Feature selection results. by Amal H. Alharbi (21755906)

“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”

ANOVA test for feature selection. by Amal H. Alharbi (21755906)

“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”

Wilcoxon test results for optimization. by Amal H. Alharbi (21755906)

“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”

Classification performance of ML and DL models. by Amal H. Alharbi (21755906)

“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”

Skipping frames and interpolating skeletons with a spline achieves similar accuracy and faster computational time. by Weheliye H. Weheliye (22022140)

“…(C) Computation time per input frame for the different models as a function of worm number. Tierpsy only uses CPU computation while Omnipose uses GPU and CPU because we use Tierpsy’s skeletonization algorithm to convert segmented regions to skeletons. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…As a case study, a curated dataset of phytocystatin sequences from the UniProt database was used to evaluate the algorithm’s performance. The PhyCysID web server enables rapid classification of both individual and batch-submitted sequences in less than 15 s, providing high-throughput analysis for an accurate identification of phytocystatin class and function. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…As a case study, a curated dataset of phytocystatin sequences from the UniProt database was used to evaluate the algorithm’s performance. The PhyCysID web server enables rapid classification of both individual and batch-submitted sequences in less than 15 s, providing high-throughput analysis for an accurate identification of phytocystatin class and function. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…As a case study, a curated dataset of phytocystatin sequences from the UniProt database was used to evaluate the algorithm’s performance. The PhyCysID web server enables rapid classification of both individual and batch-submitted sequences in less than 15 s, providing high-throughput analysis for an accurate identification of phytocystatin class and function. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…As a case study, a curated dataset of phytocystatin sequences from the UniProt database was used to evaluate the algorithm’s performance. The PhyCysID web server enables rapid classification of both individual and batch-submitted sequences in less than 15 s, providing high-throughput analysis for an accurate identification of phytocystatin class and function. …”

Structure and parameters of pipeline network. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Experimental parameter combinations. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Experimental results. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Data sources for figures and tables. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Fault events. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Dataset for "Self-Supporting Shell Lattices: Explicit Design Method and Neural Accelerated Evolutionary Optimization" (Virtual and Physical Prototyping) by Haoran Hu (22579169)

In vitro and in vivo DMS structure probing of ModT isoforms. by Tina Lenče (20428084)

Accuracy of the Generalized WTA. by Ori Hendler (21262937)

A structure determination protocol based on combined analysis of 3D-ED data, powder XRD data, solid-state NMR data and DFT-D calculations reveals the structure of a new polymorph o... by Christopher Smalley (19655662)

Role of stimulus parameters. by Jiamu Jiang (19959616)

TNP-TMA BC biomarker panels. by Robert T. Heussner (21989911)

“…Existing computational methods overlook both cell population heterogeneity across modalities and spatial information, which are critical for effectively completing this task. …”