Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill

2020 ◽  
Author(s):  
Enming Li ◽  
Jian Zhou ◽  
Xiuzhi Shi ◽  
Danial Jahed Armaghani ◽  
Zhi Yu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Support Vector ◽  
Cemented Paste Backfill ◽  
Fiber Reinforced ◽  
Salp Swarm Algorithm ◽  
Paste Backfill ◽  
Swarm Algorithm

scholarly journals A novel salp swarm clustering algorithm for prediction of the heart diseases

2022 ◽  
Vol 25 (1) ◽  
pp. 265
Author(s):  
Nitesh Sureja ◽  
Bharat Chawda ◽  
Avani Vasant
Keyword(s):  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Medical Information ◽  
Heart Diseases ◽  
Human Life ◽  
Support Vector ◽  
Medical Practitioners ◽  
Disease Clustering ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm

Heart <span>diseases have a severe impact on human life and health. Cardiovascular deaths and diseases have increased at a fast rate worldwide. The early prediction of these diseases is necessary to prevent deaths. Now a day; a considerable amount of medical information is available and collected as databases. An efficient technique is required to analyse this data and predict the disease. Clustering can help medical practitioners in diagnosis by classifying the patient’s data collected for a disease. Clustering techniques can analyse such data based on each patient-generated and predict disease. A new prediction model based on salp swarm algorithm and support vector machine is proposed in this research for predicting heart diseases. Salp swarm algorithm is used to select the useful features from the database. Support vector machine classifier is used to predict heart diseases. Results obtained are compared with the other algorithms available in the literature. It is observed that the proposed approach produces better results with accuracy 98.75% and 98.46% with the dataset 1 and 2, respectively. In addition to this, the algorithm converges in significantly less time in comparison to other algorithms. This algorithm might become a perfect supporting tool for medical </span>practitioners.

scholarly journals Short-term wind power prediction based on hybrid variational mode decomposition and least squares support vector machine optimized by improved salp swarm algorithm model

2021 ◽  
Vol 2125 (1) ◽  
pp. 012012
Author(s):  
Zhongde Su ◽  
Huacai Lu
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Power Prediction ◽  
Short Term ◽  
Salp Swarm Algorithm ◽  
Mode Decomposition ◽  
Wind Power Prediction ◽  
Swarm Algorithm

Abstract To improve the accuracy of wind power prediction, a short-term wind power prediction model based on variational mode decomposition (VMD) and improved salp swarm algorithm (ISSA) optimized least squares support vector machine (LSSVM) is proposed. In the model, the variational modal decomposition is used to decompose the wind power sequence into multiple eigenmode components with limited bandwidth. The improved salp swarm algorithm is employed to tune the regularization parameter and kernel parameter in LSSVM. The proposed wind power prediction strategy using mean one-hour historical wind power data collected from a wind farm located in zhejiang, China. Compared with other prediction models illustrate the better prediction performance of VMD-ISSA-LSSVM.

scholarly journals A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 27789-27801 ◽  
Author(s):  
Hongxin Xue ◽  
Yanping Bai ◽  
Hongping Hu ◽  
Ting Xu ◽  
Haijian Liang
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Support Vector ◽  
Classification Problems ◽  
Model Based

scholarly journals An Improved Hybrid Model for Early Prediction of Autism

2020 ◽  
Vol 8 (5) ◽  
pp. 4358-4361
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Intellectual Disabilities ◽  
Hybrid Model ◽  
Support Vector ◽  
Early Prediction ◽  
Creative Mind ◽  
Social Collaboration

Autism is described by extreme, unavoidable intellectual disabilities which are adverse on perspectives related with social collaboration, correspondence, creative mind and conduct. Treating Autism has secured an exceptional spot, as a few heuristic and measurable models are proposed by scientists working around there. Henceforth kids influenced with such issue should be upheld with recognition of an early, well-planned and singular scholarly endeavours created in adjusted settings bringing about early location and accurately diagnose the issues of Autism. Requirements of Data mining and soft computational methodologies are thought as a characteristic qualified for finding confounded examples. The paper defines a definite investigation and proposes the hybrid improved methodologies of Bee Hive Optimization with Support Vector Machine for the requirement of versatile and early prediction of Autism among developing youngsters with more Accuracy and with the less error and time.

A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams

2020 ◽  
pp. 147592172096715
Author(s):  
Mengyue He ◽  
Yishou Wang ◽  
Karthik Ram Ramakrishnan ◽  
Zhifang Zhang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Polymer Composites ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Fiber Reinforced Polymer ◽  
Support Vector ◽  
Fiber Reinforced ◽  
Reinforced Polymer ◽  
Fiber Reinforced Polymer Composites

Structural health monitoring techniques based on vibration parameters have been used to assess the internal delamination damage of fiber-reinforced polymer composites. Recently, machine learning algorithms have been adopted to solve the inverse problem of predicting delamination parameters of the delamination from natural frequency shifts. In this article, a delamination detection methodology is proposed based on the changes in multiple modes of frequencies to assess the interface, location, and size of delamination in fiber-reinforced polymer composites. Three types of machine learning algorithms including back propagation neural network, extreme learning machine, and support vector machine algorithm were adopted as inverse algorithms for assessment of the delamination parameters, with a special focus on the interface prediction. A theoretical model of fiber-reinforced polymer beam with delamination under vibration was constructed to learn how the frequencies are affected by the delaminations (“forward problem”) and to generate a database of “frequency shifts versus delamination parameters” to be used in machine learning algorithms for delamination prediction (“inverse problem”). Multiple carbon/epoxy fiber-reinforced polymer beam specimens were manufactured and measured by a laser scanning Doppler vibrometer to extract the modal frequencies. Numerical and experimental verification results have shown that support vector machine has the best prediction performance among the three machine learning algorithms, with high prediction accuracy and only requiring a small number of samples. For predicting the interface of delamination which is a discrete variable, support vector machine classification has observed better prediction accuracy and requiring less running time than regression. This study is one of the first to prove the applicability of support vector machine for structural health monitoring of delamination damage in fiber-reinforced polymer composites and has the potential to improve the prediction capability of machine learning algorithms. Another significant outcome of the study is that the interface of delamination has been predicted accurately with support vector machine.

scholarly journals Support Vector Machine and Neural Network based Model for Monthly Stream Flow Forecasting

2018 ◽  
Vol 7 (4.35) ◽  
pp. 683
Author(s):  
Nuratiah Zaini ◽  
Marlinda Abdul Malek ◽  
Marina Yusoff ◽  
Siti Fatimah Che Osmi ◽  
Nurul Hani Mardi ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Hybrid Model ◽  
Peninsular Malaysia ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Design Development ◽  
Streamflow Forecasting ◽  
Monthly Streamflow ◽  
Bpnn Model

Accurate forecasting of streamflow is desired in many water resources planning and management, flood prevention and design development. In this study, the accuracy of two hybrid model, support vector machine - particle swarm optimization (SVM-PSO) and bat algorithm – backpropagation neural network (BA-BPNN) for monthly streamflow forecasting at Kuantan River located in Peninsular Malaysia are investigated and compared to regular SVM and BPNN model. Heuristic optimization namely PSO and BA are introduced to find the optimum SVM and BPNN parameters. The input parameters to the forecasting models are antecedent streamflow, historical rainfall and meteorological parameters namely evaporation, temperature, relative humidity and mean wind speed. Two performance evaluation measure, root mean square error (RMSE) and coefficient of determination (R2) were employed to evaluate the performance of developed forecasting model. It is found that, RMSE and R2 for hybrid SVM-PSO are 24.8267 m3/s and 0.9651 respectively while general SVM model yields RMSE of 27.5086 m3/s and 0.9305 of R2 for testing phase. Besides that, hybrid BA-BPNN produces RMSE, 17.7579 m3/s and R2, 0.7740 while BPNN model produces lower RMSE and R2 of 28.1396 m3/s and 0.5015 respectively. Therefore, the results indicate that hybrid model, SVM-PSO and Bat-BPNN yield better performance as compared to general SVM and BPNN, respectively in streamflow forecasting.

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