Data Sheet 6_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.docx by Cong Peng (160287)

“…This study sought to create a robust, interpretable machine learning-based model that predicts 1-, 3-, and 5-year survival in patients with extremely aggressive prostate cancer. …”

Data Sheet 1_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.pdf by Cong Peng (160287)

“…This study sought to create a robust, interpretable machine learning-based model that predicts 1-, 3-, and 5-year survival in patients with extremely aggressive prostate cancer. …”

Transition from static to dynamic malware detection by Paul A. Gagniuc (1818325)

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Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

NRI Comparison between Machine Learning Methods and RPCE. by Sazzli Kasim (11218558)

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NRI Comparison between Machine Learning Methods and FRS. by Sazzli Kasim (11218558)

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Data Sheet 1_Fast forward modeling and response analysis of extra-deep azimuthal resistivity measurements in complex model.docx by Pan Zhang (206408)

“…Considering the increased detection range of EDARM and the requirements for computational efficiency, this paper presents a 2.5-dimensional (2.5D) finite element method (FEM). By leveraging the symmetry of simulated signals in the spectral domain, the algorithm reduces computation time by 50%, significantly enhancing computational efficiency while preserving accuracy. …”

Image 2_Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.jpg by Maliki Moustapha (22419664)

“…We tested a Kaggle dataset with more than 5,000 samples and found ResNet18 to be the best model based on genetic algorithm-based hyperparameter selection. …”

Image 1_Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.jpg by Maliki Moustapha (22419664)

“…We tested a Kaggle dataset with more than 5,000 samples and found ResNet18 to be the best model based on genetic algorithm-based hyperparameter selection. …”

SVR and MLPR algorithm flow optimized by GridSearchCV. by Jintao Li (448681)

Table 5_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Table 8_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Table 7_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Table 4_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Table 6_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Table 3_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Table 2_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Table 1_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files by Suchismita Behera (22027316)

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