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Metaverse token price forecasting using artificial neural networks (ANNs) and Adaptive neural fuzzy inference system (ANFIS)

Neural Computing and Applications · Mart 2024

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YÖKSİS Kayıtları
Metaverse token price forecasting using artificial neural networks (ANNs) and Adaptive neural fuzzy inference system (ANFIS)
Neural Computing and Applications · 2024 SCI-Expanded
Prof. Dr. FATİH BAŞÇİFTÇİ →
Metaverse token price forecasting using artificial neural networks (ANNs) and Adaptive neural fuzzy inference system (ANFIS)
NEURAL COMPUTING & APPLICATIONS · 2024 SCI
Doç. Dr. İLKER ALİ ÖZKAN →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Automatic detection and classification of rotor cage faults in squirrel cage induction motor
2010 ISSN: 0941-0643 SCI-Expanded 6 atıf
Prof. Dr. HAYRİ ARABACI →
Short term load forecasting using fuzzy logic and ANFIS
2015 ISSN: 0941-0643 SCI-Expanded 1 atıf
Dr. Öğr. Üyesi HASAN HÜSEYİN ÇEVİK →
Determination of induction motor parameters with differential evolution algorithm
2012 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Short term load forecasting using fuzzy logic and ANFIS
2015 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Fuzzy logic based induction motor protection system
2013 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Determination of induction motor parameters with differential evolution algorithm
2012 ISSN: 0941-0643 SCI-Expanded
Doç. Dr. TAHİR SAĞ →
Cost optimization of mixed feeds with the particle swarm optimization method
2013 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. ADEM ALPASLAN ALTUN →
A combination of Genetic Algorithm Particle Swarm Optimization and Neural Network for palmprint recognition
2013 ISSN: 0941-0643 SCI-Expanded 1 atıf
Prof. Dr. ADEM ALPASLAN ALTUN →
Cost optimization of mixed feeds with the particle swarm optimization method
2013 ISSN: 0941-0643 SCI-Expanded
Doç. Dr. MEHMET AKİF ŞAHMAN →
Fuzzy logic based induction motor protection system
2013 ISSN: 0941-0643 SCI
Dr. Öğr. Üyesi OKAN UYAR →
A new MILP model proposal in feed formulation and using a hybrid linear binary PSO H LBP approach for alternative solutions
2018 ISSN: 0941-0643 SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
FPGA based self organizing fuzzy controller for electromagnetic filter
2016 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
A new MILP model proposal in feed formulation and using a hybrid linear binary PSO H LBP approach for alternative solutions
2016 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. ADEM ALPASLAN ALTUN →
Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech
2018 ISSN: 0941-0643 SCI-Expanded Q1
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →
A new denoising method for fMRI based on weighted three-dimentional wavelet transform
2018 ISSN: 0941-0643 SCI-Expanded
Dr. Öğr. Üyesi GÜZİN ÖZMEN →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
A new MILP model proposal in feed formulation and using a hybrid-linear binary PSO (H-LBP) approach for alternative solutions
2018 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. ADEM ALPASLAN ALTUN →
Hybrid breast cancer detection tem via neural network and feature ion based on SBS SFS and PCA
2013 ISSN: 0941-0643 SCI-Expanded 5 atıf
Dr. Öğr. Üyesi ONUR İNAN →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →

Makale Bilgileri

Dergi Neural Computing and Applications
ISSN09410643
Yayın TarihiMart 2024
Cilt / Sayfa36 · 3267-3290
DOI10.1007/s00521-023-09228-y
Scopus ID2-s2.0-85177692804
Özet This study is about the metaverse environment which has recently heard a lot in life. Although many individuals and institutions are interested in metaverse, it is an imaginary future space, and the conceptual framework is not fully drawn. The metaverse is a mix of augmented, virtual, and mixed reality technologies that are predicted to affect our lives over the next decade including our money, possessions, and ownership. So, we examined to forecast metaverse token price using ANN and ANFIS methods. The market value of the five metaverse token firms: opening price, highest value, lowest value, closing price, volume value, and variables, is evaluated between October 17, 2017, and December 15, 2022, by YSA and ANFIS. The ANN method training was carried out using ten hidden layers and the Levenberg–Marquardt algorithm. In the ANFIS method, training was carried out with 100 iterations in a triple mesh structure. At the end of the method training, the training performed with the ANN method was 99.3% successful, and the ANFIS method was 99.1% successful. It is concluded that the model fitness of the R2 values of ANN and ANFIS methods is appropriate at 99.3 and 98.7%, respectively. As a result, ANN and ANFIS methods can be used for the prediction of metaverse token prices for the estimation of financial instruments. ANN and ANFIS are advanced tools for predicting metaverse token prices, with ANFIS having unique features like fuzzy logic. However, using only basic price data is not enough for precise predictions. While the EMA and SMA have less impact, gold (XAU), BTC, ETH, US dollar (USD), Chinese Yuan (CNY), and Brent oil (BRT) are observed to have a moderate impact on determining the market values of metaverse prices. The use of XAU and ETH prices in both ANN and ANFIS methods gives successful results. Especially, we have achieved favorable outcomes when employing the ANN method for analyzing EMA and BTC values. Additionally, we have obtained valuable and successful results by utilizing the ANFIS method for analyzing BRT. Using only opening, highest, lowest, closing prices, and volume values and USD, CNY, BRT, and SMA prices has not demonstrated usefulness in attaining favorable results. The findings of this research indicate that the utilization of the metaverse world has the potential to enhance learning capabilities and motivation, as well as make significant contributions to industrial production and the field of health. However, it is important to note that the existing body of research on the metaverse world remains limited in scope and depth. Users often hold preconceived notions and biases regarding ethical concerns associated with the metaverse world. Additionally, there are significant obstacles that need to be addressed in terms of fulfilling hardware requirements for its implementation.

Yazarlar (3)

1
İbrahim Özkal
ORCID: 0000-0002-9022-458X
2
Ilker Ali Ozkan
3
Fatih Başçiftçi

Anahtar Kelimeler

ANFIS Artificial neural networks Augmented reality Metaverse Virtual reality

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Neural Computing and Applications
Q1
SJR Skoru1,102
H-Index146
YayıncıSpringer London
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Software (Q1)
Dergi sayfasına git

Metrikler

15
Atıf
3
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

İÖ
İLKER ALİ ÖZKAN
Doç. Dr.

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