Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…We developed a mechanism which converts a given medical image to a spectral space which have a base set composed of special functions. …”

Visual depiction of the MSA algorithm. by Kayson Fakhar (12890364)

Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)

The loss function of network structures, LPC-DNN, LPC-PCA-DNN, LPC-LSTM, LPC-PCA-LSTM, MFCC-DNN, MFCC-PCA-DNN, MFCC-LSTM and MFCC-PCA-LSTM, as the epoch increases. by Cihun-Siyong Alex Gong (11874066)

Application of multi-scale community detection to subject-level and group-level resting state functional brain networks. by Arian Ashourvan (6685232)

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Fractal dimension of cortical functional connectivity networks & severity of disorders of consciousness by Thomas F. Varley (8446899)

“…<div><p>Recent evidence suggests that the quantity and quality of conscious experience may be a function of the complexity of activity in the brain and that consciousness emerges in a critical zone between low and high-entropy states. …”

Network propagation improves the correlation between mRNA and protein levels of ageing tissues (brain and liver) and PCa samples. by Konstantina Charmpi (9901053)

Reconstructing <i>sparse</i>, binary patterns using message passing algorithms and PCA. by Sebastian Goldt (14522594)

“…<p>We plot the mse per pattern obtained by the AMP algorithm, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e072" target="_blank">Eq (32)</a>, as a function of the effective noise Δ (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e032" target="_blank">9</a>), for random (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e044" target="_blank">15</a>) and informed (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e045" target="_blank">16</a>) initialisations. …”

Generating the PSC kernel using a double exponential function. by Alireza Ghadimi (13839874)

PCA visualization of LSTM hidden layer activity of an example 7-hidden-unit network (#7). by Quan Wan (1329666)

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A closed-loop, music-based brain-computer interface for emotion mediation - Fig 4 by Stefan K. Ehrlich (6475490)

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Functional PCA With Covariate-Dependent Mean and Covariance Structure by Fei Ding (134577)

“…<p>Incorporating covariates into functional principal component analysis (PCA) can substantially improve the representation efficiency of the principal components and predictive performance. …”

Loss functions between PCA-DNN model and PCA-LSTM model. by Cihun-Siyong Alex Gong (11874066)

Data_Sheet_1_Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: a data-based study.docx by Hui Dang (4396087)

“…</p>Conclusion<p>The genetic algorithms modified back propagation neural network can predict motor function in patients with traumatic brain injury, which can be used as a reference for risk and prognosis assessment and guide clinical decision-making.…”

Data_Sheet_1_Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: A data-based study.docx by Hui Dang (4396087)

“…</p>Conclusion<p>The genetic algorithms modified back propagation neural network can predict motor function in patients with traumatic brain injury, which can be used as a reference for risk and prognosis assessment and guide clinical decision-making.…”

Architecture of the 3D ResNet. by Adam L. Tyson (10892822)

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Architecture of the bottleneck 3D ResNet. by Adam L. Tyson (10892822)

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Performance of PCA and Joint Autoencoder (JAE) denoising algorithms. by Ege Altan (11768156)

Results of hyperparameter searches for neural decoding models, which varied the number of hidden layers and number of hidden units per layer in an MLP neural network. by Matthew R. Whiteway (11467528)

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