Comparative analysis of off-policy PPO algorithm and on-policy algorithm on the a) CSMAR and b) MorningStar datasets. حسب Cai Yuanqing (22513351)

Coding schematic. حسب Dongyan Wang (195482)

RF Algorithm Principles. حسب Zhiheng Zhang (9347282)

A new approach in soil organic carbon estimation using machine learning algorithms: a study in a tropical forest in Vietnam حسب Thuy Phuong Nguyen (11769999)

"…This study aimed to evaluate the ability of SOC estimation using a multiple linear regression model (MLR) and four machine learning algorithms: artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) with satellite data sources and soil nutrient indicator data to find the optimal method. …"

A machine learning framework enhanced by hybrid optimization for precise solar power generation prediction حسب Weibin Wang (1968658)

"…Therefore, improving the accuracy of solar forecasts is crucial. This study develops a solar power prediction model using machine learning, specifically employing a Multi-Layer Perceptron Regression model. …"

The pseudo-code of the WOA-LSTM-PID model. حسب Dayu Zhang (710629)

Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis حسب Alireza Enshaeian (20360253)

"…The frequency content of the vibrations below 700 Hz and across a range of different longitudinal stress and support conditions is computed using the power spectral density, which constitutes the input matrix of a machine learning algorithm able to learn the complex relationship among frequencies, axial stress, and support conditions. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …"

<b>MACEDONIA OS: A Unified Scalar Field-Based Consciousness and Operating System Architecture</b> حسب Jeffrey Siergiej (20937434)

الموضوعات:

PLS algorithm results. حسب Hao Huang (111923)

S1 Data - CSW-YOLO: A traffic sign small target detection algorithm based on YOLOv8 حسب Qian Shen (89639)

supervised.zip حسب Manuel Mendoza-Hurtado (18118441)

Corporate network anomaly detection methodology utilizing machine learning algorithms حسب Nazerke Baisholan (22048322)

Research on Vehicle Detection Algorithms Based on Deep Learning in Complex Environments حسب Xiaodan Li (22309438)