MSE for ILSTM algorithm in binary classification. by Asmaa Ahmed Awad (16726315)

“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification by Vaibhavi Patel (19843282)

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

“…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”

Binary contingency table. by Lu Xiao (314030)

Confusion matrix for a binary outcome (adapted from Fawcett [30]). by João Albuquerque (12936572)

Models’ performance without optimization. by Muhammad Usman Tariq (11022141)

“…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …”

RNN performance comparison with/out optimization. by Muhammad Usman Tariq (11022141)

“…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …”

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

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”

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)

“…However, ToxCast assays differ in the amount of data and degree of class imbalance (CI). Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”

The baseline case–the model in the absence of sentinel plants. by Francesca A. Lovell-Read (14378253)

<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>. …”

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files by Suchismita Behera (22027316)

Flow diagram of the proposed model. by Uğur Ejder (22683228)

“…<div><p>Machine learning models are increasingly applied to assisted reproductive technologies (ART), yet most studies rely on conventional algorithms with limited optimization. This proof-of-concept study investigates whether a hybrid Logistic Regression–Artificial Bee Colony (LR–ABC) framework can enhance predictive performance in in vitro fertilization (IVF) outcomes while producing interpretable, hypothesis-driven associations with nutritional and pharmaceutical supplement use. …”

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Proposed method approach. by Muhammad Usman Tariq (11022141)

“…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …”

LSTM model performance. by Muhammad Usman Tariq (11022141)

“…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …”

Descriptive statistics. by Muhammad Usman Tariq (11022141)

“…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …”

CNN-LSTM Model performance. by Muhammad Usman Tariq (11022141)

“…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …”