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

Observations that influence the accuracy of the algorithm. by Rabnawaz Khan (6509084)

Comparison of ResNet Classification Algorithm Expansion Experiments with Similar Data Enhancement Models. by Chao Tang (10925)

“…<p>Comparison of ResNet Classification Algorithm Expansion Experiments with Similar Data Enhancement Models.…”

Federated averaging algorithm. by Yan Qin (118276)

Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

Search algorithm performance comparison. by Zhaohui Du (414105)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Flowchart of algorithm principles. by Wenjuan Zhou (115285)

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Comparative results with existing representative algorithms. by Yan Qin (118276)

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

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Horizontal comparison of models. by Hui Yang (91136)

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Indicator analysis of integrated models. by Hui Yang (91136)

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Comparison of deep partitioning models. by Hui Yang (91136)

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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. …”

Decompression algorithm for the proposed HSCRC encoding. by R. Sakthivel (2589547)

Adaptive model BD rate analysis. by Hui Yang (91136)

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Minimal data set definition. by Hui Yang (91136)

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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. …”

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

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The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”