Open Access
| Issue |
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
|
|
|---|---|---|
| Article Number | 08002 | |
| Number of page(s) | 7 | |
| Section | Communication and Signal Processing | |
| DOI | https://doi.org/10.1051/e3sconf/202459108002 | |
| Published online | 14 November 2024 | |
- S. J. Hussain and R. Kiran Kumar, “Exhaustive feature set processing by multi objective optimization based on hybrid approach for CBIR,” Journal of Advanced Research in Dynamical and Control Systems, vol. 10, no. 4 Special Issue, pp. 1355–1366, 2018, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2- s2.0- 85051301233&partnerID=40&md5=4c729458e5a7 96f5926e12c3758cb951 [Google Scholar]
- S. Jakeer Hussain, N. Raghavendra Sai, B. Sai Chandana, J. Harikiran, and G. Sai Chaitanya Kumar, “A Novel Ensemble of Classification Techniques for Intrusion Detection System,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 126, 2022, pp. 405–417. doi: 10.1007/978-981-19-2069-1_28. [CrossRef] [Google Scholar]
- M. Ashok, D. R. Rinku, K. A. Jyotsna, V. Kesava Vamsi Krishna, N. Subbulakshmi, and S. J. Hussain, “Intelligent Children Safety and Security Wearable Shield Using IoT,” in Smart Innovation, Systems and Technologies, 2023, pp. 91–98. doi: 10.1007/978-981-99-4717-1_9. [Google Scholar]
- C. B. N. Lakshmi, S. J. Hussain, and M. L. Swarupa, “Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning,” in Lecture Notes in Electrical Engineering, 2024, pp. 207–215. doi: 10.1007/978- 981-99-7954-7_19. [Google Scholar]
- Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, Chen K, Mitchell R, Cano I, Zhou T, Li M, Xie J, Lin M, Geng Y, Li Y (2020) xgboost: Extreme Gradient Boosting. R packageversion,1.1.1.1. https://CRAN.Rproject.org/package=xgboost [Google Scholar]
- Donohue RJ, Lawes RA, Mata G, Gobbett D, Ouzman J (2018) Towards a national, remote- sensing based model for predicting field-scale crop yield. Field Crops Research 227, 79-90. [CrossRef] [Google Scholar]
- Filippi P, Whelan BM, Vervoort RW, Bishop TFA (2020) Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates. Agricultural Systems 184, 102894. [CrossRef] [Google Scholar]
- Liu Y, Just A (2020) SHAPforxgboost: SHAP Plots for 'XGBoost'.R package version 0.0.4 . https://CRAN.Rproject.org/package=SHAPforxgbo ost [Google Scholar]
- R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. [Google Scholar]
- Tilse M, Bishop TFA, Triantafilis J, Filippi P (2021) Quantifying the impact of subsoil constraints on soil available water capacity and potential crop yield across multiple fields/farms. Crop and Pasture Science, Under review. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.