Popularity-incidence similarity for the same four states (CA 0.70, IL 0.76, MD 0.73, RI 0.71) after time-warping for pattern recognition. by Panayiotis D. Ziakas (9510346)

An indicative example of states with a very strong (>0.80) popularity-mortality similarity (CA 0.86, IL 0.86, MD 0.87, RI 0.86) after time-warping for pattern recognition. by Panayiotis D. Ziakas (9510346)

Summary of data processing. by Hojun Lee (655617)

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)

Graphical illustration of sample size estimation for the study protocol. by Müge Yalçin (16486563)

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

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)

Time lapse imaging of gastruloids reveals hallmarks of CE. by Martijn A. de Jong (17896732)

Runtime performance of SV callers. by De-Min Duan (20685513)

SV detection workflow. by De-Min Duan (20685513)

Pseudo Code of RBMO. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

P-value on CEC-2017(Dim = 30). by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Memory storage behavior. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Elite search behavior. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Description of the datasets. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

S and V shaped transfer functions. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”