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

The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

Subjects:

CSTP-MAC algorithm backoff time-slots vs IEEE 802.15.4 binary backoff exponential algorithm backoff time-slots. by Innocent Uzougbo Onwuegbuzie (9243166)

Related Work Summary. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

Simulation parameters. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

Training losses for N = 10. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

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

Histograms of various water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Organic Carbon, Trihalomethanes, Turbidity, and a binary portability check. by Amal H. Alharbi (21755906)

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

Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

“…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …”

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

Schematic of iteration process of IDE-IIGA. by Ling Zhao (111365)

Feature selection process. by Balraj Preet Kaur (20370832)

Homomorphic binary tree. by Jianhua He (341366)

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…The percentage mean absolute residuals of the activity coefficients obtained using DEA, NMM, and the parameter estimation tool in Aspen Plus were in the ranges of 0.05–16.69, 0.05–16.69, and 0.09–326.77%, respectively. This in-house algorithm will be helpful for obtaining more accurate NRTL parameters in a timely manner and will facilitate the simulation of biochemical processes for process optimization, energy consumption estimation, and life cycle assessment.…”

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. …”