Analysis on block chain financial transaction under artificial neural network of deep learning

Gao, Wenyou; Su, Chang

doi:10.1016/j.cam.2020.112991

Cited by 52 publications

(24 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANN is a method that is widely used in machine learning [31]. The ANN method in machine learning gives promising results to produce a comprehensive review [32].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Machine learning classification of infectious disease distribution status

Wisky

Yanto

Wiyandra

et al. 2022

IJEECS

View full text Add to dashboard Cite

Infectious diseases are common diseases and are caused by microorganisms such as viruses, bacteria, and parasites. Indicators of the spread of this disease can be seen based on the population level and the number of confirmed cases. This study aims to develop a machine learning (ML) analysis model using the K-means cluster, artificial neural network (ANN), and decision tree (DT) methods. The dataset used in this study was obtained based on the number of confirmed patients and the distribution of the population. The analysis process is divided into two stages, namely preprocessing and the classification process. The pre-processing stage aims to produce a classification pattern that can describe the level of distribution status. The classification pattern will be continued at the classification analysis stage using ANN and DT. Classification analysis gave significant results with an accuracy rate of 99.77%. The results of the classification analysis can also describe the level of knowledge distribution based on the decision tree. Overall, the contribution of this research is to develop a classification analysis model that presents the latest information and knowledge. The results of the research presented also have an impact on the control process in environmental management and public health.

show abstract

“…ANN is a method that is widely used in machine learning [31]. The ANN method in machine learning gives promising results to produce a comprehensive review [32].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Machine learning classification of infectious disease distribution status

Wisky

Yanto

Wiyandra

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…BP neural network is a multi-layer feedforward neural network, whose main feature expresses that the signal is forwarded and the error is back-propagated 23 , 24 . The steps to building the GA-BP neural network are as follows: firstly, according to the errors between predictive stiffness, loss factor and test results, the network weights and thresholds are adjusted to make the predicted results approach training data.…”

Section: Prediction Of Frequency Independence Of Rubber Bushingmentioning

confidence: 99%

Frequency dependence prediction and parameter identification of rubber bushing

Zhang³

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Affected by frequency, amplitude and some other factors, the dynamic mechanical properties of rubber bushing are nonlinear. In order to study the frequency dependence of the rubber bushing, a BP neural network optimized by genetic algorithm (GA-BP neural network) is applied to predict the dynamic stiffness and loss factor under frequency of 61–100 Hz. The training data refers to the test data under frequency of 1–60 Hz. And the algorithm is demonstrated by the elastomer test of rubber bushing under amplitudes 0.2 mm, 0.4 mm and 0.6 mm. The results show that the prediction error of dynamic stiffness is less than 1%, and the prediction error of loss factor is less than 3%. In order to apply the predicted results to the software for simulation, a five-parameter mathematical model (FPM) consisting of three elastic elements and two damping elements is developed, and the model parameters are identified by least squares method. According to the fitting results and test data, the fitting error of dynamic stiffness is less than 2%, and the fitting error of loss factor is less than 3%. The GA-BP neural network and FPM model predict the dynamic mechanical behaviour of rubber bushing without the performance of iterative experiments and the incurrence of a high computational cost, making it applicable to analyze full-size vehicles with numerous rubber bushings under various vibration load conditions.

show abstract

“…Currently, a brand-new internet economics structure established by world impacts namely digital currency, peer-to-peer (P2P), blockchain (BC), and crowdfunding, might act as a majority portion in the expansion of the global monetary markets [7]. BC systems can be fixed as orderly developments in which the intrusion has huge influences by transferring the function of industries from centralized to decentralized arrangements.…”

Section: Introductionmentioning

confidence: 99%

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

Kalyani¹,

Velmurugan²,

Abdeljaber³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

show abstract

Analysis on block chain financial transaction under artificial neural network of deep learning

Cited by 52 publications

References 44 publications

Machine learning classification of infectious disease distribution status

Machine learning classification of infectious disease distribution status

Frequency dependence prediction and parameter identification of rubber bushing

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

Contact Info

Product

Resources

About