SSA4LSMOP by Zongbin Qiao (19814199)

“…<p dir="ltr">SSA4LSMOP is a multi-objective optimization algorithm library, which aims to provide researchers and developers with efficient and easy-to-use multi-objective optimization solutions. …”

S1 File - Enhanced audience sentiment analysis in IoT-integrated metaverse media communication by Hongtao Wang (570131)

Optimal progressively censored reliability sampling plans for log-location-scale distributions by Chien-Tai Lin (22673615)

“…<p>Here, we introduce a variable neighborhood search algorithm-based approach to determine the minimum sample sizes required for progressively censored reliability sampling plans within the flexible family of log-location-scale family of distributions, which includes Weibull and log-logistic distributions. …”

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

“…Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). …”

Hyperparameter and model configurations. by Hongtao Wang (570131)

“…The five interconnected modules constituting the architecture include (i) multi-source data collection using RESTful APIs; (ii) weighted preprocessing pipelines using tokenization, lemmatization, and normalization; (iii) Adam algorithm-optimized model training and cross-entropy loss minimization-based training; (iv) adaptive real-time processing using dynamic window segmentation; and (v) an ongoing refinement loop using continuous user inputs, triggered by a feedback mechanism. …”

Performance in best and worst case scenarios. by Hongtao Wang (570131)

“…The five interconnected modules constituting the architecture include (i) multi-source data collection using RESTful APIs; (ii) weighted preprocessing pipelines using tokenization, lemmatization, and normalization; (iii) Adam algorithm-optimized model training and cross-entropy loss minimization-based training; (iv) adaptive real-time processing using dynamic window segmentation; and (v) an ongoing refinement loop using continuous user inputs, triggered by a feedback mechanism. …”

Datasets and experimental settings. by Hongtao Wang (570131)

“…The five interconnected modules constituting the architecture include (i) multi-source data collection using RESTful APIs; (ii) weighted preprocessing pipelines using tokenization, lemmatization, and normalization; (iii) Adam algorithm-optimized model training and cross-entropy loss minimization-based training; (iv) adaptive real-time processing using dynamic window segmentation; and (v) an ongoing refinement loop using continuous user inputs, triggered by a feedback mechanism. …”

Response time by scenario (ms). by Hongtao Wang (570131)

“…The five interconnected modules constituting the architecture include (i) multi-source data collection using RESTful APIs; (ii) weighted preprocessing pipelines using tokenization, lemmatization, and normalization; (iii) Adam algorithm-optimized model training and cross-entropy loss minimization-based training; (iv) adaptive real-time processing using dynamic window segmentation; and (v) an ongoing refinement loop using continuous user inputs, triggered by a feedback mechanism. …”

Ablation study: Component contribution analysis. by Hongtao Wang (570131)

“…The five interconnected modules constituting the architecture include (i) multi-source data collection using RESTful APIs; (ii) weighted preprocessing pipelines using tokenization, lemmatization, and normalization; (iii) Adam algorithm-optimized model training and cross-entropy loss minimization-based training; (iv) adaptive real-time processing using dynamic window segmentation; and (v) an ongoing refinement loop using continuous user inputs, triggered by a feedback mechanism. …”

Rate of performance improvement with increased training sample size. by John Kruper (18809386)

ACF and PACF plots of the residuals. by Zixi Wang (459102)

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…This study focuses on developing an efficient classification framework for species-level tree mapping in the Hauz Khas Urban Forest, New Delhi, India, using EO-1 Hyperion hyperspectral imagery.…”

Image 1_Identification of immune-related biomarkers linked to systemic lupus erythematosus and dilated cardiomyopathy through integrated bioinformatics analysis and multiple machin... by Gaijie Li (20454743)

“…Experimental validation supports the key role of HERC6, IFI44L, and RSAD2 in SLE-related cardiac dysfunction. Additionally, we developed a nomogram for DCM based on these two genes, and the results showed that both genes exhibited AUC values greater than 0.84. …”

Supplementary Material for: The development of an intradisciplinary staff training intervention on the optimal management of behavioural and psychological symptoms of dementia: A q... by Carrier D. (15322819)

“…Experts recommended the use of an online training platform, and certain training models and indicators. Based on caregivers and experts’ input, five interactive online staff training capsules lasting from 20-25 minutes each and an algorithm guiding the evaluation and management of BPSDs were created. …”

Performance of all brain age models increases with the number of subjects in the training set. by John Kruper (18809386)

Data Sheet 1_Evaluating methods for integrating single-cell data and genetics to understand inflammatory disease complexity.docx by Hope A. Townsend (12283067)

“…Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls.…”

Prior distributions for model parameters. by Cosmin Safta (11105442)

“…Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.…”

Pipeline for extraction of parameters for ML and DL. by John Kruper (18809386)

Datasheet1_Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.docx by Chor Yiu Chloe Chung (19986408)

Visual representation of web application deployment. by Junying Wei (1710550)

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