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

"…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. …"

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

"…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. …"

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

"…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. …"

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

"…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. …"

Hardware design for IoT-based irrigation system. حسب Gourab Saha (8987405)

"…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. …"

Baseline characteristics of participants. حسب Mei Zhou (269746)

"…</p><p>Results</p><p>After DRG implementation, the logarithmic mean of total hospitalization expenditures decreased significantly (3.914 ± 0.837 vs. 3.872 ± 1.004), while rates of unplanned readmissions, unplanned reoperations, postoperative complications, and patient complaints within 30 days increased significantly (3.784% vs 4.214%, 0.083% vs 0.166%, 0.207% vs 0.258%, 3.741% vs 5.133%). …"

The framework diagram of this study. حسب Mei Zhou (269746)

"…</p><p>Results</p><p>After DRG implementation, the logarithmic mean of total hospitalization expenditures decreased significantly (3.914 ± 0.837 vs. 3.872 ± 1.004), while rates of unplanned readmissions, unplanned reoperations, postoperative complications, and patient complaints within 30 days increased significantly (3.784% vs 4.214%, 0.083% vs 0.166%, 0.207% vs 0.258%, 3.741% vs 5.133%). …"

Predictors in ordinal regression model for GDS. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

Classification of hand grip strength. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

Rating scale for functional severity [28]. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

Regression model coefficients. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

ICOPE screening positive participant’s responses. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

WHO BMI classification for adults. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

Top predictive variables for KDIGO stage ≥ 3 identified by the final Random Forest model. حسب Julian Müller-Kühnle (22265248)

Study sample. حسب Nipaporn Butsing (19470003)

"…One increased National Institute of Health Stroke Scale (NIHSS) score decreased adjusted BI scores by 3.6.</p><p>Conclusion</p><p>The time after discharge, gender, stroke subtype, and stroke severity are significant factors affecting functional outcomes after a stroke. …"

Flow chart of research object screening. حسب Wenyao Xie (21567889)

"…Estradiol exhibited significant non-linear relationship with ALI (P = 0.027), with multiple inflection points suggesting concentration-dependent effects.…"

Validation and predictive accuracy of the cerebrovascular model, حسب Hadi Esfandi (21387211)

"…<p>(a-f) dependency of blood flow in capillaries on the cortical depth and ABNP values: (a) mean and standard deviation of blood flow in Cap1 segments for various ABNP values, (b) layer-specific average blood flow in capillaries across the entire monophasic flat autoregulation range, (c) indicates that the superficial layers have a more nonuniform hemodynamic distribution in capillaries, (d) mean capillary blood flow across different vascular layers at physiological ABNP values (50-70 mmHg), (e) WT changes in superficial PA segments are more significant than in deeper segments across the autoregulation range. …"

Tuning curve sharpening and familiarity suppression. حسب Weifan Wang (4669081)

Analysis of differential microbiome and classification prediction model between case and control groups. حسب Chuan Zhang (187157)

"…<p><b>(A)</b> Linear discriminant analysis [LDA; (log10)>2] and (B) effect size (LEfSe) analysis revealed significant differences (P < 0.01) in the microbiota of the case (orange, negative score) and control groups (blue, positive score) groups. …"

Pediatric CPP with Praat and ADSV (Joshi et al., 2025) حسب Ashwini Joshi (21594817)

"…Age and <i>F</i>0 are significant predictors of CPP; however, the observed increase in CPP with increasing age in males is primarily due to the substantial decrease in <i>F</i>0 postpuberty. …"