Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg حسب Varun Sendilraj (19732510)

"…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …"

Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg حسب Varun Sendilraj (19732510)

"…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …"

Hyperspectral Camouflage Detection Dataset and Codes حسب Qiran Wang (22051883)

"…This study proposes a non-destructive classification framework integrating optimized sample partitioning, spectral preprocessing, and residual deep learning to address this challenge. …"

Table 1_Statistical and machine learning approaches for identifying biomarker associations in respiratory diseases in a population-specific region.xlsx حسب Meshari Alazmi (452881)

"…Asthma: Precision (1.00), Recall (0.95), F1-score (0.97). Other Complications: Precision (0.88), Recall (0.90), F1-score (0.90). …"

Data Sheet 1_Statistical and machine learning approaches for identifying biomarker associations in respiratory diseases in a population-specific region.pdf حسب Meshari Alazmi (452881)

"…Asthma: Precision (1.00), Recall (0.95), F1-score (0.97). Other Complications: Precision (0.88), Recall (0.90), F1-score (0.90). …"

Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE حسب Subhashree Mohapatra (17387852)

"…Given the critical role of precise PD classification in medical diagnostics, this study proposes a novel framework to enhance detection accuracy. …"

Data Sheet 1_Prediction of 1-year post-operative mortality in elderly patients with fragility hip fractures in China: evaluation of risk prediction models.pdf حسب Qiyuan Lu (13972120)

"…Risk stratification analysis revealed SHiPS as the most precise classification system.</p>Conclusion<p>ASAgeCoGeCC score, NHFS and Holt et al.showed acceptable predictive performance, where the first two are applicable to clinical rapid decision-making, while NHFS has been extensively external validated. …"

Data Sheet 1_Cerebral gray matter volume identifies healthy older drivers with a critical decline in driving safety performance using actual vehicles on a closed-circuit course.pdf حسب Handityo Aulia Putra (21430112)

"…Feature selection and classification were performed using the Random Forest machine learning algorithm, optimized to identify the most predictive GM regions.…"

Yellow River Basin Industrial Base Spatio-temporal Monitoring and Impact Assessment Dataset حسب Libing Wang (21723566)

"…The dataset aggregates data elements and core algorithm codes that underpin the key research stages of the paper, with a focus on demonstrating and reproducing the innovative methodologies employed, rather than directly sharing sensitive raw data or precise measurement values.…"

The ROC curve for the experiment. حسب Mahade Hasan (20536430)

"…Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). …"

System architecture of this study. حسب Mahade Hasan (20536430)

"…Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). …"

Description of the train test split dataset. حسب Mahade Hasan (20536430)

"…Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). …"

The dataset’s summarized description. حسب Mahade Hasan (20536430)

"…Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). …"

Feature selection procedure. حسب Mahade Hasan (20536430)

"…Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). …"

Histogram of attributes. حسب Mahade Hasan (20536430)

"…Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). …"

Illustration of all features correlation. حسب Mahade Hasan (20536430)

"…Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). …"

Data Sheet 1_A multimodal travel route recommendation system leveraging visual Transformers and self-attention mechanisms.pdf حسب Zhang Juan (11780753)

"…</p>Methods<p>This paper introduces SelfAM-Vtrans, a novel algorithm that leverages multimodal data—combining visual Transformers, LSTMs, and self-attention mechanisms—to enhance the accuracy and personalization of travel route recommendations. …"

Image 2_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... حسب Yun Zhao (48978)

"…</p>Methods<p>Multi-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …"

Table 2_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... حسب Yun Zhao (48978)

"…</p>Methods<p>Multi-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …"

Image 3_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... حسب Yun Zhao (48978)

"…</p>Methods<p>Multi-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …"