Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach
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Published:2023-03-02
Issue:5
Volume:13
Page:3186
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Kariri Elham1, Louati Hassen2, Louati Ali1, Masmoudi Fatma1
Affiliation:
1. Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia 2. SMART Lab, Higher Institute of Management, University of Tunis, Tunis 3000, Tunisia
Abstract
Artificial Neural Networks (ANNs) are machine learning algorithms inspired by the structure and function of the human brain. Their popularity has increased in recent years due to their ability to learn and improve through experience, making them suitable for a wide range of applications. ANNs are often used as part of deep learning, which enables them to learn, transfer knowledge, make predictions, and take action. This paper aims to provide a comprehensive understanding of ANNs and explore potential directions for future research. To achieve this, the paper analyzes 10,661 articles and 35,973 keywords from various journals using a text-mining approach. The results of the analysis show that there is a high level of interest in topics related to machine learning, deep learning, and ANNs and that research in this field is increasingly focusing on areas such as optimization techniques, feature extraction and selection, and clustering. The study presented in this paper is motivated by the need for a framework to guide the continued study and development of ANNs. By providing insights into the current state of research on ANNs, this paper aims to promote a deeper understanding of ANNs and to facilitate the development of new techniques and applications for ANNs in the future.
Funder
Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference47 articles.
1. McClelland, J.L., Rumelhart, D.E., and PDP Research Group (1987). Parallel Distributed Processing, Volume 2: Explorations in the Microstructure of Cognition: Psychological and Biological Models, MIT Press. 2. Hierarchical Bayesian inference in the visual cortex;Lee;JOSA A,2003 3. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7–12). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA. 4. Suganuma, M., Shirakawa, S., and Nagao, T. (2017, January 15–19). A genetic programming approach to designing convolutional neural network architectures. Proceedings of the Genetic and Evolutionary Computation Conference, Berlin, Germany. 5. Imagenet classification with deep convolutional neural networks;Krizhevsky;Commun. ACM,2017
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