Parameters Of Support Vector Machine Research Articles

Research and Application of Power Grid Maintenance Scheduling Strategy under the Interactive Mode of New Energy and Electrolytic Aluminum Load

Formulating a reasonable and feasible unit maintenance scheme is a promising way to eliminate potential risks and improve the reliability of power systems. However, the uncertainty and volatility of new energy outputs, such as wind power, increase the difficulty of scheme formulation. To overcome the complexity of uncertainty, a robust unit maintenance scheme considering the uncertainty of new energy output and electrolytic aluminum load is established in this paper. Considering the significant time-series characteristics of new energy, this paper first introduces the definition and mathematical model of information granulation (IG), through which the initial new energy output data can be transformed into fuzzy particles used for prediction and analysis. Moreover, a support-vector machine (SVM) regression prediction model is adopted, and a corresponding progressive search algorithm is designed to determine SVM parameters efficiently. Then, a robust unit maintenance model is established considering the upper and lower predicted error. In addition, electrolytic aluminum loads are allowed to participate in power system dispatch. Finally, the modified reliability test system–Grid Modernization Lab Consortium (RTS–GMLC test system) and an actual power grid in Southwest China are used to verify the accuracy and feasibility of the proposed method.

Effective predictive modelling for coronary artery diseases using support vector machine

Coronary artery disease (CAD) is a category of cardiovascular disease that causes the highest mortality rate in the world. CAD occurs due to plaque build-up on the walls of the arteries that supply blood to the heart and other organs of the body. To control the mortality rate, a practical model that is capable of predicting CAD is needed. Machine learning approaches have been used in solving various problems in various domains, including biomedicine. However, real-world data often has an unbalanced class distribution that can interfere with classifier performance. In addition, data has many features to process. This study focuses on effective modeling capable of predicting CAD using feature selection to handle high dimensional data and feature resampling to handle unbalanced data. Feature selection is very effective by eliminating irrelevant features from the training data. Hyperparameter tuning is also done to find the best combination of parameters in support vector machines (SVM). Our results show that the SVM cross-validated ten times has a more accurate training result. Furthermore, the grid search on SVM cross-validated ten times had more accurate training model results and achieved 88% accuracy on the test data.

Bayesian Optimization-Based Gradient Boosting Method of Fault Detection in Oil-Immersed Transformer and Reactors

This article aims at developing a gradient boosting (GB) model with parameters optimized by the Bayesian optimization (BO) method for detecting various types of fault in an oil-immersed transformer and reactor. The developed model depicts higher accuracy in comparison with other machine learning (ML) techniques, such as XGBoost, AdaBoost, random forest, and support vector machine parameters being optimized by BO. A comparison of the skill score depicts the superiority of the developed Bayesian optimization-based gradient boosting (BO-GB) model with respect to Bayesian optimized other ML techniques. Further comparison of the model with conventional Duval pentagon verifies the accuracy of detecting the faults, which were missed by the former. Also, the software has been developed based on the proposed BO-GB method for the on-site detection of the fault. In addition, actual values of dissolved gases with respect to fault confirmed after the internal inspection of the transformer and reactor are tabulated and compared. The data so presented will help in utilizing the authentic dissolved gas data in addition to the already available IEC TC10 databases for future works. The study undertaken in this article will help utilities in diagnosing the incipient faults more accurately and precisely, thereby preventing hazardous operating conditions along with minimizing the downtime cost.

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

As vital equipment in high-speed train power supply systems, the failure of onboard traction transformers affect the safe and stable operation of the trains. To diagnose faults in onboard traction transformers, this paper proposes a hybrid optimization method based on quickly and accurately using support vector machines (SVMs) as fault diagnosis systems for onboard traction transformers, which can accurately locate and analyze faults. Considering the limitations of traditional transformers for identifying faults, this study used kernel principal component analysis (KPCA) to analyze the feature quantity of dissolved gas analysis (DGA) data, electrical test data, and oil quality test data. The improved seagull optimization algorithm (ISOA) was used to optimize the SVM, and a Henon chaotic map was introduced to initialize the population. Combined with differential evolution (DE) based on the adaptive formula, the foraging formula of the seagull optimization algorithm (SOA) was improved to increase the diversity of the algorithm and enhance its ability to find the optimal parameters of SVM, which made the simulation results more accurate. Finally, the KPCA–ADESOA–SVM model was constructed and applied to fault diagnosis for the traction transformer. The example analysis compared the diagnosis results of the proposed diagnosis model with those of the traditional diagnosis model, showing further optimization of the feature quantity and improvements in the diagnosis accuracy. This proves that the proposed diagnosis model has high generalization performance and can effectively increase the fault diagnosis accuracy and speed of traction transformers.

A fusion of dolphin swarm optimization and improved sine cosine algorithm for automatic detection and classification of objects from surveillance videos

Surveillance is the utmost extensively used technology in the current scenario. In real-time, it is immensely applicable in all domains to monitor, identifying the moving objects, and tracking through computer vision. The object detection and classification is an important process in surveillance video. During this task, the visual appearance will change according to the viewing angles, lightening, and distance from the camera. It is necessary to improve the efficiency in real-time detection and classification of objects from surveillance videos. To obtain this, we proposed a fusion of Dolphin Swarm Optimization (DSO) and improved Sine Cosine algorithm (ISCA) based Support Vector Machine (SVM) classifier which includes the following steps: Frame differencing for foreground segmentation, Histogram of Oriented Gradients (HOG) for feature extraction, and DSO-ISCA-SVM classifier for classification. Initially, the surveillance videos are collected and the acquisition of images from the surveillance video camera. Secondly, the moving objects are detected by frame differencing in which the difference between two frames are estimated and compared with the threshold value. Then the shadow and noise are removed. Thirdly, the HOG capture local shapes through gradients. Finally, the proposed DSO-ISCA-SVM classifier accurately classifies the objects from the surveillance video, the DSO-ISCA is used to find the SVM parameters. This proposed technique effectively detects and classify the objects from the surveillance videos. The proposed technique results are compared with other existing methods. The experimental results prove that the proposed method efficiency is better than the existing methods in terms of different evaluation metrics.

A novel two‐stage Dissolved Gas Analysis fault diagnosis system based semi‐supervised learning

Dissolved Gas Analysis (DGA) is an important method for oil-immersed transformer fault diagnosis. However, collecting labelled DGA data is difficult because the determination of the transformer fault is time-consuming and expensive in the transformer substation, but DGA data without labels is easier to obtain. Therefore, the paper proposed a semi-supervised two-stage diagnostic system based DGA by using less labelled samples. The two-stage system includes a novel semi-supervised feature selection based Genetic Algorithm (GA) and Support Vector Machine (SVM) model (SSL-FS-GASVM) for selecting optimal features and a novel semi-supervised transformer fault diagnosis model based improved Artificial Fish Swarm Algorithm (AFSA) and SVM (SSL-IAFSA-SVM) for optimising the SVM parameter. Finally, the performances of SSL-FS-GASVM and SSL-IAFSA-SVM models are tested and compared with traditional supervised diagnostic models combined with other optimisation methods, respectively. The results show that the proposed two-stage system works in optimising features and parameters and has strong robustness in solving small sample classification problems.

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.

Evolutionary inspired approach for mental stress detection using EEG signal

Stress is a pensive issue in our competitive world and it has a huge impact on physical and mental health. Severe health issues may arise due to long exposure of stress. Hence, its timed detection can be helpful in managing stress periods. In this regard, electroencephalogram (EEG) based techniques have been widely explored, as stress severely impact the functioning and structure of brain. These non-invasive methods for stress detection need improvement in terms of predictive accuracy and reliability. In this work, a novel approach for stress detection has been presented using short duration of EEG signal. Entropy based features were extracted from EEG signal decomposed using stationary wavelet transform. Selected features were used for classification using different supervised machine learning algorithms. Further, different evolutionary inspired approaches were deployed to optimize the parameters of support vector machines (SVM) and perform feature weighting, simultaneously. SVM optimized using whale optimization algorithm resulted in an accuracy of 97.2559%. Accurate detection using short duration EEG signal shows potential of this technique for timed and reliable detection of stress.

Partial discharge fault diagnosis based on zernike moment and improved bacterial foraging optimization algorithm

Feature extraction and classifier optimization are important parts in the diagnosis of partial discharge (PD). Traditional feature extraction methods generally target not the images but structured data. Traditional classifier optimization methods have many disadvantages such as the many parameters that need to be adjusted, difficulty in determining the best parameters, and the ease in falling into local optimal solutions. To eliminate these defects, a PD diagnosis method of gas-insulated switchgear (GIS) based on the Zernike moment and improved support vector machine (SVM) is proposed. Firstly, five typical PD models of corona discharge, metal particle discharge, floating electrode discharge, void discharge, and surface discharge were designed. Then, the UHF sensor was used to collect discharge signals of different defects and different types of interference signals to build a PD phase-resolved pulse sequence (PRPS) patterns library. After that, the Zernike moment of the PRPS images were extracted as the PD features. Finally, the SVM model based on the differential evolution adaptive bacterial foraging optimization (DEABFO) algorithm was used to fulfill the GIS PD diagnosis. The results show that the Zernike moment feature extraction method is suitable for PD PRPS images, and DEABFO improves the convergence speed of the SVM parameter optimization process and can effectively avoid the probability of falling into the local optimum. Compared with other image feature extraction methods and other SVM optimization algorithms, this method has high accuracy in GIS PD fault diagnosis, with the recognition rate reaching 91.23%.

Enhance Teaching-Learning-Based Optimization for Tsallis-Entropy-Based Feature Selection Classification Approach

Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector Machine (SVM) is adopted as the classifier for classification purposes in this paper. We proposed an enhanced Teaching-Learning-Based Optimization (ETLBO) to optimize the SVM and Tsallis entropy parameters to improve classification accuracy. The adaptive weight strategy and Kent chaotic map are used to enhance the optimal ability of the traditional TLBO. The proposed method aims to avoid the main weaknesses of the original TLBO, which is trapped in local optimal and unbalance between the search mechanisms. Experiments based on 16 classical datasets are selected to test the performance of the ETLBO, and the results are compared with other well-established optimization algorithms. The obtained results illustrate that the proposed method has better performance in classification accuracy.

Research on ultrasonic defect identification method of well control manifold pipeline based on IAFSA-SVM

In view of the classification of corrosion defects of well controlled manifold pipelines, an ultrasonic defect recognition method based on the combination of support vector machine(SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed. Firstly, perform wavelet packet decomposition on the ultrasonic defect signal waveform to obtain the characteristic vector of characterizes the defect type; Then establish the support vector machine defect classification model, and use the improved artificial fish swarm algorithm to optimize the support vector machine parameters. Finally, a software and hardware experimental platform for the classification of pipeline corrosion defects of the well control manifold is built to carry out software simulation and experimental analysis. The experimental results show that the recognition rate of the defect classification model based on improved artificial fish swarm optimization support vector machine parameters is 94.67% for ultrasonic defect signals at different depths.

A Transformer Fault Diagnosis Based on Improved Squirrel Search Algorithm and Support Vector Machine

An improved Squirrel Search Algorithm (ISSA) is proposed to overcome the shortcoming of Support Vector Machine (SVM) parameters is difficult to select. Firstly, an adaptive probability formula is used to balance the exploration and foraging ability of SSA, so as to improve the optimization performance of SSA. In order to test the optimization performance of ISSA, two test functions are used. And results show that ISSA has better optimization performance than SSA. Finally, based on ISSA-SVM and SVM, and 3-fold cross-Validation is used, the fault diagnosis of DGA data is carried out. The diagnostic results show that ISSA-SVM has the highest diagnosis performance.

Signal-Noise Identification for Wide Field Electromagnetic Method Data Using Multi-Domain Features and IGWO-SVM

Noise tends to limit the quality of wide field electromagnetic method (WFEM) data and exploration results. The existing WFEM denoising methods lack the signal identification process and are only able to filter or eliminate abnormalities in the time or frequency domain, which easily leads to the loss of more abundant real data and to low data quality. Thus, we built the WFEM data sample library to extract the multi-domain features. Then, neighborhood search and location sharing were used to improve the grey wolf optimizer (IGWO) algorithm. The support vector machine (SVM) parameters were optimized by IGWO to train multi-domain features, and an IGWO-SVM data model was generated. We used the data model to quantitatively test the WFEM signal and noise in the simulation and measured data. This method can effectively identify the WFEM signal and noise, eliminate the identified noise, and use the identified signal to reconstruct the effective data. Finally, the digital coherence technique was used to extract the spectrum amplitude of the effective frequency points. The experiments demonstrated the advantage of the convergence of IGWO algorithms and the comparison of the SVM parameters optimization techniques. The proposed method can quickly and effectively search the optimal SVM parameters, significantly improve the identification effect of WFEM signal noise, and completely remove the abnormal noise waveform in the reconstructed data. The more stable electric field curves in the results verify the effectiveness of the algorithm design and optimized identification method.

Optimasi SVM menggunakan algoritme grid search untuk identifikasi citra biji kopi robusta berdasarkan circularity dan eccentricity

Coffee variety is one of the main factors affecting the quality and price of coffee, so it is important to recognize coffee varieties. This study aims to optimize the recognition of robusta coffee beans based on circularity and eccentricity image features using a support vector machine (SVM) and Grid search algorithm. The methods used included image acquisition, preprocessing, feature extraction, classification, and evaluation. Circularity and eccentricity are used in the feature extraction process, while the grid search algorithm is used to optimize SVM parameters in the classification process for four different kernels. This study produced the best classification model with the highest accuracy of 94% for the RBF and Polynomial kernels.

Analisis Sentimen Tweet Tentang UU Cipta Kerja Menggunakan Algoritma SVM Berbasis PSO

Support Vector Machine (SVM) is one of the most widely used classification algorithms for sentiment analysis and has been shown to provide satisfactory performance. However, despite its advantages, the SVM algorithm still has weaknesses in selecting the right SVM parameters to optimize the performance. In this study, sentiment analysis was done with the use of data called tweets about Undang-Undang Cipta Kerja which reap many pros and cons by the people in Indonesia, especially the laborers. The classification method used in this study is the Support Vector Machine algorithm which is optimized using the Particle Swarm Optimization method for the SVM parameters selection in the hope of optimizing the performance generated by the SVM algorithm in sentiment analysis. The results of the study using 10 k-fold cross-validations using the SVM algorithm resulted in an accuracy of 92,99%, a precision of 93,24%, and a recall of 93%. Meanwhile, the SVM and PSO algorithms produce an accuracy of 95%, precision of 95,08%, and recall of 94,97%. The results show that the Particle Swarm Optimization method can overcome the weaknesses of the Support Vector Machine algorithm in the problem of parameter selection and has succeeded in improving the resulting performance where the SVM-PSO is more superior to SVM without optimization in sentiment analysis.

Multiple birth support vector machine based on dynamic quantum particle swarm optimization algorithm

At present, the parameters of the multiple birth support vector machine (MBSVM) are mainly determined by experience or artificially specified by the grid method. Both of these methods rely too much on the experience value, which is easy to cause the selection of the parameter value to be insufficient. The selection of parameters directly affects the performance of MBSVM, so how to find a set of optimal parameters plays a vital role in improving the performance of MBSVM. To solve this problem, a dynamic quantum particle swarm optimization algorithm (DQPSO) was proposed. Focusing on the contraction – expansion (CE) coefficient control mode, authors propose the concept of the ability factor of particle search and realize the dynamic regulation of CE coefficient by taking it as feedback. Then DQPSO algorithm is used to optimize the parameters of MBSVM. The experimental results show that the proposed DQPSO algorithm has better optimization performance and faster convergence speed than that of the classical QPSO. Meanwhile, experiments on the UCI datasets show that the proposed DQPSO-MBSVM algorithm is effective to improve the classification performance of MBSVM.

Face Recognition Method based on Support Vector Machine and Rain Optimization Algorithm (ROA)

One basic study direction in pattern recognition research domain is Face recognition. Face recognition-based Authentication is used widely. Face recognition is related to non-linear issue; therefore, some techniques of artificial intelligence have been used in last few years to face recognition. According to recent results, support vector system classifiers (SVM) have excellent face recognition accuracy in pattern recognition in comparison with other classification methods. Although, support vector machine training parameters selection has great effect on the performance of support vector machine. Here in the research, the novel Rain optimization algorithm and support vector machine algorithm (ROA-SVM)-based method of face recognition is provided. In ROA-SVM, Rain optimization algorithm is applied for optimizing SVM parameters at the same time. The average classification accuracy for the YALE dataset in the proposed method was 86% and for the base paper was 81%. Furthermore, in order to find optimal parameters in support vector machine, proposed ROA-SVM method efficiency has been improved by 5 percent in comparison with PSO-SVM basic research.

CLASSIFICATION OF TRANSLATIONAL LANDSLIDE ACTIVITY USING VEGETATION ANOMALIES INDICATOR (VAI) IN KUNDASANG, SABAH

Abstract. This paper introduced a novel method of landslide activity mapping using vegetation anomalies indicators (VAIs) obtained from high resolution remotely sensed data. The study area was located in a tectonically active area of Kundasang, Sabah, Malaysia. High resolution remotely sensed data were used to assist manual landslide inventory process and production on VAIs. The inventory process identified 33, 139, and 31 of active, dormant, and relict landslides, respectively. Landslide inventory map were randomly divided into two groups for training (70%) and validation (30%) datasets. Overall, 7 group of VAIs were derived including (i) tree height irregularities; (ii) tree canopy gap; (iii) density of different layer of vegetation; (iv) vegetation type distribution; (v) vegetation indices (VIs); (vi) root strength index (RSI); and (vii) distribution of water-loving trees. The VAIs were used as the feature layer input of the classification process with landslide activity as the target results. The landslide activity of the study area was classified using support vector machine (SVM) approach. SVM parameter optimization was applied by using Grid Search (GS) and Genetic Algorithm (GA) techniques. The results showed that the overall accuracy of the validation dataset is between 61.4–86%, and kappa is between 0.335–0.769 for deep-seated translational landslide. SVM RBF-GS with 0.5m spatial resolution produced highest overall accuracy and kappa values. Also, the overall accuracy of the validation dataset for shallow translational is between 49.8–71.3%, and kappa is between 0.243–0.563 where SVM RBF-GS with 0.5m resolution recorded the best result. In conclusion, this study provides a novel framework in utilizing high resolution remote sensing to support labour intensive process of landslide inventory. The nature-based vegetation anomalies indicators have been proved to be reliable for landslide activity identification in Malaysia.

Improving generalisation capability of artificial intelligence-based solar radiation estimator models using a bio-inspired optimisation algorithm and multi-model approach.

One way of reducing environmental pollution is to reduce our dependence on fossil fuels by replacing them with solar radiation (Rs), which is one of the main sources of clean and renewable energy. In this study, daily Rs values at seven meteorological stations in Iran (Ahvaz, Isfahan, Kermanshah, Mashhad, Bandar Abbas, Kerman and Tabriz) over 2010-2019 were estimated using empirical models, support vector machine (SVM), SVM coupled with cuckoo search algorithm (SVM-CSA) and multi-model approach in the form of two structures. In structure 1, data from each station were divided into training and testing sets. In structure 2, data from the former four stations were used for model training, and those from the latter three stations were used to test the models. The results showed that using meteorological parameters improved estimation accuracy compared with the use of geographical parameters for both SVM and SVM-CSA models. Coupling the CSA to SVM did improve the accuracy of radiation estimates, reducing RMSE by up to 38% (Kermanshah station) and 36% (Tabriz station) for the first structure and about 42.4% (Tabriz station) for the second. Performance analysis of the models over three intervals including, the first, middle and last third of measured radiation values at each station showed that for both structures (except at Tabriz station), the best model performance in under- and over-estimation sets of radiation values was obtained, respectively, in the first third interval (first structure, Mashhad station, RMSE = 28.39J.cm-2.day-1) and the last third interval (first structure, Bandar Abbas station, RMSE = 12.23J.cm-2.day-1). Determining the effects of climate change on Rs estimation and using remotely sensed data as inputs of the models could be considered as future works.

A Predict Method of Water Pump Operating State Based on Improved Particle Swarm Optimization of Support Vector Machine

In order to improve prediction accuracy of water pump operating state, a chaotic prediction model of the pump vibration data based on improved particle swarm optimization of support vector machine is proposed in this paper. Firstly, a grouping optimization strategy particle swarm algorithm based on cosine function is proposed. Then, the training set is obtained on the time series of vibration data by phase space reconstruction. Secondly, The improved particle algorithm is used to optimize the penalty parameters, insensitive loss coefficient and width parameters of support vector machine. Then, a prediction model of vibration data is established by using support vector machine combined with training set and optimal parameters. Finally, the operating state of the pump is predicted according to pump vibration measurement and evaluation method. Compared with the method of linear decreasing weight strategy, the method proposed in this paper is more accurately.