The effect of test set size and number of nodes on the values of confusion matrix using SVM combined with the uniform window. by Abeer Aljohani (18497914)

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Graphical illustration of a spectral approach for training support vector machine. by Abeer Aljohani (18497914)

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Accuracy of the support vector machines for different proportion of test samples and different length of nodes used for training. by Abeer Aljohani (18497914)

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Confusion matrix representation of classification outcomes. by Abeer Aljohani (18497914)

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The parametric study of the confusion matrix values for different proportion of test samples vs number of nodes from the spectral space. by Abeer Aljohani (18497914)

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ROC and precision plots of SVM showing the details of accuracy. by Abeer Aljohani (18497914)

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Classification Report of the SVM classifier without using any window. by Abeer Aljohani (18497914)

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Accuracy of the support vector machines for different proportion of test samples. by Abeer Aljohani (18497914)

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Accuracy of the proposed method using SVM combined with the uniform window. by Abeer Aljohani (18497914)

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Classification Report of the SVM classifier using a uniform window. by Abeer Aljohani (18497914)

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DataSheet2_Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis.PDF by Miguel Castresana-Aguirre (12521683)

“…<p>Functional analysis of gene sets derived from experiments is typically done by pathway annotation. …”

DataSheet1_Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis.CSV by Miguel Castresana-Aguirre (12521683)

“…<p>Functional analysis of gene sets derived from experiments is typically done by pathway annotation. …”

ClaritySpectra: Raman spectra analysis tool by Aaron Celestian (9395696)

“…</li></ul><h3>PEAK FITTING </h3><ul><li>Automated background subtraction using asymmetric least squares fitting</li><li>A new suggested background feature that lets you preview the background that you like best</li><li>Interactive background fitting lets you further tune the background to perfection</li><li>Four choice of peaks: Gaussian, Lorentzian, Pseudo-Voigt, and the new Asymmetric Voigt functions</li><li>Overlapping view of how well the peaks fit with quality metrics</li><li>No need to define regions, the algorithm is smart enough to what a peak looks like.…”

Python code, input data, and outputs of Fourier analysis and Dynamic Time Warping from McGlasson et al. by Riley McGlasson (13876874)

“…</p> <h4><strong>Python Function Files:</strong></h4> <p>radarfuncs.py: Holds three functions used for FFT and DTW analysis.…”

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

Membership function of output for tank 1. by Tamiru Demelash Kassie (20678515)

Membership function of output for tank 2. by Tamiru Demelash Kassie (20678515)

Table_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Table_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)