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General ML benchmarks, tabular data, AutoML, recommendation systems, anomaly detection, evaluation suites
141,962 datasets
A 5.5 KB Excel file containing a unified deep learning framework evaluated on three benchmark clinical datasets. The framework, proposed by Vijay U. Rathod, integrates MLP, CNN, FT-Transformer, and autoencoder architectures. It was last updated on May 8, 2026, and demonstrated competitive performance, with an FT-Transformer + autoencoder ensemble achieving an AUC of 0.8980 for heart disease prediction.
Three heterogeneous clinical datasets—UCI Heart Disease (303 samples), PIMA Indians Diabetes (768 samples), and Parkinson's disease voice recordings (195 samples)—were used to evaluate a unified deep learning framework. The study by Vijay U. Rathod, last updated in May 2026, reports model performance metrics, including an AUC of 0.8980 for heart disease prediction. It compares multiple deep learning architectures against classical machine learning baselines for multi-disease prediction tasks.
An AUC of 0.8451 (±0.0270) was achieved for diabetes classification using a CNN and autoencoder ensemble. This 5.5 KB Excel file contains results from a study proposing a unified deep learning framework evaluated on three benchmark clinical datasets. The framework was authored by Vijay U. Rathod and last updated on May 8, 2026.
Three benchmark datasets for evaluating a unified deep learning framework for disease prediction. The data includes 303 samples for heart disease, 768 for diabetes, and 195 voice recordings for Parkinson's disease. Author Vijay U. Rathod published the dataset on figshare in 2026.
Vijay U. Rathod's 2026 study proposes a unified deep learning framework for disease prediction, evaluated on three benchmark datasets. The framework achieved an AUC of 0.8980 (±0.0483) for heart disease prediction using an FT-Transformer and autoencoder ensemble. This 5.5 KB Excel file contains the performance results for the heart disease classification task.
Vijay U. Rathod published a 5.5 KB Excel file on May 8, 2026, detailing cross-validation configurations for a proposed deep learning framework. The framework was evaluated on three benchmark datasets: the UCI Heart Disease dataset (303 samples), the PIMA Indians Diabetes dataset (768 samples), and a Parkinson's disease voice dataset (195 recordings). The study reports model performance metrics, including an AUC of 0.8980 for heart disease prediction.
Vijay U. Rathod's study, last updated in 2026, provides performance benchmarks for a unified deep learning framework on three clinical datasets. The framework was evaluated on the UCI Heart Disease dataset (303 samples), the PIMA Indians Diabetes dataset (768 samples), and a Parkinson's disease voice dataset (195 recordings). Results include AUC scores for various model ensembles, such as 0.8980 for heart disease prediction.
Vijay U. Rathod's study, last updated in May 2026, proposes a unified deep learning framework for disease prediction. The framework is evaluated on three benchmark datasets: the UCI Heart Disease dataset (303 samples, 13 features), the PIMA Indians Diabetes dataset (768 samples, 8 features), and a Parkinson's disease voice dataset (195 recordings, 22 features). The associated data file is a 5.5 KB Excel spreadsheet.
5.5 KB of Wilcoxon signed-rank test results comparing deep learning models on three clinical benchmark datasets. The data, authored by Vijay U. Rathod and last updated on 2026-05-08, supports the evaluation of a unified deep learning framework for disease prediction. Results include AUC scores for models like FT-Transformer and CNN ensembles on heart disease, diabetes, and Parkinson's disease detection tasks.
A 5.5 KB Excel file containing model configurations for a proposed deep learning framework evaluated on three benchmark clinical datasets. The framework, authored by Vijay U. Rathod, integrates multiple architectures like MLP, CNN, and FT-Transformer. Experimental results from 2026 show the framework achieved an AUC of 0.8980 for heart disease prediction and 0.8451 for diabetes classification.
A study by Vijay U. Rathod proposes a dataset-aware deep learning framework for disease prediction, evaluated on three benchmark clinical datasets. The framework integrates multiple architectures like MLP, CNN, and FT-Transformer, using robust preprocessing and nested cross-validation. Results include an AUC of 0.8980 for heart disease prediction and 0.8451 for diabetes classification on the tested datasets.
195 voice recordings with 22 acoustic features form one of three benchmark datasets used to evaluate a deep learning framework for disease prediction. The dataset, shared by Vijay U. Rathod on figshare, includes results from models like MLP achieving an AUC of 0.7538 for Parkinson's detection. It was last updated on 2026-05-08.
Model configuration details for a deep learning framework evaluated on the UCI Heart Disease dataset. The dataset contains 303 samples and 13 clinical features. Author Vijay U. Rathod published the configuration on figshare under a CC-BY-4.0 license, with a last update recorded as 2026-05-08.
Vijay U. Rathod's 2026 study proposes a unified deep learning framework evaluated on three benchmark clinical datasets. The framework integrates MLP, CNN, FT-Transformer, and autoencoder architectures, achieving AUC scores up to 0.8980 for heart disease prediction. Experimental results demonstrate competitive performance against classical machine learning baselines across heterogeneous biomedical data.
Vijay U. Rathod's study, last updated in May 2026, proposes a unified deep learning framework for disease prediction. The framework was evaluated on three benchmark datasets: the UCI Heart Disease dataset (303 samples), the PIMA Indians Diabetes dataset (768 samples), and a Parkinson's disease voice dataset (195 recordings). The dataset contains the results of this ablation study, showing the incremental impact on mean AUC for different model ensembles.
Vijay U. Rathod's study proposes a unified deep learning framework for disease prediction, evaluated on three clinical datasets. The framework, last updated in May 2026, integrates multiple architectures like MLP, CNN, and FT-Transformer with autoencoders. It was tested on the UCI Heart Disease, PIMA Indians Diabetes, and Parkinson's disease voice datasets.
A neural network potential (NNP) predicts the mechanical and thermophysical properties of the high-energy-density material β-HMX with density functional theory-level accuracy. The model, developed by Jiyuan Wei and last updated in May 2026, achieves orders-of-magnitude computational efficiency improvements. Its predictions for bulk modulus, thermal expansion coefficient, and heat capacities show deviations from experiments within 13% and 1%, respectively.
A transcriptomic analysis dataset identifies key mitochondria-related genes associated with liver fibrosis. The data was generated by Yupeng Ma and last updated in May 2026. It includes bulk and single-cell RNA-seq datasets from GEO and results from an XGBoost model prioritizing targets like ACOT9.
Representative Aboriginal/Torres Strait Islander Body (RATSIB) areas are defined under section 203AD of the Native Title Act (NTA). These bodies perform statutory functions including facilitating native title applications, certifying agreements, resolving disputes, and notifying constituents about land and water matters. The dataset provides the geographic boundaries for these representative bodies.
A study by Xia Li, last updated in May 2026, developed an interpretable prediction model for lung adenocarcinoma immune subtyping. The model quantifies spatial distribution patterns of tumor-infiltrating lymphocytes in H&E-stained whole-slide images, linking morphological phenotypes to molecular subtypes. It was validated on internal and external cohorts, achieving AUCs of 0.839 and 0.927, respectively.