Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</p><p dir="ltr">When applied to AMR prediction, SMOTE enhances the ability of classification models to accurately identify resistant <i>Escherichia coli</i> strains by balancing the dataset, ensuring that machine learning algorithms do not overlook rare resistance patterns. …”

Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx by Yuan Liu (88411)

“…In this predictor, we introduced a sequence-based feature algorithm consisting of two feature representations, (1) k-mer spectrum and (2) positional nucleotide binary vector, to capture the sequential characteristics of 5hmC sites. …”

Seed mix selection model by Bethanne Bruninga-Socolar (10923639)

“…Classic genetic algorithms consider a population of chromosomes and apply principles of natural selection (selection, mutation, and crossover processes) to generate optimal solutions. …”

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

“…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2  = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …”

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

“…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2  = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …”

Data_Sheet_1_The impact of family urban integration on migrant worker mental health in China.docx by Xiaotong Sun (6535064)

“…The results of this study lead the authors to recommend formulating a family-centered policy for migrant workers to reside in urban areas, optimizing the allocation of medical resources and public services, and improving family urban integration among migrant workers in order to avoid mental health problems in the process of urban integration.…”

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

“…</p><p dir="ltr">IC50 (µg/mL): The concentration at which 50% of cells are inhibited, used as a toxicity threshold.</p><p dir="ltr">These biological metrics were used to define a binary toxicity label: entries were classified as toxic (1) or non-toxic (0) based on thresholds from standardized guidelines (e.g., ISO 10993-5:2009) and literature consensus. …”