Here are the samples prediction of cattle weight using our proposed best 3Conv3Dense model. by Md Junayed Hossain (22615268)

Fig 8 - by Maryam Shadi (14237349)

Fig 9 - by Maryam Shadi (14237349)

Data_Sheet_1_Bi-objective goal programming for balancing costs vs. nutritional adequacy.pdf by Melissa F. Koenen (14262764)

“…Introduction<p>Linear programming (LP) is often used within diet optimization to find, from a set of available food commodities, the most affordable diet that meets the nutritional requirements of an individual or (sub)population. …”

Data_Sheet_1_Bi-objective goal programming for balancing costs vs. nutritional adequacy.pdf by Melissa F. Koenen (14262764)

“…Introduction<p>Linear programming (LP) is often used within diet optimization to find, from a set of available food commodities, the most affordable diet that meets the nutritional requirements of an individual or (sub)population. …”

Image_2_Detecting Marine Heatwaves With Sub-Optimal Data.pdf by Robert W. Schlegel (8055026)

“…We also show that the output of our MHW algorithm for time series missing less than 25% data did not differ appreciably from a complete time series, and that the level of allowable missing data could cautiously be increased to 50% when gaps were filled by linear interpolation. …”

Image_4_Detecting Marine Heatwaves With Sub-Optimal Data.jpg by Robert W. Schlegel (8055026)

“…We also show that the output of our MHW algorithm for time series missing less than 25% data did not differ appreciably from a complete time series, and that the level of allowable missing data could cautiously be increased to 50% when gaps were filled by linear interpolation. …”

Image_1_Detecting Marine Heatwaves With Sub-Optimal Data.pdf by Robert W. Schlegel (8055026)

“…We also show that the output of our MHW algorithm for time series missing less than 25% data did not differ appreciably from a complete time series, and that the level of allowable missing data could cautiously be increased to 50% when gaps were filled by linear interpolation. …”

Image_3_Detecting Marine Heatwaves With Sub-Optimal Data.jpg by Robert W. Schlegel (8055026)

“…We also show that the output of our MHW algorithm for time series missing less than 25% data did not differ appreciably from a complete time series, and that the level of allowable missing data could cautiously be increased to 50% when gaps were filled by linear interpolation. …”

Image_5_Detecting Marine Heatwaves With Sub-Optimal Data.pdf by Robert W. Schlegel (8055026)

“…We also show that the output of our MHW algorithm for time series missing less than 25% data did not differ appreciably from a complete time series, and that the level of allowable missing data could cautiously be increased to 50% when gaps were filled by linear interpolation. …”

Decoding results. by Sam Hall-McMaster (10343795)

Table_1_Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow.pdf by Amir Sahraei (10915302)

“…For the SVM, Polynomial kernel achieved the best performance, whereas Linear yielded the weakest performance among the kernel functions. …”

Table_1_Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients.DOCX by María Tubío-Fungueiriño (12069125)

“…Machine learning may be valuable tool for helping clinicians to rapidly identify patients at higher risk and therefore provide optimized care, especially in future pandemics. However, further validation of these models is required to ensure greater reliability of the algorithms for clinical implementation to specific objectives of interest.…”