ARHMM body movement detector for two-view data. by Matthew R. Whiteway (11467528)

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The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

Benchmark functions. by Qinghe Guo (21519322)

Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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A flow diagram to explain the algorithm named 2BDP (Biomarker Discovery Process at Binomial Decision Point) that we used to find the best fitting model to predict RRiF. by Nabarun Chakraborty (535772)

Training accuracy/loss as a function of epochs for different datasets. by Hiam Alquran (17636906)

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

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The support vector machine based on linear kernel function has the highest model accuracy when the number of variables is 16. by Xin Luo (428648)

Constructing the dynamical law. by Robert Marsland (8616483)

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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AP values of three methods on the three datasets, including four indicators: AP @50, AP @75, AP @M, and AP @L. Experiment one is the baseline model without adding other modules, ex... by Xunqian Xu (21261899)

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Body point saliency for all infants. by E. Passmore (18010656)

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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. by Gursimran Singh (575288)

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Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…The most widely employed tool to perform this task is MODELLER. This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”

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

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<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Freiberg plus human equivalent circuit model. by Yunpeng Bai (1714087)

“…Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. …”