Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

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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... by Itxasne Antúnez Sáenz (22794696)

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

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

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

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Flowchart of the study. by Usman Haider (12630481)

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

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

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

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

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

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Sanitized databases using MLHProtector algorithm. by Loan T. T. Nguyen (20660789)

“…To address this issue, this work suggests two PPUM algorithms, namely <b>MLHProtector</b> and <b>FMLHProtector</b>, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. …”

Clinical use algorithm. by Chibuike K. Uwakwe (22655742)

Algorithm factor level change curve. by Kuiyuan Pan (21145338)

Risk element category diagram. by Yao Hu (3479972)

Algorithm for optimizing the model using transformer. by Weiling Liu (1465480)

Proposed and existing workflows to obtain RF-shim PTX in safe mode. by 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 by 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. …”