scholarly journals A Hybrid Model Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4784-4796
Author(s):  
Ibrahim Omara ◽  
Ahmed Hagag ◽  
Souleyman Chaib ◽  
Guangzhi Ma ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Support Vector ◽  
Distance Metric ◽  
Biometric Recognition ◽  
Model Combining ◽  
Multimodal Biometric Recognition

scholarly journals Hybrid modeling method of dust suppression efficiency for dry fog dust suppression process

2022 ◽  
Vol 355 ◽  
pp. 02017
Author(s):  
Hailiang Li ◽  
Hongyang Li
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Support Vector ◽  
Actual Process ◽  
Dust Suppression ◽  
Mechanism Model ◽  
Suppression Efficiency ◽  
Model Combining ◽  
Simulation Results ◽  
Suppression Process

The original mechanism model of dust suppression efficiency for dry fog dust suppression could not guide the actual process. In order to accurately predict the dust suppression efficiency in the process, a new mechanism model was established by my analysis of the process. But there were some factors that I could not establish the model. So a hybrid model combining mechanism model and support vector machine (SVM) was proposed. Using the data of dry fog dust suppression oval process, the hybrid model was simulated. The simulation results show that the hybrid model can accurately predict the dry fog dust suppression.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document