Algorithm results based on experimental likelihood functions. by Nicolas Herzig (9230425)

BOFdat Step 3: Identifying species-specific metabolic end goals. by Jean-Christophe Lachance (6619307)

“…(C) Schematic representation of the implementation of the genetic algorithm (GA) using the metabolic network presented in B. …”

Biological functions and network analysis of highly interrelated genes selected by ADC across modalities. by Qunlun Shen (11668968)

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

Subjects:

Approximate energy consumed per particles in <i>nano Joules</i> (<i>nJ</i>) for various computation platforms and algorithms (A: first algorithm, B: second algorithm, C: third algo... by Abedalmuhdi Almomany (18547669)

Conservation and functional variation across TcdB subtypes. by Michael J. Mansfield (9922934)

Results of searching performance of the improved NAG algorithm on Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

Results of searching performance of the improved NAG algorithm on Sphere function and Griewank function. by Rui Zhang (13940)

PSNR (dB) and SSIM of the deblurred images, and the algorithms’ running time (in seconds) on VOC2012 database. by Wei Zhou (24328)

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

3D modeling qualities of the AM multiple-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

Comparison of the detection results. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

IMFs component. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

IMFs component. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

BFO-PSO-VMD-R-LMS-BPNN detection pseudocodes. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

Freiberg plus human equivalent circuit model. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

Experimental setup parameters. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”

Kolmogorov-Smirnov test data analysis table. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”