Regression model coefficients. by 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. by 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. by 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.…”

Correlation in site-level DNA methylation estimates between TMS and RRBS data after permutation. by Amy Longtin (21409957)

Data file without outliers. by Helena Wallin (6103205)

“…The association between TRP, KYN, KYNA and ExCap in CKD was assessed using a generalised linear model.</p><p>Results</p><p>At baseline, there were significant differences between all groups in aerobic ExCap, KYN, KYNA, TRP and KYN/TRP. …”

Mean parameter values for the selected crops. by Gourab Saha (8987405)

“…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. …”

Performance comparison of ML models. by Gourab Saha (8987405)

“…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. …”

Comparative data of different soil samples. by Gourab Saha (8987405)

“…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. …”

Confusion matrix of random forest model. by Gourab Saha (8987405)

“…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. …”

Sensor value scenario for fuzzy logic algorithm. by Gourab Saha (8987405)

“…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. …”

Evaluation metrics of selected ML models. by Gourab Saha (8987405)

“…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 of the proposed system. by Gourab Saha (8987405)

“…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. …”

Chart for applicable amount of fertilizers. by Gourab Saha (8987405)

“…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. …”

Cost analysis of irrigation controller unit. by Gourab Saha (8987405)

“…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. …”

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

“…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. by Gourab Saha (8987405)

“…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. by Gourab Saha (8987405)

“…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. by Gourab Saha (8987405)

“…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. by Gourab Saha (8987405)

“…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. …”

Highly Sensitive and Selective Electrochemical Sensor via Cu-BTC/Au@Cu-BTC Modified Screen-Printed Electrode for the Detection of Chemical Agents by Xiaosen Li (6263651)

“…Chemical agents present significant threat to international peace, security, and human health due to their potential toxicity. …”