Decrease in loss during the training process. by Muhammad Idrees (92773)

Youthful systemic milieu in young recipients decreased apoptosis and oxidative stress damage in allografts, and promoted proliferation. by Chengjun Yu (12136227)

Training -validation accuracy & lost for ANN. by Gaurav Sandeep Dave (21519884)

“…The validated data is employed to train machine learning classifiers and deep learning algorithms, targeting a 27.25% enhancement in operational efficiency based on F1 score. …”

ANN architecture. by Abdelfattah Dani (22226715)

Deep learning-based locomotive syndrome prediction method. by Junichi Kushioka (12236447)

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ANN architecture and configuration. by Abdelfattah Dani (22226715)

Block diagram for Average Value-based irrigation system. by Gourab Saha (8987405)

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Flow chart of Average Value-based control system. by Gourab Saha (8987405)

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Oviduct Glycoprotein 1 (OVGP1) Diagnoses Polycystic Ovary Syndrome (PCOS) Based on Machine Learning Algorithms by Fengjuan Wang (768949)

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TITAN decreaser diatom heatmap. by Brent J. Bellinger (21156150)

P values of Kruskal–Wallis test at 95% significance level. by Fahd Saeed Alakbari (10701871)

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Illustration of BSC control using ANN for WECS. by Abdelfattah Dani (22226715)

Sensor value scenario for average value algorithm. by Gourab Saha (8987405)

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The refracted opposition-based learning mechanism. by Yang Guo (399435)

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Comparison of environmental perception time results at different learning rates. by Wenhui Wang (34265)

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Performance comparison between the OTC-FOC and ANN-BSC. by Abdelfattah Dani (22226715)

Training process flowchart of ANN-based MPPT controller. by Abdelfattah Dani (22226715)

Analysis of tea output value distribution and growth- decrease rate of provinces. by Kun Qin (177235)

Differences between extreme values of different indicators. by Weijia Zhu (65481)

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Table of values for <i>p.</i> by Long Di (9977453)

“…Furthermore, a probabilistic obstacle motion prediction framework is established through motion pattern analysis to actively optimize the robot’s motion strategy and reduce tracking errors. Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”