Journal: Bioengineering

Article Title: Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

doi: 10.3390/bioengineering11080766

Figure Lengend Snippet: Mean Squared Error analysis for different FE techniques without feature selection.

Article Snippet: An interesting observation is the significant reduction in Test MSE for SDC (Softmax Discriminant Classifier) compared to the previous table (Train MSE: 2.21 × 10 −5 , Test MSE: 1.6 × 10 −5 to Train MSE: 2.81 × 10 −6 , Test MSE: 2.81 × 10 −4 ).

Techniques: Selection

Journal: Bioengineering

Article Title: Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

doi: 10.3390/bioengineering11080766

Figure Lengend Snippet: Mean Squared Error analysis for different feature extraction techniques with Bald Eagle Search Optimization (BESO) feature selection.

Article Snippet: An interesting observation is the significant reduction in Test MSE for SDC (Softmax Discriminant Classifier) compared to the previous table (Train MSE: 2.21 × 10 −5 , Test MSE: 1.6 × 10 −5 to Train MSE: 2.81 × 10 −6 , Test MSE: 2.81 × 10 −4 ).

Techniques: Extraction, Selection

Journal: Bioengineering

Article Title: Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

doi: 10.3390/bioengineering11080766

Figure Lengend Snippet: Mean Squared Error analysis for different feature extraction techniques with Red Deer Optimization (RDO) feature selection.

Article Snippet: An interesting observation is the significant reduction in Test MSE for SDC (Softmax Discriminant Classifier) compared to the previous table (Train MSE: 2.21 × 10 −5 , Test MSE: 1.6 × 10 −5 to Train MSE: 2.81 × 10 −6 , Test MSE: 2.81 × 10 −4 ).

Techniques: Extraction, Selection

Journal: Bioengineering

Article Title: Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

doi: 10.3390/bioengineering11080766

Figure Lengend Snippet: Classifiers optimal parameters.

Article Snippet: An interesting observation is the significant reduction in Test MSE for SDC (Softmax Discriminant Classifier) compared to the previous table (Train MSE: 2.21 × 10 −5 , Test MSE: 1.6 × 10 −5 to Train MSE: 2.81 × 10 −6 , Test MSE: 2.81 × 10 −4 ).

Techniques:

Journal: PLoS ONE

Article Title: Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

doi: 10.1371/journal.pone.0178410

Figure Lengend Snippet: Performance for all categories using the proposed method (ConvNet & LRBSF). Best and worst performance of individual participant is also mentioned.

Article Snippet: Because ConvNet is a complete framework, most of the studies have used the ConvNet classifier (softmax) for prediction/classification [ ].

Techniques: Selection

Journal: PLoS ONE

Article Title: Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

doi: 10.1371/journal.pone.0178410

Figure Lengend Snippet: Significant difference (p-value) of accuracies between the proposed and other methods.

Article Snippet: Because ConvNet is a complete framework, most of the studies have used the ConvNet classifier (softmax) for prediction/classification [ ].

Techniques: