Flow chart of multiphase sampling of ICMR MINDS. -н Johnson-Pradeep Ruben (22367283)

Нөхцлүүд:

Service outcome measures. -н Johnson-Pradeep Ruben (22367283)

Нөхцлүүд:

Spirit Figure of Phases of ICMR MINDS. -н Johnson-Pradeep Ruben (22367283)

Нөхцлүүд:

Pre-facility, facility, and post-facility components of the logic model and specific aspects of the logic model for the implementation framework. -н Johnson-Pradeep Ruben (22367283)

Нөхцлүүд:

Conceptual framework for costing of intervention package. -н Johnson-Pradeep Ruben (22367283)

Нөхцлүүд:

Error-specific accuracy comparison results. -н Weiling Liu (1465480)

“…It shows the excellent effect of this method in grammar error recognition and correction, and has far-reaching significance in providing accurate error correction suggestions, helping English learners improve their writing ability and ensuring the quality of English writing. …”

Comparison of Model Five-classification Results. -н Chao Tang (10925)

PCA-CGAN Ablation Experiment Results. -н Chao Tang (10925)

The Lang-8 corpus of learner English results. -н Weiling Liu (1465480)

“…It shows the excellent effect of this method in grammar error recognition and correction, and has far-reaching significance in providing accurate error correction suggestions, helping English learners improve their writing ability and ensuring the quality of English writing. …”

Radar plots compare the performance of models with different qubit configurations across evaluation metrics: accuracy (ACC), specificity (SPEC), F1-score (F1), precision (PRE), and... -н Mominul Islam (3513758)

Нөхцлүүд:

Training configurations for the segmentation (), diffusion (), and classification () models. -н Mominul Islam (3513758)

Нөхцлүүд:

Subfigure (a) shows the 3D MRI volume represented as voxels in a three-dimensional coordinate system; (b) presents example 2D slices from the axial, coronal, and sagittal planes; a... -н Mominul Islam (3513758)

Нөхцлүүд:

The graphs present the training and validation accuracy curves for the CQ-CNN models across different MRI planes (axial, coronal, sagittal, and 3-plane) and model configurations (c... -н Mominul Islam (3513758)

Нөхцлүүд:

Schematic depiction of a classical neural network (a) and a quantum neural network (b) for binary classification. -н Mominul Islam (3513758)

Нөхцлүүд:

Visualization of the 3D-to-2D slice extraction strategy from volumetric MRI data (axial view). -н Mominul Islam (3513758)

Нөхцлүүд:

Performance analysis of CQ-CNN models across axial, coronal, sagittal, and combined 3-plane views. -н Mominul Islam (3513758)

Нөхцлүүд:

Layer-by-layer configuration and parameter count of the proposed CQ-CNN model, where <i>ω</i> represents the number of qubits in the PQC, and indicates the number of trainable para... -н Mominul Islam (3513758)

Нөхцлүүд:

The schematic depicts our PQC using ZZFeatureMap encoding, with subfigure (a) showing a 2-qubit circuit and subfigure (b) showing a 3-qubit circuit. -н Mominul Islam (3513758)

Нөхцлүүд:

Distribution of training and testing images for the moderate dementia and non-dementia classes in the OASIS-2 dataset, shown across four variations with images from different plane... -н Mominul Islam (3513758)

Нөхцлүүд:

Control experiments illustrate feature separability and training stability between MNIST binary pairs (0v1, 2v3, 4v5) and the OASIS-2 MRI dataset for AD classification across axial... -н Mominul Islam (3513758)

Нөхцлүүд: