Evolutionary Support Vector Machine Research Articles

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning.

Prediction of river suspended sediment load using machine learning models and geo-morphometric parameters

Estimating sediment load of rivers is one of the major problems in river engineering that has been using various data mining algorithms and variables. It is desirable to obtain accurate estimates of sediment load while using techniques that limit computational intensity when datasets are large. This study investigates the usefulness of geo-morphometric factors and machine learning (ML) models for predicting suspended sediment load (SSL) in several river basins in Lorestan and Gilan, Iran. Six ML models, namely, multiple linear regression (MLR), artificial neural networks (ANN), K-nearest neighbor (KNN), Gaussian processes (GP), support vector machines (SVM), and evolutionary support vector machines (ESVM), were evaluated for estimating minimum and average SSL for the study regions. Geo-morphometric parameters and river discharge data were utilized as the main predictors in modeling process. In addition, an attribute reduction technique was applied to decrease the algorithm complexity and computational resources used. The results showed that all models estimated both target variables well. However, the optimal models for predicting average sediment load and minimum sediment load were the GP and ESVM models, respectively.

A Reliability Evaluation Method for Intermittent Jointed Rock Slope Based on Evolutionary Support Vector Machine

A Reliability Evaluation Method for Intermittent Jointed Rock Slope Based on Evolutionary Support Vector Machine

Early Recognition and Discrimination of COVID-19 Severity Using Slime Mould Support Vector Machine for Medical Decision-Making

COVID-19 has spread rapidly across the world, leading to the insufficiency of medical resources in many regions. Early detection and identification of high-risk COVID-19 patients will contribute to early intervention and optimize medical resource allocation. Using the clinical data from the Affiliated Yueqing Hospital of Wenzhou Medical University (Yueqing, China), an evolutionary support vector machine model is designed to recognize and discriminate the severity of the COVID-19 by patients basic information and hematological indexes. The support vector machine is a frequently used pattern classification tool affected by both the kernel parameter setting and feature selection for its classification accuracy. This study recommends an enhanced Slime Mould Algorithm (ESMA), mixing a new movement strategy of white holes, black holes, and wormholes, to perform parameter optimization and feature selection simultaneously for SVM. Therefore, the proposed SVM framework (ESMA-SVM) can also obtain high-quality classification results, and it is less prone to stagnation in the classification process. To verify the capabilities of the proposed methodology, first, the performance of the ESMA is thoroughly verified by using IEEE CEC2017 benchmark functions and the diversity and compared with other similar methods experimentally using these standard benchmark functions. Moreover, the balance between diversification and intensification capability of the enhanced ESMA and the original SMA is also investigated statistically. Finally, the designed model ESMA-SVM and other competitive SVM models based on other optimization algorithms are applied to early recognition and discrimination of COVID-19 severity. Through the analysis of experimental results, the core compensations of ESMA are confirmed, and the ESMA-SVM can obtain strong performance in terms of several performance evaluation indexes on discrimination of COVID-19 severity.

An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks

Software-Defined Network (SDN) has become a promising network architecture in current days that provide network operators more control over the network infrastructure. The controller, also called as the operating system of the SDN, is responsible for running various network applications and maintaining several network services and functionalities. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential targets. Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to the Internet. As the control layer is vulnerable to DDoS attacks, the goal of this paper is to detect the attack traffic, by taking the centralized control aspect of SDN. Nowadays, in the field of SDN, various machine learning (ML) techniques are being deployed for detecting malicious traffic. Despite these works, choosing the relevant features and accurate classifiers for attack detection is an open question. For better detection accuracy, in this work, Support Vector Machine (SVM) is assisted by kernel principal component analysis (KPCA) with genetic algorithm (GA). In the proposed SVM model, KPCA is used for reducing the dimension of feature vectors, and GA is used for optimizing different SVM parameters. In order to reduce the noise caused by feature differences, an improved kernel function (N-RBF) is proposed. The experimental results show that compared to single-SVM, the proposed model achieves more accurate classification with better generalization. Moreover, the proposed model can be embedded within the controller to define security rules to prevent possible attacks by the attackers.

Automated Edge-based Segmentation and Evolutionary-SVM Algorithm of Fingertips Bones for Radiographs

Skeletal Bone is world-wide used to standard growth for prediction and assessment for children in endocrinology. A survey of the various papers and found these two methods used for BAA are GPs and TW2 (Greulich Pyle and Tanner Whitehouse) methods. Radiograph bone of the patient matched with SR (Standard Radiographs) using Graphics and consequences, determined in GP technique, whereas in TW2 technique scoring method is used for assessment for BA. The clinical practice depends on the level of maturity for 20*20 features including the features based on Epiphysis, Diaphysis and Metaphysis in radius features, Ulna, 1st and 3rd fingers and the carpal Bones, so it is difficult and time-consuming. The major problems are time-consuming to assess the bone using clinical methods and scanning phase challenging to predict the X-ray images. The research work has implemented a novel method using evolutionary support vector machine method to assess the bone age based on hand, wrist X-ray images, and resolve the issues in existing processes. To develop a filtration and optimized feature vector extraction and selection method to smooth the hand wrist X-ray images. To implement in-depth learning approach using ESVM to classify the assessment rate based on the X-ray Bone Images. After that evaluation of the performance metrics such as error rate, and Accuracy Rate and compared with the various methods. In the proposed work’s conclusion has accurately achieved value is 98.7% and existing method performance in 1-year assessment 98%, and 2 years 97.5 %.

Evolutionary support vector machine for RMB exchange rate forecasting

The volatility of exchange rate is very important to a country’s trading. Accurately forecasting exchange rate time series appears to be a challenging task for the scientific community on account of its nonstationary and nonlinear structural nature. In order to improve the performance of exchange rate forecasting, this study develops two evolutionary support vector regression models to forecast four typical RMB exchange rates (CNY against USD, EUR, JPY and GBP), and employs four evaluation criteria to assess the performance of out-of-sample exchange rate forecasting. In this study, the evolutionary algorithm optimizes the SVR parameters by balancing search between the global and local optima. However, the inputs of models are selected though phase space reconstruction method from historical data of exchange rate series. The empirical results demonstrate that our proposed evolutionary support vector regression outperforms all other benchmark models in terms of level forecasting accuracy, directional forecasting accuracy and statistical accuracy. As it turns out, our proposed evolutionary support vector regression is a promising approach for RMB exchange rate forecasting.

Risk-based maintenance strategy for deteriorating bridges using a hybrid computational intelligence technique: a case study

The current bridge inspection and maintenance protocol that is used in most countries focuses primarily on the visible aspects of bridge fitness and underestimates the invisible aspects, such as resistance to scouring and earthquake hazards. To help transportation authorities to better consider both aspects, the present study developed a new computational intelligence system, the so-called risk-based evaluation model for bridge life-cycle maintenance strategy (REMBMS). This model considers the three main risk factors of component deterioration, scouring and earthquakes in order to minimise the expected life-cycle cost of bridge maintenance. Monte Carlo simulation is used to estimate the probability of bridge maintenance. The evolutionary support vector machine inference model (ESIM) was applied to estimate the risk-related maintenance cost using historical data from the Taiwan Bridge Management System (TBMS) database. The time-influenced expected costs were obtained by multiplying each maintenance probability with its associated cost. Finally, the symbiotic organisms search (SOS) algorithm is used to identify the bridge maintenance schedule that optimises the life-cycle maintenance cost. The present study provides to bridge management authorities an effective approach for determining the optimal timing and budget for maintaining transportation bridges.

Evolutionary support vector machine for evapotranspiration estimation (case study: Haji Abad region, Hormozgan province)

Evolutionary support vector machine for evapotranspiration estimation (case study: Haji Abad region, Hormozgan province)

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

ABSTRACTNowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.

Influence of the number and location of design parameters in the aerodynamic shape optimization of a transonic aerofoil and a wing through evolutionary algorithms and support vector machines

ABSTRACTSurrogate-based optimization (SBO) has recently found widespread use in aerodynamic shape design owing to its promising potential to speed up the whole process by the use of a low-cost objective function evaluation, to reduce the required number of expensive computational fluid dynamics simulations. However, the application of these SBO methods for industrial configurations still faces several challenges. The most crucial challenge nowadays is the ‘curse of dimensionality’, the ability of surrogates to handle a high number of design parameters. This article presents an application study on how the number and location of design variables may affect the surrogate-based design process and aims to draw conclusions on their ability to provide optimal shapes in an efficient manner. To do so, an optimization framework based on the combined use of a surrogate modelling technique (support vector machines for regression), an evolutionary algorithm and a volumetric non-uniform rational B-splines parameterization are applied to the shape optimization of a two-dimensional aerofoil (RAE 2822) and a three-dimensional wing (DPW) in transonic flow conditions.

Stego anomaly detection in images exploiting the curvelet higher order statistics using evolutionary support vector machine

Steganalysis is an important extension to existing security infrastructure, and is gaining more research focus of forensic investigators and information security researchers. This paper reports the design principles and evaluation results of a new experimental blind image steganalysing system. This work approaches the steganalysis task as a pattern classification problem. The detection accuracy of the steganalyser depends on the selection of low-dimensional informative features. We investigate this problem as a three step process and propose a novel steganalyser with the following implications: a) Selection of the Curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, than other conventional wavelet transforms. b) Exploiting the empirical moments of the transformation as effective steganalytic features c) Realizing the system using an efficient classifier, evolutionary-Support Vector Machine (SVM) model that provides promising classification rate. An extensive empirical evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.

Stock market prediction using evolutionary support vector machines: an application to the ASE20 index

The main motivation for this paper is to introduce a novel hybrid method for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use an alternative computational methodology named evolutionary support vector machine (ESVM) stock predictor for modeling and trading the ASE20 Greek stock index extending the universe of the examined inputs to include autoregressive inputs and moving averages of the ASE20 index and other four financial indices. The proposed hybrid method consists of a combination of genetic algorithms with support vector machines modified to uncover effective short-term trading models and overcome the limitations of existing methods. For comparison purposes, the trading performance of the ESVM stock predictor is benchmarked with four traditional strategies (a naïve strategy, a buy and hold strategy, a moving average convergence/divergence and an autoregressive moving average model), and a multilayer perceptron neural network model. As it turns out, the proposed methodology produces a higher trading performance, even during the financial crisis period, in terms of annualized return and information ratio, while providing information about the relationship between the ASE20 index and DAX30, NIKKEI225, FTSE100 and S&P500 indices.

PREDICTING PRODUCTIVITY LOSS CAUSED BY CHANGE ORDERS USING THE EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL

Change orders in construction projects are very common and result in negative impacts on various project facets. The impact of change orders on labor productivity is particularly difficult to quantify. Traditional approaches are inadequate to calculate the complex input-output relationship necessary to measure the effect of change orders. This study develops the Evolutionary Fuzzy Support Vector Machines Inference Model (EFSIM) to more accurately predict change-order-related productivity losses. The EFSIM is an AI-based tool that combines fuzzy logic (FL), support vector machine (SVM), and fast messy genetic algorithm (fmGA). The SVM is utilized as a supervised learning technique to solve classification and regression problems; the FL is used to quantify vagueness and uncertainty; and the fmGA is applied to optimize model parameters. A case study is presented to demonstrate and validate EFSIM performance. Simulation results and our validation against previous studies demonstrate that the EFSIM predicts the impact of change orders significantly better than other AI-based tools including the artificial neural network (ANN), support vector machine (SVM), and evolutionary support vector machine inference model (ESIM).

Research of Financial Early-warning Model for the Listed Electric Power Companies on Evolutionary Support Vector Machines Based on Genetic Algorithms

The development of the electric power enterprise concerns the national economic lifeline. In this paper, the Support Vector Machines (SVMs) early-warning model which is based on Genetic Algorithm (GA) optimization is established, with GA’s ameliorating SVMs. Using the penalty parameters and the kernel parameters of the process of GA’s optimizing SVMs, this paper gives full play to the global searching ability of GA and overcomes the problems generated from the selection of the SVMs model parameters. As a result, it is possible to initiate the financial risk analysis of the electric power enterprises and enable them to take timely measures to deal with issues that have emerged during the process of their development. It is displayed in the instance verification results of the listed companies in the electric power industry that SVMs which are based on GA optimization can predict the financial risks of the listed companies in the electric power industry accurately and effectively.

Reactive Power Planning for Maximum Load Margin Improvement Using Fast Artificial Immune Support Vector Machine (FAISVM)

Load margin improvement is an important issue in power system planning and operation. This paper, first, presents a newly voltage stability index called Voltage Stability Condition Indicator (VSCI) to evaluate the voltage stability state of load buses in a system. It also proposes a fast optimization algorithm for reactive power planning problem (RPP) through Fast Artificial Immune Support Vector Machine (FAISVM). FAISVM is a hybrid algorithm that incorporates the application of Artificial Immune System (AIS) and Support Vector Machine (SVM) in solving RPP problems. The newly proposed algorithm can determine the optimal tap settings of tap changing transformers, the value of reactive power injection at the reactive power sources and the injection at the reactive power generator buses. The performances of the techniques proposed were verified using the IEEE 30-bus test system and compared with another newly developed hybrid Evolutionary Support Vector Machine (ESVM). The simulation results have shown that FASIVM outperformed ESVM in terms of maximum load margin improvement and computation time significantly and also reduce active power losses.

Seismic Assessment of Bridge Diagnostic in Taiwan Using the Evolutionary Support Vector Machine Inference Model (ESIM)

Because earthquakes cause bridge damage resulting in incidents such as traffic gridlock and casualties, it is necessary to assess possible bridge damage under different seismic intensity levels in order to reduce the incidence of disasters. However, because there are many bridges in Taiwan, the time and budget will be restricted to conduct traditional structural analysis (preliminary assessment, detailed analysis) of each bridge to obtain its yield acceleration (Ay) and collapse acceleration (Ac). Hence, this study developed an inference model by integrating two AI techniques: support vector machines (SVM) and fast messy genetic algorithms (fmGA). The study applied historical cases to infer Ay and Ac values by the mapping relation between preliminary assessment factors (input) of historical cases and the detailed assessment of Ay and Ac values (output). According to the above inference model to predict Ay and Ac values, the probability of possible bridge damage by earthquakes can be predicted as a suggestion for bridge management personnel in a short period of time. This study adopted 121 RC bridges in Taiwan and selected 109 bridges for training cases and 12 bridges for testing cases to calculate the root mean square error (RMSE). The results indicate that the RMSE of training is 0.087 and of testing is 0.0869.

Global and decomposition evolutionary support vector machine approaches for time series forecasting

Multi-step ahead time series forecasting (TSF) is a key tool for supporting tactical decisions (e.g., planning resources). Recently, the support vector machine (SVM) emerged as a natural solution for TSF due to its nonlinear learning capabilities. This paper presents two novel evolutionary SVM (ESVM) methods for multi-step TSF. Both methods are based on an estimation distribution algorithm search engine that automatically performs a simultaneous variable (number of inputs) and model (hyperparameters) selection. The global ESVM (GESVM) uses all past patterns to fit the SVM, while the decomposition ESVM (DESVM) separates the series into trended and stationary effects, using a distinct ESVM to forecast each effect and then summing both predictions into a single response. Several experiments were held, using six time series. The proposed approaches were analyzed under two criteria and compared against a recent evolutionary artificial neural network (EANN) and two classical forecasting methods, Holt–Winters and autoregressive integrated moving average. Overall, the DESVM and GESVM obtained competitive and high-quality results. Furthermore, both ESVM approaches consume much less computational effort when compared with EANN.

Adaptation of Evolutionary Algorithms for Decision Making on Building Construction Engineering (TSP Problem)

Abstract The report revolve on building construction engineering and management, in which there are a lot of requirements such as well supervision and accuracy and being in position to forecast uncertainties that may arise and mechanisms to solve them. It also focuses on the way the building and construction can minimise the cost of building and wastages of materials. The project will be based of heuristic methods of Artificial Intelligence (AI). There are various evolution methods, but report focus on two experiments Pattern Recognition and Travelling Salesman Problem (TSP). The Pattern Recognition focuses Evolutionary Support Vector Machine Inference System for Construction Management. The construction is very dynamic are has a lot of uncertainties, no exact data this implies that the inference should change according to the environment so that it can fit the reality, therefore there a need of Support Vector Machine Inference System to solve these problems. TSP focus on reducing cost of building construction engineering and also reduces material wastages, through its principals of finding the minimum cost path of the salesman.

TIME DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL FOR PREDICTING DIAPHRAGM WALL DEFLECTION

Brace diaphragm walls are commonly used in underground structures in metropolitan areas, where avoiding costly damage to adjacent infrastructure / buildings is critical to project success. It is necessary to make accurate diaphragm wall deflection predictions to ensure actual deflection falls within allowable limits, and thus ensure the safety of both the project and adjacent structures. Numer- ous studies and approaches, such as empirical, semi-empirical as well as numerical approaches, have addressed excavation-induced deflection in diaphragm walls. Ar- tificial intelligence (AI) has been used recently by several researchers to improve diaphragm wall deflection prediction capabilities. This paper proposes a hybrid ar- tificial intelligence system, namely the evolutionary fuzzy support vector machine inference model for time series data (EFSIMT ), to predict diaphragm wall deflection in deep excavation through the application of historical project data. Simulations were performed on 1,083 instances, segregated into a total of 988 training data sets and 95 test data sets. Validation results show that the EFSIMT achieves higher performance in comparison with Artificial Neural Networks and the Evolutionary Support Vector Machine Inference Model (ESIM). Therefore, EFSIMT has great potential as a predictive tool for diaphragm wall deflection problems and assisting project managers/engineers to ensure safety during the construction process.