In the application of regression prediction through big data technology, error between predicted value and true is often large. order to reduce prediction, this paper proposes an Intelligent Data Prediction (IDP) scheme for Smart Service. It uses Least Squares Support Vector Machine (LSSVM) as basic model. Since there no standard procedure determining main parameters LSSVM, improved Particle Sw...

Data recorded from monitoring the health condition of industrial equipment are often high-dimensional, nonlinear, nonstationary and characterised by high levels uncertainty. These factors limit efficiency machine learning techniques to produce desirable results when developing effective fault classification frameworks. This paper sought propose a hybrid artificial intelligent predictive mainten...

The internal temperature of the pigsty has a great impact on pigs. Keeping in within certain range is pressing problem environmental control. current regulation method based mainly manual and simple automatic There rarely intelligent control, such direct methods have problems as low control accuracy, high energy consumption untimeliness, which can easily lead to occurrence heat stress condition...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...

This paper proposed a method to identify nonlinear systems via the fuzzy weighted least squares support machine (FW-LSSVM). At first, we describe the proposed modeling approach in detail and suggest a fast learning scheme for its training. Because the training sample data of independent variable and dependent variable has a certain error, and we obtain the sample which has a certain fuzziness f...

In this study, we use least square support vector machines (LSSVM) to construct a credit scoring model and introduce conjoint analysis technique to analyze the relative importance of each input feature for making the decision in the model. A test based on a real-world credit dataset shows that the proposed model has good classification accuracy and can help explain the decision. Hence, it is an...

In this paper, a classifier is proposed and trained to distinguish between bulking and non-bulking situations in an activated sludge wastewater treatment plant, based on available image analysis information and with the goal of predicting and monitoring filamentous bulking. After selecting appropriate activated sludge parameters (filament length, floc fractal dimension and floc roundness), an L...

Mengetahui perusahaan akan mengalami financial distress adalah hal yang penting bagi banyak pihak. Banyak metode digunakan dalam memprediksi distress, seperti Multivariate Discriminant Analysis (MDA), logistic regression, hiingga paling terbaru menggunakan artificial intelligence. Dalam membuat model prediksi, akurasi mendekati sempurna ingin dicapai, sehingga terus dilakukan penelitian agar ma...