Application of improved convolution neural network in financial forecasting

2022 ◽  
Vol 34 (3) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
National Economy ◽  
Principal Component ◽  
Data Set ◽  
Specific Data ◽  
Financial Status ◽  
Financial Credit ◽  
Mean And Variance ◽  
The Mean ◽  
Pca Algorithm

Financial status and its role in the national economy have been increasingly recognized. In order to deduce the source of monetary funds and determine their whereabouts, financial information and prediction have become a scientific method that can not be ignored in the development of national economy. This paper improves the existing CNN and applies it to financial credit from different perspectives. Firstly, the noise of the collected data set is deleted, and then the clustering result is more stable by principal component analysis. The observation vectors are segmented to obtain a set of observation vectors corresponding to each hidden state. Based on the output of PCA algorithm, we recalculate the mean and variance of all kinds of observation vectors, and use the new mean and covariance matrix as credit financial credit, and then determine the best model parameters.The empirical results based on specific data from China's stock market show that the improved convolutional neural network proposed in this paper has advantages and the prediction accuracy reaches.

scholarly journals A PROPOSED SIGN LANGUAGE MODEL FOR SPEECHLESS PERSONS USING EEG SIGNALS

2021 ◽  
Vol 4 (3) ◽  
pp. 23-29
Author(s):  
Areej H. Al-Anbary ◽  
Salih M. Al-Qaraawi ‎
Keyword(s):  
Neural Network ◽  
Sign Language ◽  
Language Model ◽  
Motor Execution ◽  
Eeg Signals ◽  
Data Set ◽  
Learning Classifier ◽  
Software Model ◽  
Computer Interfaces ◽  
Pca Algorithm

Recently, algorithms of machine learning are widely used with the field of electroencephalography (EEG)-Brain-Computer interfaces (BCI). In this paper, a sign language software model based on the EEG brain signal was implemented, to help the speechless persons to communicate their thoughts to others.  The preprocessing stage for the EEG signals was performed by applying the Principle Component Analysis (PCA) algorithm to extract the important features and reducing the data redundancy. A model for classifying ten classes of EEG signals, including  Facial Expression(FE) and some Motor Execution(ME) processes, had been designed. A neural network of three hidden layers with deep learning classifier had been used in this work. Data set from four different subjects were collected using a 14 channels Emotiv epoc+ device. A classification results with accuracy 95.75% were obtained ‎for the collected samples. An optimization process was performed on the predicted class with the aid of user, and then sign class will be connected to the specified sentence under a predesigned lock up table.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network

Endoscopy ◽  
2020 ◽  
Vol 52 (09) ◽  
pp. 786-791 ◽  
Author(s):  
Keita Otani ◽  
Ayako Nakada ◽  
Yusuke Kurose ◽  
Ryota Niikura ◽  
Atsuo Yamada ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Bowel ◽  
Capsule Endoscopy ◽  
Characteristic Curve ◽  
Vascular Lesions ◽  
Single Type ◽  
Data Set ◽  
Normal Tissues ◽  
Computer Aided ◽  
The Mean

Abstract Background Previous computer-aided detection systems for diagnosing lesions in images from wireless capsule endoscopy (WCE) have been limited to a single type of small-bowel lesion. We developed a new artificial intelligence (AI) system able to diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. Methods We trained the deep neural network system RetinaNet on a data set of 167 patients, which consisted of images of 398 erosions and ulcers, 538 vascular lesions, 4590 tumors, and 34 437 normal tissues. We calculated the mean area under the receiver operating characteristic curve (AUC) for each lesion type using five-fold stratified cross-validation. Results The mean age of the patients was 63.6 years; 92 were men. The mean AUCs of the AI system were 0.996 (95 %CI 0.992 – 0.999) for erosions and ulcers, 0.950 (95 %CI 0.923 – 0.978) for vascular lesions, and 0.950 (95 %CI 0.913 – 0.988) for tumors. Conclusion We developed and validated a new computer-aided diagnosis system for multiclass diagnosis of small-bowel lesions in WCE images.

Expected consequences of the segregation of a major gene in a sheep population in relation to observations on the ovulation rate of a flock of Cambridge sheep

1990 ◽  
Vol 51 (2) ◽  
pp. 277-282 ◽  
Author(s):  
J. B. Owen ◽  
C. J. Whitaker ◽  
R. F. E. Axford ◽  
I. Ap Dewi
Keyword(s):  
Gene Frequency ◽  
Major Gene ◽  
Ovulation Rate ◽  
Phenotypic Effect ◽  
Data Set ◽  
Sheep Population ◽  
Mean Values ◽  
Population Mean ◽  
Mean And Variance ◽  
The Mean

ABSTRACTA simple model was derived relating the phenotypic effect (g) of a major gene to observed values of the population mean and variance for a trait, at specified values of the major gene frequency and at specified basal values of the population mean and variance (in the absence of the major gene). This model was applied to a total of 549 observed values of ovulation rate in ewes of the Cambridge breed at Bangor under a range of assumptions. The mean values of ovulation rate were 2·44 for 243 ewes of 1 year of age and 37·54 for 306 ewes of 2 and 3 years of age with a coefficient of variation for both age sets of 0·50.The results indicate a minimum value for g, in this data set, of 1·07 for 1 year old and 1·72 for 2 and 3 year old ewes. The results are also consistent with a frequency value in the region of 0·3 to 0·4, with the absence of dominance and with a reasonable concordance with Hardy-Weinburg equilibrium. The results also indicate that the value of g varies according to the background phenotype since it is lower for younger as compared with older ewes.

scholarly journals DEVELOPMENT OF BIOMETRIC METHODS AND INFORMATION SECURITY TOOLS

2021 ◽  
Vol 2 (336) ◽  
pp. 121-124
Author(s):  
T. Zh. Mazakov ◽  
D. N. Narynbekovna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Future Perspective ◽  
Data Set

Now a day’s security is a big issue, the whole world has been working on the face recognition techniques as face is used for the extraction of facial features. An analysis has been done of the commonly used face recognition techniques. This paper presents a system for the recognition of face for identification and verification purposes by using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) and the implementation of face recognition system is done by using neural network. The use of neural network is to produce an output pattern from input pattern. This system for facial recognition is implemented in MATLAB using neural networks toolbox. Back propagation Neural Network is multi-layered network in which weights are fixed but adjustment of weights can be done on the basis of sigmoidal function. This algorithm is a learning algorithm to train input and output data set. It also calculates how the error changes when weights are increased or decreased. This paper consists of background and future perspective of face recognition techniques and how these techniques can be improved.

Aerodynamic Prediction on the Off-Design Performance of a S-CO2 Turbine Based on Deep Learning

2021 ◽  
Author(s):  
Yuqi Wang ◽  
Tianyuan Liu ◽  
Di Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Performance Prediction ◽  
Aerodynamic Performance ◽  
Operating Conditions ◽  
Training Data ◽  
Brayton Cycle ◽  
Data Set ◽  
Set Size ◽  
The Mean

Abstract The research on the supercritical carbon dioxide (S-CO2) Brayton cycle has gradually become a hot spot in recent years. The off-design performance of turbine is an important reference for analyzing the variable operating conditions of the cycle. With the development of deep learning technology, the research of surrogate models based on neural network has received extensive attention. In order to improve the inefficiency in traditional off-design analyses, this research establishes a data-driven deep learning off-design aerodynamic prediction model for a S-CO2 centrifugal turbine, which is based on a deep convolutional neural network. The network can rapidly and adaptively provide dynamic aerodynamic performance prediction results for varying blade profiles and operating conditions. Meanwhile, it can illustrate the mechanism based on the field reconstruction results for the generated aerodynamic performance. The training results show that the off-design aerodynamic prediction convolutional neural network (OAP-CNN) has reduced the mean and maximum error of efficiency prediction compared with the traditional Gaussian Process Regression (GPR) and Artificial Neural Network (ANN). Aiming at the off-design conditions, the pressure and temperature distributions with acceptable error can be obtained without a CFD calculation. Besides, the influence of off-design parameters on the efficiency and power can be conveniently acquired, thus providing the reference for an optimized operation strategy. Analyzing the sensitivity of AOP-CNN to training data set size, the prediction accuracy is acceptable when the percentage of training samples exceeds 50%. The minimum error appears when the training data set size is 0.8. The mean and maximum errors are respectively 1.46% and 6.42%. In summary, this research provides a precise and fast aerodynamic performance prediction model in the analyses of off-design conditions for S-CO2 turbomachinery and Brayton cycle.

Financial Crisis Warning Analysis for Companies Based on BP Neural Network

2014 ◽  
Vol 519-520 ◽  
pp. 1529-1533
Author(s):  
Hong Min Zhang
Keyword(s):  
Neural Network ◽  
Financial Crisis ◽  
Capital Market ◽  
Bp Neural Network ◽  
Common Factor ◽  
Principal Component ◽  
Special Treatment ◽  
Good Prediction ◽  
Prediction Ability ◽  
Financial Status

This paper chose 11 financial criteria based on the principle for criteria selection. Due to the relevance of criteria, this paper collected 5 common factors with the method of principal component analysis of factor. The comprehensive score of every sample is got from the score of every common factor. Considering whether enterprises are special treated as auxiliary reference, 3 ranges of score are divided into 3 financial status accordingly: health, focus, crisis, disregarding the division criteria of special treatment or otherwise.Finally, the network got trained and tested by regarding 5 common factor as input and the financial status as output. It is found that BP neural network has a good prediction ability, high accuracy and application value, which will give a great assistance to the development of capital market in our country.BP: financial crisis warning, empirical analysis, factor analysis, BP neural network.

scholarly journals A Robust Procedure for Damage Detection from Strain Measurements Based on Principal Component Analysis

2013 ◽  
Vol 558 ◽  
pp. 128-138 ◽  
Author(s):  
Alfredo Guemes ◽  
J. Sierra-Pérez ◽  
J. Rodellar ◽  
L. Mujica
Keyword(s):  
Principal Component Analysis ◽  
Multivariable Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Complex Data ◽  
Data Set ◽  
Strain Measurements ◽  
Before And After ◽  
Small Cracks ◽  
Pca Algorithm

FBGs are excellent strain sensors, because of its low size and multiplexing capability. Tens to hundred of sensors may be embedded into a structure, as it has already been demonstrated. Nevertheless, they only afford strain measurements at local points, so unless the damage affects the strain readings in a distinguishable manner, damage will go undetected. This paper show the experimental results obtained on the wing of a UAV, instrumented with 32 FBGs, before and after small damages were introduced. The PCA algorithm was able to distinguish the damage cases, even for small cracks. Principal Component Analysis (PCA) is a technique of multivariable analysis to reduce a complex data set to a lower dimension and reveal some hidden patterns that underlie.

scholarly journals VSS: Variance-stabilized signals for sequencing-based genomic signals

2020 ◽  
Author(s):  
Faezeh Bayat ◽  
Maxwell Libbrecht
Keyword(s):  
Negative Binomial ◽  
Empirical Relationship ◽  
Data Sets ◽  
Data Set ◽  
Variance Stabilization ◽  
Mean And Variance ◽  
The Mean ◽  
Genomic Signals ◽  
Mean Variance ◽  
Inverse Hyperbolic Sine

AbstractMotivationA sequencing-based genomic assay such as ChIP-seq outputs a real-valued signal for each position in the genome that measures the strength of activity at that position. Most genomic signals lack the property of variance stabilization. That is, a difference between 100 and 200 reads usually has a very different statistical importance from a difference between 1,100 and 1,200 reads. A statistical model such as a negative binomial distribution can account for this pattern, but learning these models is computationally challenging. Therefore, many applications—including imputation and segmentation and genome annotation (SAGA)—instead use Gaussian models and use a transformation such as log or inverse hyperbolic sine (asinh) to stabilize variance.ResultsWe show here that existing transformations do not fully stabilize variance in genomic data sets. To solve this issue, we propose VSS, a method that produces variance-stabilized signals for sequencingbased genomic signals. VSS learns the empirical relationship between the mean and variance of a given signal data set and produces transformed signals that normalize for this dependence. We show that VSS successfully stabilizes variance and that doing so improves downstream applications such as SAGA. VSS will eliminate the need for downstream methods to implement complex mean-variance relationship models, and will enable genomic signals to be easily understood by [email protected]://github.com/faezeh-bayat/Variance-stabilized-units-for-sequencing-based-genomic-signals.

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