14 citations as recorded by crossref.

1. Should artificial neural networks replace linear models in tree ring based climate reconstructions? J. Jevšenak & T. Levanič https://doi.org/10.1016/j.dendro.2016.08.002
2. Interface depth modelling of gravity data and altitude variations: a Bayesian neural network approach S. Maiti et al. https://doi.org/10.1007/s00521-019-04276-9
3. A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction S. Maiti & R. Tiwari https://doi.org/10.1007/s12665-013-2702-7
4. A non-linear bayesian neural network model for global temperature change modelling and quantifying relative influence of climate forcing S. Maiti https://doi.org/10.1016/j.physd.2026.135191
5. Advances in Geo-Time Series Modelling R. Tiwari & R. Rekapalli https://doi.org/10.1007/s12594-021-1862-4
6. ANN-Based Assessment of the Influence of Natural and Anthropogenic Forcing on Surface Air Temperature Variability Over the Indian Subcontinent B. Padmavathi et al. https://doi.org/10.1007/s00024-021-02724-z
7. Using artificial neural networks to predict future dryland responses to human and climate disturbances C. Buckland et al. https://doi.org/10.1038/s41598-019-40429-5
8. A Machine Learning Approach to Analyzing the Relationship Between Temperatures and Multi-Proxy Tree-Ring Records J. Jevšenak et al. https://doi.org/10.3959/1536-1098-74.2.210
9. Predicting lithology using neural networks from downhole data of a gas hydrate reservoir in the Krishna–Godavari basin, eastern Indian offshore A. Singh et al. https://doi.org/10.1093/gji/ggz522
10. Delineation of shallow resistivity structure around Malvan, Konkan region, Maharashtra by neural network inversion using vertical electrical sounding measurements S. Maiti et al. https://doi.org/10.1007/s12665-012-1779-8
11. Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks S. Maiti et al. https://doi.org/10.1007/s10661-012-2802-y
12. Short term memory efficient pore pressure prediction via Bayesian neural networks at Bering Sea slope of IODP expedition 323 M. Karmakar & S. Maiti https://doi.org/10.1016/j.measurement.2018.12.034
13. Fusing pollen-stratigraphic and dendroclimatic proxy data to reconstruct summer temperature variability during the past 7.5 ka in subarctic Fennoscandia S. Helama et al. https://doi.org/10.1007/s10933-012-9598-1
14. Sediment classification using neural networks: An example from the site-U1344A of IODP Expedition 323 in the Bering Sea M. Ojha & S. Maiti https://doi.org/10.1016/j.dsr2.2013.03.024