Principle of the SVM algorithm. by Anfeng Zhu (17817144)

Machine learning deployment strategies and schematic illustration of the proposed generative adversarial algorithm for domain adaptation. by Aly A. Valliani (13251484)

Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

Confusion matrix for multiclass classification. by Ebru Ergün (21395498)

“…The experimental protocol involved eight participants performing tasks across four classes of scrolling text. To optimize system accuracy and speed, EEG and NIRS data were segmented into discrete temporal windows. …”

General flow chart of the proposed method. by Ebru Ergün (21395498)

“…The experimental protocol involved eight participants performing tasks across four classes of scrolling text. To optimize system accuracy and speed, EEG and NIRS data were segmented into discrete temporal windows. …”

Data_Sheet_1_Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence.PDF by Vito Janko (14605643)

Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png by Minjin Guo (22751300)

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP by Xiaoyuan Wang (492534)

DataSheet_1_Stronger wind, smaller tree: Testing tree growth plasticity through a modeling approach.docx by Haoyu Wang (429641)

Data_Sheet_2_Computational Neural Modeling of Auditory Cortical Receptive Fields.pdf by Jordan D. Chambers (6732224)

Data_Sheet_1_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”

Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_2_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Data_Sheet_1_Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision.docx by Qunfeng Niu (13263975)

DATASET AI by Elena Stamate (18836305)

“…</li></ul><p dir="ltr">The dataset includes model-ready variables suitable for classification tasks and has been used to train and evaluate algorithms such as Extra Trees, Support Vector Machines (SVM), Random Forest, and Quadratic Discriminant Analysis (QDA). …”