The document describes a project that aims to simplify non-fungible tokens (NFTs) using recurrent neural network algorithms in blockchain. It provides details of the project members and supervisor. The objectives are to identify digital assets and ensure unique identifiers. The document outlines the scope, issues, proposed methodology, modules, algorithms, and concludes that the project investigates NFTs and evaluates them as a core building block for a blockchain-based event ticketing system.

1. SIMPLIFICATION OF NON-FUNGIBLE TOKENS USING RNN
ALGORITHM IN BLOCKCHAIN
DETAILS OF THE PROJECT
MEMBERS: SAMMED SUNIL PATIL
NIKHIL PRAKASH .J.S
SUNAISH KUMAR
SHIKHA RAI
SUPERVISOR DETAILS
MS. AARTHI.S
ASSITANT PROFESSOR
DEPARTMENT OF COMPUTER SCIENCE
AND ENGINEERING
SRM Institute of Science and Technology,
Ramapuram Campus, Chennai-89
DEPARTMENT OF COMPUTER SCIENCE AND
ENGINEERING
Batch No: 2
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2. OBJECTIVE
• To identify the digital assets and ensure unique identifiers.
• NFTs differ from the crypto currencies since digital currencies are fungible
like each currency of specific denomination.
• Hide their best content behind paywalls
• NFT marketplaces may vary from new to hardcore traders thus the digital
assets tagged to NFTs will be exposed to a large set of buyers.
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3. SCOPE
• Gaming
• Real Estate
• Music
• Hyper-Realistic VR
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4. ABSTRACT
• Current blockchain-based “Web 3.0” solutions provide appealing
alternatives by offering increased permanence, performance, and
transparency in application development.
• Unfortunately, most decentralized apps (“dApps”) suffer from sky-high
transaction costs, expensive infrastructure, and issues with scalability.
• Ultimately, they have failed to help the large majority of internet users
graduate from being exploited in Web 2.0 into flourishing in Web 3.0.
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5. INTRODUCTION
• Blockchain is a shared, immutable ledger that facilitates the process of
recording transactions and tracking assets in a business network.
• An asset can be tangible (a house, car, cash, land) or intangible (intellectual
property, patents, copyrights, branding).
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6. SYSTEM REQUIREMENTS
Hardware Requirements
• Processor : Any Processor above 500 MHz
• RAM : 1 GB
• Hard Disk : 80 GB
• System : Pentium IV 2.4 GHz
• Any system with above or higher configuration is compatible for this project.
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7. SYSTEM REQUIREMENTS
Software Requirements
A layer of peer devices connected to the consensus process form the core of the
Network. These are called Nodes (also referred to as Voting Nodes or Voters). In order
to track state data permanently, Nodes interact with a Smart weave Contract hosted on
the AR weave blockchain. Every time a new proposal is made (be it part of the daily
1,000 KOI distribution or a node-sponsored Koi Task), the nodes collectively review it.
Tasks also provide an avenue to earn through participation, without an up front token
purchase.
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8. Literature survey - Paper 1
• Title : Bitcoin Price Prediction an ARIMAApproach
• Authors : Amin Azari
• Year : 2018
• Inference : Analyzing the tweets of Bitcoin collected from different
news account sources are classified to positive or
negative sentiments.
• Advantages : To present the efficient method for selecting the features
that influences the crypto currency prices most.
• Disadvantages: Bitcoin transfer can only be performed using user account.
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9. Literature survey - Paper 2
• Title : Automated Bitcoin Trading via Machine Learning
Algorithms
• Author : Isaac Madan, Saluja, Aojia Zhao
• Year : 2017
• Inference : LSTM and GRU architecture are considered to be state of
the art in problem related sequential data.
• Advantages : It is very effective in sequence modelling tasks.
• Disadvantage : The performance of this model is 78.9% accurate only.
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10. Literature survey - Paper 3
• Title : The Effect of Cryptocurrency on Investment
Portfolio Effectiveness
• Authors : Andrianto
• Year : 2019
• Inference : It consist of hybrid model which have common input and
their output is concatenated before passing through dense layer.
• Advantages : Short and long term forecast models predict both the exact
price and direction of NFT price.
• Disadvantages: It supports only one non-fungible token that it ETHERIUM
with an accuracy of 84.2%.
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11. Literature survey - Paper 4
• Title : A Deep Learning-based Cryptocurrency Price
Prediction Scheme for Financial Institutions
• Authors : Patel, M.M, Tanwar, Gupta, Kumar
• Year : 2016
• Inference : CatBoost uses combined category features, which can
make use of the connection between features.
• Advantages : It is trained on the Google cloud with the learning rate of
0.05%.
• Disadvantages: The performance is accurate 84.8%.
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12. Literature survey - Paper 5
• Title : Prediction of Bitcoin prices with machine
learning methods using time series data
• Authors : Karasu, Altan, Sarac, Hacioglu
• Year : 2018
• Inference : Analyzing the tweets of Bitcoin collected from different
news account sources are classified to positive or
negative sentiments.
• Advantages : To present the efficient method for selecting the features
that influences the crypto currency prices most.
• Disadvantages: Bitcoin transfer can only be performed using user account.
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13. Literature survey - Paper 6
• Title : Towards characterizing blockchain-based
cryptocurrencies for highly-accurate predictions.
• Authors : Saad.M, Mohaisen.A.
• Year : 2020
• Inference : Black box is a algorithm used to train Machine Learning
Model for datasets.
• Advantages : It is very effective in sequence modelling tasks.
• Disadvantages: The performance of this model is 80.9% accurate only.
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14. Literature survey - Paper 7
• Title : LSTM-Method for Bitcoin Price Prediction.
• Authors : Ferdiansyah.F, Othman.S.H, Radzi, R.Z.R.M.
Stiawan.D, Sazaki.Y, Ependi.
• Year : 2019
• Inference : It consist of hybrid model which have common input and
their output is concatenated before passing through dense layer.
• Advantages : Short and long term forecast models predict both the exact
price and direction of NFT price.
• Disadvantages: It supports only one non-fungible token that it
BLOCKCHAIN with an accuracy of 75.9%.
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15. Literature survey - Paper 8
• Title : Forecasting Price of Cryptocurrencies Using
Tweets Sentiment Analysis.
• Authors : Tripathi.S, Dwived.H.D, Saxena.P.
• Year : 2020
• Inference : Black box uses combined category features, which can
make use of the connection between features.
• Advantages : It is very effective in sequence modelling tasks.
• Disadvantages: The performance of this model is 80.9% accurate only.
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16. Literature survey - Paper 9
• Title : A Comparison between ARIMA, LSTM,
and GRU for Time Series Forecasting.
• Authors : Yamak.P.T, Yujian.L, Gadosey.P.K.
• Year : 2018
• Inference : Analyzing the tweets of Bitcoin collected from different
news account sources are classified to positive or
negative sentiments.
• Advantages : To present the efficient method for selecting the features
that influences the crypto currency prices most.
• Disadvantages: It supports only one non-fungible token that it
BLOCKCHAIN with an accuracy of 75.9%.
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17. Literature survey - Paper 10
• Title : Automated Bitcoin Trading via Machine Learning
Algorithms
• Author : Mohan Das
• Year : 2021
• Inference : LSTM and GRU architecture are considered to be state of
the art in problem related sequential data.
• Advantages : It is very effective in sequence modelling tasks.
• Disadvantage : The performance of this model is 78.9% accurate only.
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18. Architecture Diagram
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19. ISSUES
● Monetize by exploiting their users
● Websites sell user data
● Post un-skippable ads
● Hide their best content behind paywalls
● Lack of Transparency
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20. Algorithm Used
Time series data :
• Normally a time series is a sequence of numbers along time. Premeawb for sequence
prediction acts as a supervised algorithm unlike its autoencoder version.
• Moreover, LSTM is great in comparison with classic statistics linear models, since it can
easier handle multiple input forecasting problems.
• In our approach, the LSTM will use previous data to predict 30 days ahead of closing price.
First, we have a need to decide on how many previous days one forecast will have access to.
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21. Algorithm Used
Recurrent neural networks (RNNs)
• They are the state of the art algorithm for sequential data and are used by Apple's Siri and
Google's voice search.
• It is the first algorithm that remembers its input, due to an internal memory, which makes it
perfectly suited for machine learning problems that involve sequential data.
• A Recurrent Neural Network (RNN) is a type of neural network well-suited to time series
data.
• RNNs process a time series step-by-step, maintaining an internal state from time-step to time-
step.
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22. Proposed Methodology
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The main options of the projected system are:
● To create d-Apps in under 5 minutes
● Create a better proof of attention protocol
● Store NFTs as a medium media assets are served over the internet

23. MODULES
AR WEAVE NETWORK
● The AR weave network is used to store Atomic NFTs permanently across thousands of
devices worldwide.
● AR weave makes information permanence sustainable.
● AR weave is a new type of storage that backs data with sustainable and perpetual
endowments, allowing users and developers to truly store data forever— for the very first
time. As a collectively owned hard drive that never forgets, AR weave allows us to remember
and preserve valuable information, apps, and history, indefinitely. By preserving history, it
prevents others from rewriting it.
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24. MODULES
THE PERM WEB
• On top of the AR weave network lives the perm web: a global, community-owned web that
anyone can contribute to or get paid to maintain.
• The first web connected people over vast distances, the perm web connects people over
extremely long periods of time.
• The perm web looks just like the normal web, but all of its content.
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25. MODULES
LAZY CONTRACTS
• In contrast to most popular blockchains, every Node does not all need to execute each action
in order for the network to function
• When a contract is executed by a node, the result is written back to the perm web, and the
Node completes their participation.
• Any Node can then check the result, and vote to slash the participating nodes.
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26. MODULES
DATA PREPROCESSING
• The primary knowledge collected from the web sources remains within the raw kind of
statements, digits and qualitative terms.
• The raw data contains error, omissions and inconsistencies.
• The process comprises:
1. Data Gathering
2. Data Cleaning
3. Data Normalization
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27. CONCLUSION:
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We have investigated NFTs as an emerging phenomenon and evaluated NFTs as a
core building block for a blockchain-based event ticketing system. We followed a
design science approach based on the guidelines by Hevner et al. (2004) and
iteratively developed a prototype. Through the process of designing, building, and
evaluating the NFT-based prototype, we were able to generate several relevant
findings regarding benefits and challenges of the new token type. We propose further
research to re-assess the state of these challenges in the near future.

28. REFERENCES
● Bitcoin: A Peer-to-Peer Electronic Cash System, https://bitcoin.org/bitcoin.pdf, last acessed:
2018/1/27.
● All cryptocurrencies, https://coinmarketcap.com/all/views/all/, last accessed: 2018/1/27 0
5000 10000 15000 20000 25000 6.18.17 7.18.17 8.18.17 9.18.17 10.18.17 11.18.17 12.18.17
1.18.18 Original Linear Regression Random Forests LSTM 455
● 2018 Will See Many More Cryptocurrencies Double In Value,
https://www.forbes.com/sites/kenrapoza/2018/01/02/2018-will-see-many-
morecryptocurrencies-double-in-value, last accessed: 2018/1/27
● Nilson, N.J.: Introduction to Machine Learning. An early draft of a proposed textbook,
Robotics Laboratory Department of Computer Science, Stanford University, Stanford, CA
94305 (2005)
● , Stanford University, Stanford, CA 94305 (2005)
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29. REFERENCES
● W. Brock, J. Lakonishok, and B. LeBARON, ‘‘Simple technical trading rules and the
stochastic properties of stock returns,’’ J. Finance, vol. 47, no. 5, pp. 1731–1764, Dec. 1992.
● R. U. Khan, X. Zhang, M. Alazab, and R. Kumar, ‘‘An improved convolutional neural
network model for intrusion detection in networks,’’ in Proc. Cybersecurity Cyberforensics
Conf. (CCC), May 2019, pp. 74–77.
● 8.Bitinfocharts. Accessed: May 1, 2020. [Online]. Available: https://bitinfocharts.com/
● D. Garcia, C. J. Tessone, P. Mavrodiev, and N. Perony, ‘‘The digital traces of bubbles:
Feedback cycles between socio-economic signals in the bitcoin economy,’’ J. Roy. Soc.
Interface, vol. 11, no. 99, Oct. 2014, Art. no. 20140623.
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30. REFERENCES
● 10. S. Rahman, J. N. Hemel, S. Junayed Ahmed Anta, H. A. Muhee, and J. Uddin, ‘‘Sentiment
analysis using R: An approach to correlate cryptocurrency price fluctuations with change in
user sentiment using machine learning,’’ in Proc. Joint 7th Int. Conf. Informat., Electron. Vis.
(ICIEV) 2nd Int. Conf. Imag., Vis. Pattern Recognit. (icIVPR), Jun. 2018, pp. 492–497.
● H. Jang and J. Lee, "An Empirical Study on Modeling and Prediction of Bitcoin Prices With
Bayesian Neural Networks Based on Blockchain Information," in IEEE Access, vol. 6, pp.
5427-5437, 2018
● Hota HS, Handa R & Shrivas AK, Time Series Data Prediction Using Sliding Window Based
RBF Neural Network, International Journal of Computational Intelligence Research,
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31. REFERENCES
● Jang Huisu,Jaewook Lee,Hyungjin Ko,Woojin Le, aPredicting Bitcoin price using
Rolling Window LSTM modela ,DSF, ACM ISBN 123-4567-24-
567/08/06,vol.4,pp.550- 580,2018.
● Dibakar Raj Pant, Prasanga Neupane, Anuj Poudel, Anup Kumar Pokhrel, Bishnu
Kumar “Recurrent Neural Network Based Bitcoin Price Prediction by Twitter
Sentiment Analysis” Lama 2018IEEE
● Amin Azari “Bitcoin Price Prediction an ARIMA Approach” 2018IEEE.
● Sean Mc Nally, Jason Roche, Simen Caton “Predicting the Price of Bitcoin Using
Machine Learning” 2018IEEE.
● Science, Stanford University, Stanford, CA 94305 (2005)
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