Homomorphic binary tree. حسب Jianhua He (341366)

Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. حسب Benjamin Gallois (10131683)

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

Microsoft excel file containing exhaustive list of Abbreviated-Injury Scale codes used to generate binary variables encoding the presence or absence of injury to various body regio... حسب Michael D. Cobler-Lichter (22492930)

Zimbabwe model outputs on actual and optimized 2016 spending and impact by intervention. حسب Nicole Fraser-Hurt (2636833)

Côte d’Ivoire model outputs on actual and optimized 2016 spending and impact by care delivery platform. حسب Nicole Fraser-Hurt (2636833)

Armenia model outputs on optimized resource allocation and health impact by EUHC care package (2019 vs. higher budget scenario). حسب Nicole Fraser-Hurt (2636833)

Datasets used for the study and their sources. حسب Peter N-jonaam Mahama (15347793)

Overview of the five stages of the HIPtool application. حسب Nicole Fraser-Hurt (2636833)

Schematic representation of semi-automated algorithm for organoids analyzing. حسب Anna Demchenko (18449439)

Fig 3 - حسب Ali Ahmed (2567584)

Machine learning deployment strategies and schematic illustration of the proposed generative adversarial algorithm for domain adaptation. حسب Aly A. Valliani (13251484)

"…<b>(C)</b> Schematic of the proposed algorithm. a) Real data from a source domain is translated by the generator to resemble data from a specified target domain while maintaining underlying semantic qualities of the input image. b) Translated data is reconstructed by the generator to resemble data from the source domain to maintain domain-agnostic image characteristics with a semantic consistency constraint ensuring that reconstructed images maintain the semantic characteristics of the source data. c) The discriminator aims to distinguish between real and synthetic images and identify the domain of input images to constrain the generator to produce realistic-looking synthetic images from a specified domain. d) A target discriminator is fine-tuned on synthetic images to better identify opacity in the target domain.…"

Comparison analysis of computation time. حسب Indhumathi S. (19173013)

"…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection حسب Samin Aref (4683934)

"…</p><p dir="ltr"><br></p><p dir="ltr">For more information about the data, one may refer to the article below:</p><p dir="ltr">Aref S, Ng B (2025) Troika algorithm: Approximate optimization for accurate clique partitioning and clustering of weighted networks. …"

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment حسب Jianfang Cao (1881379)

"…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …"

Proposed IDS architecture. حسب Saad Hammood Mohammed (20623506)

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

"…In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. …"

ECE6379_PSOM.zip حسب Xingpeng Li (11825663)

"…Optimization algorithms that are commonly used to solve these problems will also be covered including linear programming, mixed-integer linear programming, Lagrange relaxation, dynamic programming, branch and bound, and duality theory.…"

Results of Comprehensive weighting. حسب Hao Yang (328526)

"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"

The prediction error of each model. حسب Hao Yang (328526)

"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"