Comparison of different optimization algorithms. by Hang Zhao (143592)

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The optimal number of clusters identified using the GMM clustering algorithm. by Dinh K. Tang (20469082)

Optimal number of clusters identified using the GMM clustering algorithm. by Dinh K. Tang (20469082)

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>" by Yize Wang (19535173)

“…<p dir="ltr">Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>"</p>…”

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

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Performance of algorithms outside the training species. by Sven B. Gould (12237287)

Schematic diagram of the experimental algorithm. by Ronghua Meng (20978441)

“…On the basis of stable egg carrying, a carrying motion mathematical model is established, and the genetic algorithm (GA) is chosen to optimize the structural parameters. …”

A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

Performance of machine learning algorithms. by Wenxue Liu (6877754)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

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The genome coding scheme. by Wenbing Shi (5806160)

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Risk element category diagram. by Yao Hu (3479972)

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Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

Temporal response patterns determined by Dirichlet Process-based clustering algorithm across amplitudes and stimuli. by Dinh K. Tang (20469082)

“…<p>The average neuronal activity for each cluster of the Contact <b>(a)</b>, Rough <b>(b)</b> and Smooth stimulus <b>(c)</b> were determined using the Dirichlet Process Mixture Model (DPM) clustering algorithm. The optimal number of clusters was ascertained from the Gaussian Mixture Model (GMM) analysis to ensure a fair comparison between the two techniques. …”

Scatter diagram of different principal elements. by Jizhong Wang (7441697)

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The structure of the NANEAT model corresponds to the optimal species genome. by Wenbing Shi (5806160)

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The structure of the ANEAT model corresponds to the optimal species genome. by Wenbing Shi (5806160)

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Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)