Convergence Analysis. by Nadim Rana (11424583)

“…Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. …”

Proposed Thorax Disease Detection Model. by Nadim Rana (11424583)

“…Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. …”

Structure of EnAE model for feature extraction. by Nadim Rana (11424583)

“…Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. …”

AUC Analysis for Dataset 1. by Nadim Rana (11424583)

“…Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. …”

Comparative Analysis for Datasets 1 and 2. by Nadim Rana (11424583)

“…Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. …”

Accuracy-Loss Analysis for Dataset 1. by Nadim Rana (11424583)

“…Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. …”

AUC Analysis for Dataset 2. by Nadim Rana (11424583)

“…Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. …”

Accuracy-Loss Analysis for Dataset 2. by Nadim Rana (11424583)

“…Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. …”

Oscillatory Field Genesis: The Emergent Architecture of Spacetime, Matter, and Memory by Drippy Stone (21218843)

Subjects:

3D Deformable cell based model. by Roland M. Dries (6620132)

Table 4_Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.xlsx by Anthony Wong (428070)

“…Performance was evaluated using 5-fold cross-validation and independent validation on 24 literature-derived sequences. A genetic algorithm framework was developed for peptide sequence optimization, incorporating multi objective fitness evaluation based on predicted binding affinity, biological plausibility, and sequence novelty.…”

Image 1_Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.tiff by Anthony Wong (428070)

“…Performance was evaluated using 5-fold cross-validation and independent validation on 24 literature-derived sequences. A genetic algorithm framework was developed for peptide sequence optimization, incorporating multi objective fitness evaluation based on predicted binding affinity, biological plausibility, and sequence novelty.…”

Image 2_Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.tiff by Anthony Wong (428070)

“…Performance was evaluated using 5-fold cross-validation and independent validation on 24 literature-derived sequences. A genetic algorithm framework was developed for peptide sequence optimization, incorporating multi objective fitness evaluation based on predicted binding affinity, biological plausibility, and sequence novelty.…”

Image 4_Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.tiff by Anthony Wong (428070)

“…Performance was evaluated using 5-fold cross-validation and independent validation on 24 literature-derived sequences. A genetic algorithm framework was developed for peptide sequence optimization, incorporating multi objective fitness evaluation based on predicted binding affinity, biological plausibility, and sequence novelty.…”

Table 3_Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.xlsx by Anthony Wong (428070)

“…Performance was evaluated using 5-fold cross-validation and independent validation on 24 literature-derived sequences. A genetic algorithm framework was developed for peptide sequence optimization, incorporating multi objective fitness evaluation based on predicted binding affinity, biological plausibility, and sequence novelty.…”

Table 2_Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.xlsx by Anthony Wong (428070)

“…Performance was evaluated using 5-fold cross-validation and independent validation on 24 literature-derived sequences. A genetic algorithm framework was developed for peptide sequence optimization, incorporating multi objective fitness evaluation based on predicted binding affinity, biological plausibility, and sequence novelty.…”

Image 3_Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.tiff by Anthony Wong (428070)

“…Performance was evaluated using 5-fold cross-validation and independent validation on 24 literature-derived sequences. A genetic algorithm framework was developed for peptide sequence optimization, incorporating multi objective fitness evaluation based on predicted binding affinity, biological plausibility, and sequence novelty.…”

Table 1_Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.xlsx by Anthony Wong (428070)

“…Performance was evaluated using 5-fold cross-validation and independent validation on 24 literature-derived sequences. A genetic algorithm framework was developed for peptide sequence optimization, incorporating multi objective fitness evaluation based on predicted binding affinity, biological plausibility, and sequence novelty.…”

data and code by PENGCHENG LIU (20680641)

“…Combined with the road network data, this algorithm regards the trajectory data as a signal that changes dynamically with time, converts it from time domain to frequency domain through Fourier transform, fits the trajectory points in the spectrum domain, and converts the discrete trajectory points into time continuous line elements.…”

Data Sheet 1_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

“…PIs collectively achieved high-precision predictions (mean 0.42 ≤ R² ≤ 0.94), with the prediction of PH using green leaf index (GLI) in BP algorithm attaining peak accuracy (R² = 0.94). VIs and PIs exhibited comparable predictive capacity for yield, with multi-indicators integrated modeling significantly enhancing performance: VIs achieved R² = 0.87 under RF algorithms, whereas PIs reached R² = 0.81 using BP algorithms. …”