hybrid model
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Author(s):  
Tamilarasi Suresh ◽  
Tsehay Admassu Assegie ◽  
Subhashni Rajkumar ◽  
Napa Komal Kumar

Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.


2022 ◽  
Vol 3 (4) ◽  
pp. 322-335
Author(s):  
C. R. Nagarathna ◽  
M. Kusuma

Since the past decade, the deep learning techniques are widely used in research. The objective of various applications is achieved using these techniques. The deep learning technique in the medical field helps to find medicines and diagnosis of diseases. The Alzheimer’s is a physical brain disease, on which recently many research are experimented to develop an efficient model that diagnoses the early stages of Alzheimer’s disease. In this paper, a Hybrid model is proposed, which is a combination of VGG19 with additional layers, and a CNN deep learning model for detecting and classifying the different stages of Alzheimer’s and the performance is compared with the CNN model. The Magnetic Resonance Images are used to analyse both models received from the Kaggle dataset. The result shows that the Hybrid model works efficiently in detecting and classifying the different stages of Alzheimer’s.


Author(s):  
Po Yun ◽  
Chen Zhang ◽  
Yaqi Wu ◽  
Yu Yang

The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.


Author(s):  
Vladimir Kolobov ◽  
Juan Alonso Guzmán ◽  
R R Arslanbekov

Abstract A self-consistent hybrid model of standing and moving striations was developed for low-current DC discharges in noble gases. We introduced the concept of surface diffusion in phase space (r,u) (where u denotes the electron kinetic energy) described by a tensor diffusion in the nonlocal Fokker-Planck kinetic equation for electrons in the collisional plasma. Electrons diffuse along surfaces of constant total energy ε=u-eφ(r) between energy jumps in inelastic collisions with atoms. Numerical solutions of the 1d1u kinetic equation for electrons were obtained by two methods and coupled to ion transport and Poisson solver. We studied the dynamics of striation formation in Townsend and glow discharges in Argon gas at low discharge currents using a two-level excitation-ionization model and a “full-chemistry” model, which includes stepwise and Penning ionization. Standing striations appeared in Townsend and glow discharges at low currents, and moving striations were obtained for the discharge currents exceeding a critical value. These waves originate at the anode and propagate towards the cathode. We have seen two types of moving striations with the 2-level and full-chemistry models, which resemble the s and p striations previously observed in the experiments. Simulations indicate that processes in the anode region could control moving striations in the positive column plasma. The developed model helps clarify the nature of standing and moving striations in DC discharges of noble gases at low discharge currents and low gas pressures.


Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 36
Author(s):  
Kejing Zhao ◽  
Jinliang Zhang ◽  
Qing Liu

The reasonable pricing of options can effectively help investors avoid risks and obtain benefits, which plays a very important role in the stability of the financial market. The traditional single option pricing model often fails to meet the ideal expectations due to its limited conditions. Combining an economic model with a deep learning model to establish a hybrid model provides a new method to improve the prediction accuracy of the pricing model. This includes the usage of real historical data of about 10,000 sets of CSI 300 ETF options from January to December 2020 for experimental analysis. Aiming at the prediction problem of CSI 300ETF option pricing, based on the importance of random forest features, the Convolutional Neural Network and Long Short-Term Memory model (CNN-LSTM) in deep learning is combined with a typical stochastic volatility Heston model and stochastic interests CIR model in parameter models. The dual hybrid pricing model of the call option and the put option of CSI 300ETF is established. The dual-hybrid model and the reference model are integrated with ridge regression to further improve the forecasting effect. The results show that the dual-hybrid pricing model proposed in this paper has high accuracy, and the prediction accuracy is tens to hundreds of times higher than the reference model; moreover, MSE can be as low as 0.0003. The article provides an alternative method for the pricing of financial derivatives.


2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Lijun Zhang ◽  
Yongchao Dong ◽  
Dejian Meng ◽  
Wenbo Li

In recent years, the problem of automotive brake squeal during steering braking has attracted attention. Under the conditions of squealing, the loading of sprung mass is transferred, and lateral force is generated on the tire, resulting in stress and deformation of the suspension system. To predict the steering brake squeal propensity and explore its mechanism, we established a hybrid model of multibody dynamics and finite element methods to transfer the displacement values of each suspension connection point between two models. We successfully predicted the occurrence of steering brake squeal using the complex eigenvalue analysis method. Thereafter, we analyzed the interface pressure distribution between the pads and disc, and the results showed that the distribution grew uneven with an increase in the steering wheel angle. In addition, changes in the contact and restraint conditions between the pads and disc are the key mechanisms for steering brake squeal.


2022 ◽  
pp. 1-16
Author(s):  
Zhang Tingting ◽  
Tang Zhenpeng ◽  
Zhan Linjie ◽  
Du Xiaoxu ◽  
Chen Kaijie

An important feature of the outbreak of systemic financial risk is that the linkage and contagion of risk amongst the various sub-markets of the financial system have increased significantly. In addition, research on the prediction of systemic financial risk plays a significant role in the sustainable development of the financial market. Therefore, this paper takes China’s financial market as its research object, considers the risks co-activity among major financial sub-markets, and constructs a financial composite indicator of systemic stress (CISS) for China, describing its financial systemic stress based on 12 basic indicators selected from the money market, bond market, stock market, and foreign exchange market. Furthermore, drawing on the decomposition and integration technology in the [email protected] complex system research methodology, this paper introduces advanced variational mode decomposition (VMD) technology and extreme learning machine (ELM) algorithms, constructing the VMD-DE-ELM hybrid model to predict the systemic risk of China’s financial market. According to e RMSE , e MAE , and e MAPE , the prediction model’s multistep-ahead forecasting effect is evaluated. The empirical results show that the China’s financial CISS constructed in this paper can effectively identify all kinds of risk events in the sample range. The results of a robustness test show that the overall trend of China’s financial CISS and its ability to identify risk events are not affected by parameter selection and have good robustness. In addition, compared with the benchmark model, the VMD-DE-ELM hybrid model constructed in this paper shows superior predictive ability for systemic financial risk.


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