Information Security: An Artificial Intelligence and Data Mining Based Approach

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.

Download Full-text

Cognitive Information Systems for Protection of Museum Complexes

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.38.24326 ◽

2018 ◽

Vol 7 (4.38) ◽

pp. 82

Author(s):

Aleksey Valentinovich Bogdanov ◽

Igor Gennadievich Malygin

Keyword(s):

Artificial Intelligence ◽

Information Systems ◽

Information Security ◽

Security System ◽

Structural Scheme ◽

Cognitive Information ◽

Technological Platform ◽

Network Technologies ◽

Cognitive Information Systems ◽

Stratified Model

The paper considers the conceptual provisions of building a promising cognitive information security system of the museum complex on a cyber-physical basis. The stratified model of cognitive information security system of the museum complex was presented. It was shown that the key technological platform for the security of the museum complex is information and network technologies integrated (converged) with the technologies of industrial artificial intelligence. The generalized structural scheme of the cognitive cycle of the information security system of the museum complex was considered. The characteristic of the basic processes realized in a cognitive contour was given.

Download Full-text

Data Mining Usage in Corporate Information Security: Intrusion Detection Applications

Business Systems Research Journal ◽

10.1515/bsrj-2017-0005 ◽

2017 ◽

Vol 8 (1) ◽

pp. 51-59 ◽

Cited By ~ 2

Author(s):

Masoud Al Quhtani

Keyword(s):

Data Mining ◽

Information Security ◽

Intrusion Detection ◽

Mining System ◽

Systematic Analysis ◽

New Methods ◽

Use Of Data ◽

Efficient Data ◽

Abuse Risk ◽

Corporate Information

AbstractBackground: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.

Download Full-text

Improved differentiation classification of variable precision artificial intelligence higher education management

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219036 ◽

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.

Download Full-text

Data Mining Approach to Analyze COVID-19 Clinical Dataset

10.53350/pjmhs211561812 ◽

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

Download Full-text