QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm by Z.Y. Algamal (5547620)

“…Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”

Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data by Seokho Lee (10088)

“…<p>We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. …”

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

DataSheet_1_Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT I... by Mengshi Dong (5181833)

Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model by Ramya Chinnasamy (21633527)

Comparisons of prediction of phenotypes by the microbiome. by Daphna Rothschild (561439)

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

“…The dataset was stratified into training (80%) and testing (20%) sets. Multiple SVM models were trained and evaluated, including configurations with linear and RBF (Radial Basis Function) kernels. …”

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

“…Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”

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

“…Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”

An AI-based Ecosystem for Real-time Gravitational Wave Analyses by Erik Katsavounidis (19369348)

“…These libraries enhance efficiency and AI model robustness in the context of a broad range of gravitational wave analyses with an emphasis on real-time application, scalability to heterogeneous computing resources, and streamlining the training to deployment cycles for machine learning models. …”