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

Principle of the SVM algorithm. حسب Anfeng Zhu (17817144)

The CaM objective spaces have low effective dimensionalities. حسب Lu Hong (399882)

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.…"

Table_1_A Brain Tumor Image Segmentation Method Based on Quantum Entanglement and Wormhole Behaved Particle Swarm Optimization.pdf حسب Tianchi Zhang (1740631)

Supplementary information for Efficient distributed edge computing for dependent delay-sensitive tasks in multi-operator multi-access networks حسب Alia Asheralieva (17562462)

"…Tasks are executed by MEC servers with the assistance of nearby edge devices, so that the MEC network can be viewed as a distributed “primary-secondary node” system where each MEC server acts as a primary node (PN) deciding on sub-tasks assigned to its secondary nodes (SNs), i.e., nearby edge devices. …"

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_A robust ensemble deep learning framework for accurate diagnoses of tuberculosis from chest radiographs.pdf حسب Xin Sun (127355)

Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX حسب Hui Tang (226667)

"…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. …"

Table_2_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX حسب Hui Tang (226667)

"…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. …"

Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx حسب Hui Tang (226667)

"…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. …"

Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS حسب Hui Tang (226667)

"…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. …"

Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX حسب Hui Tang (226667)

"…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. …"

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

"…., 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. …"