The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

Outcomes of the per-schema linear regression models to test whether participants show similar schema patterns in open as in closed thought records of the same scenario type (interp... by Franziska Burger (11576795)

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

“…Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

“…Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

DataSheet1_Study on Dynamic Process Characteristics of CHP Unit With Variable Load Based on Working Point Linearization Modeling.pdf by Yuehua Huang (3920081)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

Running time comparison of different algorithms. by Shuang Guo (294074)

“…Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction.…”

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

“…Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”

DataSheet1_A Novel Sequential Approach for the Design of Heat Exchanger Networks.docx by Jose A. Caballero (3116736)

A. Top – 14 days of continuous glucose monitor (CGM) data for a patient that spends all of their time within an optimal glucose range. by Jamison H. Burks (21029607)

Testing results for classifying AD, MCI and NC. by Nicodemus Songose Awarayi (18414494)

“…Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

Summary of existing CNN models. by Nicodemus Songose Awarayi (18414494)

“…Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

Post scan feature estimates and confidence ratings. by Sam Hall-McMaster (10343795)

DataSheet1_Minimal Cycle Representatives in Persistent Homology Using Linear Programming: An Empirical Study With User’s Guide.pdf by Lu Li (14069)

Table_1_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. This approach offers significant flexibility, enlarges the solution space and mitigates bias that may arise from manual or semi-manual hyperparameter tuning and feature selection and presents the opportunity to select the inner machine learning algorithm based on the results of optimized learning for the problem at hand.…”

Table_2_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. This approach offers significant flexibility, enlarges the solution space and mitigates bias that may arise from manual or semi-manual hyperparameter tuning and feature selection and presents the opportunity to select the inner machine learning algorithm based on the results of optimized learning for the problem at hand.…”

Video1_NSPG: An Efficient Posture Generator Based on Null-Space Alteration and Kinetostatics Constraints.MP4 by figshare admin frontiersin (4545187)

DataSheet1_v1_Multi-Criteria Optimization of a Biomass-Based Hydrogen Production System Integrated With Organic Rankine Cycle.docx by Xiaoqi Zhang (49443)

Notations table. by Shuang Guo (294074)

“…Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction.…”

Comparison of average traffic throughput. by Shuang Guo (294074)

“…Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction.…”