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)

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

“…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …”

Example medication days generated for patients P4 and P6, medication times (red dots), patient’s state—TRS score (blue line) with bounds for the states (red lines) and optimal stat... by Tomasz Gutowski (17200042)

Learning dynamics on training tasks. by Sam Hall-McMaster (10343795)

Texas Rio Grande Valley Red–crowned Parrot predicted general use habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence... by Elise Varaela Voltura (17592996)

Texas Rio Grande Valley Red–crowned Parrot predicted roost site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence)... by Elise Varaela Voltura (17592996)

Texas Rio Grande Valley Red–crowned Parrot predicted nest site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence)... by Elise Varaela Voltura (17592996)

Simulation of dense parameter pairs and optimal model output. by Yen-Ju Chen (354876)

The overlap of Texas Rio Grande Valley Red–crowned Parrot predicted general use, roost site, and nest site habitat distributions as determined using consensus-based feature ensembl... by Elise Varaela Voltura (17592996)

Results for the Pattern Generation task. by Paolo Muratore (8331585)

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)

“…Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”

Performance comparison of neural UCB strategies based on the basal ganglia model against other UCB algorithms in different bandit tasks. by Yuhao Wang (196321)

Post scan feature estimates and confidence ratings. by Sam Hall-McMaster (10343795)

Decoding results. by Sam Hall-McMaster (10343795)

Subgroup analysis. by Sam Hall-McMaster (10343795)

Behavioral results. by Sam Hall-McMaster (10343795)

“…Rows are grouped into training and test tasks. <b>A</b>, <b>B</b>, <b>F</b>, <b>G</b>: Theoretical predictions are shown for a model-based algorithm on training (A) and test tasks (F), as well as an SF&GPI algorithm on training (B) and test tasks (G). …”

Comparison of city decoders trained on session one and session two data. by Sam Hall-McMaster (10343795)

Validation tests of feature decoding during the associative memory paradigm. by Sam Hall-McMaster (10343795)

An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. by Olaide N. Oyelade (14047002)