Machine learning prediction algorithm flow chart. by Ahmad M. Al-Omari (20460931)

Coarse-grained motion segmentation process. by Shinan Ding (20783580)

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Ultrafast Hydrogen Detection System Using Vertical Thermal Conduction Structure and Neural Network Prediction Algorithm Based on Sensor Response Process by Ruilin Yang (151028)

“…Here we report an ultrafast (0.4 s) hydrogen detection system based on a wafer-scale fabrication process. It consists of a low power (20.2 mW) hydrogen sensor based on vertical thermal conduction structure and a signal processing circuit introduced with a neural network prediction algorithm based on sensor response process. …”

Ultrafast Hydrogen Detection System Using Vertical Thermal Conduction Structure and Neural Network Prediction Algorithm Based on Sensor Response Process by Ruilin Yang (151028)

“…Here we report an ultrafast (0.4 s) hydrogen detection system based on a wafer-scale fabrication process. It consists of a low power (20.2 mW) hydrogen sensor based on vertical thermal conduction structure and a signal processing circuit introduced with a neural network prediction algorithm based on sensor response process. …”

Data inclusion and exclusion process. by Jingru Dong (14076094)

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Pareto optimal front result of MOCOA. by Hang Zhao (143592)

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Confusion matrix. by Hang Zhao (143592)

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Performance validation on the MIT-BIH database. by Hang Zhao (143592)

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Exponentially attenuated sinusoidal function. by Hang Zhao (143592)

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Performance comparison with other papers. by Hang Zhao (143592)

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Improved DAE based on LSTM. by Li Yuan (102305)

“…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. …”

Chart showing the target tracking performances of the three control algorithms under noise. by Xiaosong Liu (66661)

Data supporting figures and tables in Attention-Based Framework for Automated Symbol Recognition and Wiring Design in Electrical Diagrams by IKENNA CLEMENT EKEKE (20995226)

“…</p><h4><b>Use Cases</b></h4><p dir="ltr">This dataset is useful for:</p><ul><li>Benchmarking attention-based models for electrical symbol recognition.</li><li>Studying the impact of class imbalance on detection accuracy.…”

Improved random forest algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation. by Spencer Caplan (10188380)

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Algorithmic flow chart highlighting a possible path of NGM behavior under parallel-presentation. by Spencer Caplan (10188380)

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Comparison of different processing methods. by Jinyu Wang (381467)

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1d-MSCNN + GRU model process. by Jianpeng Zhang (528185)

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T2-Flair model training process using all data. by Fengning Liang (20927493)

“…Firstly, our model uses a rough set algorithm to remove the redundant medical image features extracted by radiomics, while adding pseudo-labels for non-labeled data via statistical. …”