ARTICLE | doi:10.20944/preprints201906.0055.v2
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: supercritical carbon dioxide; machine learning modeling; acid; artificial intelligence; solubility; artificial neural networks (ANN); adaptive neuro-fuzzy inference system (ANFIS); least-squares support-vector machine (LSSVM); multi-layer perceptron (MLP); engineering applications of artificial intelligence
Online: 31 July 2019 (04:35:26 CEST)
In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.
ARTICLE | doi:10.20944/preprints202001.0227.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: transportation; mobility; prediction model; pavement management; pavement condition index; falling weight deflectometer; multilayer perceptron; radial basis function; artificial neural network; intelligent machine system committee
Online: 20 January 2020 (11:08:32 CET)
Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., Levenberg-Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS model outperforms other models with the promising results of APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.
ARTICLE | doi:10.20944/preprints201908.0019.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: emotion classification; machine learning classifiers; ISEAR dataset; data mining; performance evaluation; data science; opinion-mining
Online: 2 August 2019 (08:49:27 CEST)
Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.