This paper concentrates on a new procedure which experimentally recognizes 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 consists of three steps, firstly the six different base wavelets are considered. Out of these six wavelets, the base wavelet is selected based on wavelet selection criterion to extract statistical features from wavelet coefficients of raw vibration signals. Based on wavelet selection criterion, Daubechies wavelet and Meyer are selected as the best base wavelet among the other wavelets considered from the Maximum Relative Energy and Maximum Energy to Shannon Entropy criteria respectively. Finally, the gearbox faults are classified using these statistical features as input to LSSVM technique. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of fault classification demonstrate that the LSSVM identified the fault categories of gearbox more accurately with multi kernel and OAOT strategy.

Background: The recent progress and achievements in the advanced, accurate, and rigorously evaluated algorithms has revolutionized different aspects of the predictive microbiology including bacterial growth.Objectives: In this study, attempts were made to develop a more accurate hybrid algorithm for predicting the bacterial growth curve which can also be applicable in predictive microbiology studies.Materials and Methods: Sigmoid functions, including Logistic and Gompertz, as well as least square support vector machine (LSSVM) based algorithms were employed to model the bacterial growth of the two important strains comprising Listeria monocytogenes and Escherichia coli. Even though cross-validation is generally used for tuning the parameters in LSSVM, in this study, parameters tuning (i.e.,‘c’ and ‘σ’) of the LSSVM were optimized using non-dominated sorting genetic algorithm-II (NSGA-II), named as NSGA-II-LSSVM. Then, the results of each approach were compared with the mean absolute error (MAE) as well as the mean absolute percentage error (MAPE).Results: Applying LSSVM, it was resulted in a precise bacterial growth modeling compared to the sigmoid functions. Moreover, our results have indicated that NSGA-II-LSSVM was more accurate in terms of prediction than LSSVM method.Conclusion: Application of the NSGA-II-LSSVM hybrid algorithm to predict precise values of ‘c’ and ‘σ’ parameters in the bacterial growth modeling resulted in a better growth prediction. In fact, the power of NSGA-II for estimating optimal coefficients led to a better disclosure of the predictive potential of the LSSVM.

Accurate prediction of electrical conductivity (EC) concentrations in river water is essential for effective water quality management and environmental protection. This study develops a novel hybrid model, named HOA-CEEMD-LSSVM, that integrates the hiking optimization algorithm (HOA), complementary ensemble empirical mode decomposition (CEEMD), and least square support vector machine (LSSVM) to forecast daily EC concentrations in the Aidoghmoush River, Iran. HOA simultaneously optimizes key parameters of CEEMD and LSSVM to enhance prediction accuracy. CEEMD decomposes complex time series into intrinsic mode functions (IMFs) with more predictable patterns, which serve as inputs to the LSSVM predictor. The model’s performance is evaluated through multiple metrics, demonstrating significant improvements over benchmark models in terms of R² and Kling-Gupta Efficiency (KGE). The proposed model enhances the R2 and KGE values of other prediction models by 1%-10 % and 3. 17%-17%, respectively. Our findings show that the HAO-CEEMD-LSSVM model can precisely forecast EC concentration. This approach provides a robust framework for capturing the nonlinear, nonstationary characteristics of EC time series data. The model is applicable in water resource planning, pollution control, and river ecosystem management. While showing high forecasting accuracy, its computational complexity and black-box nature present limitations. Future work should explore parallel computing and explainable artificial intelligence techniques to enhance efficiency and interpretability.

One of the challenging problems in the oil and gas industry is accurate and reliable multiphase flow rate measurement in a three-phase flow. The application of methods with minimized uncertainty is required in the industry. Previously developed correlations for two-phase flow are complex and not capable of three-phase flow. Hence phase behavior identification in different conditions of designing and modeling of three-phase flow is important. Numerous laboratory and theoretical studies have been done to describe the Venturi multiphase flow meter in both horizontal and vertical flow. However, it is not possible to select the measurement devices for all similar conditions. In this study, a new venturi model is developed to implement in Simulink/Matlab for predicting the mass flow rate of gas, water, and oil. This model is simple and semi-linear. Several classified configurations of three-phase flow are simulated using computational fluid dynamics analysis to get hydrodynamics parameters of the flows to use as inputs of the model. The obtained data is used as a test and train data in the least squares support vector machine (LSSVM) algorithm. The pressure drop and mass flow rate of gas, oil, and water are calculated with the LSSVM method. Two tuning parameters of LSSVM, namely γ and 𝜎 2, are obtained as 1150954 and 0. 4384, 53. 9199 and 0. 18163, 8. 8714 and 0. 14424, and 1003913. 2214 and 0. 74742 for the pressure drop, the mass flow rate of oil, gas mass flow rate, and the water mass flow rate, respectively. Developed models are found to have an average relative error of 5. 81%, 6. 31%, and 2. 58% for gas, oil, and water, respectively.

This work presents a technique for the analysis of facial electromyogram signal activities to classify five different facial expressions for computer-muscle interfacing applications. Facial electromyogram (FEMG) is a technique for recording the asynchronous activation of neuronal inside the face muscles with non-invasive electrodes. FEMG pattern recognition is a difficult task for the researcher, where classification accuracy is key concerns. Artifacts, such as eyeblink activity and electroencephalogram (EEG) signals interference, can corrupt these FEMG signals and directly affected the classification results. In this work, a robust wavelet-based thresholding technique, which comprised of a wavelet transform (WT) method and the statistical threshold, is proposed to remove the different artifacts from FEMG datasets and improve recognition accuracy rate. A set of five different raw FEMG data set was analyzed. Four wavelet basis functions, namely, haar, coif3, sym3, and bior4.4, were considered. The performance parameters (signal-to-artifact ratio (SAR) and normalized mean square error (NMSE) were utilized to understand the effect of the proposed signal denoising protocol. After denoising, the effectiveness of different statically features has been extracted. Two pattern recognition algorithms support vector machine (SVM) and the least square support vector machine (LSSVM) are implemented to classify extracted features. The performance accuracy of SVM and LSSVM classifier was evaluated and compared to know which classifier is the best for facial expression classification.  The results showed that: (i) the proposed technique for denoising, improves the performance parameter results; (ii) The proposed work gives the best 95.24% classification accuracy.