Height-Weight correlation and breed distribution of cattle in the dataset. by Md Junayed Hossain (22615268)

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Comparison of Actual vs. Predicted values across different models used in this study. by Md Junayed Hossain (22615268)

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This figure illustrates the four distinct custom Convolutional Neural Network (CNN) architectures evaluated in this study. by Md Junayed Hossain (22615268)

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

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Data collision free processing unit. by Ayesha Waris (21368446)

Surround Suppression mechanism. by Elnaz Nemati (21402730)

CNN-GRU based on GA. by Jianpeng Zhang (528185)

“…GA is used to optimize the feature selection process to identify the key feature subsets that have the greatest impact on model performance. …”

Block diagram of 2-DOF PIDA controller. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 7. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 10. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Iterative Registration and Clustering (IREC) Algorithm: The input image stack (<i>S</i>) is first aligned and grouped using registration and clustering (REC) based on an initial re... by Creighton M. Lisowski (22756444)

Wrapper-based feature selection methods. by Xiaohua Zeng (793632)

Construction process of XGBoost model. by Huijun Chen (148680)

Training process of model construction. by Huijun Chen (148680)

Training implementation process of the model. by Huijun Chen (148680)

1d-MSCNN + GRU model process. by Jianpeng Zhang (528185)

“…GA is used to optimize the feature selection process to identify the key feature subsets that have the greatest impact on model performance. …”

Comparison of methods on the USPS dataset. by Mohammed Shalaby (18297650)

Comparison of methods on the Banana dataset. by Mohammed Shalaby (18297650)

Comparison of methods on the Adult9a dataset. by Mohammed Shalaby (18297650)

Data_Sheet_1_Computational Methods for Continuous Eye-Tracking Perimetry Based on Spatio-Temporal Integration and a Deep Recurrent Neural Network.pdf by Alessandro Grillini (4391917)