Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Artificial Intelligence in Healthcare · Currency Recognition and Detection · Stock Market Forecasting Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
SMART Lab, Higher Institute of Management, University of Tunis, Tunis 3000, Tunisia
Funding
Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia
References (47)
Show source-provided references
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.10.7551/mitpress/5237.001.0001 ↗
2. Hierarchical Bayesian inference in the visual cortex10.1364/JOSAA.20.001434 ↗
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.10.1109/CVPR.2015.7298594 ↗
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.10.1145/3071178.3071229 ↗
5. Imagenet classification with deep convolutional neural networks10.1145/3065386 ↗
6. Receptive Fields of Single Neurons in the Cat’s Striate Cortex10.1113/jphysiol.1959.sp006308 ↗
7. A Fast Learning Algorithm for Deep Belief Nets10.1162/neco.2006.18.7.1527 ↗
8. Salakhutdinov, R., and Larochelle, H. (2010, January 13–15). Efficient Learning of Deep Boltzmann Machines. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Chia Laguna Resort, Sardinia, Italy.
9. Liu, J.-W., Chi, G.-H., and Luo, X.-L. (2012, January 18–20). Contrastive Divergence Learning of Restricted Boltzmann Machine. Proceedings of the 2012 Second International Conference on Electric Technology and Civil Engineering, Washington, DC, USA.10.1109/ICNC.2013.6817936 ↗
10. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks10.1109/TNNLS.2015.2469673 ↗
11. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5–10). Distributed representations of words and phrases and their compositionality. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA.
12. Zhang, Y., and Wallace, B. (2015). A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv.
13. Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis10.1016/j.compenvurbsys.2018.10.008 ↗
14. Recurrent neural networks for multivariate time series with missing values10.1038/s41598-018-24271-9 ↗
15. Novel optimization approach for stock price forecasting using multi-layered sequential LSTM10.1016/j.asoc.2022.109830 ↗
16. Generative adversarial networks10.1145/3422622 ↗
17. Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv.
18. Louati, A., Lahyani, R., Aldaej, A., Mellouli, R., and Nusir, M. (2021). Mixed integer linear programming models to solve a real-life vehicle routing problem with pickup and delivery. Appl. Sci., 11.10.3390/app11209551 ↗
19. Louati, A., Masmoudi, F., and Lahyani, R. (2022, January 21–24). Traffic disturbance mining and feedforward neural network to enhance the immune network control performance. Proceedings of the Seventh International Congress on Information and Communication Technology: ICICT 2022, London, UK.10.1007/978-981-19-1607-6_9 ↗
20. Deep learning and case-based reasoning for predictive and adaptive traffic emergency management10.1007/s11227-020-03435-3 ↗
21. Deep learning10.1038/nature14539 ↗
22. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv.
23. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
24. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups10.1109/MSP.2012.2205597 ↗
25. Wen, T.-H., Vandyke, D., Mrkšić, N., Gašić, M., Rojas-Barahona, L.M., Su, P.-H., Ultes, S., and Young, S. (2018, January 18–21). A network-based end-to-end trainable task-oriented dialogue model. Proceedings of the 2018 IEEE Spoken Language Technology Workshop (SLT), Athens, Greece.
26. Widrow, B., and Lehr, M.E. (1990). 30 Years of Adaptive Neural Networks: Perception, Motor Control, and Cognition, World Scientific.10.1109/5.58323 ↗
27. Alpaydin, E. (2010). Introduction to Machine Learning, MIT Press.
28. Beyond the hype: Big data concepts, methods, and analytics10.1016/j.ijinfomgt.2014.10.007 ↗
29. Deep convolutional neural network architecture design as a bi-level optimization problem10.1016/j.neucom.2021.01.094 ↗
30. Louati, H., Bechikh, S., Louati, A., Aldaej, A., and Said, L.B. (2021). Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices, Proceedings of the 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, 26–29 July 2021, Springer.
31. Joint design and compression of convolutional neural networks as a Bi-level optimization problem10.1007/s00521-022-07331-0 ↗
32. A hybridization of deep learning techniques to predict and control traffic disturbances10.1007/s10462-020-09831-8 ↗
33. Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicles guidance10.1007/s12652-020-01921-3 ↗
34. Louati, H., Louati, A., Bechikh, S., Masmoudi, F., Aldaej, A., and Kariri, E. (2022). Topology optimization search of deep convolution neural networks for CT and X-ray image classification. BMC Med. Imaging, 22.10.1186/s12880-022-00847-w ↗
35. Louati, H., Louati, A., Bechikh, S., and Ben Said, L. (2022). Computational Collective Intelligence, Proceedings of the 14th International Conference, ICCCI 2022, Hammamet, Tunisia, 28–30 September 2022, Springer.
36. Louati, H., Bechikh, S., Louati, A., Aldaej, A., and Said, L.B. (2022). Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence, Proceedings of the 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Kitakyushu, Japan, 19–22 July 2022, Springer.
37. Design research on user experience based on text mining
38. Text mining in product design research: A literature review
39. Text mining in industrial design research: A literature review
40. Text mining in design thinking research: A literature review
41. Text mining in design for sustainability research: A literature review
42. Text mining in design education research: A literature review
43. Sentiment analysis with text mining techniques and artificial neural networks
44. Text classification using artificial neural networks and text mining techniques
45. A review on artificial neural network based named entity recognition using text mining techniques
46. Opinion mining and sentiment analysis with text mining and artificial neural networks
47. Stock price prediction with text mining and artificial neural networks