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This study aims to use regression Least-Square Support Vector Machine (LS-SVM) as a probabilistic model to determine the factor of safety (FS) and probability of failure (PF) of anisotropic soil slopes.

This research uses machine learning (ML) techniques to predict soil slope failure. Due to the lack of analytical solutions for measuring FS and PF, it is more convenient to use surrogate models like probabilistic modeling, which is suitable for performing repetitive calculations to compute the effect of uncertainty on the anisotropic soil slope stability. The study first uses the Limit Equilibrium Method (LEM) based on a probabilistic evaluation over the Latin Hypercube Sampling (LHS) technique for two anisotropic soil slope profiles to assess FS and PF. Then, using one of the supervised methods of ML named LS-SVM, the outcomes (FS and PF) were compared to evaluate the efficiency of the LS-SVM method in predicting the stability of such complex soil slope profiles.

This method increases the computational performance of low-probability analysis significantly. The compared results by FS-PF plots show that the proposed method is valuable for analyzing complex slopes under different probabilistic distributions. Accordingly, to obtain a precise estimate of slope stability, all layers must be included in the probabilistic modeling in the LS-SVM method.

Combining LS-SVM and LEM offers a unique and innovative approach to address the anisotropic behavior of soil slope stability analysis. The initiative part of this paper is to evaluate the stability of an anisotropic soil slope based on one ML method, the Least-Square Support Vector Machine (LS-SVM). The soil slope is defined as complex because there are uncertainties in the slope profile characteristics transformed to LS-SVM. Consequently, several input parameters are effective in finding FS and PF as output parameters.

The purpose of this paper is to present extended least squares support vector machines (LS‐SVM) where data selection methods are used to get sparse LS‐SVM solution, and to overview and compare the most important data selection approaches.

The selection methods are compared based on their theoretical background and using extensive simulations.

The paper shows that partial reduction is an efficient way of getting a reduced complexity sparse LS‐SVM solution, while partial reduction exploits full knowledge contained in the whole training data set. It also shows that the reduction technique based on reduced row echelon form (RREF) of the kernel matrix is superior when compared to other data selection approaches.

Data selection for getting a sparse LS‐SVM solution can be done in the different representations of the training data: in the input space, in the intermediate feature space, and in the kernel space. Selection in the kernel space can be obtained by finding an approximate basis of the kernel matrix.

The RREF‐based method is a data selection approach with a favorable property: there is a trade‐off tolerance parameter that can be used for balancing complexity and accuracy.

The paper gives contributions to the construction of high‐performance and moderate complexity LS‐SVMs.

Convection is one of the main heat transfer mechanisms in both high to low temperature media. The accurate convection heat transfer coefficient (HTC) value is required for exact prediction of heat transfer. As convection HTC depends on many variables including fluid properties, flow hydrodynamics, surface geometry and operating and boundary conditions, among others, its accurate estimation is often too hard. Homogeneous dispersion of nanoparticles in a base fluid (nanofluids) that found high popularities during the past two decades has also increased the level of this complexity. Therefore, this study aims to show the application of least-square support vector machines (LS-SVM) for prediction of convection heat transfer coefficient of nanofluids through circular pipes as an accurate alternative way and draw a clear path for future researches in the field.

The proposed LS-SVM model is developed using a relatively huge databank, including 253 experimental data sets. The predictive performance of this intelligent approach is validated using both experimental data and empirical correlations in the literature.

The results show that the LS-SVM paradigm with a radial basis kernel outperforms all other considered approaches. It presents an absolute average relative deviation of 2.47% and the regression coefficient (R²) of 0.99935 for the estimation of the experimental databank. The proposed smart paradigm expedites the procedure of estimation of convection HTC of nanofluid flow inside circular pipes.

Therefore, the focus of the current study is concentrated on the estimation of convection HTC of nanofluid flow through circular pipes using the LS-SVM. Indeed, this estimation is done using operating conditions and some simply measured characteristics of nanoparticle, base fluid and nanofluid.

The purpose of this paper is to use artificial intelligence to evaluate the risks of urban power network planning.

A fuzzy Bayesian least squares support vector machine (LS_SVM) model is established in this paper, which can learn the risk information of urban power network planning through artificial intelligence and acquire expert knowledge for its risk evaluation. With the advantage of possessing learning analog simulation precision and speed, the proposed model can be effectively applied in conducting a risk evaluation of an urban network planning system. First, fuzzy theory is applied to quantify qualitative risk factors of the planning to determine the fuzzy comprehensive evaluation value of the risk factors. Then, Bayesian evidence framework is utilized in LS_SVM model parameter optimization to automatically adjust the LS_SVM regularization parameters and nuclear parameters to obtain the best parameter values. Based on this, a risk comprehensive evaluation of urban network planning based on artificial intelligence is established.

The fuzzy Bayesian LS_SVM model established in this paper is an effective artificial intelligence method for risk comprehensive evaluation in urban network planning through empirical study.

The paper breaks new ground in using artificial intelligence to evaluate urban power network planning risks.

The purpose of this paper is to propose a robust control scheme for near space vehicle's (NSV's) reentry attitude tracking problem under aerodynamic parameter variations and external disturbances.

The robust control scheme is composed of dynamic surface control (DSC) and least squares support vector machines (LS‐SVM). DSC is used to design a nonlinear controller for HSV; then, to increase the robustness and improve the control performance of the controller. LS‐SVM is presented to estimate the lumped uncertainties, including aerodynamic parameter variations and external disturbances. The stability analysis shows that all closed‐loop signals are bounded, with output tracking error and estimate error of LS‐SVM weights exponentially converging to small compacts.

Simulation results demonstrate that the proposed method is effective, leading to promising performance.

First, a robust control scheme composed of DSC and adaptive LS‐SVM is proposed for NSV's reentry attitude tracking problem under aerodynamic parameter variations and external disturbances; second, the proposed method can achieve more favorable tracking performances than conventional dynamic surface control because of employing LS‐SVM to estimate aerodynamic parameter variations and external disturbances.

Nowadays, automotive engines are controlled by electronic control units (ECUs), and the engine idle speed performance is significantly affected by the setup of control parameters in the ECU. The engine ECU tune‐up is done empirically through tests on a dynamometer (dyno). In this way, a lot of time, fuel and human resources are consumed, while the optimal control parameters may not be obtained. The purpose of this paper is to propose a novel ECU setup optimization approach for engine idle speed control.

In the first phase of the approach, Latin hypercube sampling (LHS) and a multi‐input/output least squares support vector machine (LS‐SVM) is proposed to build up an engine idle speed model based on dyno test data, and then a genetic algorithm (GA) is applied to obtain optimal ECU setting automatically subject to various user‐defined constraints.

The study shows that the predicted results using the estimated model from LS‐SVM are in good agreement with the actual test results. Moreover, the optimization results show a significant improvement on idle speed performance in a test engine.

As the methodology is generic it can be applied to different vehicle control optimization problems.

The research is the first attempt to integrate a couple of paradigms (LHS, multi‐input/output LS‐SVM and GA) into a general framework for constrained multivariable optimization problems under insufficient system information. The proposed multi‐input/output LS‐SVM for modelling of multi‐input/output systems is original, because the traditional LS‐SVM modelling approach is suitable for multi‐input, but single output systems. Finally, this is the first use of the novel integrated framework for automotive engine idle‐speed control optimization.

This study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.

The authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.

Empirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).

Efficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics.

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r _p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively.

The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics.

It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

The purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics.

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models using 100 wavelet coefficients. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with the LS-SVM model.

The correlation coefficient of prediction (r_p) and root mean square errors of prediction were 0.99 and 4.37 percent, respectively. The results suggest that NIR spectroscopy, combining with the LS-SVM method, has significant potential to quantitatively analyze cotton content in blend fabrics.

It may have commercial and regulatory potential to avoid time-consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

The purpose of this paper is to give an approach for categorization of diverse textile designs using their textural features as extracted from their gray images by means of multi-class least-square support vector machines (LS-SVM).

In this work, the authors endeavor to devise a pattern recognition system based on LS-SVM which performs a multi-class categorization of three basic woven designs namely plain, twill and sateen after analyzing their features.

The result establishes that LS-SVM is able to classify the fabric design with a reasonable degree of accuracy and it outperforms the standard SVM.

The algorithmic simplicity of LS-SVM resulting from replacement of inequality constraints by equality ones and ability of handling noisy data by accommodating an error variable in its algorithm make it eminently suitable for textile pattern recognition. This paper offers a maiden application of LS-SVM in textile pattern recognition.