1,619 research outputs found
Papaya Dataset
No full textThree Variety Papaya Fruits according to Levels of Maturity.
Cite as: Behera, S.K., Rath, A.K., Sethy, P.K., Maturity status classification of papaya fruits based on machine learning and transfer learning approach.Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2020.05.00
Papaya Dataset
No full textThree Variety Papaya Fruits according to Levels of Maturity.
Cite as: Behera, S.K., Rath, A.K., Sethy, P.K., Maturity status classification of papaya fruits based on machine learning and transfer learning approach.Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2020.05.00
FDI Spillovers, Innovation and the Role of Industrial Clusters: Evidence from Innovative Indian Manufacturing Firms
No full text© 2025, Elsevier B.V. The attached document (embargoed until 08/09/2026) is an author produced version of a paper published in Economic Modelling uploaded in accordance with the publisher’s self-archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it
PRIMITIVE PADDY VARITIES OF ODISHA, INDIA
No full textTen Varieties of Primitive Paddy like Goswami, Jamuna, Jamut, Mamata, Pooja, Rambanbas, Sanghmitra, Sanjeevani, Sadhana and Sannasriya are shared.Please cite N.K. Naik, P.K. Sethy, A.G. Devi, S.K. Behera, Few-shot learning convolutional neural network for primitive indian paddy grain identification using 2D-DWT injection and grey wolf optimizer algorithm, Journal of Agriculture and Food Research (2024), doi: https://doi.org/10.1016/j.jafr.2023.1009THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV
Multi-robot path planning in a dynamic environment using improved gravitational search algorithm
No full textAbstractThis paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm (IGSA) in a dynamic environment. GSA is improved based on memory information, social, cognitive factor of PSO (particle swarm optimization) and then, population for next generation is decided by the greedy strategy. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position. Finally, the analytical and experimental results of the multi-robot path planning have been compared with those obtained by IGSA, GSA and PSO in a similar environment. The simulation and the Khepera environmental results outperform IGSA as compared to GSA and PSO with respect to performance matrix
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