Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. حسب Tehnan I. A. Mohamed (16846175)

Sample image for illustration. حسب Indhumathi S. (19173013)

Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level حسب Giovanni Nattino (561797)

"…We fill the gap by developing an iterative matching algorithm for the three-group setting. Our algorithm outperforms the nearest neighbor algorithm and is shown to produce matched samples with total distance no larger than twice the optimal distance. …"

Thesis-RAMIS-Figs_Slides حسب Felipe Santibañez-Leal (10967991)

"…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…"

MCLP_quantum_annealer_V0.5 حسب Anonymous Anonymous (4854526)

"…This paper first proposes the QUBO-MCLP algorithm workflow and designs the Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (TOICCAP), which accounts for the scale of accessible supply points. …"

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP حسب Xiaoyuan Wang (492534)

"…The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. …"

Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx حسب Ali Nabavi (21097424)

"…Demographic, clinical, and heavy metal biomarker data (e.g., blood lead and cadmium levels) were analyzed as features, with hearing loss status—defined as a pure-tone average threshold exceeding 25 dB HL across 500, 1,000, 2000, and 4,000 Hz in the better ear—serving as the binary outcome. Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …"

Supplementary Material 8 حسب Nishitha R Kumar (19750617)

"…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…"