values scored by the different regression algorithms. Linear Regression (LR), Random Forest (RF), Decision Tree (DT), X Gradient Boosting(XGB), Support Vector Machine (SVM), Gradie... حسب Itxasne Antúnez Sáenz (22794696)

Performance of machine learning algorithms. حسب Wenxue Liu (6877754)

(a) Proposed dynamic low RF-complexity DBF architecture. حسب Usman Haider (12630481)

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A chart of associated parameters, along with various other miscellaneous parameters [39]. حسب Usman Haider (12630481)

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Flowchart of the study. حسب Usman Haider (12630481)

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Number of selected antennas per group, NLOS street canyon scenario. حسب Usman Haider (12630481)

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Channel capacity of MIMO system with variable . حسب Usman Haider (12630481)

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Channel capacity of MIMO system with NLOS street canyon scenario. حسب Usman Haider (12630481)

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Channel capacity of MIMO system with LOS street canyon scenario. حسب Usman Haider (12630481)

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Probability of LOS models and average transmit correlation analysis for MIMO system in street canyon and shopping mall scenarios, (a) Probability of LOS models. حسب Usman Haider (12630481)

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Statistical analysis of the (a) Percentage of several subarrays formed (same as the required number of RF chains). حسب Usman Haider (12630481)

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Number of selected antennas per group, LOS street canyon scenario. حسب Usman Haider (12630481)

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Main code. حسب Jingguo Bi (21439766)

Risk element category diagram. حسب Yao Hu (3479972)

Proposed and existing workflows to obtain RF-shim PTX in safe mode. حسب Maryam Arianpouya (12299222)

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Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …"

Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …"

Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …"

Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …"

Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …"