Prediksi Detak Jantung Berbasis LSTM pada Raspberry Pi untuk Pemantauan Kesehatan Portabel by Ahmad Foresta Azhar Zen, Eko Sakti Pramukantoro, Kasyful Amron, Viera Wardhani, Putri Annisa Kamila

“…LSTM models are a type of artificial neural network architecture known for their ability to handle sequential data effectively, making them highly suitable for sequential heart rate monitoring and prediction. …”
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A Navier–Stokes-Informed Neural Network for Simulating the Flow Behavior of Flowable Cement Paste in 3D Concrete Printing by Tianjie Zhang, Donglei Wang, Yang Lu

“…The results show that the presented NSINN has a higher accuracy compared to a traditional artificial neural network (ANN) as the Mean Square Errors (MSEs) of the u, v, and p predicted by NSINN are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.25</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.85</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.91</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>, respectively. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
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Advances in machine learning applications to resource technology for organic solid waste by Hongzhi MA, Yichan LIU, Jihua ZHAO, Fan FEI, Ming GAO, Qunhui WANG

“…This study explores a range of commonly used ML models, including artificial neural network (ANN), support vector machine (SVM), decision tree, random forest, and extreme gradient boosting (XGBoost). …”
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Eye Collateral Channel Characteristic Analysis and Identification Model Construction of Mild Cognitive Impairment by WU Tiecheng, CAO Lei, YIN Lianhua, HE Youze, LIU Zhizhen, YANG Minguang, XU Ying, WU Jinsong

“…Different MCI identification models were constructed using support vector machine, decision tree, artificial neural network and random forest algorithm, with MCI eye collateral channel characteristics and TCM syndrome elements as independent variables and onset of MCI as a dependent variable. …”
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