بدائل البحث:
significant decrease » significant increase (توسيع البحث), significantly increased (توسيع البحث)
linear decrease » linear increase (توسيع البحث)
significant decrease » significant increase (توسيع البحث), significantly increased (توسيع البحث)
linear decrease » linear increase (توسيع البحث)
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101
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102
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103
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104
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105
Results of the LMM analysis for IOP change.
منشور في 2025"…The mean GMS was 2.46 ± 1.33 preoperatively, and decreased to 1.32 ± 1.31 at 3 months, and 1.60 ± 1.41 at 12 months postoperatively. …"
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106
Results of the LMM analysis for GMS change.
منشور في 2025"…The mean GMS was 2.46 ± 1.33 preoperatively, and decreased to 1.32 ± 1.31 at 3 months, and 1.60 ± 1.41 at 12 months postoperatively. …"
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107
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108
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109
Cross-sectional dependence results.
منشور في 2024"…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …"
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110
Autocorrelation test results.
منشور في 2024"…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …"
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111
Pesaran’s CADF test results for Model I.
منشور في 2024"…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …"
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112
Descriptive statistics of related variables.
منشور في 2024"…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …"
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113
Performance comparison of ML models.
منشور في 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. …"
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114
Comparative data of different soil samples.
منشور في 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. …"
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115
Confusion matrix of random forest model.
منشور في 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. …"
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116
Sensor value scenario for fuzzy logic algorithm.
منشور في 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. …"
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117
Evaluation metrics of selected ML models.
منشور في 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. …"
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118
Block diagram of the proposed system.
منشور في 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. …"
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119
Chart for applicable amount of fertilizers.
منشور في 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. …"
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120
Cost analysis of irrigation controller unit.
منشور في 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. …"