code&data by Liyuan Liu (18298084)

“…The study identified outdoor sports facilities in Shanghai using advanced deep-learning techniques on remote sensing data. It then developed a greedy heuristic algorithm based on the Gaussian Two-Step Floating Catchment Area method and Gini coefficient analysis for evaluating and optimizing facility accessibility and fairness. …”

Data and code. by Hao Cao (2248066)

“…The results demonstrate that the VMD-BILSTM-AEAM algorithm achieves a mean True Positive Rate (TPR) of 0.919 with a 95% confidence interval of 0.915 to 0.924, a mean False Positive Rate (FPR) of 0.090 with a 95% confidence interval of 0.087 to 0.092, and a mean Area Under the Curve (AUC) of 0.919 with a 95% confidence interval of 0.915 to 0.923. …”

G R code algorithm. by R. Sakthivel (2589547)

“…The algorithm was developed and coded in Verilog and simulated using Modelsim. …”

Code and data. by Shangyang Jin (21772466)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

Maple code for algorithm 3. by Peter Thompson (165468)

The metro zone algorithm pseudo-code. by Sarah Alhumoud (22258183)

A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

“…Increasing values of κ flattening the relationship between prediction errors and learning rates, leading to lower learning rates for a given magnitude of the prediction error. …”

Dual deconvolution algorithm: analysis code with experimental data by Wonshik Choi (18877660)

“…<p dir="ltr">This software package provides key algorithms for dual-deconvolution microscopy analysis, along with both experimental and numerical data. …”

data_code.zip by Mengmeng Gong (20129994)

data_codes.rar by Zhe Li (22011704)

Performance comparison of different learning rates. by Chong Zhang (418677)

Underlying predictions and human ratings necessary for reproducing the LINNDA algorithm by Paul Naser (17776497)

Data and code availability: Machine Learning on systematically curated data reveals key determinants of magnetic hyperthermia performance by Edgar Vega (21981590)

“…<p dir="ltr">The accurate prediction of the specific absorption rate (SAR) of superparamagnetic iron oxide nanoparticles (SPIONs) is critical for optimizing their performance in magnetic hyperthermia applications. …”

Data&Code by anonymous author (20478659)

Effects of processing rate and algorithms performance on estimates of biodiversity. by Valentine Fleuré (12822137)

Subjects:

code & data for GeoEvo by Wenhui Zhang (20474243)

Data and code resources. by Sam Hall-McMaster (10343795)

Boxplots of classification error rates for all algorithms. by Genliang Li (5816264)