scholarly journals Tweaking Business Planning With Artificial Intelligence

2021 ◽  
Vol 2 (4) ◽  
pp. 1-22
Author(s):  
Jing Rui Chen ◽  
P. S. Joseph Ng
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
Learning Experience ◽  
Compulsory Education ◽  
Large Data ◽  
Data Sets ◽  
Chinese Market ◽  
Artificial Intelligence Technology ◽  
Education Support

Griffith AI&BD is a technology company that uses big data platform and artificial intelligence technology to produce products for schools. The company focuses on primary and secondary school education support and data analysis assistance system and campus ARTIFICIAL intelligence products for the compulsory education stage in the Chinese market. Through big data, machine learning and data mining, scattered on campus and distributed systems enable anyone to sign up to join the huge data processing grid, and access learning support big data analysis and matching after helping students expand their knowledge in a variety of disciplines and learning and promotion. Improve the learning process based on large data sets of students, and combine ai technology to develop AI electronic devices. To provide schools with the best learning experience to survive in a competitive world.

Data Classification

2017 ◽  
pp. 34-51 ◽  
Author(s):  
A. Sheik Abdullah ◽  
R. Suganya ◽  
S. Selvakumar ◽  
S. Rajaram
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Classification ◽  
Large Data ◽  
Classification Problem ◽  
Data Sets ◽  
Credit Risk Analysis ◽  
Predicted Values ◽  
Class Labels ◽  
The One

Classification is considered to be the one of the data analysis technique which can be used over many applications. Classification model predicts categorical continuous class labels. Clustering mainly deals with grouping of variables based upon similar characteristics. Classification models are experienced by comparing the predicted values to that of the known target values in a set of test data. Data classification has many applications in business modeling, marketing analysis, credit risk analysis; biomedical engineering and drug retort modeling. The extension of data analysis and classification makes the insight into big data with an exploration to processing and managing large data sets. This chapter deals with various techniques, methodologies that correspond to the classification problem in data analysis process and its methodological impacts to big data.

Tensor Numerical Methods: Actual Theory and Recent Applications

2019 ◽  
Vol 19 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Ivan Gavrilyuk ◽  
Boris N. Khoromskij
Keyword(s):  
Numerical Modeling ◽  
Big Data ◽  
Data Analysis ◽  
Numerical Methods ◽  
Differential Equations ◽  
Numerical Solution ◽  
Large Data ◽  
High Dimensional ◽  
Data Sets ◽  
Integral Differential Equations

AbstractMost important computational problems nowadays are those related to processing of the large data sets and to numerical solution of the high-dimensional integral-differential equations. These problems arise in numerical modeling in quantum chemistry, material science, and multiparticle dynamics, as well as in machine learning, computer simulation of stochastic processes and many other applications related to big data analysis. Modern tensor numerical methods enable solution of the multidimensional partial differential equations (PDE) in {\mathbb{R}^{d}} by reducing them to one-dimensional calculations. Thus, they allow to avoid the so-called “curse of dimensionality”, i.e. exponential growth of the computational complexity in the dimension size d, in the course of numerical solution of high-dimensional problems. At present, both tensor numerical methods and multilinear algebra of big data continue to expand actively to further theoretical and applied research topics. This issue of CMAM is devoted to the recent developments in the theory of tensor numerical methods and their applications in scientific computing and data analysis. Current activities in this emerging field on the effective numerical modeling of temporal and stationary multidimensional PDEs and beyond are presented in the following ten articles, and some future trends are highlighted therein.

Copyright Exceptions Reform and AI Data Analysis in ChinaA Modest Proposal

2021 ◽  
pp. 196-220
Author(s):  
Tianxiang He
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Incomplete Data ◽  
Copyright Law ◽  
Data Sets ◽  
Copyright Exception ◽  
Using Data

The development of artificial intelligence (AI) technology is firmly connected to the availability of big data. However, using data sets involving copyrighted works for AI analysis or data mining without authorization will incur risks of copyright infringement. Considering the fact that incomplete data collection may lead to data bias, and since it is impossible for the user of AI technology to obtain a copyright licence from each and every right owner of the copyrighted works used, a mechanism that can free the data from copyright restrictions under certain conditions is needed. In the case of China, it is crucial to check whether China’s current copyright exception model can take on the role and offer that kind of function. This chapter suggests that a special AI analysis and data mining copyright exception that follows a semi-open style should be added to the current exceptions list under the Copyright Law of China.

scholarly journals FinTech as an innovation challenge: from big data to sustainable development

2020 ◽  
Vol 166 ◽  
pp. 13027
Author(s):  
Anzhela Ignatyuk ◽  
Olena Liubkina ◽  
Tetiana Murovana ◽  
Alina Magomedova
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Data ◽  
Big Data Analysis ◽  
Current Approach ◽  
Data Sets ◽  
Human Society ◽  
Public And Private ◽  
Global Trends ◽  
Green Business

Driving force of human society development is elimination contradiction between unlimited usage of natural resources during economic activity of enterprises, environment pollution as a result of such activity and limited natural, energy and other resources. Research results on economic and environmental issues of green business management showed that there are several basic types of problems at present which arise at enterprises during collecting and processing data on the results of their activities. The authors analyzed how public sector and green business is catching up on global trend towards broader use of the big data analysis to serve public interests and increase efficiency of business activities. In order to detect current approach to big data analysis in public and private sectors authors conduct interviews with stakeholders. The paper concludes with the analysis what changes in approaches to the big data analysis in public and private sectors have to be made in order to comply with the global trends in greening the economy. Application of FinTech, methods of processing large data sets and tools for implementing the principles of greening the economy will enable to increase the investment attractiveness of green business and will simplify the interaction between the state and enterprises.

scholarly journals Deep Learning Security Systems

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1823-1826
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Security Systems ◽  
Abstract Knowledge

Big Data Analytics and Deep Learning are not supposed to be two entirely different concepts. Big Data means extremely huge large data sets that can be analyzed to find patterns, trends. One technique that can be used for data analysis so that able to help us find abstract patterns in Big Data is Deep Learning. If we apply Deep Learning to Big Data, we can find unknown and useful patterns that were impossible so far. With the help of Deep Learning, AI is getting smart. There is a hypothesis in this regard, the more data, the more abstract knowledge. So a handy survey of Big Data, Deep Learning and its application in Big Data is necessary.

scholarly journals Data Science Methods for Psychology

Psychology ◽  
2020 ◽  
Author(s):  
Jeffrey Stanton
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Data Science ◽  
Large Data ◽  
Large Data Sets ◽  
Predictive Analysis ◽  
Data Sets ◽  
The Impact

The term “data science” refers to an emerging field of research and practice that focuses on obtaining, processing, visualizing, analyzing, preserving, and re-using large collections of information. A related term, “big data,” has been used to refer to one of the important challenges faced by data scientists in many applied environments: the need to analyze large data sources, in certain cases using high-speed, real-time data analysis techniques. Data science encompasses much more than big data, however, as a result of many advancements in cognate fields such as computer science and statistics. Data science has also benefited from the widespread availability of inexpensive computing hardware—a development that has enabled “cloud-based” services for the storage and analysis of large data sets. The techniques and tools of data science have broad applicability in the sciences. Within the field of psychology, data science offers new opportunities for data collection and data analysis that have begun to streamline and augment efforts to investigate the brain and behavior. The tools of data science also enable new areas of research, such as computational neuroscience. As an example of the impact of data science, psychologists frequently use predictive analysis as an investigative tool to probe the relationships between a set of independent variables and one or more dependent variables. While predictive analysis has traditionally been accomplished with techniques such as multiple regression, recent developments in the area of machine learning have put new predictive tools in the hands of psychologists. These machine learning tools relax distributional assumptions and facilitate exploration of non-linear relationships among variables. These tools also enable the analysis of large data sets by opening options for parallel processing. In this article, a range of relevant areas from data science is reviewed for applicability to key research problems in psychology including large-scale data collection, exploratory data analysis, confirmatory data analysis, and visualization. This bibliography covers data mining, machine learning, deep learning, natural language processing, Bayesian data analysis, visualization, crowdsourcing, web scraping, open source software, application programming interfaces, and research resources such as journals and textbooks.

scholarly journals New Framework of Digital Entrepreneurship Model Based on Artificial Intelligence and Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jiahui Li ◽  
Meifang Yao
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Algorithm Analysis ◽  
Model Based ◽  
Artificial Intelligence Technology ◽  
New Framework ◽  
Digital Entrepreneurship

With the rapid development of entrepreneurial enterprises and the widespread application of emerging technologies, the commercialization of new technologies for entrepreneurial enterprises is particularly important. This research mainly discusses the new framework of digital entrepreneurship model based on artificial intelligence and cloud computing. Through artificial intelligence technology, the products provided by existing competitors only have the characteristics of one-way value; that is, data is only collected and displayed, and the application of artificial intelligence technology in products makes the value of products develop in two directions; that is, the machine can self-identify faults and errors are resolved and reported. Let customers experience the convenience, accuracy, and safety brought by technology through intelligent acquisition equipment hardware with artificial intelligence algorithm analysis and camera hardware with artificial intelligence image analysis. Customers can pay flexibly according to their needs. This model greatly enhances the high possibility of artificial intelligence companies landing. Use big data analysis and cloud computing technology to provide customers with a series of solutions such as warehouse management, sales forecasting, big data analysis, and financial management. In the SaaS market, in terms of market segmentation, there are no domestic enterprises with scale and brand effect; the incentive and welfare module will focus on the outsourcing and outsourcing of employee benefits. Relevant value-added services and derivative services are the core business, which can give play to the competitive advantages of specialization, scale, and platform. From 2018 to 2020, the cash paid to employees shows a gradual increase, and the taxes and fees paid are also increasing year by year. The cash paid for other operating activities reached a maximum of 12303 million yuan in 2018. This research will promote the innovation of new types of enterprises.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

Big Data Technology, Evolving Knowledge Skills and Emerging Roles

2016 ◽  
Vol 16 (4) ◽  
pp. 219-224 ◽  
Author(s):  
Alex Smith
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Emerging Technology ◽  
Law Firms ◽  
Support Functions ◽  
Legal Problems ◽  
Artificial Intelligence Technology ◽  
Domain Expertise ◽  
New Generation ◽  
Big Data Technology

AbstractIn a world where articles and tweets are discussing how artificial intelligence technology will replace humans, including lawyers and their support functions in firms, it can be hard to understand what the future holds. This article, written by Alex Smith, is based on his presentation at the British and Irish Association of Law Librarians conference in Dublin 2016 and looks at demystifying the emerging technology boom and identifies the expertise needed to make these tools work and be deployed in law firms. The article then looks at the skills and expertise of the knowledge and information teams, based in law firms, and suggests how they are ideally placed to lead these challenges as a result of their domain expertise and their existing, well defined skills that are essential to this new generation of technology. The article looks at the new technical environment, the emerging areas of products and legal problems, the skills needed for the new roles that this revolution is creating and how this could fit into a reimagined knowledge team.

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