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

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

Fig 1 - by T. Anuradha Devi (18554708)

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. …”

Human expert versus automated processing: Running dataset. by Keenon Werling (17512156)

Table2_Nonintrusive Load Monitoring Method Based on Color Encoding and Improved Twin Support Vector Machine.XLS by Ruoyuan Zhang (13136175)

“…Second, the two-dimension Gabor wavelet is used to extract the texture features of the image, and the dimension is reduced by means of local linear embedding (LLE). Finally, the artificial fish swarm algorithm (AFSA) is used to optimize the twin support vector machine (TWSVM), and the ITWSM is used to train the load recognition model, which greatly enhances the model training speed. …”

Table1_Nonintrusive Load Monitoring Method Based on Color Encoding and Improved Twin Support Vector Machine.XLS by Ruoyuan Zhang (13136175)

“…Second, the two-dimension Gabor wavelet is used to extract the texture features of the image, and the dimension is reduced by means of local linear embedding (LLE). Finally, the artificial fish swarm algorithm (AFSA) is used to optimize the twin support vector machine (TWSVM), and the ITWSM is used to train the load recognition model, which greatly enhances the model training speed. …”

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

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. …”

Physical performance parameters of rock samples. by Tong Zhuang (17847113)

“…However, under constant velocity loading, the relationship between force and displacement in sandstone showed linearity after compaction. …”

MTS-816 and acoustic emission monitoring system. by Tong Zhuang (17847113)

“…However, under constant velocity loading, the relationship between force and displacement in sandstone showed linearity after compaction. …”

Smart metering and energy access programs: an approach to energy poverty reduction in sub-Saharan Africa by Bennour Bacar (14761288)

“…</li> <li>The datasets (CSV, XLSX), sequentially named, are part of the process of extracting, transforming and loading the data into a machine learning algorithm, identifying the best regression model based on metrics, and predicting the data.…”

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. …”

Video1_Fast Online Optimization for Terrain-Blind Bipedal Robot Walking With a Decoupled Actuated SLIP Model.MP4 by Ke Wang (82395)

Synthetic walking data results. by Keenon Werling (17512156)

“…Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at <a href="https://addbiomechanics.org" target="_blank">AddBiomechanics.org</a>, which is available at no cost and asks that users agree to share processed and de-identified data with the community. …”

Block diagram of 2-DOF PIDA controller. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 7. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 10. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”