S1 Data - حسب Zhengyu Xu (8550660)

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The processed data and results of manuscript "A Unified Framework for Deciphering Cellular Dynamics byLearning and Interpreting Vector Field" حسب Ruihong Xu (19517776)

The pseudo-code of the WOA-LSTM-PID model. حسب Dayu Zhang (710629)

The training process and validation process of SE-MAConvLSTM and its variants on two datasets. حسب Rong Zhu (172093)

The steps of applying RF algorithm. حسب Zuzhen Ji (17593348)

Graphical representation of the dial data and its classification into epochs using our algorithm. حسب Eleftheria Pistolas (20313429)

Positive predictive values (PPVs) of the register-based algorithms to identify individuals with poststroke epilepsy (PSE). All algorithms required (i) a diagnostic code for stroke... حسب André Idegård (22041446)

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Structure of the Kuhn-Munkres Algorithm. حسب Qingnan Ji (22662198)

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The weight optimization process. حسب Guomei Cui (20721578)

"…However, the training data of this model comes from the simulation environment, which may deviate from the real game data. …"

Experimental software and hardware configuration and algorithm initialization parameter settings. حسب Lu Lin (201289)

Evaluation of model aggregation algorithms. حسب JiaMing Wang (334865)

"…This algorithm leverages a federated learning architecture and combines it with Paillier homomorphic encryption technology to ensure the security of the training process. …"

Comparison of homomorphic encryption algorithms. حسب JiaMing Wang (334865)

"…This algorithm leverages a federated learning architecture and combines it with Paillier homomorphic encryption technology to ensure the security of the training process. …"

Overview of the proposed process with data processing, model development, model evaluation and explainability. حسب Md Amir Hamja (20495951)

The parameter settings of the RF algorithm after parameter optimization. حسب Xini Fang (20861990)

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Illustration of PyNoetic’s 2D and 3D simulation module with visual feedback. حسب Gursimran Singh (575288)

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PyNoetic’s feature extraction module that supports time domain features, frequency domain features, time-frequency domain features, spatial features as well as Brain Connectivity m... حسب Gursimran Singh (575288)

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Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. حسب Gursimran Singh (575288)

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List of Abbreviations حسب Gursimran Singh (575288)

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PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. حسب Gursimran Singh (575288)

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PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. حسب Gursimran Singh (575288)

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