 159-173
Author(s):  
Jay Rodge ◽  
Swati Jaiswal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Comprehensive Overview ◽  
Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

Congestion Prediction System With Artificial Neural Networks

2020 ◽  
Vol 12 (3) ◽  
pp. 28-43
Author(s):  
Fatma Gumus ◽  
Derya Yiltas-Kaplan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Prediction Algorithm ◽  
Learning Stage ◽  
Network Algorithms ◽  
Prediction Scheme ◽  
Machine Learning Approach ◽  
Congestion Prediction ◽  
Nonlinear Autoregressive

Software Defined Network (SDN) is a programmable network architecture that provides innovative solutions to the problems of the traditional networks. Congestion control is still an uncharted territory for this technology. In this work, a congestion prediction scheme has been developed by using neural networks. Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm was performed on the data collected from the OMNET++ simulation. The novelty of this study also covers the implementation of mRMR in an SDN congestion prediction problem. After evaluating the relevance scores, two highest ranking features were used. On the learning stage Nonlinear Autoregressive Exogenous Neural Network (NARX), Nonlinear Autoregressive Neural Network, and Nonlinear Feedforward Neural Network algorithms were executed. These algorithms had not been used before in SDNs according to the best of the authors knowledge. The experiments represented that NARX was the best prediction algorithm. This machine learning approach can be easily integrated to different topologies and application areas.

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