Showing 1,741 - 1,760 results of 4,211 for search 'significantly ((((mean decrease) OR (nn decrease))) OR (linear decrease))', query time: 0.34s Refine Results
  1. 1741
  2. 1742
  3. 1743
  4. 1744
  5. 1745
  6. 1746
  7. 1747

    Performance comparison of ML models. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  8. 1748

    Comparative data of different soil samples. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  9. 1749

    Confusion matrix of random forest model. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  10. 1750

    Sensor value scenario for fuzzy logic algorithm. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  11. 1751

    Evaluation metrics of selected ML models. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  12. 1752

    Block diagram of the proposed system. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  13. 1753

    Chart for applicable amount of fertilizers. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  14. 1754

    Cost analysis of irrigation controller unit. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  15. 1755

    Run times of two algorithms. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  16. 1756

    Metals concentrations in selected coal samples. by Ruchika Kishor Jain (19704102)

    Published 2024
    “…After BAI-RCD treatment, both cell lines showed a decrease in antioxidant stress measures (SOD, CAT, and GSH) and a significant (<i>p</i> < 0.001) increase in oxidative stress parameters (NADPH, MPO, LPO, and PC). …”
  17. 1757

    BAI as a percent release at pH 4.5. by Ruchika Kishor Jain (19704102)

    Published 2024
    “…After BAI-RCD treatment, both cell lines showed a decrease in antioxidant stress measures (SOD, CAT, and GSH) and a significant (<i>p</i> < 0.001) increase in oxidative stress parameters (NADPH, MPO, LPO, and PC). …”
  18. 1758

    Flow chart of Fuzzy Logic based control system. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  19. 1759

    Block diagram for IoT-based irrigation system. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
  20. 1760

    Flow chart of Average Value-based control system. by Gourab Saha (8987405)

    Published 2025
    “…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”