Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

Data and code resources. by Sam Hall-McMaster (10343795)

Sanitized databases using MLHProtector algorithm. by Loan T. T. Nguyen (20660789)

“…To address this issue, this work suggests two PPUM algorithms, namely <b>MLHProtector</b> and <b>FMLHProtector</b>, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. …”

Technological Fitness and Coherence explanation. Schematic representation of the workings of the Fitness and Complexity algorithm and Coherence diversification. by Matteo Straccamore (15083799)

“…<p>In the top panel, we show the computation of the Fitness and Complexity algorithm. The Fitness of the entity <i>e</i>₁ is the technological Complexity sum of all the technology done by <i>e</i>₁. …”

Description of the dataset used for the machine learning algorithm. by Tri Ngo Quang (20390189)

Algorithm factor level change curve. by Kuiyuan Pan (21145338)

Computational Domain: The computational domain was discretized using conformal tetrahedral elements, resulting in a total of 926K elements. by Martina Bukač (20161460)

Algorithm used for dose reconstruction in workers. by Jonathan Côté (527819)

Clinical use algorithm. by Chibuike K. Uwakwe (22655742)

a. How various statistical models account for modulation classification performance across the entire dataset. by Chris Scholes (3309477)

Correlation matrices of handcrafted features before and after feature selection. The figure presents two heatmaps illustrating the correlation among 52 handcrafted features derived from the 3-level wavelet transform (WT) at four resolutions. These features were used as inputs for traditional ML algorithms. The left map demonstrates the correlation matrix of 52 features crafted from 4 resolutions of the 3-level WT, which are used as inputs for traditional ML algorithms.... by Quang Dung Dinh (20995766)

“…These features were used as inputs for traditional ML algorithms. The left map demonstrates the correlation matrix of 52 features crafted from 4 resolutions of the 3-level WT, which are used as inputs for traditional ML algorithms. …”

The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

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The genome coding scheme. by Wenbing Shi (5806160)

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Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

Comparison of different algorithms. by Jiandong Qiu (20389944)

An overview of the organization of the study program. by Marialuisa Gandolfi (8639238)

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Overview of the development of the biomarkers in the motor, exteroceptive, interoceptive, and cerebral domains. by Marialuisa Gandolfi (8639238)

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The schedule of enrolment, interventions, and assessments. by Marialuisa Gandolfi (8639238)

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The frequency distribution of ranking levels for different algorithms. by Yating Ji (11819687)

Algorithm used for the determination of the model parametric values. by Jonathan Côté (527819)