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)

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

Accuracy comparison between the proposed algorithm and existing algorithms. by Xin Li (51274)

The main workflow of the proposed algorithm. by Xin Li (51274)

Performance comparison based on feature groups and their combinations. by Rinrada Tanthanathewin (18611171)

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

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. …”

Illustrations of distances used as the 5^th to 7^th features (f5-f7) from a blob (orange circle) to the fovea. by Rinrada Tanthanathewin (18611171)

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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. …”

Examples of results from two different OCT images shown in the left and right columns. by Rinrada Tanthanathewin (18611171)

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Illustration of Area of interest (left) and all HRF blobs used as EX-AN candidates (right). by Rinrada Tanthanathewin (18611171)

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Examples of the fovea locations (stars) a typical case (left) and an abnormal case (right). by Rinrada Tanthanathewin (18611171)

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ILM (pink line) and RPE (green line) layers in a normal OCT image. by Rinrada Tanthanathewin (18611171)

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Examples of input (first column) and areas between ILM and RPE layers of ground truth (second column) and U-Net (third columns) and U-Net++ (fourth column) of four OCT cases. by Rinrada Tanthanathewin (18611171)

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Illustrations of fovea detection procedure. The typical case (left) and the abnormal case (right) with ILMs (black lines), fitted lines (red lines), intersection points (blue verti... by Rinrada Tanthanathewin (18611171)

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An example of a histogram of the normalized intensity of an OCT image. by Rinrada Tanthanathewin (18611171)

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The flowchart depicting the overall procedures of the proposed work. by Rinrada Tanthanathewin (18611171)

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Performance comparison of the area between ILM and RPE layers. by Rinrada Tanthanathewin (18611171)

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