Characteristic chart of algorithm efficiency. حسب Wang Xiaoqian (10502222)

Improved BP network algorithm flow. حسب Wang Xiaoqian (10502222)

Hyper parameter settings for ML Algorithms. حسب R. S. Jayaram (22139155)

Comparative analysis of relative errors in 4 schemes. حسب Zhouzhou Liu (21560758)

Algorithm for MFISTA-VA [30]. حسب Faisal Najeeb (20542714)

"…Reconstruction results of the proposed method are evaluated by using: (i) convergence error, (ii) peak and mean values of arterial signal intensity in the selected region of interest (ROI) of DCE MR Images, and (iii) reconstruction time. …"

Confusion matrix: Describes the two errors based on ground truth and predicted values of the SCP in the frame. حسب Sukhvinder Kaur (20297968)

Measurement parameters of five BF. حسب Lintao Chen (4634617)

"…Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. …"

Parameters required in DEM simulation. حسب Lintao Chen (4634617)

"…Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. …"

BP neural network topology structure. حسب Lintao Chen (4634617)

"…Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. …"

Raw materials obtained from BFs. حسب Lintao Chen (4634617)

"…Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. …"

Schematic diagram of the JKR bonding model. حسب Lintao Chen (4634617)

"…Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. …"

Particle size distribution of arrow BF. حسب Lintao Chen (4634617)

"…Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. …"

Particle size distribution of palm BF. حسب Lintao Chen (4634617)

"…Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. …"

Data from an Investigation of Music Analysis by the Application of Grammar-based Compressor حسب David Humphreys (19079318)

"…<br>        <br>    Compressor output:<br>The size of the unaltered, compressed model.<br>Increase in size from initial_model_size as an error is introduced to each position in sequence.…"

Flowchart of node scheduling. حسب Zhouzhou Liu (21560758)

"…<div><p>To tackle the challenges of extensive data transmission and high redundancy in wireless sensor networks (WSNs), this study proposes a novel data collection scheme based on expected network coverage and clustered compressive sensing (CS). First, the K-medoids clustering algorithm organizes nodes within the WSN coverage area into clusters. …"

Key parameters of the data collection scheme. حسب Zhouzhou Liu (21560758)

"…<div><p>To tackle the challenges of extensive data transmission and high redundancy in wireless sensor networks (WSNs), this study proposes a novel data collection scheme based on expected network coverage and clustered compressive sensing (CS). First, the K-medoids clustering algorithm organizes nodes within the WSN coverage area into clusters. …"

Comparative analysis of data collection schemes. حسب Zhouzhou Liu (21560758)

"…<div><p>To tackle the challenges of extensive data transmission and high redundancy in wireless sensor networks (WSNs), this study proposes a novel data collection scheme based on expected network coverage and clustered compressive sensing (CS). First, the K-medoids clustering algorithm organizes nodes within the WSN coverage area into clusters. …"

WSNs Data Collection Model. حسب Zhouzhou Liu (21560758)

"…<div><p>To tackle the challenges of extensive data transmission and high redundancy in wireless sensor networks (WSNs), this study proposes a novel data collection scheme based on expected network coverage and clustered compressive sensing (CS). First, the K-medoids clustering algorithm organizes nodes within the WSN coverage area into clusters. …"

Data collection performance for a single round. حسب Zhouzhou Liu (21560758)

"…<div><p>To tackle the challenges of extensive data transmission and high redundancy in wireless sensor networks (WSNs), this study proposes a novel data collection scheme based on expected network coverage and clustered compressive sensing (CS). First, the K-medoids clustering algorithm organizes nodes within the WSN coverage area into clusters. …"

Flowchart of network clustering. حسب Zhouzhou Liu (21560758)

"…<div><p>To tackle the challenges of extensive data transmission and high redundancy in wireless sensor networks (WSNs), this study proposes a novel data collection scheme based on expected network coverage and clustered compressive sensing (CS). First, the K-medoids clustering algorithm organizes nodes within the WSN coverage area into clusters. …"