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)

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

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

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

“…<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>. …”

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

Optimal Timings Codebook.xlsx by Emma Spillane (11249121)

Image processing workflow. by Denis Tamiev (7404980)

“…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”

Optimal Time Intervals of Breech Births Dataset by Emma Spillane (11249121)

Silibinin solubilization: combined effect of co-solvency and inclusion complex formation by Azam Dehghan (18434551)

“…The optimized solution was examined to study SLB release from chitosan nanoparticles (4.5 ± 0.2% drug loading) at 37 °C under static conditions. …”

Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... by Ramsés Noguez Imm (6658637)

Thesis-RAMIS-Figs_Slides by 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.…”

DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

“…Cooking data were classified into binary and multiclass variables (CT4C and CT6C). Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. …”

Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

“…Cooking data were classified into binary and multiclass variables (CT4C and CT6C). Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…The consistent accuracy of 70.28% does not represent a flaw in the SVM. algorithm, but rather the predictive performance ceiling of the feature itself, whose simplicity and class overlap limit the model's discriminatory ability. …”

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. …”

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

“…</p><p dir="ltr"><b>Data Cleaning and Normalization:</b></p><p dir="ltr">To ensure model reliability and generalizability, extensive preprocessing was undertaken:</p><p dir="ltr">Outlier management: Features with wide value ranges, such as hydrodynamic size or ROS scores, were log-transformed to reduce skewness.…”