An efficient drug compound analysis model based on Mutual Spectral Scaling Feature Selection using Spectral Deep Feature Classification for Drug recommendation from health care

Monetary policy is an important means for a country to regulate macroeconomic operations and achieve established economic goals. Moreover, a reasonable monetary policy improves the efficiency of financial operations on a global scale and effectively resolves the financial crisis. At present, scholars from various countries have begun to pay attention to the issue of differentiated formulation of monetary policy among regions. This paper combines machine learning to construct a monetary policy differentiation effect analysis model based on the GVAR model. Moreover, this paper uses the gray correlation analysis method to obtain the gray correlation matrix between industries, and then introduces the industry’s own characteristics, industry relevance and macroeconomic factors into the macro stress test of credit risk. In addition, this paper constructs a conduction model based on the industry GVAR model, and uses the first-order difference sequence of GDP growth rate, CPI growth rate and M2 growth rate of each economic region to construct a GVAR model to test the impulse response function. The results of the test show that the monetary policy shocks of various economic regions are significantly different. All in all, the research results show that the performance of the model constructed in this paper is good.

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Cosine-grey Correlation Analysis Model Based on Combination Weighting and Its Application

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) ◽

10.1109/icaica50127.2020.9182404 ◽

2020 ◽

Author(s):

Yin Pei ◽

Liu Yian

Keyword(s):

Correlation Analysis ◽

Analysis Model ◽

Grey Correlation ◽

Model Based ◽

Grey Correlation Analysis

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The Design of Customer Satisfaction Analysis Model Based on Hierarchical Fuzzy System

2009 International Conference on Business Intelligence and Financial Engineering ◽

10.1109/bife.2009.116 ◽

2009 ◽

Cited By ~ 1

Author(s):

Huiyu Cao ◽

Bin Liu ◽

Jianmin He

Keyword(s):

Customer Satisfaction ◽

Fuzzy System ◽

Analysis Model ◽

Model Based ◽

Hierarchical Fuzzy System ◽

Satisfaction Analysis

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Forecasting air pollutant concentration using a novel spatiotemporal deep learning model based on clustering, feature selection and empirical wavelet transform

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.149654 ◽

2021 ◽

pp. 149654

Author(s):

Jusong Kim ◽

Xiaoli Wang ◽

Chollyong Kang ◽

Jinwon Yu ◽

Penghui Li

Keyword(s):

Feature Selection ◽

Deep Learning ◽

Wavelet Transform ◽

Learning Model ◽

Air Pollutant ◽

Pollutant Concentration ◽

Model Based ◽

Empirical Wavelet Transform ◽

Deep Learning Model

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Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Sensors ◽

10.3390/s21092910 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2910

Author(s):

Kei Suzuki ◽

Tipporn Laohakangvalvit ◽

Ryota Matsubara ◽

Midori Sugaya

Keyword(s):

Feature Selection ◽

Single Channel ◽

Feature Selection Method ◽

Classification Model ◽

Features Selection ◽

Selection Methods ◽

Emotion Classification ◽

Model Based ◽

Physiological Indexes ◽

Emotion Estimation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

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Mountain Road Planning and Analysis Model Based on Data Mining

10.1109/icicta51737.2020.00036 ◽

2020 ◽

Author(s):

Jing Hu

Keyword(s):

Data Mining ◽

Analysis Model ◽

Model Based ◽

Road Planning

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Study on Portrait Tracking Technology of Deep Feature Learning in Monitoring Image Acquisition

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.4.040502 ◽

2021 ◽

Author(s):

Senlin Yang ◽

Xin Chong

Keyword(s):

Feature Selection ◽

Image Acquisition ◽

Recognition Rate ◽

Particle Swarm ◽

Feature Subset ◽

Particle Structure ◽

Recognition Time ◽

Traditional Methods ◽

Deep Feature ◽

Global Optimal

In a network information society, there are many occasions where people’s behaviors need to be tracked, photographed, and recognized. Biometric recognition technologies are considered to be one of the most effective solutions. Traditional methods mostly use graph structure and deformed component model to design two-dimensional (2D) human body component detectors, and apply graph models to establish the connectivity of each component. The recognition design process is simple, but the accuracy of recognition and tracking effect applied in monitoring image acquisition is not high. The improved particle swarm optimization algorithm is used to determine the particle structure, and the binary bit string is used to represent the particle structure. The support vector machine (SVM) parameters of discrete particles are optimized, and the synchronous optimization design of feature selection and SVM parameters is carried out to realize the synchronous optimization of portrait feature subset and SVM parameters in discrete space. Through in-depth research, the extracted feature subsets can be effectively optimized and selected, and the parameters of SVM model can be optimized synchronously. The discrete particle structure is associated with the SVM parameters to achieve feature selection and SVM parameter synchronization and optimization. It is not only superior to traditional algorithms in terms of recognition rate, but also reduces the feature dimension and shortens the recognition time. The deep feature recognition built on the learning machine is not easy to diverge and can effectively adjust the particle speed to the global optimal, which is more effective than the particle swarm algorithm to search for the global optimal solution, and has better robustness. In the experiments, the research content of the article is compared with the traditional methods to test and analysis. The results show that the method optimizes the selection of feature subset and eliminates a large number of invalid features. The method not only reduces space complexity and shortens recognition time, but also improves recognition rate. The dimension of feature subset dimensions are superior to those extracted by other algorithms.

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