Search alternatives:
greater decrease » greatest decrease (Expand Search), greater increase (Expand Search), greater disease (Expand Search)
linear decrease » linear increase (Expand Search)
nn decrease » _ decrease (Expand Search), a decrease (Expand Search), mean decrease (Expand Search)
greater decrease » greatest decrease (Expand Search), greater increase (Expand Search), greater disease (Expand Search)
linear decrease » linear increase (Expand Search)
nn decrease » _ decrease (Expand Search), a decrease (Expand Search), mean decrease (Expand Search)
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1201
Time and flowrate used for proteomics.
Published 2024“…Protein kinase C (PKC) activity was greater in the CF cells compared to the non-CF cells, but the activity was significantly attenuated in both cell types after infection with <i>M</i>. …”
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1202
S1 Graphical abstract -
Published 2024“…Protein kinase C (PKC) activity was greater in the CF cells compared to the non-CF cells, but the activity was significantly attenuated in both cell types after infection with <i>M</i>. …”
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1203
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1204
Mean parameter values for the selected crops.
Published 2025“…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. …”
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1205
Performance comparison of ML models.
Published 2025“…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. …”
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1206
Comparative data of different soil samples.
Published 2025“…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. …”
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1207
Confusion matrix of random forest model.
Published 2025“…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. …”
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1208
Sensor value scenario for fuzzy logic algorithm.
Published 2025“…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. …”
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1209
Evaluation metrics of selected ML models.
Published 2025“…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. …”
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1210
Block diagram of the proposed system.
Published 2025“…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. …”
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1211
Chart for applicable amount of fertilizers.
Published 2025“…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. …”
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1212
Cost analysis of irrigation controller unit.
Published 2025“…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. …”
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1213
Run times of two algorithms.
Published 2025“…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. …”
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1214
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1215
Flow chart of Fuzzy Logic based control system.
Published 2025“…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. …”
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1216
Block diagram for IoT-based irrigation system.
Published 2025“…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. …”
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1217
Flow chart of Average Value-based control system.
Published 2025“…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. …”
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1218
Hardware design for IoT-based irrigation system.
Published 2025“…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. …”
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1219
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1220