The figures showed a comparison of the balanced and imbalanced sequences and performance of classifiers. by Solomon Shiferaw Beyene (9143178)

Image_1_Formulas to Explain Popular Oscillometric Blood Pressure Estimation Algorithms.JPEG by Anand Chandrasekhar (44553)

Truth table for self-dual function. by Sonal Arvind Barge (20454967)

“…Functional behaviour is tested using ModelSim SE-64. …”

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

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Statistical results of fitness function acquired in 10 runs (in p.u.). by Junfang Hao (9656905)

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators by Philipp Renz (5690312)

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TimeGAN instantiated with RNN, solid lines denote function application, dashed lines denote recurrence, and orange lines indicate loss computation. by Xiaorui Wu (8322705)

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

Data Sheet 1_Understanding acceptance and resistance toward generative AI technologies: a multi-theoretical framework integrating functional, risk, and sociolegal factors.docx by Priyanka Shrivastava (472059)

“…Additionally, the manuscript now highlights demographic diversity, including variations in age, gender, and academic discipline, as relevant to AI adoption patterns. Ethical concerns, including algorithmic bias, data ownership, and the labor market impact of AI, are addressed to offer a more holistic understanding of resistance behavior. …”

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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Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

Immune microenvironment (A) Comparison of immune cell infiltration between the high-risk and low-risk groups using the ssGSEA algorithm. by Chengting Wu (21485612)

Hash function construct used in SKINNY-tk3-hash. by Sonal Arvind Barge (20454967)

“…Functional behaviour is tested using ModelSim SE-64. …”

Immune infiltration analysis by ssGSEA algorithm. by Yubin Xue (1537042)

Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling by Shan He (121220)

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

“…<div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. …”

The correlation coefficient matrix, means, standard deviation, correlation coefficient, and square root of AVE (N = 368). by Linsha Liu (20378293)

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Bootstrap results (N = 368). by Linsha Liu (20378293)

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