An Innovative Classification Approach for Predicting Physical Properties in Nanoparticles
| Authors | V.M. Tidake1, P.M. Patare2, P.B. Khatkale3, A.A. Khatri4, P.M. Yawalkar5, S.S. Ingle2, N.K. Darwante6 |
| Affiliations |
1Department of MBA, Sanjivani College of Engineering, Kopargaon, SPPU, Pune, India 2Department of Mechanical Engineering, Sanjivani College of Engineering Kopargaon, SPPU, Pune, India 3Sanjivani University, Kopargaon, MH, India 4Department of Computer Engineering, Jaihind College of Engineering, Kuran, SPPU, Pune, MH, India 5Department of Computer Engineering, MET's Institute of Engineering, Nashik, India 6Department of Electronics and Computer Engineering, Sanjivani College Engineering, Kopargaon, SPPU, Pune, India |
| Е-mail | khatrianand@gmail.com |
| Issue | Volume 16, Year 2024, Number 5 |
| Dates | Received 02 June 2024; revised manuscript received 20 October 2024; published online 30 October 2024 |
| Citation | V.M. Tidake, P.M. Patare, P.B. Khatkale, et al., J. Nano- Electron. Phys. 16 No 5, 05011 (2024) |
| DOI | https://doi.org/10.21272/jnep.16(5).05011 |
| PACS Number(s) | 77.84.Bw |
| Keywords | Zinc oxide (10) , Nanotechnology (6) , Physical properties (5) , Bat based Random Forest (B-RF). |
| Annotation |
Zinc oxide (ZnO) nanoparticles (NP) are generating substantial attention across multiple areas due to the distinctive Structural and Molecular Features. Predicting and understanding these properties is crucial for designing effective applications in areas such as catalysis, sensors, and biomedical devices. Nanotechnology has emerged as a pivotal field, particularly in materials science, where the unique properties of NP are harnessed for various applications. Understanding and predicting the physical properties of NP, such as those in ZnO, is crucial for optimizing their performance. For the classification approach, we introduced a novel method, Bat based Random Forest (B-RF) to enhance the accuracy and efficiency of predicting major physical properties of ZnO NP. In this research, we utilize a relevant dataset encompassing various physical properties of ZnO NP. The model is fine-tuned to achieve optimal performance. The proposed Random Forest-based classification approach demonstrates superior predictive performance compared to traditional methods. Our model attains high accuracy and reliability in predicting diverse physical properties of ZnO NP. By the end of the study, our suggested approach outperforms other methods in terms of Accuracy (92.8%), Sensitivity (90.8%), and Specificity (93.9%). This can contribute to improve the overall performance and functioning of the existing model in a better way. |
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List of References |
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