Artificial intelligence and text and data mining

2021 ◽  
pp. 299-316
Author(s):  
Alain Strowel ◽  
Rossana Ducato
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Text And Data Mining

scholarly journals Limitations to Text and Data Mining and Consumer Empowerment. Making the Case for a Right to “Machine Legibility”

2020 ◽  
Author(s):  
Rossana Ducato ◽  
Alain Strowel
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Intellectual Property Rights ◽  
Data Protection ◽  
Consumer Empowerment ◽  
Protection Measures ◽  
Legal Constraints ◽  
Access To Data ◽  
Legal Barriers ◽  
Text And Data Mining

The paper focuses on the current legal barriers to text and data mining (TDM) in the context of smart disclosure systems (SDSs) whose aim is to provide consumers with improved access to the data needed to make informed decisions. The use of intellectual property rights and contracts, combined with technological protection measures, can hinder TDM and the deployment of SDSs.Further, those legal constraints can negatively impact artificial intelligence innovation that requires improved access to data. There are thus various arguments for enhanced “machine legibility”.However, the TDM exception included in the draft Copyright in the DSM Directive and the various amendments proposed by the European Parliament or the Council do not appear to clear the way for enhanced “machine legibility”. In relation to SDSs, we also argue that the principle of transparency, embedded in consumer and data protection laws, can serve as a last line of defence against prohibition of TDM.

Text and Data Mining Exception in South America: A Way to Foster AI Development in the Region

2021 ◽  
Author(s):  
Matías Jackson Bertón
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
South America ◽  
International Organizations ◽  
Computational Analysis ◽  
Digital Technologies ◽  
Current State ◽  
Research Techniques ◽  
Competitive Disadvantage ◽  
Text And Data Mining

  In 2015, authors wondered if Europe was falling behind in the artificial intelligence (AI) race because of the lack of a text and data mining (TDM) exception. What can then be said for South America? Copyright regimes and their interaction with the development of digital technologies in this continent have been overlooked by authors. This paper intends to start filling this gap by mapping the current state of copyright exceptions that serve computational analysis in South America. After reviewing the copyright regimes of the five largest economies of the region (i.e. Argentina, Brazil, Chile, Colombia and Peru), I concluded that they are not prepared for digital research techniques such as text and data mining. Researchers in these countries are at a competitive disadvantage, as rigid and outdated copyright regimes act as a constraint against keeping pace with the latest developments in subsequent years. If policymakers want to develop their nations’ AI capabilities, as many governments and international organizations claim they do, they will need to look for a more flexible and enabling approach to copyright.

Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining

2021 ◽  
pp. 1-10
Author(s):  
Wan Hongmei ◽  
Tang Songlin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classroom Teaching ◽  
Functional Modules ◽  
Semantic Ambiguity ◽  
Calculation Methods ◽  
Multiple Factors ◽  
Quantitative Calculation

In order to improve the efficiency of sentiment analysis of students in ideological and political classrooms, under the guidance of artificial intelligence ideas, this paper combines data mining and machine learning algorithms to improve and propose a method for quantifying the semantic ambiguity of sentiment words. Moreover, this paper designs different quantitative calculation methods of sentiment polarity intensity, and constructs video image sentiment recognition, text sentiment recognition, and speech sentiment recognition functional modules to obtain a combined sentiment recognition model. In addition, this article studies student emotions in ideological and political classrooms from the perspective of multimodal transfer learning, and optimizes the deep representation of images and texts and their corresponding deep networks through single-depth discriminative correlation analysis. Finally, this paper designs experiments to verify the model effect from two perspectives of single factor sentiment analysis and multi-factor sentiment analysis. The research results show that comprehensive analysis of multiple factors can effectively improve the effect of sentiment analysis of students in ideological and political classrooms, and enhance the effect of ideological and political classroom teaching.

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

Data Mining Approach to Analyze COVID-19 Clinical Dataset

2021 ◽  
Vol 15 (6) ◽  
pp. 1812-1819
Author(s):  
Azita Yazdani ◽  
Ramin Ravangard ◽  
Roxana Sharifian
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Clinical Signs ◽  
Study Data ◽  
Mining Machine ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

Text and data mining of in-copyright works

2021 ◽  
Vol 64 (11) ◽  
pp. 20-22
Author(s):  
Pamela Samuelson
Keyword(s):  
Data Mining ◽  
Copyright Law ◽  
Text And Data Mining

How copyright law might be an impediment to text and data mining research.

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