Artificial Intelligence and Traditional Machine Learning to Deep Neural Networks: A Study for Social Implications
DOI:
https://doi.org/10.18034/ajhal.v7i2.622Keywords:
Machine learning, deep learning, explicable machine learning, transfer learning, convolution neural networks, artificial intelligenceAbstract
There are intriguing opportunities on the road to broad usage of artificial intelligence, as well as challenges that must be solved in order to use machine learning and artificial intelligence technology in industries. On the basis of Google Scholar data, this work systematizes artificial intelligence sections and analyzes the dynamics of changes in the number of scientific papers in machine learning sections. The process of data collection and calculation of dynamic indicators of changes in publication activity, such as the growth rate and acceleration of growth of scientific publications, is presented in detail. Particularly evident in the analysis of publishing activity was the high level of interest in current transformer models, the production of datasets for specific industries, and a significant growth in interest in explainable machine learning approaches. As indicated by the negative correlation between the number of publications published and the number of articles published in a given year, relatively minor study topics are gaining an increasing amount of attention. Results demonstrate that, despite the method's limitations, it is possible to (1) identify rapidly expanding fields of research, irrespective of the number of papers published, and (2) anticipate publication activity in the short term with sufficient accuracy for practical purposes. Results for more than 400 search queries relating to designated study topics and the application of machine learning models to industries are presented in this article in a single document. The suggested method assesses the growth and fall of scientific areas that are related with specific key phrases in terms of their dynamics of growth and decline. It does not necessitate access to huge bibliometric archives and enables for the generation of quantitative estimates of dynamic indicators in a reasonably short time frame.
Metrics
Downloads
References
Adusumalli, H. P. (2016a). Digitization in Production: A Timely Opportunity. Engineering International, 4(2), 73-78. https://doi.org/10.18034/ei.v4i2.595
Adusumalli, H. P. (2016b). How Big Data is Driving Digital Transformation?. ABC Journal of Advanced Research, 5(2), 131-138. https://doi.org/10.18034/abcjar.v5i2.616
Adusumalli, H. P. (2017a). Mobile Application Development through Design-based Investigation. International Journal of Reciprocal Symmetry and Physical Sciences, 4, 14–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/58
Adusumalli, H. P. (2017b). Software Application Development to Backing the Legitimacy of Digital Annals: Use of the Diplomatic Archives. ABC Journal of Advanced Research, 6(2), 121-126. https://doi.org/10.18034/abcjar.v6i2.618
Adusumalli, H. P. (2018). Digitization in Agriculture: A Timely Challenge for Ecological Perspectives. Asia Pacific Journal of Energy and Environment, 5(2), 97-102. https://doi.org/10.18034/apjee.v5i2.619
Adusumalli, H. P. (2019). Expansion of Machine Learning Employment in Engineering Learning: A Review of Selected Literature. International Journal of Reciprocal Symmetry and Physical Sciences, 6, 15–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/65
Adusumalli, H. P., & Pasupuleti, M. B. (2017). Applications and Practices of Big Data for Development. Asian Business Review, 7(3), 111-116. https://doi.org/10.18034/abr.v7i3.597
Ayodele, T. O. (2010). Types of machine learning algorithms. New Adv. Mach. Learn, 3, 19–48.
Daim, T.U., Rueda, G. R., Martin, H. T. (2005). Technology forecasting using bibliometric analysis and system dynamics. In Proceedings of the A Unifying Discipline for Melting the Boundaries Technology Management, Portland, OR, USA, pp. 112–122.
David, B. M.; Andrew, N. Y.; Michael, J. I. (2003). Latent dirichlet allocation. J. Mach. Learn. Res.,3, 993–1022.
Fadziso, T., Adusumalli, H. P., & Pasupuleti, M. B. (2018). Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews. Asian Journal of Applied Science and Engineering, 7, 85–92. Retrieved from https://upright.pub/index.php/ajase/article/view/63
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Ann. Stat., 1189–1232.
Haseeb, M., Mihardjo, L. W., Gill, A. R., Jermsittiparsert, K. (2019). Economic impact of artificial intelligence: New look for the macroeco-nomic assessment in Asia-Pacific region. Int. J. Comput. Intell. Syst., 12, 1295–1310.
Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural Comput. 9, 1735–1780.
Ibrahim, H. A. H., Nor, S. M., Mohammed, A., Mohammed, A. B. (2012). Taxonomy of machine learning algorithms to classify real time interactive applications. Int. J. Comput. Netw. Wirel. Commun., 2, 69–73.
Muhamedyev, R. (2015). Machine learning methods: An overview. Comput. Model. New Technol, 19, 14–29.
Muhamedyev, R. I., Aliguliyev, R. M., Shokishalov, Z. M., Mustakayev, R. R. (2018). New bibliometric indicators for prospectively estimation of research fields. Ann. Libr. Inf. Stud. 65, 62–69.
Mukhamedyev, R.I., Kuchin, Y., Denis, K., Murzakhmetov, S., Symagulov, A., Yakunin, K. (2019). Assessment of the dynamics of publication activity in the field of natural language processing and deep learning. In Proceedings of the International Conference on Digital Transformation and Global Society, Saint Petersburg, Russia, 744–753.
Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., Shaalan, K. (2019). Speech recognition using deep neural networks: A systematic review. IEEE Access, 7, 19143–19165.
Pasupuleti, M. B. (2015). Stimulating Statistics in the Epoch of Data-Driven Innovations and Data Science. Asian Journal of Applied Science and Engineering, 4, 251–254. Retrieved from https://upright.pub/index.php/ajase/article/view/55
Pasupuleti, M. B. (2016a). Data Scientist Careers: Applied Orientation for the Beginners. Global Disclosure of Economics and Business, 5(2), 125-132. https://doi.org/10.18034/gdeb.v5i2.617
Pasupuleti, M. B. (2016b). The Use of Big Data Analytics in Medical Applications. Malaysian Journal of Medical and Biological Research, 3(2), 111-116. https://doi.org/10.18034/mjmbr.v3i2.615
Pasupuleti, M. B., & Adusumalli, H. P. (2018). Digital Transformation of the High-Technology Manufacturing: An Overview of Main Blockades. American Journal of Trade and Policy, 5(3), 139-142. https://doi.org/10.18034/ajtp.v5i3.599
Pasupuleti, M. B., & Amin, R. (2018). Word Embedding with ConvNet-Bi Directional LSTM Techniques: A Review of Related Literature. International Journal of Reciprocal Symmetry and Physical Sciences, 5, 9–13. Retrieved from https://upright.pub/index.php/ijrsps/article/view/64
Pasupuleti, M. B., Miah, M. S., & Adusumalli, H. P. (2019). IoT for Future Technology Augmentation: A Radical Approach. Engineering International, 7(2), 105-116. https://doi.org/10.18034/ei.v7i2.601
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Netw. 61, 85–117.
--0--
