首页|Researchers from Nanjing University of Science and Technology Provide Details of New Studies and Findings in the Area of Machine Learning (Combustion Condition Predictions for C 2-c 4 Alkane and Alkene Fuels Via Machine Learning Methods)
Researchers from Nanjing University of Science and Technology Provide Details of New Studies and Findings in the Area of Machine Learning (Combustion Condition Predictions for C 2-c 4 Alkane and Alkene Fuels Via Machine Learning Methods)
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2024 OCT 08 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Machine Lea rning. According to news reporting from Nanjing, People's Republic of China, by NewsRx journalists, research stated, "The accurate and rapid prediction of hydro carbon type was a precondition for the utilization of fossil fuels with high eff iciency and safety. In this study, machine learning based techniques were used t o predict the type and equivalence ratio of flames of C 2-C 4 alkane and alkene fuels based on the differences in flame morphology between various combustion co nditions." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from the Nanjing Univ ersity of Science and Technology, "The test results of different machine learnin g algorithms, including ANN, SVM, SVR, KNN, MLR, and RF were compared in detail using statistical methods. Results indicated that ANN, SVM, KNN, and RF all exhi bited an outstanding performance in predicting the types of C 2-C 4 alkane and a lkene flames, achieving accuracies of 95.7 %, 96.3 %, 93.8 %, and 96.5 %, respectively. For the prediction o f the equivalence ratio among these fuels, the mean absolute percentage errors o f the ANN, SVR, MLR, and RF were only 5.6 %, 3.8 %, 8. 2 %, and 3.8 %, respectively. The performance of SVM, SVR, and RF algorithms was significantly superior to that of ANN, MLR, and KNN a lgorithms for flame prediction. Moreover, the data of feature analysis revealed that the importance level of designed features exhibited a significant distincti on between different prediction targets. For predicting the type of C 2-C 4 alka ne and alkene fuels, the features associated with blue region showed a stronger importance level."
NanjingPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningNanjing Univers ity of Science and Technology
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