Landscape Change Monitoring System (LCMS) Alaska Annual Change by U.S. Forest Service (17476914)

“…</p><p>Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). …”

Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach by Mar Roca (20435036)

“…Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. …”

Table 1_A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities.xlsx by José Luis Uc Castillo (20516885)

“…These models are recognized as powerful tools in many fields due to their capability to carry out several tasks such as forecasting, image classification, recognition, natural language processing, machine translation, etc. …”

Table 2_A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities.docx by José Luis Uc Castillo (20516885)

“…These models are recognized as powerful tools in many fields due to their capability to carry out several tasks such as forecasting, image classification, recognition, natural language processing, machine translation, etc. …”

Data Sheet 1_Application and research progress on artificial intelligence in the quality of Traditional Chinese Medicine.pdf by Mei-Yu Li (8505678)

“…This review provides a comprehensive synthesis of commonly applied machine learning and deep learning algorithms, systematically outlining recent advances in AI-enabled sensing applications such as image recognition, odor analysis, authenticity verification, origin tracing, quality grading, and storage-age determination. …”

Mean squared Error on all unseen data. by Edward Antonian (21453161)

“…The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. Next, we examine the statistical effect of correlated prediction error and propose a method for Generalized Least Squares (GLS) on graphs. …”

Possible graph filter functions. by Edward Antonian (21453161)

“…The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. Next, we examine the statistical effect of correlated prediction error and propose a method for Generalized Least Squares (GLS) on graphs. …”

The notational conventions used in this paper. by Edward Antonian (21453161)

“…The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. Next, we examine the statistical effect of correlated prediction error and propose a method for Generalized Least Squares (GLS) on graphs. …”

A Tutorial on the Use of Artificial Intelligence Tools for Facial Emotion Recognition in R by Austin Wyman (17559054)

“…We present their advantages, disadvantages, and provide sample code so that researchers can immediately begin designing, collecting, and analyzing emotion data. …”

Data analyzed for the article of <b>Evaluating photoplethysmography-based pulsewave parameters and composite scores for assessment of cardiac function: A comparison with echocardio... by Kulin (20907929)

“…The algorithm automatically generated the endocardial contours of the cavities, which were manually corrected throughout the entire cardiac cycle. …”

A dataset mapping the temperate savanna in Northeastern China (2022) by Xiaoya Li (19531363)

“…The derived method provides a new insight to explore the extent of regional and global savannain the future.</p><p dir="ltr">The code and generated maps are publicly accessible. …”

<b>AutoMated tool for Antimicrobial resistance Surveillance System version 3.1 (AMASS3.1)</b> by Chalida Rangsiwutisak (10501496)

“…</li><li>We have revised the text and corrected bugs encountered during data import and translation.</li><li>We have updated SaTScan codes for AnnexC: Cluster signals and report layouts.…”

Raw dataset. by Jun Zhang (48506)

“…<p>Original files of investigated data and algorithm codes.</p> <p>(ZIP)</p>…”

Landscape Change Monitoring System (LCMS) Puerto Rico USVI Annual Change by U.S. Forest Service (17476914)

“…</p><p>Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). …”

Data Sheet 2_Characterization of the salivary microbiome in healthy individuals under fatigue status.docx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

Table 3_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

Data Sheet 1_Characterization of the salivary microbiome in healthy individuals under fatigue status.docx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

Table 5_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

Table 4_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

Table 2_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”