Graph of convergence of PSO algorithm fitness function. by Mohammed F. Elnaggar (18297954)

Functional fitness test results of different algorithms. by Ling Zhao (111365)

Convergence rate of the proposed algorithm update. by Pingxin Wang (16747662)

Comparison results of the loss functions. by Jia Li (160557)

Subjects:

Comparison of the curves of the DCIOU loss function and the CIOU loss function. by Xunqian Xu (21261899)

Subjects:

Rate of change of loss function (0.01, 1000, 100 runs). by Weizheng Jiang (21647509)

Loss function curve graphs. by Huiying Zhang (200681)

Algorithm operation steps. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Byte loss rate (%). by Alisson Assis Cardoso (7861175)

Subjects:

Comparative analysis of the change of the loss function with the number of iterations k under different strategies. by Ming Li (91180)

DE algorithm flow. by Ling Zhao (111365)

“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”

Comparative experimental data of loss functions. by Huiying Zhang (200681)

“…Additionally, three innovative loss functions—focalerDIoU, focalerGIOU and focalerShapeIoU are proposed to reduce losses during the training process. …”

Test results of different algorithms. by Ling Zhao (111365)

“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”

Comparison of average precision mean and loss function for different models. by Ting Wang (16292)

Evaluation metrics graphs for different loss functions. by Huiying Zhang (200681)

Training loss function curve. by Mingtao Gao (21722035)

WERs for different SE algorithms. by Fahad Khalil Peracha (15453204)

Comparison of packet loss quantity. by Jianpeng Zhang (528185)

Subjects:

Run times of two algorithms. by Gourab Saha (8987405)

Subjects:

PSO algorithms flowchart. by Mohammed F. Elnaggar (18297954)