| Data Set | 2D | 5D | |||||
| Participant | Algorithm | Train | Dev | Eval | Train | Dev | Eval |
| Elseify, Tarek (Baseline - Fall 2019)) | PyTorch: Multilayer Perceptrin (MLP) | 09.34% | 10.05% | 09.15% | 37.47% | 37.14% | 37.42% |
| Elseify, Tarek (Baseline - Fall 2019)) | TensorFlow: Multilayer Perceptron (MLP) | 08.63% | 09.10% | 09.00% | 36.89% | 37.30% | 36.87% |
| Bala, Animesh (Fall 2019)) | Scikit-Learn: Random Forests (RNF) | 06.86% | 08.90% | 08.50% | 30.27% | 36.88% | 36.48% |
| Bala, Animesh (Fall 2019)) | PyTorch: Multilayer Perceptron (MLP) | 08.07% | 07.90% | 08.40% | 39.91% | 39.93% | 39.94% |
| Begaj, Brandon (Fall 2019)) | Python: Support Vector Machines (SVM) | 08.11% | 07.90% | 08.35% | 36.56% | 36.35% | 36.68% |
| Begaj, Brandon (Fall 2019)) | PyTorch: Multilayer Perceptron (MLP) | 09.02% | 09.70% | 09.05% | 36.80% | 36.79% | 36.85% |
| Bruno, Casey (Fall 2019)) | Scikit-Learn: Random Forests (RNF) | 07.22% | 08.55% | 08.35% | 24.62% | 36.96% | 36.60% |
| Bruno, Casey (Fall 2019)) | PyTorch: Extreme Learning Machine (ELM) | 08.22% | 08.50% | 08.40% | 36.47% | 38.48% | 38.32% |
| Campbell, Christopher (Fall 2019)) | Python: K Nearest Neighbors (KNN) | 07.69% | 07.85% | 08.50% | 38.49% | 38.93% | 39.16% |
| Campbell, Christopher (Fall 2019)) | TensorFlow: Generative Adversarial Networks (GAN) | 09.57% | 08.80% | 09.25% | 40.11% | 40.20% | 40.07% |
| Jiang, Kuang (Fall 2019)) | Scikit-Learn: Random Forests (RNF) | 06.66% | 06.40% | 08.30% | 13.72% | 37.42% | 37.65% |
| Jiang, Kuang (Fall 2019)) | PyTorch: Multilayer Perceptron (MLP) | 07.83% | 07.50% | 08.15% | 36.43% | 36.19% | 37.11% |
| Khalkhali, Vahid (Fall 2019)) | Scikit-Learn: Gaussian Mixture Models (GMM) | 07.97% | 07.65% | 08.05% | 37.23% | 36.80% | 37.13% |
| Khalkhali, Vahid (Fall 2019)) | PyTorch: Multilayer Perceptron (MLP) | 08.07% | 08.15% | 08.30% | 36.31% | 36.60% | 36.96% |
| Mills, Kenneth (Fall 2019)) | MATLAB: Support Vector Machines (SVM) | 07.92% | 08.15% | 08.45% | 36.92% | 36.72% | 37.13% |
| Mills, Kenneth (Fall 2019)) | MATLAB: Deep Neural Network (DNN) | 17.61% | 16.75% | 17.45% | 41.68% | 41.58% | 41.91% |
| Pale, Andrew (Fall 2019)) | MATLAB: K Nearest Neighbors (KNN) | 07.93% | 07.90% | 08.05% | 34.42% | 38.24% | 38.31% |
| Pale, Andrew (Fall 2019)) | MATLAB: Multilayer Perceptron (MLP) | 08.35% | 08.00% | 07.75% | 36.96% | 37.33% | 37.81% |
| Xiao, Ya (Fall 2019)) | Scikit-Learn: Random Forests (RNF) | 00.00% | 08.25% | 09.10% | 00.00% | 38.38% | 38.54% |
| Xiao, Ya (Fall 2019)) | PyTorch: Multilayer Perceptron (MLP) | 08.09% | 08.25% | 08.30% | 40.24% | 40.36% | 40.25% |
| Xie, Zhanteng (Fall 2019)) | MATLAB: K Nearest Neighbors (KNN) | 08.03% | 08.10% | 08.05% | 36.89% | 37.08% | 36.71% |
| Xie, Zhanteng (Fall 2019)) | PyTorch: Multilayer Perceptron (MLP) | 08.22% | 08.05% | 08.10% | 37.22% | 36.97% | 37.26% |
| Zhou, Tongdi (Fall 2019)) | MATLAB: Gaussian Mixture Modeling (GMM) | 08.00% | 07.80% | 08.20% | 36.84% | 36.00% | 36.30% |
| Zhou, Tongdi (Fall 2019)) | MATLAB: Recurrent Neural Network (RNN) | 08.09% | 08.25% | 07.80% | 36.78% | 36.79% | 36.74% |
| Zlotnikov, Sivan (Fall 2019)) | MATLAB: Kernel Linear Discriminant Analysis (LDA) | 09.24% | 09.45% | 09.50% | 38.04% | 37.63% | 38.13% |
| Zlotnikov, Sivan (Fall 2019)) | MATLAB: Multilayer Perceptron (MLP) | 08.54% | 09.25% | 08.90% | 39.99% | 39.75% | 40.45% |
| Campbell, Chris (Fall 2019) | Python: Majority Vote (MAJ) | 07.62% | 08.05% | 07.80% | 33.74% | 36.57% | 36.57% |