Bitcoin Prediction Using Multi-Layer Perceptron Regressor, PCA, and Support Vector Regression (SVR)

2022 ◽  
pp. 225-236
Author(s):  
Aatif Jamshed ◽  
Asmita Dixit
Keyword(s):  
Neural Network ◽  
Software Engineering ◽  
Support Vector Regression ◽  
Evaluation Process ◽  
Support Vector ◽  
Actual Rate ◽  
Multi Layer Perceptron ◽  
Bayesian Neural Network ◽  
Price Schedule ◽  
Tremendous Amount

Bitcoin has gained a tremendous amount of attention lately because of the innate nature of entering cryptographic technologies and money-related units in the fields of banking, cybersecurity, and software engineering. This chapter investigates the effect of Bayesian neural structures or networks (BNNs) with the aid of manipulating the Bitcoin process's timetable. The authors also choose the maximum extensive highlights from Blockchain records that are carefully applied to Bitcoin's marketplace hobby and use it to create templates to enhance the influential display of the new Bitcoin evaluation process. They endorse actual inspection to check and expect the Bitcoin technique, which compares the Bayesian neural network and other clean and non-direct comparison models. The exact tests show that BNN works well for undertaking the Bitcoin price schedule and explain the intense unpredictability of Bitcoin's actual rate.

COMPARISON OF SUPPORT VECTOR MACHINES AND BAYESIAN NEURAL NETWORKS PERFORMANCE FOR BREAST TISSUES USING GEOSTATISTICAL FUNCTIONS IN MAMMOGRAPHIC IMAGES

2010 ◽  
Vol 09 (04) ◽  
pp. 271-288
Author(s):  
GERALDO BRAZ JUNIOR ◽  
LEONARDO DE OLIVEIRA MARTINS ◽  
ARISTÓFANES CORREA SILVA ◽  
ANSELMO CARDOSO PAIVA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Region Of Interest ◽  
Female Breast Cancer ◽  
Support Vector ◽  
Risk Minimization ◽  
Bayesian Neural Network ◽  
Cad System ◽  
Mammographic Images ◽  
Breast Tissues

Female breast cancer is a major cause of deaths in occidental countries. Computer-aided Detection (CAD) systems can aid radiologists to increase diagnostic accuracy. In this work, we present a comparison between two classifiers applied to the separation of normal and abnormal breast tissues from mammograms. The purpose of the comparison is to select the best prediction technique to be part of a CAD system. Each region of interest is classified through a Support Vector Machine (SVM) and a Bayesian Neural Network (BNN) as normal or abnormal region. SVM is a machine-learning method, based on the principle of structural risk minimization, which shows good performance when applied to data outside the training set. A Bayesian Neural Network is a classifier that joins traditional neural networks theory and Bayesian inference. We use a set of measures obtained by the application of the semivariogram, semimadogram, covariogram, and correlogram functions to the characterization of breast tissue as normal or abnormal. The results show that SVM presents best performance for the classification of breast tissues in mammographic images. The tests indicate that SVM has more generalization power than the BNN classifier. BNN has a sensibility of 76.19% and a specificity of 79.31%, while SVM presents a sensibility of 74.07% and a specificity of 98.77%. The accuracy rate for tests is 78.70% and 92.59% for BNN and SVM, respectively.

scholarly journals Artificial intelligence techniques applied to predict teams position of the Brazilian football championship

1989 ◽  
Author(s):  
Mariana Kleina ◽  
◽  
Mateus Noronha dos Santos ◽  
Tiago Noronha dos Santos ◽  
Marcos Augusto Mendes Marques ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Group Classification ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Post Processing ◽  
Specific Training ◽  
Reasonable Prediction ◽  
True Group ◽  
Better Than

This study presents a classifier prediction in groups for the Brazilian Football Championship of both A and B leagues, from the results of the first half of each championship. With assertive predictions of the group where a team will end the championship, strategic planning can be performed in the squad, such as new hiring, specific training for athletes, and possible championships that the team will be entitled to participate in according to the group classification. In order to find the predictions, two techniques of artificial intelligence were applied: Multi-Layer Perceptron (MLP), which is a type of artificial neural network, and Support Vector Machine (SVM). Preliminary results show that the proposed methodology is very promising, with more than 40% successful cases with MLP and almost 50% with SVM. Moreover, results indicate that the methodology is able to make a reasonable prediction by missing one group of the true group at the end of the championship. The SVM technique was slightly better than MLP. A post-processing analysis of the SVM results was applied to the 2018 A league data from the Brazilian championship, resulting in 85% success indicator of groups.

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