Designing an adaptive learning model for predictive database management in Internet of Things applications
Pandurang Pralhadrao Todsampandurang.todsam@vit.eduDepartment of Artificial Intelligence & Data ScienceVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , V. Dhanalakshmidhanalakshmi@gmail.comDepartment of Computer ScienceMeenakshi College of Arts and ScienceMeenakshi Academy of Higher Education and ResearchChennai, Tamil Nadu, 600078, IndiaView full profile → , *Pradnya BorkarCorresponding authorpradnyaborkar2@gmail.comDepartment of Computer Science and EngineeringNagpur Campus, Symbiosis International (Deemed University)Symbiosis Institute of TechnologyNagpur, Maharashtra, 440008, IndiaView full profile → , Sunil Thakursunil.thakur@niu.edu.inSchool of Engineering & TechnologyNoida International UniversityGreater Noida, Uttar Pradesh, 203201, IndiaView full profile → , Dipannita Mondalddmondal2684@gmail.comDepartment of Artificial Intelligence - Data Science and Machine LearningTalegaonDr. D. Y. Patil College of Engineering and InnovationPune, Maharashtra, 410507, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2025
- Published Online:
- 23 Apr 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2276
- Pages:
- 1843–1850
Abstract
Keywords
Subject Classifications
References
[1] S. Cereda, S. Valladares, P. Cremonesi, and S. Doni, “CGPTuner: a contextual Gaussian process bandit approach for the automatic tuning of IT configurations under varying workload conditions,” Proc. VLDB Endow., vol. 14, no. 8, pp. 1401–1413 (2021). doi: 10.14778/3457390.3457404.
[2] J. Lao, Y. Wang, Y. Li, J. Wang, Y. Zhang, Z. Cheng, and W. Wang, “GPTuner: A manual-reading database tuning system via GPT-guided Bayesian optimization,” Proc. VLDB Endow., vol. 17, no. 8, pp. 1939–1952 (2024). doi: 10.14778/3659437.3659449.
[3] J. Arulraj, R. Xian, L. Ma, and A. Pavlo, “Predictive indexing,” arXiv preprint, arXiv:1901.07064 (2019). doi: 10.48550/arXiv.1901.07064.
[4] D. Goyal, A. Kumar, Y. Gandhi, and V. Khetani, “Securing wireless sensor networks with novel hybrid lightweight cryptographic protocols,” J. Discrete Math. Sci. Cryptogr., vol. 27, no. 2-B, pp. 703–714 (2024).
[5] R. Cao, L. Bao, P. Zhangsun, C. Wu, S. Wei, R. Sun, and Z. Zhang, “PTSSBench: a performance evaluation platform in support of automated parameter tuning of software systems,” Automated Software Eng., vol. 30 (2023). doi: 10.1007/s10515-023-00402-z.
[6] N. Sassi and W. Jaziri, “Efficient AI-driven query optimization in large-scale databases: A reinforcement learning and graph-based approach,” Mathematics, vol. 13, no. 11, p. 1700 (2025). doi: 10.3390/math13111700.
[7] X. Zhang, H. Wu, Y. Li, J. Tan, and B. Cui, “Towards dynamic and safe configuration tuning for cloud databases,” arXiv preprint, arXiv:2203.14473 (2022). doi: 10.48550/arXiv.2203.14473.
[8] C.-W. Chang, “Virtual and real integration and online learning course learning effect difference analysis during COVID-19,” J. Inf. Optim. Sci., vol. 46, no. 3, pp. 601–623 (2025), doi: 10.47974/JIOS-1467.
[9] U. Erol, F. Raimondo, J. Pope, S. Gunner, V. Kumar, I. Mavromatis, and G. Oikonomou, “Evaluating concept drift detectors on real-world data,” in Proc. Conf. on Machine Learning and Applications (2023). doi: 10.5555/3639940.3640003.
[10] Anwarul Siddiqui, “Generative Ai Coding Tech - An Advance Pathway”, Int Journal Adv Comp Theory Engg, vol. 14, no. 1, pp. 203–207 (May 2025).
