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)

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …”

Network structure diagram of LMD-YOLO algorithm. by Weiyu Han (3448742)

Network structure diagram of YOLOv8 algorithm. by Weiyu Han (3448742)

The results of curve fitting analysis. by Hai-bo Ai (20790405)

Comparison with the BST algorithm for the AF and CST bundles. by Thomas Durantel (20933812)

Statistical results of various algorithms. by ZeSheng Lin (20501356)

“…Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. …”

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

Subjects:

Knowledge graph model rating function. by Chunjuan Li (7890182)

Iteration curves of different algorithms. by Tengfei Ma (597633)

“…During experimental evaluation, the efficiency of OP-ZOA was verified using the CEC2017 test functions, demonstrating superior performance compared to seven recently proposed meta-heuristic algorithms (Bloodsucking Leech Algorithm (BSLO), Parrot Optimization Algorithm (PO), Polar Lights Algorithm (PLO), Red-tailed Hawk Optimization Algorithm (RTH), Bitterling Fish Optimization Algorithm (BFO), Spider Wasp Optimization Algorithm (SWO) and Zebra Optimization Algorithm (ZOA)). …”

Flowchart of OP-ZOA algorithm. by Tengfei Ma (597633)

“…During experimental evaluation, the efficiency of OP-ZOA was verified using the CEC2017 test functions, demonstrating superior performance compared to seven recently proposed meta-heuristic algorithms (Bloodsucking Leech Algorithm (BSLO), Parrot Optimization Algorithm (PO), Polar Lights Algorithm (PLO), Red-tailed Hawk Optimization Algorithm (RTH), Bitterling Fish Optimization Algorithm (BFO), Spider Wasp Optimization Algorithm (SWO) and Zebra Optimization Algorithm (ZOA)). …”

Occurrence probabilities of behavioral switching in multi-agent predator–prey interactions when both <i>D</i>_<i>w</i> and <i>D</i>_<i>s</i> are 2. by Hiroyuki Ichijo (13891153)

Loss Function Comparison. by Hongwei Yue (574068)

Loss Function Comparison. by Hongwei Yue (574068)

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. …”

Comparison curve of objective function (). by Wanjun Zhang (496858)

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. …”

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

Test of polymethyl methacrylate (C₅O₂H₈) through algorithm: Curves of both objective function and fractional weight in the process of identifying third chemical constituent after H was assumed, i.e., step 2 in Figure 2. by Dong Hyeok Choi (12562048)

<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.…”