Optimized Support Vector Machine Research Articles

Debris Flow Scale Prediction Based on Correlation Analysis and Improved Support Vector Machine

The occurrence of debris flows are a significant threat to human lives and property. Estimating the debris flow scale is a crucial parameter for assessing disaster losses in such events. Currently, the commonly used method for estimating debris flow runoff relies on fitting techniques, which often yield low prediction accuracy and limited data representation capabilities. Addressing these challenges, this study proposes an improved grey wolf algorithm optimized support vector machine prediction model. The model’s effectiveness is validated using data from 72 debris flow events in Beichuan County. The results demonstrate a prediction accuracy of 95.9% using this approach, indicating its strong predictive capabilities for debris flow scale. Additionally, it is observed that the basin area, the basin relative, and the main channel length are the key factors influencing debris flow scale in Beichuan County.

A rapid and non-destructive identification for paper cup evidence based on shifted-excitation Raman difference spectroscopy and SOM clustering.

In order to achieve rapid, non-destructive, efficient, and accurate classification of paper cup samples, we propose a classification model that integrates shifted-excitation Raman difference spectroscopy (SERDS) with self-organizing map (SOM) and Bayesian optimization-support vector machine (BO-SVM). We collected differential Raman data from 52 paper cup samples using SERDS, with an excitation wavelength range of 784-785 nm, a laser power of 440 mW, an integration time of 10 s, and a spectral range spanning from 280 to 2700 cm-1 . Subsequently, principal component analysis (PCA) was applied to reduce the dimensionality of the data. The SOM clustering outcomes were utilized as the foundation for constructing the discriminant analysis (FDA) and BO-SVM classification models. The primary constituent of the paper cup samples was identified as cellulose, while additional fillers such as talc, calcium carbonate, and kaolin were also present. The SOM clustering categorized the samples into seven distinct groups. The FDA model achieved a classification accuracy of 92.3%, and the BO-SVM model reached a classification accuracy of 96.2%. The SOM clustering effectively discerned samples with different fillers, as evidenced by distinct peak numbers and shapes in the differential Raman spectra, thereby underscoring the practical significance of SOM clustering. In comparison with FDA, BO-SVM exhibited enhanced classification accuracy and exceptional performance in handling outliers and linearly inseparable data, indicating its superior generalization capabilities.

Trust And Energy-Aware Routing Protocol for Wireless Sensor Networks Based on Secure Routing

Wireless Sensor Network (WSN) is a network area that includes a large number of nodes and the ability of wireless transmission. WSNs are frequently employed for vital applications in which security and dependability are of utmost concern. The main objective of the proposed method is to design a WSN to maximize network longevity while minimizing power usage. In a WSN, trust management is employed to encourage node collaboration, which is crucial for achieving dependable transmission. In this research, a novel Trust and Energy Aware Routing Protocol (TEARP) in wireless sensors networks is proposed, which use blockchain technology to maintain the identity of the Sensor Nodes (SNs) and Aggregator Nodes (ANs). The proposed TEARP technique provides a thorough trust value for nodes based on their direct trust values and the filtering mechanisms generate the indirect trust values. Further, an enhanced threshold technique is employed to identify the most appropriate clustering heads based on dynamic changes in the extensive trust values and residual energy of the networks. Lastly, cluster heads should be routed in a secure manner using a Sand Cat Swarm Optimization Algorithm (SCSOA). The proposed method has been evaluated using specific parameters such as Network Lifetime, Residual Energy, Throughpu,t Packet Delivery Ratio, and Detection Accuracy respectively. The proposed TEARP method improves the network lifetime by 39.64%, 33.05%, and 27.16%, compared with Energy-efficient and Secure Routing (ESR), Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (MOSFA) , and Optimal Support Vector Machine (OSVM).

Optimal cleaning schedule in solar PV using the biography-based helianthus optimization enabled coupled deep network

The research aims to develop a system that determines the optimal intervals for cleaning solar PV panels to maximize their efficiency and minimize power loss due to dust accumulation. The research utilizes data related to daily solar photovoltaic tariff rates, power generation, and conditions of dust accumulation on PV panels. To monitor the performance of PV panels, the research involves forecasting power generation. If the power generation falls below a predefined threshold, it indicates a need for cleaning. Dust particle loss conditions are analyzed using an optimized Support Vector Machine (SVM) coupled with a Deep Convolutional Neural Network (CNN) model. This model processes PV panel images to identify dusty conditions. The Deep CNN model extracts features from PV panel images, such as color-based statistical features and local gradient patterns. These features are crucial for identifying dusty conditions. The optimal cleaning schedule is determined based on the detection of dust particle loss conditions. Cleaning is initiated when the conditions warrant it. The novelty of this research lies in the development of a deep learning model for both detecting dust particle loss and forecasting power generation. This model is trained using data that includes daily tariff rates and dust particle loss information. The research proposes a new optimization method called “biography-based Helianthus optimization (HBBHO)” specifically designed to suit the characteristics of sunlight. This method is employed to schedule cleaning activities. The proposed approach achieves impressive accuracy, precision, sensitivity, and specificity, with values reaching 96.53% for most metrics, except for specificity, which is at 96.21%.

Multi-objective diesel engine emission management and control technology based on SVM and NSGA-II

To improve the emission control effect of diesel engines, a pre-emission model for diesel engines using biofuels is constructed using support vector machine. Considering that traditional support vector machine training relies on initial parameters and is prone to local convergence, a genetic algorithm model is adopted to construct an improved diesel engine pre-emission model. At the same time, with the optimization goal of diesel engine emissions of particles and gases, the improved diesel engine pre-emission model is used to obtain emission data as training parameters. The second generation non-dominated genetic algorithm is introduced to construct a multi-objective diesel engine emission control model, achieving optimal control of diesel engine emissions. In the experimental analysis of diesel engine pre emission models, the proposed pre emission model has a better prediction effect in nitrogen oxides, with an average prediction accuracy of 0.986, which is better than the Particle Swarm Optimization-Support Vector Machine model's 0.906, and is more accurate in detecting emissions. Simultaneously predicting particulate matter, the average prediction accuracy of the proposed model is 0.978, while the prediction accuracy of the Particle Swarm Optimization-Support Vector Machine model is 0.902, indicating that the overall prediction effect of the proposed method is better. In the experimental analysis of multi-objective optimization models, the proposed multi-objective optimization model performs best in diesel emission optimization. The proposed model can achieve convergence in the shortest time, and the minimum nitrogen oxide emissions are 210.7 ppm. Compared with Non-dominated Sorting Genetic Algorithm II and Multi-Objective Particle Swarm Optimization models, the particle emission optimization effect is improved by 23.5 % and 18.6 %. From this, it can be seen that the proposed method has excellent performance in diesel engine emission monitoring and pollutant optimization, surpassing related technologies in the same period. The research technology provides important technical references for the monitoring and emission control of harmful substances in diesel engines.

Predicting pKa of the carboxylic acid group in water solutions of amino acids based on molecular structures using machine learning QSPR methods

Amino acids as biomolecules are essential building blocks of proteins, which are fundamental to life and living organisms. The pKa values of amino acid residues in proteins can impact their structure and function. Furthermore, the pKa can be used to optimize experimental conditions for protein purification. In this research, a data set containing the pKa of the carboxylic acid group for 52 amino acids from various families in a water solution was used. The quantity structure property relationship (QSPR) approach was applied in our work for predicting the pKa using different machine learning methods. Initially, the amine molecules were drawn, and optimized and 3224 molecular descriptors were calculated for each molecule. Selecting among molecular descriptors using genetic algorithm-multi linear regression (GA-MLR) shows that the model with 8 descriptors is the best economic one. The comparison of results shows that the feed forward neural network (FFNN) is better than the particle swarm optimization-support vector machine (PSO-SVM) model. The coefficients of determination at different models follow the order of R2 FFNN= 0.9987 > R2 PSO-SVM= 0.9971 > R2 GA-MLR= 0.9951. The R2test for FFNN and PSO-SVM are near the R2train and this evidence proves the validation of models. Consequently, by using FFNN, the pKa of the carboxylic acid group in water solutions of amino acids can be predicted without any experimental costs with a low average absolute relative error (AARE%) of 2.82.

Landslide susceptibility prediction by gray wolf optimized support vector machine model under different factor states

Landslide susceptibility prediction by gray wolf optimized support vector machine model under different factor states

Mechanomyography signals pattern recognition in hand movements using swarm intelligence algorithm optimized support vector machine based on acceleration sensors

On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human-machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements based on MMG signal is studied with swarm intelligence algorithms introduced to optimize support vector machine (SVM). Time domain (TD) features, wavelet packet node energy (WPNE) features, frequency domain (FD) features, convolution neural network (CNN) features were extracted from each channel to constitute different feature sets. Three novel swarm intelligence algorithms (i.e., bald eagle search (BES), sparrow search algorithm (SSA), grey wolf optimization (GWO)) optimized SVM is proposed to train the models and recognition of hand movements are tested for each MMG feature extraction method. Using GWO as the optimization algorithm, time consumption is less than using the other two swarm algorithms. Using GWO with TD+FD features can obtain the classification accuracy of 93.55 %, which is higher than other methods while using CNN to extract features can be independent of domain knowledge. The results confirm GWO-SVM with TD + FD features is superior to some other methods in the classification problem for tiny samples based on MMG.

Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion

The multiple loads of the Regional Integrated Energy System (RIES) possess characteristics of randomness and relatively higher complexity. The current forecasting methods struggle to effectively handle the non-stationary sequence of these multiple loads, leading to less accurate load forecasting. To address this problem, this paper proposes a multi-model fusion prediction method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), Genetic Algorithm-Long Short Term Memory (GA-LSTM), Radial Basis Fusion-Autoencoder (RBF-AE), and Particle Swarm Optimization-Support Vector Machine (PSO-SVM). First, the load sequence is decomposed into different frequency Intrinsic Mode Functions (IMFs) components using CEEMD. The IMFs components are then grouped based on their zero-crossing rate and Sample Entropy (SE), resulting in three distinct groups: high-, medium-, and low-frequency components. Next, the high-frequency load component, which exhibit strong randomness, are predicted using GA-LSTM. The medium-frequency load component, which have weaker randomness, are predicted using RBF-AE. The smooth and periodic low-frequency load component are predicted using PSO-SVM. The prediction results from these three models are reconstructed to obtain the final predictive value. Finally, experimental results confirm that the forecasting model can effectively handle non-stationary load sequences and demonstrate the highest level of forecasting accuracy.

Intelligent Selection Method for Gravity-suitable Area Based on Improved Support Vector Machine by the Genetic Algorithm

The gravity-aided inertial navigation system (GAINS) can achieve precise positioning of underwater vehicles in a gravity-suitable area. However, there are generally shortcomings in the existing intelligent suitable area selection methods in terms of selecting gravity feature parameters and learning parameters. In this paper, an intelligent suitable area selection method is proposed based on an improved support vector machine by the genetic algorithm (GA-SVM) to address the aforementioned problems. Firstly, the genetic algorithm (GA) is utilized to independently pick out the optimal feature subset of 15 existing gravity feature parameters and obtain the optimal support vector machine (SVM) learning parameters while eliminating irrelevant redundant features, thus improving the classifier’s performance and generalization ability. Then, the SVM classifier is trained according to the optimal information output by GA, and the accuracy of the test set is 95.5%. Finally, the classifier is utilized to distinguish suitable and unsuitable areas in the application region to evaluate the proposed method’s performance. The TERCOM experiment in the suitable area resulted in an average positioning error of less than 170 m.

Feature recognition of complex systems using cumulative residual Tsallis signal entropy and grey wolf optimized support vector machine

In this paper, the cumulative residual Tsallis singular entropy (CRTSE) is introduced to measure the complex characteristics of nonlinear signals. Firstly, we do singular value decomposition on time series, which can reduce the interference of noise on information extraction, and the singular values represent the information characteristics of the signal. Then the statistical distribution of the signal is described by the cumulative residual function of singular values, and the Tsallis entropy is calculated to quantify the complexity. We verify the effectiveness and robustness of CRTSE through simulation experiments. Finally, we propose a grey wolf optimized support vector machine based on CRTSE called CRTSE-GWOSVM to intelligently diagnose complex systems. The results show that CRTSE can effectively measure the complex characteristics of time series, GWOSVM is superior to SVM and particle swarm optimized support vector machine (PSOSVM) in data identification, and CRTSE-GWOSVM model can identify complex systems more effectively and accurately.

Multilevel Thresholding Segmentation based on Levy-Horse Optimization Algorithm: A Review

Abstract: A novel multi-level thresholding segmentation algorithm is proposed concerning machine learning and a metaheuristic algorithm is used to select multiple threshold value for segmenting the image. Here for the analysis, two, three and five-level of threshold value for the segmentation process is made. The threshold values for two, three and five level is generated by Levy horse optimized support vector machine (LHSVM). This optimized machine learning approach selects a reduced error threshold value to segment every object or region of interest in an image. Then the method is evaluated using Berkeley segmentation dataset and benchmark (BSDS300) dataset. This kind of multi-level threshold segmentation splits the image into class and thereby considers several classes for segmenting each object in the image

A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps.

The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%.

Enhanced marine predators algorithm optimized support vector machine for IGBT switching power loss estimation

As the use of renewable energy generation continues to grow, improving power conversion efficiency has become an urgent task. This study aims to propose a high-precision power module loss estimation method, with a focus on predicting the switching losses of insulated gate bipolar transistor (IGBT) modules, which is crucial for the reliability assessment of IGBT modules. The main objective of this study is to establish a high-precision predictive model for IGBT module switching losses to enhance the reliability and efficiency of these devices. Firstly, a dynamic characteristic test platform was established to acquire relevant data for in-depth analysis of IGBT behavior. Secondly, given the excellent performance of support vector machine (SVM) in handling both strong and small-sized datasets, SVM was chosen as the foundational model for high-precision switching loss estimation. Subsequently, an enhanced marine predatory algorithm (EMPA), known for its superior convergence precision and speed, was introduced to optimize the random parameters of SVM. Finally, a method based on the optimized EMPA-SVM was constructed for predicting IGBT switching power losses. The proposed approach was validated using dynamic characteristic test data. And it indicated that the predictive model achieved the value of R 2 exceeding 99.8% for switching losses. Additionally, the mean absolute error and root mean square error metrics of EMPA-SVM model outperformed other models. Therefore, the research results unambiguously demonstrate the significant benefits of accurately predicting IGBT switching losses in enhancing device performance.

Maize disease identification based on optimized support vector machine using deep feature of DenseNet201

In recent times, maize diseases have become widespread globally, adversely impacting agricultural productivity and causing significant financial losses. Recognizing these diseases in maize leaves presents formidable challenges due to the complexities involved in extracting features from a dynamic environment. Issues such as inconsistent lighting conditions, reflections from light sources, and other variables add to the intricacies of this task. This paper introduces a novel classification model that leverages deep features and advanced optimization strategies to categorize four variations of maize leaves: blight, common rust, gray leaf spot, and healthy. Our approach harnesses the power of DenseNet201, a specialized deep-learning architecture tailored for image-classification tasks. This model excels in extracting crucial features from maize leaf images. Furthermore, we meticulously fine-tuned the Support Vector Machine (SVM) using Bayesian optimization techniques to discern and categorize diseases based on these extracted features. In our experimental assessment, we meticulously curated a dataset comprising 4988 images across four distinct maize leaf classes. The outcomes of our experiments are highly compelling, showcasing that our proposed model achieved an outstanding classification accuracy of 94.6%. Notably, this accuracy surpasses the performance attained by an SVM operating without the assistance of deep features and optimization techniques.

PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems

There are currently many studies on data-driven optimization scheduling, but only a few studies have combined “closed-loop optimization” with “performance-driven”. Therefore, this research proposed a PSO-SVM-based (particle swarm optimization optimized support vector machine) scheduling method that reconciles the composite dispatching rules (CDR), performance-driving ideology, and feedback mechanism ideology. Firstly, the composite dispatching rules coalesce flexible equipment maintenance, multiple process constraints, and dynamic dispatching. Secondly, the performance-driving ideology is carried out through two learning models based on the PSO-SVM algorithm, based on targeted optimizing performances. Thirdly, the feedback mechanism ideology makes the scheduling method realize closed-loop optimizations adaptively. Finally, the superiority of the proposed scheduling method is validated in a semiconductor manufacturing system in China. Compared with CDR, the proposed scheduling method combines MOV, PR, and EU, respectively EU_ O, EU_ P, PCSR and ODR increased by 7.85%, 5.11%, 8.76%, 8.14%, 6.60%, and 7.33%, indicating the superiority of this method.

Landslide Susceptibility Evaluation of Bayesian Optimized CNN Gengma Seismic Zone Considering InSAR Deformation

Landslides are one of the most common geological disasters in China, characterized by suddenness and uncertainty. Traditional methods are not sufficient for the accurate identification, early warning, and forecasting of landslide disasters. As high-resolution remote sensing satellites and interferometric synthetic aperture radar (InSAR) surface deformation monitoring technology have been leaping forward, the traditional methods of landslide monitoring data sources are limited, and there have been few effective methods to excavate the characteristics of the spatial distribution of landslide hazards and their triggering factors, etc. In this study, an area extending 10 km from the VII isobar of the Gengma earthquake was taken as the study area, and 13 evaluation factors were screened out by integrating the factors of InSAR surface deformation, topography, and geological environment. Landslide susceptibility was evaluated through the Bayesian optimized convolutional neural network (BO-CNN), and the Bayesian optimized random forests (BO-RF) and particle swarm optimization support vector machines (PSO-SVM) models were selected for comparative analyses. The accuracy of the model was evaluated by using three indices, including the ROC curve, the AUC value, and the FR value. Specifically, the ROC curves of PSO-SVM, BO-RF, and BO-CNN were close to the upper-left corner, indicating excellent model performance. Moreover, the AUC values were computed as 0.9388, 0.9529, and 0.9535, respectively, and the FR value of landslides in the high susceptibility area of BO-CNN reached up to 14.9 and exceeded those of PSO-SVM and BO-RF, respectively. Furthermore, the mentioned values of the SVM and BO-RF models were 4.55 and 3.69 higher. The experimental results indicated that, compared with other models, the BO-CNN model used in this study had a better effect on landslide susceptibility evaluation, and the research results are of great significance to the disaster prevention and mitigation measures of local governments.

Prediction of Surface Settlement in the Cross-passage by FIG-SVM Approach

Introduction: An optimized support vector machine prediction model (SVM) with fuzzy information granulation (FIG) is established for surface prediction in this paper. Based on the measured data, cubic spline interpolation was processed and FIG approach was applied to granulate the surface settlement parameters into fuzzy particles Low, R and Up, and the particles are used to represent the range of the measured data variation. Method: For each fuzzy particle, particle swarm optimization (PSO) was used to select the best penalty and kernel function parameters to minimize the K-fold cross-validation (K-CV) error. With the optimized parameters, the prediction model was trained for the nonlinear prediction of fuzzy particles. Finally, the surface settlement trend in terms of Low, R, Up for the next construction stage can be predicted effectively based on the previous test data. Taking the surface settlement monitoring point D5-5 as an example, the error of the predicted range of surface settlement at D5-5 is 5.82%, 5.42%, and 8.0%, respectively Result: The error of other predicted points is also less than 10%, indicating the effectiveness of the prediction model. Compared with the numerical simulation results, the accuracy of the prediction model was further verified. Conclusion: Combined with the simulation method, the "simulation - prediction" patent scheme for monitoring the surface settlement of the Cross Passage is proposed in this paper. The research results indicate that the model proposed in this paper can easily and effectively predict the range and trend of changes in surface settlement, and is suitable for practical engineering applications.

Distributed photovoltaic short‐term power forecasting using hybrid competitive particle swarm optimization support vector machines based on spatial correlation analysis

AbstractIn order to further improve the accuracy of distributed photovoltaic (DPV) power prediction, this paper proposes a support vector machine (SVM) model based on hybrid competitive particle swarm optimization (HCPSO) with consideration of spatial correlation (SC), for realizing short‐term PV power prediction tasks. Firstly, the spatial correlation analysis is conducted on the distributed PV stations. The k‐means clustering method based on morphological similarity distance improvement and mutual information function is used to select the best reference station and the best delay, which generates strongly correlated PV sequences. Then, a hybrid algorithm of particle swarm optimization (PSO) and sine cosine algorithm (SCA) in a competitive framework (HCPSO) is proposed, aiming to fuse the fast convergence capability of PSO algorithm with the global search capability of SCA algorithm, while enabling the algorithm to effectively handle high‐dimensional optimization problems based on a competitive mechanism. Finally, the HCPSO algorithm is combined with SVM algorithm, which expands the applicable scenarios of the SVM model and effectively improves the accuracy of PV short‐term prediction.

Working condition perception for froth flotation based on NSCT multiscale features

Visual features at the froth surface of a flotation cell are closely related to the process condition and performance. Accurate identification of flotation conditions is crucial. However, the multi-scale properties of froth images are not all extracted based on existing perception methods. Considering that multi-scale frequency domain analysis can obtain more informative statistical froth features, a new working condition recognition based on nonsubsampled contourlet transform (NSCT) multiscale features is proposed and successfully validated by industrial froth images taken under different process conditions. NSCT was used to enhance the froth image, including the decomposition and reconstruction, contrast-limited adaptive histogram equalization (CLAHE), and multi-scale features of the enhanced image are extracted and analyzed. The fuzzy binarization method is adopted for extracting the morphological feature. Texture feature parameters are extracted from other high-frequency images. The particle swarm optimization support vector machine is used to recognize the working condition. The results indicate that the proposed algorithms, in particular the multiscale features classification based on NSCT, have the highest classification accuracy (93.33 %), and can accurately and reliably identify the working condition in the actual froth images.