Performance of machine learning algorithms. by Wenxue Liu (6877754)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

<b>Discrimination of gold deposit types based on big data analysis and machine learning techniques</b> by Lihao Feng (20837036)

Risk element category diagram. by Yao Hu (3479972)

“…The LSTM algorithm can play a role in providing early warning, assisting decision-making, optimizing resources, and enhancing real-time monitoring in airport security assurance. …”

Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' by Fanzhi Su (21005174)

Subjects:

Elements: A Sustainable, Resource-Efficient Cyberinfrastructure for Notebook Interactive ML Training Workloads by Yue Cheng (22087328)

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

Subjects:

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

Subjects:

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

Subjects:

Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction by Mohamad A. Raja (19640297)

“…To preform accurate forecasts on energy storage, a data-driven machine learning approach based on artificial neural networks (ANN) was optimized via a Bayesian optimization algorithm to predict the structural battery’s future capacity. …”

A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

“…<p>The proposed algorithm approximates this relationship under the control of a single free-parameter (thick green line, κ = 0.25). …”

Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

“…<b>(E)</b> Venn diagrams of hub genes screened by two machine learning algorithms.</p>…”

Comparison of federated learning algorithms. by Duy-Dong Le (22057362)

Results of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Types of machine learning algorithms. by Md. Merajul Islam (12646837)

“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.</p><p>Materials and methods</p><p>This study used the latest nationally representative cross-sectional Bangladesh demographic health survey (BDHS), 2017–18 data. …”

Performance of the machine learning algorithms. by Novel Chandra Das (19742953)

“…<div><p>Introduction</p><p>Hypertension is a leading contributor to maternal and cardiometabolic morbidity in Bangladesh. …”