Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

Armaghani, Danial Jahed; Asteris, Panagiotis G.; Askarian, Behnam; Hasanipanah, Mahdi; Tarinejad, Reza; Khang, Huynh Van

doi:10.3390/su12062229

Cited by 86 publications

(44 citation statements)

References 69 publications

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“…Further tests and evaluation on the MI-based machine learning models revealed that the maximum possible accuracy of the best performer algorithm (SVM radial) remains bracketed around 96–97% in terms of accuracy, precision, and recall. The rbf kernel was chosen because it is most frequently used by contemporary researchers [ 30 , 74 , 75 ]. It would be logical to evaluate the other kernels before moving to the improvisation of the kernel function for improving the performance of the MI-based algorithm.…”

Section: Resultsmentioning

confidence: 99%

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Dhindsa

Bhatia

Agrawal

et al. 2021

Entropy

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The accurate classification of microbes is critical in today’s context for monitoring the ecological balance of a habitat. Hence, in this research work, a novel method to automate the process of identifying microorganisms has been implemented. To extract the bodies of microorganisms accurately, a generalized segmentation mechanism which consists of a combination of convolution filter (Kirsch) and a variance-based pixel clustering algorithm (Otsu) is proposed. With exhaustive corroboration, a set of twenty-five features were identified to map the characteristics and morphology for all kinds of microbes. Multiple techniques for feature selection were tested and it was found that mutual information (MI)-based models gave the best performance. Exhaustive hyperparameter tuning of multilayer layer perceptron (MLP), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM) was done. It was found that SVM radial required further improvisation to attain a maximum possible level of accuracy. Comparative analysis between SVM and improvised SVM (ISVM) through a 10-fold cross validation method ultimately showed that ISVM resulted in a 2% higher performance in terms of accuracy (98.2%), precision (98.2%), recall (98.1%), and F1 score (98.1%).

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Section: Resultsmentioning

confidence: 99%

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Dhindsa

Bhatia

Agrawal

et al. 2021

Entropy

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“…Such strings are used by biologically motivated operators (e.g., cross-over and mutation) to produce a new set of strings. The selection, crossover, and mutation process will continue for a fixed number of generations until certain conditions are matched [139,140]. The process of the RCGA experimental search diagram is presented in Figure 2.…”

Section: Real-coded Genetic Algorithm (Rcga)mentioning

confidence: 99%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

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“…In the formula, K(x i , x j ) is the kernel function, which needs to meet Mercer condition. In this paper, Gaussian RBF kernel function [46] is selected as…”

Section: Abc-svrmentioning

confidence: 99%

A New Hybrid Model for Underwater Acoustic Signal Prediction

Chang

Yang

2020

Complexity

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The prediction of underwater acoustic signal is the basis of underwater acoustic signal processing, which can be applied to underwater target signal noise reduction, detection, and feature extraction. Therefore, it is of great significance to improve the prediction accuracy of underwater acoustic signal. Aiming at the difficulty in underwater acoustic signal sequence prediction, a new hybrid prediction model for underwater acoustic signal is proposed in this paper, which combines the advantages of variational mode decomposition (VMD), artificial intelligence method, and optimization algorithm. In order to reduce the complexity of underwater acoustic signal sequence and improve operation efficiency, the original signal is decomposed by VMD into intrinsic mode components (IMFs) according to the characteristics of the signal, and dispersion entropy (DE) is used to analyze the complexity of IMF. The subsequences (VMD-DE) are obtained by adding the IMF with similar complexity. Then, extreme learning machine (ELM) is used to predict the low-frequency subsequence obtained by VMD-DE. Support vector regression (SVR) is used to predict the high-frequency subsequence. In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. The experimental results show that the proposed new hybrid model can provide enhanced accuracy with the reduction of prediction error compared with other existing prediction methods.

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Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

Cited by 86 publications

References 69 publications

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

A New Hybrid Model for Underwater Acoustic Signal Prediction

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