scholarly journals Information Fusion for Multi-Source Material Data: Progress and Challenges

2019 ◽  
Vol 9 (17) ◽  
pp. 3473 ◽  
Author(s):  
Zhou ◽  
Hong ◽  
Jin
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Fusion ◽  
Manufacturing Industry ◽  
Data Representation ◽  
Source Material ◽  
Future Research ◽  
Unified Framework ◽  
Open Problems ◽  
Material Data

The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion.

scholarly journals ANALYSIS ON COGNITIVE SOCIAL DATA FUSION

2020 ◽  
pp. 27-30
Author(s):  
Anandakumar H ◽  
Tamilselvan T ◽  
Nandni S ◽  
Subashree R ◽  
Vinodhini E
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Fusion ◽  
Data Processing ◽  
Pattern Mining ◽  
Data Representation ◽  
Map Reduce ◽  
User Interest ◽  
Social Data ◽  
Algorithm Mapping

Big data stands for effective handling of large amount of data, research, mining, intelligence. In social media large amount of data uploaded every.Social media handle large amount of data like photo, video, songs and so many using big data. When it comes for big data, a large amount of data should be effectively handled. Big data face various challenges like clustering of data, visualizing, data representation, data processing, pattern mining, tracking of data and analysing behaviour of users. In this paper the Emoji in messages are decoded and Unicode will be set. Based on the Emoji the user interest can be understood in a better way. Then another part involves the replacement of repeated data by using the map Reduce algorithm. Mapping of data with key values used to reduce the size of storage.

scholarly journals Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

2020 ◽  
Vol 47 (5) ◽  
pp. 577-597 ◽  
Author(s):  
Ziaul Haque Munim ◽  
Mariia Dushenko ◽  
Veronica Jaramillo Jimenez ◽  
Mohammad Hassan Shakil ◽  
Marius Imset
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Future Research ◽  
Maritime Industry ◽  
Research Directions ◽  
Bibliometric Review ◽  
Future Research Directions

Artificial Intelligence and Big Data Analytics in Support of Cyber Defense

2021 ◽  
pp. 1738-1753
Author(s):  
Louise Leenen ◽  
Thomas Meyer
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Data Sets ◽  
Data Types ◽  
Cyber Defense ◽  
Intelligent Decision Making ◽  
Big Data Technologies

Cybersecurity analysts rely on vast volumes of security event data to predict, identify, characterize, and deal with security threats. These analysts must understand and make sense of these huge datasets in order to discover patterns which lead to intelligent decision making and advance warnings of possible threats, and this ability requires automation. Big data analytics and artificial intelligence can improve cyber defense. Big data analytics methods are applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends, and other useful information. Artificial intelligence provides algorithms that can reason or learn and improve their behavior, and includes semantic technologies. A large number of automated systems are currently based on syntactic rules which are generally not sophisticated enough to deal with the level of complexity in this domain. An overview of artificial intelligence and big data technologies in cyber defense is provided, and important areas for future research are identified and discussed.

scholarly journals Audit and tax in the context of emerging technologies: A retrospective analysis, current trends, and future opportunities

2021 ◽  
pp. 95-128
Author(s):  
Osama F. Atayah ◽  
◽  
Muneer M. Alshater ◽  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Content Analysis ◽  
Tax Evasion ◽  
Emerging Technologies ◽  
Future Research ◽  
Prolific Author ◽  
Future Research Agenda ◽  
Publication Number ◽  
The Usa

This study aims to review the existing literature on audit and tax in the context of emerging technologies, besides providing future research agenda. A meta literature approach by combining bibliometric and content analysis was adopted to analyze 154 relevant English articles published in Scopus indexed journals, published over the last 35 years. Using RStudio, VOSviewer, and Microsoft Excel. Quantitative findings reveal that the USA is the top contributor and the most cited in the world. Brigham Young University, on the institutional level, is the most relevant affiliation. Concerning publication number, the Journal of Emerging Technologies in Accounting is the most relevant source. At the same time, the most cited source is the Decision Sciences journal. While the most prolific author is Miklos Vasarhelyi. Moreover, the emerging technologies, including big data, blockchain, and artificial intelligence, have significantly drawn accounting scholars interest from 2015 and thereafter. From the perspective of qualitative findings, the main focus shows that employing advanced technologies offers promising opportunities to mitigate the risk of tax evasion and enhance the auditors' efficiency. The content analysis reports two mainstreams tax and audit; each one is classified into three sub-streams, big data, artificial intelligence, and blockchain. This study contributes to present a clear and coherent understanding of the relevant exact literature and propose future research. However, the study review confines only on audit and tax fields, relying on the Scopus database.

scholarly journals Recent Development of Automation in Vehicle Manufacturing Industries

2019 ◽  
Vol 8 (6S4) ◽  
pp. 410-413 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Motor Vehicle ◽  
Manufacturing Industries ◽  
Research Trend ◽  
Future Research ◽  
Automation System ◽  
Safety Feature ◽  
Final Assembly ◽  
Wheel Loading

This paper presents the application of automation in different areas of motor vehicle manufacturing industry such as; robotic wheel loading, defect tracking in process, automated machine adjustment and restructuring, decision making, multi arm operation, final assembly and most important safety feature. In this work explore the present automation application and also find out the better way to implement in future to minimize the human efforts and time. These technologies based on the automation and artificial intelligence that will helps to make the process more efficient, stable and flexible. Moreover, aspects of changeability and adaptiveness of automation system have to be considered. The aim of this study to identified the opportunities and scope for future research trend in the field of automobile industries.

Artificial Intelligence and Big Data Analytics in Support of Cyber Defense

2019 ◽  
pp. 42-63 ◽  
Author(s):  
Louise Leenen ◽  
Thomas Meyer
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Data Sets ◽  
Data Types ◽  
Cyber Defense ◽  
Intelligent Decision Making ◽  
Big Data Technologies

Cybersecurity analysts rely on vast volumes of security event data to predict, identify, characterize, and deal with security threats. These analysts must understand and make sense of these huge datasets in order to discover patterns which lead to intelligent decision making and advance warnings of possible threats, and this ability requires automation. Big data analytics and artificial intelligence can improve cyber defense. Big data analytics methods are applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends, and other useful information. Artificial intelligence provides algorithms that can reason or learn and improve their behavior, and includes semantic technologies. A large number of automated systems are currently based on syntactic rules which are generally not sophisticated enough to deal with the level of complexity in this domain. An overview of artificial intelligence and big data technologies in cyber defense is provided, and important areas for future research are identified and discussed.

scholarly journals Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Haifei Yu ◽  
Songjian Han ◽  
Dongsheng Yang ◽  
Zhiyong Wang ◽  
Wei Feng
Keyword(s):  
Big Data ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Intelligent Manufacturing ◽  
Future Research ◽  
Cloud Platform ◽  
Actual Problem ◽  
Shop Scheduling ◽  
Digital Twin

The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology.

scholarly journals Application of Artificial Intelligence and Collaborative Knowledge for Manufacturing Design

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Tingting Luo ◽  
Guangyao Li ◽  
Naijiang Yu
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Decision Support ◽  
Collaborative Design ◽  
Product Life Cycle ◽  
Manufacturing Industry ◽  
Product Information ◽  
Rapid Development ◽  
Influence Coefficient ◽  
Sensitivity Index

With the rapid development of science and technology, digital technology has brought the world economy and management into a new stage. Collaborative design can realize product design process by cross-regional and cross-industry designers and share and exchange product information through network. With the rapid development of big data and artificial intelligence, knowledge services have gradually developed into multirole collaborative design activities based on artificial intelligence decision support. Traditional manufacturing industry has gradually transformed into modern manufacturing service industry after integrating information technology means such as Internet, communication, computer, and modern management methods. This article focuses on artificial intelligence decision support systems and the complex product manufacturing industry. We present a detailed analysis of how to integrate the knowledge generated by the product life cycle in the era of big data. We calculate the influence coefficient and sensitivity index of four different industries and propose a metadata architecture to improve the value of products as a whole. The findings of the research study imply that a knowledge-based collaborative platform should be designed by the enterprises and industries and a platform-based construction approach for economical, practical, and reliable production. We also present a detailed discussion about other factors such as the network cost of symmetric services, raw data and forecast data, and the number of nodes and the processing complexity.

Artificial Intelligence as a Technological Innovation to Accelerate Economic Development

2019 ◽  
Vol 12 (3) ◽  
pp. 125-133
Author(s):  
S. V. Shchurina ◽  
A. S. Danilov
Keyword(s):  
Artificial Intelligence ◽  
Economic Development ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Retail Trade ◽  
High Tech ◽  
Industrial Complex ◽  
Technological Gap ◽  
The Government

The subject of the research is the introduction of artificial intelligence as a technological innovation into the Russian economic development. The relevance of the problem is due to the fact that the Russian market of artificial intelligence is still in the infancy and the necessity to bridge the current technological gap between Russia and the leading economies of the world is coming to the forefront. The financial sector, the manufacturing industry and the retail trade are the drivers of the artificial intelligence development. However, company managers in Russia are not prepared for the practical application of expensive artificial intelligence technologies. Under these circumstances, the challenge is to develop measures to support high-tech projects of small and medium-sized businesses, given that the technological innovation considered can accelerate the development of the Russian economy in the energy sector fully or partially controlled by the government as well as in the military-industrial complex and the judicial system.The purposes of the research were to examine the current state of technological innovations in the field of artificial intelligence in the leading countries and Russia and develop proposals for improving the AI application in the Russian practices.The paper concludes that the artificial intelligence is a breakthrough technology with a great application potential. Active promotion of the artificial intelligence in companies significantly increases their efficiency, competitiveness, develops industry markets, stimulates introduction of new technologies, improves product quality and scales up manufacturing. In general, the artificial intelligence gives a new impetus to the development of Russia and facilitates its entry into the five largest world’s economies.

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