Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods

Year 2023, , 233 - 237, 30.09.2023

Yasin Kırelli

https://doi.org/10.22399/ijcesen.1330363

Abstract

Artificial Intelligence is a sub-branch of artificial intelligence used to produce new data or content. These methods can create recent examples in different categorical fields such as natural language processing, image processing, music, and video creation by using models from learning clusters with artificial intelligence (AI) tools in this field. AI tools that can solve real-world problems are also created using different methods apart from generative AI methods. With generative-based artificial intelligence tools, it can facilitate people's work in jobs that require creativity. However, they can offer the opportunity to build advanced models that learn from data with other artificial intelligence methods. In the study, the public dataset has been used. This dataset includes trending artificial intelligence tools, AI methods, and user scores. In this study the working area and user trend of the ai tools in the dataset and the effect of generative AI methods on the development of the tool are discussed. Random Forest and Naive Bayes algorithms from classification methods have been used to measure the impact and estimation. Several AI tools help solve real-life problems. Identifying what type of category is needed for AI tools and method selection are interlinked, and the research provides an overview of this connection.

Keywords

AI Tools, Generative Artificial Intelligence, Machine Learning

References

• [1] A. Holzinger, K. Keiblinger, P. Holub, K. Zatloukal, and H. Müller, (2023). AI for life: Trends in artificial intelligence for biotechnology,” N Biotechnol, 74;16–24 doi: 10.1016/j.nbt.2023.02.001.
• [2] N. Eslamirad, F. De Luca, K. Sakari Lylykangas, and S. Ben Yahia, (2023). Data generative machine learning model for the assessment of outdoor thermal and wind comfort in a northern urban environment, Frontiers of Architectural Research, doi: 10.1016/j.foar.2022.12.001.
• [3] V. Couteaux et al., (2023). Synthetic MR image generation of macrotrabecular-massive hepatocellular carcinoma using generative adversarial networks, Diagn Interv Imaging, doi: 10.1016/j.diii.2023.01.003.
• [4] H. Woldesellasse and S. Tesfamariam, (2022). Data Augmentation Using conditional Generative Adversarial Network (cGAN): Application for Prediction of Corrosion Pit Depth and Testing Using Neural Network, Journal of Pipeline Science and Engineering, p. 100091 doi: 10.1016/j.jpse.2022.100091.
• [5] S. O’Connor and ChatGPT, (2023). Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse?, Nurse Education in Practice, vol. 66. Elsevier Ltd, doi: 10.1016/j.nepr.2022.103537.
• [6] T. Ching et al., (2018). Opportunities and obstacles for deep learning in biology and medicine,” J R Soc Interface, 15;141 doi: 10.1098/RSIF.2017.0387.
• [7] D. Dana et al., (2018). Deep Learning in Drug Discovery and Medicine; Scratching the Surface, Molecules, 23;9 doi: 10.3390/molecules23092384.
• [8] E. Lin, P. H. Kuo, Y. L. Liu, Y. W. Y. Yu, A. C. Yang, and S. J. Tsai, (2018). A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers. Front Psychiatry, 9 doi: 10.3389/FPSYT.2018.00290.
• [9] Y. Chauvin and D. E. Rumelhart, Backpropagation : theory, architectures, and applications. Lawrence Erlbaum Associates, 1995. Accessed: Mar. 13, 2023. [Online]. Available: https://www.routledge.com/Backpropagation-Theory-Architectures-and-Applications/Chauvin-Rumelhart/p/book/9780805812596
• [10]H. Zhao, H. Li, S. Maurer-Stroh, and L. Cheng, (2018). Synthesizing retinal and neuronal images with generative adversarial nets. Med Image Anal, 49;14–26, doi: 10.1016/j.media.2018.07.001.
• [11]B. Hu, Y. Tang, E. I.-C. Chang, Y. Fan, M. Lai, and Y. Xu, (2017). Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks. doi: 10.1109/JBHI.2018.2852639.
• [12]M. Mardani et al., (2019). Deep Generative Adversarial Neural Networks for Compressive Sensing MRI. IEEE Trans Med Imaging, 38(1);167–179, doi: 10.1109/TMI.2018.2858752.
• [13]E. Lin, S. Mukherjee, and S. Kannan, (2020). A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis. BMC Bioinformatics, 21(1);64, doi: 10.1186/s12859-020-3401-5.
• [14]D. O. Eke, (2023). ChatGPT and the rise of generative AI: Threat to academic integrity?. Journal of Responsible Technology, 13;100060, doi: 10.1016/j.jrt.2023.100060.
• [15]C. Kruchko, Q. T. Ostrom, H. Gittleman, and J. S. Barnholtz-Sloan, (2018). The CBTRUS story: providing accurate population-based statistics on brain and other central nervous system tumors for everyone. Neuro Oncol, 20(3);295–298, doi: 10.1093/neuonc/noy006.
• [16]I. J. Goodfellow et al., “Generative Adversarial Networks,” Jun. 2014.
• [17]R. Ranjbarzadeh, A. Caputo, E. B. Tirkolaee, S. Jafarzadeh Ghoushchi, and M. Bendechache, (2023). Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools,” Computers in Biology and Medicine, 152 doi: 10.1016/j.compbiomed.2022.106405.
• [18]W. M. Lim, A. Gunasekara, J. L. Pallant, J. I. Pallant, and E. Pechenkina, (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2);100790, doi: 10.1016/j.ijme.2023.100790.
• [19]S. Liu, H. Jiang, Z. Wu, Y. Liu, and K. Zhu, (2022). Machine fault diagnosis with small sample based on variational information constrained generative adversarial network. Advanced Engineering Informatics, 54 doi: 10.1016/j.aei.2022.101762.
• [20]Cutting-Edge AI Tools: An Up-to-Date Dataset Kaggle”https://www.kaggle.com/datasets/yasirabdaali/740-ai-tools-for-everyone (accessed Mar. 13, 2023).