Nomogram for determining the average interest for a range of years going from 1 to 20. by Trevor Blight (20685529)

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Nomogram obtained from the example. by Trevor Blight (20685529)

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Screenshot showing the basic starting Nomogen’s code structure. by Trevor Blight (20685529)

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Nomogram for determining the average interest for a range of years going from 5 to 20. by Trevor Blight (20685529)

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Nomogram for the variables <i>u</i>, <i>v</i>, and <i>w</i> that satisfies the Eq 1. by Trevor Blight (20685529)

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Nomogram for determining the geometric burden in blast design. by Trevor Blight (20685529)

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Mean and standard deviation of the survey answers. by Trevor Blight (20685529)

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Variables in the Eq 4 for two retaining walls. by Trevor Blight (20685529)

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Nomogram for determining the hydrocyclone apex diameter in mining engineering studies. by Trevor Blight (20685529)

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Example nomogram for deriving the hydraulic radius of a circular section. by Trevor Blight (20685529)

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Diagram showing the 5-step approach to assess the student perceived importance of nomograms in academic context. by Trevor Blight (20685529)

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Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’... by Enock Niyonkuru (21246879)

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Schematic of the approach: This schematic illustrates the entire workflow of the project. by Enock Niyonkuru (21246879)

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Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). by Enock Niyonkuru (21246879)

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Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

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Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... by Enock Niyonkuru (21246879)

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Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. by Enock Niyonkuru (21246879)

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Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... by Enock Niyonkuru (21246879)

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Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... by Enock Niyonkuru (21246879)

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HCC Evaluation Dataset and Results by Jens-Rene Giesen (18461928)

“…The only requirement for running this script is a Python 3.6+ interpreter as well as an installation of the <code>numpy</code> package. …”