Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

“…</p> <p><br></p> <p>3. Algorithm</p> <p>Below is the main body of the test data generator program:</p> <p>  </p> <p>the main body of a Python program to generate test data for Python functions.…”

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

3D modeling qualities of the AM multiple-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

Accelerating Fock Build via Hybrid Analytical-Numerical Integration by Yong Zhang (5893)

“…A hybrid analytical-numerical integration scheme is introduced to accelerate the Fock build in self-consistent field (SCF) and time-dependent density functional theory (TDDFT) calculations. To evaluate the Coulomb matrix <b>J</b>[<b>D</b>], the density matrix <b>D</b> is first decomposed into two parts, the superposition of atomic density matrices <b>D</b>_⊕^<i>A</i> and the rest <b>D</b>^<i>R</i> = <b>D</b>–<b>D</b>_⊕^<i>A</i>. …”

Accelerating Fock Build via Hybrid Analytical-Numerical Integration by Yong Zhang (5893)

“…A hybrid analytical-numerical integration scheme is introduced to accelerate the Fock build in self-consistent field (SCF) and time-dependent density functional theory (TDDFT) calculations. To evaluate the Coulomb matrix <b>J</b>[<b>D</b>], the density matrix <b>D</b> is first decomposed into two parts, the superposition of atomic density matrices <b>D</b>_⊕^<i>A</i> and the rest <b>D</b>^<i>R</i> = <b>D</b>–<b>D</b>_⊕^<i>A</i>. …”

Different leaf species with the corresponding Mean Square Error (MSE) and RBFNN curve fitting. by Ali Ahmed (2567584)

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Two different leaf images belonging to the same plant species and their corresponding contour feature curves. by Ali Ahmed (2567584)

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Binary leaf image rotated anti-clock wise from the origin degree to 40^<i>o</i>, 80^<i>o</i>, 120^<i>o</i>, and 160^<i>o</i> from left to ri... by Ali Ahmed (2567584)

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The SVM kernel parameters optimization using Bayesian optimization. by Ali Ahmed (2567584)

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Summarization of related literature. by Ali Ahmed (2567584)

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The RBFNN as a classifier. by Ali Ahmed (2567584)

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Classification parameters of the proposed three classifiers. by Ali Ahmed (2567584)

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The SVM kernel parameters optimization using SSA optimization. by Ali Ahmed (2567584)

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Pairs of features from the 36 features defining the leaf and their corresponding leaf classes. by Ali Ahmed (2567584)

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The proposed system accuracies with respect to different selected angles (number of features). by Ali Ahmed (2567584)

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Information of nodes and pipes of NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The topology of the NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The result of Wilcoxon signed-rand test. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The Simulation and optimization process of pipe diameter selection. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Optional pipe diameter and unit price of NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”