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

“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”

Failure Samples and Fitting Results. by Dongyan Wang (195482)

Region division of traditional SVPWM algorithm. by Jun Li (6494)

Small area control of the conventional algorithm. by Jun Li (6494)

General overview of the total model. by Xueliang Guo (4797057)

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

Study design flow chart. by Müge Yalçin (16486563)

FixbbGCN benchmark results. by Jack B. Maguire (5123183)

Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data by Fei Xue (24567)

Free vibration analysis and optimal design of adhesively bonded double-strap joints by using artificial neural networks by Qi Guo (133147)

“…The effects of the adhesive material properties and joint geometrical parameters on the joint dynamic characteristics were investigated in detail using the trained ANNs. The optimum design problem is defined as a multi-objective optimization problem considering maximizing the first natural frequency and corresponding loss factor while minimizing the total structural weight. …”

Optimal contrast analysis with heterogeneous variances and budget concerns by Show-Li Jan (3538697)

“…Optimal allocation procedures for the Welch-Satterthwaite tests of standardized and unstandardized contrasts are presented to minimize the total sample size with the designated ratios, to meet a desirable power level for the least cost, and to attain the maximum power performance under a fixed cost. …”

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Study flowchart. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Risk of bias graph. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Characteristics of included studies. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”