A SCIENTOMETRIC STUDY IN IEEE COMPUTER JOURNALS IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN Academic Libraries-Reference-Cited by-同舟云学术

A SCIENTOMETRIC STUDY IN IEEE COMPUTER JOURNALS IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN Academic Libraries

Published:2024-08-08 Issue:2 Volume:1 Page:103-107
ISSN:3023-459X
Container-title:International journal of Modern Achievement in Science, Engineering and Technology
language:
Short-container-title:IJSETPUB

Author:

Dixit Chitransh,Lata Dixit Kanchan,Kumar Dixit Chandra,Kumar Pandey Praveen,Chauhan Deepali,Ali Siddiqui Shavej

Abstract

In the modern 21st century the development of Information Communication Technology creates an enormous development known as Artificial Intelligence and Machine Learning. These technologies reduce the work of all Human Beings. The growth of these technologies has reached its enormous growth before five to two decades. The need for machine learning is increasing day in nowadays. The father of Artificial Intelligence was John MC Carthy. In modern trends, libraries have convertedthemselves to Artificial Intelligence to promote their digital knowledge among the people. The term “Scientometric” was introduced in the year 1978. The “Sciento” means Science and “Metric” means quantitate measurement. The systematic study of scientific collection of data using statistical tools. The data collectionis taken from Scimago Journal and Country Ranking. The main objective of the study is to analyzethe further growth of the journal in Artificial Intelligence and Machine Learning in Education in IEE journals Publications. The study is made on descriptive research methodology. The study uses a quantitative approach.The growth of journal publications will be increased in a further ten years in the field of artificial intelligence. It helps us to know about the growth of artificial intelligence and machine learning in education.

Publisher

Arvin Arena

Reference27 articles.

1. Brown, D., & Jones, E. (2024). Deep learning approaches for malware detection: A survey. IEEE Transactions on Cybersecurity, 15(3), 178-195.

2. Chen, L., & Wang, Q. (2024). Scalability challenges in deploying AI-driven cybersecurity solutions. Journal of Scalable Computing, 30(2), 145-162.

3. Garcia, M., & Martinez, L. (2024). Ethical considerations in deploying AI-driven cybersecurity solutions. Journal of Computer Ethics, 20(2), 89-104.

4. Ghillani, D. (2022). Deep learning and artificial intelligence framework to improve the cyber security. Authorea Preprints.

5. Johnson, B., & Williams, C. (2022). Artificial intelligence techniques for anomaly detection in computer networks. International Journal of Engineering and Technology, 10(4), 112-129.