Analysis of corner extraction results. by Guangmei Ma (21432861)

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Feature recognition accuracy testing. by Guangmei Ma (21432861)

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Summary of raw and processed dataset. by Elena Escobar-Linero (16052848)

“…Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. …”

Grid search process for ANN classifier. by Elena Escobar-Linero (16052848)

“…Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. …”

Grid search process for RF classifier. by Elena Escobar-Linero (16052848)

“…Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. …”

Grid search process for SVM classifier. by Elena Escobar-Linero (16052848)

“…Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. …”

The training process in the proposed CNN model. by Mohsen Ahmadi (633584)

Post scan feature estimates and confidence ratings. by Sam Hall-McMaster (10343795)

Decoding results. by Sam Hall-McMaster (10343795)

Video1_NSPG: An Efficient Posture Generator Based on Null-Space Alteration and Kinetostatics Constraints.MP4 by figshare admin frontiersin (4545187)

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping by P. Vangeenderhuysen (15854812)

“…To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. …”

Approximating Partial Likelihood Estimators via Optimal Subsampling by Haixiang Zhang (10144283)

Table2_Nonintrusive Load Monitoring Method Based on Color Encoding and Improved Twin Support Vector Machine.XLS by Ruoyuan Zhang (13136175)

“…Second, the two-dimension Gabor wavelet is used to extract the texture features of the image, and the dimension is reduced by means of local linear embedding (LLE). Finally, the artificial fish swarm algorithm (AFSA) is used to optimize the twin support vector machine (TWSVM), and the ITWSM is used to train the load recognition model, which greatly enhances the model training speed. …”

Table1_Nonintrusive Load Monitoring Method Based on Color Encoding and Improved Twin Support Vector Machine.XLS by Ruoyuan Zhang (13136175)

“…Second, the two-dimension Gabor wavelet is used to extract the texture features of the image, and the dimension is reduced by means of local linear embedding (LLE). Finally, the artificial fish swarm algorithm (AFSA) is used to optimize the twin support vector machine (TWSVM), and the ITWSM is used to train the load recognition model, which greatly enhances the model training speed. …”

Performance comparison of our proposed model with existing approaches for cattle weight prediction using machine learning and deep learning techniques. by Md Junayed Hossain (22615268)

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Visualization of Residuals across different models applied in our research. by Md Junayed Hossain (22615268)

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Proposed architecture of our research. by Md Junayed Hossain (22615268)

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Validation Loss vs. Training Loss Curves for the CNN architectures and EfficientNet B3 model evaluated in this study. by Md Junayed Hossain (22615268)

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A summary of the dataset containing information on each cattle’s weight, breed, and height. by Md Junayed Hossain (22615268)

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Error case analysis using LIME visualization on cattle’s weight prediction. by Md Junayed Hossain (22615268)

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