A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... by Olaide N. Oyelade (14047002)

Subjects: “…requires approximate algorithms…”

Large-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

Subjects: “…requires approximate algorithms…”

Parameter settings. by Olaide N. Oyelade (14047002)

Subjects: “…requires approximate algorithms…”

Fig 2 - by Olaide N. Oyelade (14047002)

Subjects: “…requires approximate algorithms…”

An example of a type of texture of binary preferred/non-preferred ranges of raw vegetation index values type feature. by Elise Varaela Voltura (17592996)

Homomorphic binary tree. by Jianhua He (341366)

An example of a type of percent cover of preferred ranges of vegetation index texture values type feature. by Elise Varaela Voltura (17592996)

The data processing and analysis pipeline. by Xin Zhang (35492)

UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<p><b>(A)</b> Input data is a list of genotype-level SNPs. <b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”

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

“…Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…The shape of the cell was then approximated using the Ramer-Douglas-Peucker algorithm, which involved adjusting the level of detail (epsilon value) iteratively until an approximation with five corners was achieved. …”