Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

Pseudocode of artificial dragonfly algorithm. by Ghassan Ahmed Ali (17041488)

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The Hopfield artificial neural network algorithm. by Ghassan Ahmed Ali (17041488)

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Data_Sheet_1_Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S..pdf by Xiao Zhou (122661)

Comparison of MAPE mean and RMSE mean for statistical significance. by Ghassan Ahmed Ali (17041488)

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Flow diagram of Wan Abdullah method for HNN. by Ghassan Ahmed Ali (17041488)

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G<i>m</i>R performance of various HNN-EB<i>k</i>SAT models. by Ghassan Ahmed Ali (17041488)

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List of some parameters of the ADA-HNN- EB<i>k</i>SAT model. by Ghassan Ahmed Ali (17041488)

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<i>MAPE performance of various</i> HNN-EB<i>k</i>SAT models. by Ghassan Ahmed Ali (17041488)

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RMSE performance of various HNN-EB<i>k</i>SAT models. by Ghassan Ahmed Ali (17041488)

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Flowchart for HNN-EB<i>k</i>SAT implementation procedure. by Ghassan Ahmed Ali (17041488)

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Training error and accuracy for all HNN models. by Ghassan Ahmed Ali (17041488)

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Implementation time performance of various HNN-EBkSAT models. by Ghassan Ahmed Ali (17041488)

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Iteration curve of the optimization process. by Meijun Shang (22806461)

“…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …”

Sample image for illustration. by Indhumathi S. (19173013)

“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning by Lu Xin (728966)

“…Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. …”

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning by Lu Xin (728966)

“…Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. …”

Quadratic polynomial in 2D image plane. by Indhumathi S. (19173013)

“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”

Data Sheet 1_TBESO-BP: an improved regression model for predicting subclinical mastitis.pdf by Kexin Han (10933209)

“…The model is based on TBESO (Multi-strategy Boosted Snake Optimizer) and utilizes monthly Dairy Herd Improvement (DHI) data to forecast the status of subclinical mastitis in cows.…”

Comparison analysis of computation time. by Indhumathi S. (19173013)

“…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”