Research on Artificial Intelligence Modeling Methods for Internet Public Opinion Information

Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots.

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AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

10.5772/intechopen.99442 ◽

2021 ◽

Author(s):

Naser Zaeri

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Ct Scan ◽

Machine Learning Algorithms ◽

World Health ◽

Related Data ◽

Recent Developments ◽

Intelligence Modeling ◽

Health Organization ◽

Imaging Algorithms

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

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Public Opinion on Artificial Intelligence Development

KnE Social Sciences ◽

10.18502/kss.v5i2.8401 ◽

2021 ◽

Author(s):

A.V. Merenkov ◽

R. Campa ◽

N.P. Dronishinets

Keyword(s):

Artificial Intelligence ◽

Public Opinion ◽

Western Europe ◽

Social Groups ◽

Active Role ◽

The United States ◽

Master's Programs ◽

Intelligence Development ◽

And Control

In connection with the active role of Russia and other countries in the design and implementation of devices with artificial intelligence (AI), there is a need to study the opinion of different social groups on this technology and the problems that arise when using it. The purpose of this work is to analyze public opinion on AI, in Russia and various foreign countries, and the possible consequences of its implementation in different areas of human activity. The research has revealed students’ opinions about AI devices and the problems related to their development in Russia. The research methods adopted are a content analysis of foreign publications devoted to the study of public opinion on AI and a questionnaire survey. Overall, 190 students of the Ural Federal University enrolled in Bachelor’s and Master’s programs were interviewed. The analysis of publications devoted to the study of public opinion in the United States, Japan, and Western Europe, as well as the results of our survey, has led to the conclusion that the majority of people have only a vague idea of what AI devices are. Our study has revealed that 23.6% of the respondents know nothing about AI. 36% of the respondents believe that in the near future the most demanded specialists in the labor market will be those who create robots and control their work. The survey has also shown the important role of mass media and general and special education institutions in informing the population about the opportunities and problems that arise when devices that exceed human mental capabilities are created and enter the social fabric. Keywords: public opinion, artificial intelligence, subjects of public opinion, representations of social groups about artificial intelligence

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Insight Artificial to Cyborg Intelligence Modeling

Archives of Industrial Engineering ◽

10.31829/2637-9252/aie2018-1(1)-102 ◽

2018 ◽

pp. 1-5

Keyword(s):

Artificial Intelligence ◽

Short Communication ◽

Human Life ◽

Vital Role ◽

The Commons ◽

Intelligence Modeling

Today Artificial Intelligence play vital role to everyday changing and made easy to human life advance automation, but more than of it is Cyborg Intelligence where instead of machine mankind themselves can able to make extreme powerful with implementing and interfacing artificial/Bionic parts with their biological organs and those work together. Hence I have shown in my short communication how one can move for Cyborg Intelligence from artificial intelligence and what are the commons and what are the different to set engineering skills in it.

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A Training Gesture-Based-Scroll Visual Artificial Intelligence And Measuring Its Effectiveness Using Hidden-Markov Modeling Methods

Journal of Information Systems and Informatics ◽

10.33557/journalisi.v2i1.58 ◽

2020 ◽

Vol 2 (1) ◽

pp. 163-168

Author(s):

Arif Wibisono

Keyword(s):

Artificial Intelligence ◽

Gesture Recognition ◽

Motion Detection ◽

Visual Motion ◽

Hidden Markov ◽

Hand Gesture ◽

Markov Modeling ◽

Image Capture ◽

Modeling Methods ◽

Hidden Markov Modeling

In this article I discuss the method of hand gesture recognition as a visual motion detection based on artificial intelligence by training three main movements namely, scrolling up, scrolling down and stopping based on capturing the front camera image capture speed of 3 fps and measuring its efficiency against the control movements that performed using Hidden-Markov Modeling (HMM) with each catch object scroll up 3 fps / 15 frames scroll down scroll down 3 fps / 15 frames and stop 3 fps / 9 frames, the result is that the most effective hand gesture object training movement is stop gesture with 3 fps / 9 frames because the object's movement is able to be recognized by the system only in the 3rd second image capture frame.

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Analysis of Public Opinion in Colleges and Universities Based on Wireless Web Crawler Technology in the Context of Artificial Intelligence

Mobile Information Systems ◽

10.1155/2022/7745028 ◽

2022 ◽

Vol 2022 ◽

pp. 1-10

Author(s):

WenNing Wu ◽

ZhengHong Deng

Keyword(s):

Artificial Intelligence ◽

Public Opinion ◽

Colleges And Universities ◽

Web Crawler ◽

The Public ◽

User Influence ◽

Proposed Model ◽

Wide Range ◽

Different Dimensions ◽

The Web

Wi-Fi-enabled information terminals have become enormously faster and more powerful because of this technology’s rapid advancement. As a result of this, the field of artificial intelligence (AI) was born. Artificial intelligence (AI) has been used in a wide range of societal contexts. It has had a significant impact on the realm of education. Using big data to support multistage views of every subject of opinion helps to recognize the unique characteristics of each aspect and improves social network governance’s suitability. As public opinion in colleges and universities becomes an increasingly important vehicle for expressing public opinion, this paper aims to explore the concepts of public opinion based on the web crawler and CNN (Convolutional Neural Network) model. Web crawler methodology is utilised to gather the data given by students of college and universities and mention them in different dimensions. This CNN has robust data analysis capability; this proposed model uses the CNN to analyse the public opinion. Preprocessing of data is done using the oversampling method to maximize the effect of classification. Through the association of descriptions, comprehensive utilization of image information like user influence, stances of comments, topics, time of comments, etc., to suggest guidance phenomenon for various schemes, helps to enhance the effectiveness and targeted social governance of networks. The overall experimentation was carried out in python here in which the suggested methodology was predicting the positive and negative opinion of the students over the web crawler technology with a low rate of error when compared to other existing methodology.

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