Araştırma Makalesi

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

Cilt: 9 Sayı: 3 30 Eylül 2023
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Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods

Öz

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.

Anahtar Kelimeler

Kaynakça

  1. [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. [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. [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. [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. [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. [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. [7] D. Dana et al., (2018). Deep Learning in Drug Discovery and Medicine; Scratching the Surface, Molecules, 23;9 doi: 10.3390/molecules23092384.
  8. [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.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Altyapı Mühendisliği ve Varlık Yönetimi

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

17 Ağustos 2023

Yayımlanma Tarihi

30 Eylül 2023

Gönderilme Tarihi

20 Temmuz 2023

Kabul Tarihi

15 Ağustos 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 9 Sayı: 3

Kaynak Göster

APA
Kırelli, Y. (2023). Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods. International Journal of Computational and Experimental Science and Engineering, 9(3), 233-237. https://doi.org/10.22399/ijcesen.1330363
AMA
1.Kırelli Y. Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods. IJCESEN. 2023;9(3):233-237. doi:10.22399/ijcesen.1330363
Chicago
Kırelli, Yasin. 2023. “Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods”. International Journal of Computational and Experimental Science and Engineering 9 (3): 233-37. https://doi.org/10.22399/ijcesen.1330363.
EndNote
Kırelli Y (01 Eylül 2023) Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods. International Journal of Computational and Experimental Science and Engineering 9 3 233–237.
IEEE
[1]Y. Kırelli, “Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods”, IJCESEN, c. 9, sy 3, ss. 233–237, Eyl. 2023, doi: 10.22399/ijcesen.1330363.
ISNAD
Kırelli, Yasin. “Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods”. International Journal of Computational and Experimental Science and Engineering 9/3 (01 Eylül 2023): 233-237. https://doi.org/10.22399/ijcesen.1330363.
JAMA
1.Kırelli Y. Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods. IJCESEN. 2023;9:233–237.
MLA
Kırelli, Yasin. “Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods”. International Journal of Computational and Experimental Science and Engineering, c. 9, sy 3, Eylül 2023, ss. 233-7, doi:10.22399/ijcesen.1330363.
Vancouver
1.Yasin Kırelli. Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods. IJCESEN. 01 Eylül 2023;9(3):233-7. doi:10.22399/ijcesen.1330363