One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

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)

Training process. by Ning Zhao (84707)

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

Quantitative experimental results of the present algorithm and other methods. by Tingting Liu (267387)

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. …”

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-01. by Tingting Liu (267387)

Flow chart of particle swarm algorithm. by Nour Eldeen Mahmoud Khalifa (19259450)

“…The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50).…”

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-01. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Proposed architecture testing phase. by Yasir Khan Jadoon (21433231)

“…The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. The trained model is employed in the testing phase for the feature extraction from the self-attention layer and passed to the shallow wide neural network classifier for the final classification. …”

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. …”

Proposed Algorithm. by Hend Bayoumi (22693738)

“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

Parameter settings of the comparison algorithms. by Ying Li (38224)

“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”