Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

2021 ◽  
Author(s):  
E. B. Priyanka ◽  
S. Thangavel ◽  
K. Martin Sagayam ◽  
Ahmed A. Elngar
Keyword(s):  
Artificial Intelligence ◽  
Wireless Network ◽  
Data Aggregation ◽  
Internet Applications

scholarly journals The Intelligent Integration of Interactive Installation Art Based on Artificial Intelligence and Wireless Network Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ze Gao ◽  
Lin Lin
Keyword(s):  
Artificial Intelligence ◽  
Wireless Network ◽  
Bit Error Rate ◽  
Error Rate ◽  
Installation Art ◽  
Communication Delay ◽  
Network Communication ◽  
Positioning Accuracy ◽  
Interactive Installation ◽  
Communication Equipment

With the development of technology and the times, the development of new media technology and interactive installation art has slowly entered the vision of our audience. It is simply “silent art.” The public no longer “retires” like the traditional one, but participates in it and swims with the artists in the world of art. This article is aimed at studying the application of artificial intelligence and wireless network communication to the application of interactive installation art. Through the optimization of various communication equipment and the continuous advancement of various algorithms, we can strengthen the communication and connection between our interactive installation art. This article proposes that with the addition of artificial intelligence and wireless network communication, the interaction between artists and audiences may be more fun, so that we can be more colorful in our lives. The experimental results in this article show that when performing wireless network communication, the communication delay rate of the intelligent algorithm with artificial intelligence is much lower than that of the one without it, which shows that they can better transmit information to the control end. When affected by the outside world, the bit error rate of wireless network communication will increase, however, the artificial intelligence algorithm is added to his impact range, and his bit error rate increase is obviously not so high. In the process of wireless network communication, the improved algorithm is definitely better than the nonimproved algorithm in terms of energy consumption, communication delay, and bit error rate. Through the enhancement of signals and the selection of materials for communication equipment, these are all in continuous progress, and in this respect, are in continuous exploration. Compared with other algorithms, the ml algorithm has improved positioning accuracy by about 70%, 65%, and 30%. Increasing the number of nodes in the transmission signal can greatly reduce the number of hops between nodes, correspondingly reducing the hop distance error, correspondingly reducing the distance estimation error, and improving the positioning accuracy. It can solve the technical barriers of interactive installation art faster.

Research of ISIC-CDIO Question Answer System for Materials Teaching Based on Android

2012 ◽  
Vol 214 ◽  
pp. 596-600
Author(s):  
Hui Yan ◽  
Hai Yan Hu
Keyword(s):  
Artificial Intelligence ◽  
Wireless Network ◽  
Pattern Matching ◽  
Question Answering ◽  
Mobile Wireless ◽  
Question Answering System ◽  
Cultivation Mode ◽  
Similarity Algorithm ◽  
Artificial Intelligence Technology ◽  
The Individual

This essay innovatively proposes the education mode of ISIC-CDIO and introduces the Android mobile platform to teaching procedures by utilizing the existing mobile wireless network and artificial intelligence technology. It also realizes the question-answering system for teaching under the cultivation mode of ISIC-CDIO, focusing on two similarity algorithm- stereotyped matching and no pattern matching studying. The improvement of algorithm has increased the efficiency of successful matching of problems, which will promote the individual learning of students and thus will cultivate the comprehensive talents with ISIC-CDIO quality.

A Multi-Disciplinary Perspective for Conducting Artificial Intelligence-enabled Privacy Analytics

2021 ◽  
Vol 12 (1) ◽  
pp. 1-18
Author(s):  
Sagar Samtani ◽  
Murat Kantarcioglu ◽  
Hsinchun Chen
Keyword(s):  
Artificial Intelligence ◽  
Web Sites ◽  
Data Aggregation ◽  
National Science Foundation ◽  
Modern Society ◽  
Data Sources ◽  
Risk Modeling ◽  
Legal Documents ◽  
Funding Sources ◽  
Assistance Systems

Events such as Facebook-Cambridge Analytica scandal and data aggregation efforts by technology providers have illustrated how fragile modern society is to privacy violations. Internationally recognized entities such as the National Science Foundation (NSF) have indicated that Artificial Intelligence (AI)-enabled models, artifacts, and systems can efficiently and effectively sift through large quantities of data from legal documents, social media, Dark Web sites, and other sources to curb privacy violations. Yet considerable efforts are still required for understanding prevailing data sources, systematically developing AI-enabled privacy analytics to tackle emerging challenges, and deploying systems to address critical privacy needs. To this end, we provide an overview of prevailing data sources that can support AI-enabled privacy analytics; a multi-disciplinary research framework that connects data, algorithms, and systems to tackle emerging AI-enabled privacy analytics challenges such as entity resolution, privacy assistance systems, privacy risk modeling, and more; a summary of selected funding sources to support high-impact privacy analytics research; and an overview of prevailing conference and journal venues that can be leveraged to share and archive privacy analytics research. We conclude this paper with an introduction of the papers included in this special issue.

scholarly journals Developing the Operational City: Sidewalk Labs & Toronto’s Eastern Waterfront

2018 ◽  
Author(s):  
T.F. Tierney ◽  
Keyword(s):  
Artificial Intelligence ◽  
Urban Design ◽  
Data Aggregation ◽  
Feedback Loops ◽  
Private Partnership ◽  
Waterfront Development ◽  
Citizen Data ◽  
Future Vision ◽  
The City

In social media and the popular press, there is much discussion over the City of Toronto’s decision to partner with Google on their Eastern Waterfront development, however, there has not been enough scholarly research on its long-term implications. First, this public-private partnership signals a new model for urban design professionals. Second, intelligent infrastructure will be harvesting citizen data continuously and autonomously twenty-four hours per day. Google will build on its reputation as the world’s largest search and data aggregation company by layering the city with a ubiquitous wireless network on top of city services, forming an informational stack that will invisibly orchestrate communication, economics, and energy. Artificial intelligence software will analyze the resultant mass of citizen data, and use it to automatically inform decisions that will shape future city services. Those analytical feedback loops will create an operational city, one where cars drive themselves and smartphones know what residents want and where to find it – all in real time. Is this the future vision for our cities?

scholarly journals Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Haifeng Zhang ◽  
Lianzhu Zhou
Keyword(s):  
Artificial Intelligence ◽  
Wireless Network ◽  
Nearest Neighbor ◽  
New Technologies ◽  
Chemical Characterization ◽  
Chloride Ions ◽  
High Density ◽  
Bayesian Optimization ◽  
Chlorinated Polyethylene ◽  
Artificial Intelligence Technology

Chemical enterprises are presently confronted with several difficult issues, including high power consumption, dangerous risk evaluation, and environmental regulation, all of which push industrial and academic institutions to develop new technologies, catalysts, and materials. Chlorinated polyethylene (CPE) is a polymer made by replacing H2 molecules in high density-(C2H4)n with chloride ions. CPE elastomers are made from a high density-(C2H4) backbone, and it was chlorinated using a free radical aqueous slurry technique. However, such fundamental polymer characteristics are insufficient to explain the performance characteristics of chlorinated polyethylene elastomers. Artificial intelligence (AI) has had a massive effect on all sections of the chemical sector, with tremendous potential that has revolutionized value supply chains, enhanced efficiency, and opened up new ways to the marketplace. As a result, in this research, we offer a methodology for the performance characterization of chlorinated polyethylene based on artificial intelligence (AI) and wireless network technology. The AI tools can search through enormous databases of known compounds and their attributes, leveraging the data to generate new possibilities. The dataset is first gathered. The chemical characterization is classified using the K -nearest neighbor (KNN) technique. This program was created to examine molecule structures and forecast the outcomes of new chemical reactions. Bayesian optimization is used to improve characterization performance. The proposed method will contribute to the future usage of AI in the chemical sector.

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