Public available data on NAIIS multiplexing. by Ernest L. Yufenyuy (12494849)

“…This is similar to the unweighted incidence using the LAg-based RITA (recent infection testing algorithm) of 0.80 per 1,000 [95% CI: 0.60-1.10]. …”

<b>Machine learning-enhanced monitoring of global copper mining areas</b>, by Houxuan Li (20948624)

“…</p><p dir="ltr">Data collection involved preprocessing Sentinel-2 satellite imagery via the Google Earth Engine (GEE) platform, applying cloud-free median composites, and training a Random Forest classification algorithm using manually collected sample points for accurate delineation of mining features. …”

iNCog-EEG (ideal vs. Noisy Cognitive EEG for Workload Assessment) Dataset by Fariya Bintay Shafi (21692408)

“…</p><h3>Applications</h3><p dir="ltr">This dataset can be applied to a wide range of research areas, including:</p><ul><li>EEG signal denoising and artifact rejection</li><li>Binary and hierarchical <b>cognitive workload classification</b></li><li>Development of <b>robust Brain–Computer Interfaces (BCIs)</b></li><li>Benchmarking algorithms under <b>ideal and noisy conditions</b></li><li>Multitasking and mental workload assessment in <b>real-world scenarios</b></li></ul><p dir="ltr">By combining controlled multitasking protocols with deliberately introduced environmental noise, <b>iNCog-EEG provides a comprehensive benchmark</b> for advancing EEG-based workload recognition systems in both clean and challenging conditions.…”

Data Sheet 1_Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.csv by Zohreh Javanmard (20270430)

“…</p>Conclusion<p>The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. …”

Table 1_Predicting emotional responses in interactive art using Random Forests: a model grounded in enactive aesthetics.xlsx by Xiaowei Chen (200607)

“…Model evaluation was conducted using cross-validation and held-out test sets, applying classification and regression metrics to assess performance.…”

Table 1_Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.docx by Zohreh Javanmard (20270430)

“…</p>Conclusion<p>The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. …”

Table 2_Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.docx by Zohreh Javanmard (20270430)

“…</p>Conclusion<p>The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. …”

Supplementary Material 9 by Nishitha R Kumar (19750617)

“…<p dir="ltr">CD-HIT (Cluster Database at High Identity with Tolerance) is a widely used clustering algorithm that reduces redundancy in large genomic datasets. …”

<b>Climate pressure and protection gaps threaten endemic flora of Brazil’s semi-arid region - Supplementary file</b> by Paulo Weslem Portal Gomes (11786705)

“…For each species, the table presents the climate scenario, future period, GAP analysis category (based on overlap with Protected Areas), total suitable area (km²), preliminary classification according to IUCN Criterion B1 (Extent of Occurrence), and the percentage of change in suitable area relative to the baseline (%), where negative values indicate area loss and positive values indicate area gain.…”

Table 1_Adverse events in the neonatal intensive care unit identified by triggers.pdf by Fabiana Bragança Albanese (21450395)

“…AE severity was assessed using the Neonatal Adverse Event Severity Scale (NAESS) and the World Health Organization (WHO) classification, while ADR causality was evaluated using the WHO criteria and the algorithm proposed by Du et at. …”

Data Sheet 1_Machine learning assisted breathomic approach for early-stage thoracic cancer detection.docx by Zhenguang Chen (518931)

“…A logistic regression algorithm was employed to construct a classification model for benign and malignant thoracic lesions. …”

RASP performance across different scales in the Mouse Sagittal brain dataset. by Ian K. Gingerich (22822222)

“…<p><b>A: Cell Type Annotations</b> — Ground truth cell type labels (top) alongside predicted labels identified by RASP (bottom), demonstrating cell-level classification accuracy. <b>B: Cell Type Clustering Accuracy</b> — Adjusted Rand Index (ARI) values for all methods based on cell type annotations in <b>A</b>, with colors indicating clustering algorithms. …”

The dataset of main grain land changes in China over 1985–2020 by Shidong Liu (11590769)

“…Here, we developed the change map of MGL with resolution 30m in China for the period 1985–2020 using the Landsat image-based random forest algorithm on the GEE platform. …”

Collaborative Research: SCIPE: Interdisciplinary Research Support Community for Artificial Intelligence and Data Sciences by Sanmukh Kuppannagari (21798863)

“…<br><br>To achieve 10x-100x reduction in DNN energy consumption, a holistic approach is pursued, which encompasses: (1) new circuit designs that leverage emerging CMOS+X technologies; (2) a novel near-memory architecture in which processing elements are seamlessly integrated with traditional Dynamic RAM (DRAM); (3) novel 3D-matrix-based per-layer DNN computations and data-layout optimizations for kernel weights; and (4) algorithms and hardware/software co-design tailored for near-real-time DNN-based signal classification in next-generation wireless systems. …”

CIAHS-Data.xls by Yingchang Li (22195585)

“…For this purpose, we employed the Natural Breaks classification method to reclassify factor values. This method identifies inherent natural grouping points within the data through the Jenks optimization algorithm, maximizing between-class differences while minimizing within-class differences37. …”

EEG Signals for Robust Cognitive Workload Recognition Under Noisy Conditions.zip by Fariya Bintay Shafi (21692408)

“…Unlike traditional lab-controlled datasets, this collection deliberately includes a broad range of physiological and environmental artifacts, providing a realistic testing ground for robust EEG-based algorithm development.</p><h3>Key Characteristics:</h3><ul><li><b>Noise Sources Captured:</b> ECG (cardiac activity), EMG (muscle movements), EOG (eye blinks/movements), respiratory artifacts, and power line interference.…”

Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay by Younseong Song (9967421)

“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”

Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay by Younseong Song (9967421)

“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”

Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay by Younseong Song (9967421)

“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”

Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay by Younseong Song (9967421)

“…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”