Zainab Khalid Mohammed, A. A. Zaidan, Hazleen Aris, Hassan A. Alsattar · 7 authors
Abstract Metaverse is a new technology expected to generate economic growth in Industry 5.0. Numerous studies have shown that current bitcoin networks offer remarkable prospects for future developments involving metaverse with anonymity and privacy. Hence, modelling effective Industry 5.0 platforms for the bitcoin network is crucial for the future metaverse environment. This modelling process can be classified as multiple-attribute decision-making given three issues: the existence of multiple anonymity and privacy attributes, the uncertainty related to the relative importance of these attributes and the variability of data. The present study endeavours to combine the fuzzy weighted with zero inconsistency method and Diophantine linear fuzzy sets with multiobjective optimisation based on ratio analysis plus the multiplicative form (MULTIMOORA) to determine the ideal approach for metaverse implementation in Industry 5.0. The decision matrix for the study is built by intersecting 22 bitcoin networks to support Industry 5.0's metaverse environment with 24 anonymity and privacy evaluation attributes. The proposed method is further developed to ascertain the importance level of the anonymity and privacy evaluation attributes. These data are used in MULTIMOORA. A sensitivity analysis, correlation coefficient test and comparative analysis are performed to assess the robustness of the proposed method.
This research paper presents a stacked ensemble model for next day Bitcoin price prediction, incorporating diverse look-back windows and evaluating the performance of various models within the ensemble framework using metrics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The base layer, layer-0, comprises LSTM and GRU models with different look-back windows. The layer-1 models, including CNN, SVR, Linear Regression, Random Forest Regressor, LSTM, and KNN, are tested individually in conjunction with the base layer models. Extensive experiments demonstrate the effectiveness of the stacked ensemble approach, improving prediction accuracy. The comparative analysis provides insights into the strengths and weaknesses of each model, aiding in the identification of optimized combinations for Bitcoin price prediction. This research contributes to the field by showcasing the value of diverse look-back windows and evaluating models in a stacked ensemble framework, enhancing the accuracy of Bitcoin price forecasting.
Parth Daxesh Modi, Kamyar Arshi, Pertami J. Kunz, Abdelhak M. Zoubir
Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality data to leverage their power. There are some techniques such as augmentation that can help us with increasing the dataset size, but we cannot exploit them on historical bitcoin data. As a result, we propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.
Gelişen teknolojinin sağladığı olanaklar sayesinde internet kullanımıyla gerçekleştirilen işlemlerde artış olmuş ve bu da verilerde artışa neden olmuştur. Bu durum işletmeler için verilerin güvenli bir şekilde saklanması, paylaşılması, kontrolünün sağlaması ve yönetilmesine yönelik yeni teknoloji ihtiyacı doğurmuştur. Bu kapsamda faydalanılabilecek güncel teknolojilerden birisi de blok zinciri (Blockchain) yapısıdır. Blok zinciri yapısı birçok alanda kullanılabilecek bir teknoloji olup günümüzde en popüler kullanım alanı kripto paralar üzerinde olmaktadır. Bu çalışmada önemli alt kripto para birimlerinden biri olan Polkadot kripto para birimi için tahminleme işlemi yapılması amaçlanmıştır. Yapılan çalışmada 20.08.2020 ve 27.02.2023 tarihleri arasındaki veriler kullanılmış olup, bu verilere göre çıktı değer olarak günlük ortalama Polkadot değerinin tahmin edilmesi amaçlanmıştır. Girdi değerleri için kümeler iki farklı şekilde oluşturulmuştur. İlk girdi değerlerinde; Polkadot YouTube arama sayısı, Polkadot Google arama sayısı ve Polkadot hacmi kullanılmıştır. İkinci girdi değerlerinde ise ilk girdi değerlerinden farklı olarak alt kripto paraların lideri Ethereum eklenmiştir. İki farklı girdi yapısından oluşan bu çalışmada Polkadot para birimi günlük ortalama değerlerinin tahminlenebilmesi için yapay sinir ağlarında çok katmanlı algılayıcılar ile derin öğrenme yöntemlerinden olan uzun kısa süreli bellek yapısı kullanılarak tahminleme çalışması yapılmıştır. Sonuçlar incelendiğinde elde edilen yapay sinir ağlarında 4 girdi kümesinden oluşan değerlerin 0,93 korelasyon katsayısı ile daha iyi sonuç verdiği belirlenmiştir.
Open access
Currency Recognition and Detection
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Non-fungible token (NFT) is a tradable unit of data stored on the blockchain which can be associated with some digital asset as a certification of ownership. The past several years have witnessed the exponential growth of the NFT market. In 2021, the NFT market reached its peak with more than $40 billion trades. Despite the booming NFT market, most NFT-related studies focus on its technical aspect, such as standards, protocols, and security, while our study aims at developing a pioneering recommender system for NFT buyers. In this paper, we introduce an extreme deep factorization machine (xDeepFM)-based recommender system, NFT.mine, which achieves real-time data collection, data cleaning, feature extraction, training, and inference. We used data from OpenSea, the most influential NFT trading platform, to testify the performance of NFT.mine. As a result, experiments showed that compared to traditional models such as logistic regression, naive Bayes, random forest, etc., NFT.mine outperforms them with higher AUC and lower cross entropy loss and outputs personalized recommendations for NFT buyers.
Abstract To measure the diversification capability of Bitcoin, this study employs wavelet analysis to investigate the coherence of Bitcoin price with the equity markets of both the emerging and developed economies, considering the COVID-19 pandemic and the recent Russia-Ukraine war. The results based on the data from January 9, 2014 to May 31, 2022 reveal that compared with gold, Bitcoin consistently provides diversification opportunities with all six representative market indices examined, specifically under the normal market condition. In particular, for short-term horizons, Bitcoin shows favorably low correlation with each index for all years, whereas exception is observed for gold. In addition, diversification between Bitcoin and gold is demonstrated as well, mainly for short-term investments. However, the diversification benefit is conditional for both Bitcoin and gold under the recent pandemic and war crises. The findings remind investors and portfolio managers planning to incorporate Bitcoin into their portfolios as a diversification tool to be aware of the global geopolitical conditions and other uncertainty in considering their investment tools and durations.
Ayesha Kalhoro, Asif Ali Wagan, Abdullah Ayub Khan, Jim‐Min Lin · 7 authors
Non-fungible tokens (NFTs) are individual tokens with valuable information stored inside them over blockchain technology. They can be purchased and sold like other physical and virtual art pieces because their worth is mostly determined by the market and demand. The unique data of NFTs render it simple to verify and authenticate their ownership and transfer of tokens between owners. However, in Pakistan, developers cannot acquire different licences to accomplish their projects not because they cannot afford it, but because they cannot invest in every piece of software to accomplish each new sensitive task. Rather, they can render the product platform independent. Considering this technology, this paper provides IT professionals with a new NFT approach and business policies that solely belong to the information technology domain. In addition, this paper also introduces how NFT tokens can hold software applications. Since we can store files, we can let NFTs also store complete applications to help developers in further utilising virtuality and having the metaverse at their fingertips. Whenever they succeed in a project, they never receive rewards, and their skills only pay the bills. In a nutshell, this paper presents a prototype of NFTs that would be further polished to save and utilise applications in a decentralised manner while rewarding the developers.
Raden Aditya Kristamtomo Putra, Mita Wahidiyat, Donna Carollina, Fairuz Iqbal Maulana
Non-Fungible Tokens (NFTs) now becomes the latest digital currency phenomenon. This phenomenon began in 2014 and widely used in nowadays. Based on this phenomenon, this research was carried out. The research conducted to do overview research related to Non-Fungible Tokens based on the SCOPUS database from 2017-2021. From the search results of the SCOPUS database, it was found that there were 68 studies related to Non-Fungible Tokens from 2017-2021. The most numerous documents are conference papers (N=37) and the publication source with the most documents (N=5) is Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics. The country with the most Non-Fungible Token keyword research is the United States (N=14). While the subject area of research that discusses the most Non-Fungble Token is Computer Science (N = 54). Based on the results, the research trend over Non Fungible Token is related to 2 cluster which is about Blockchain and Technology. So, there is there are still many opportunities for other research to be carried out outside the two clusters and their relationships.
Amogh Shukla, Tapan Kumar Das, Sanjiban Sekhar Roy
TRON is a decentralized digital platform that provides a reliable way to transact in cryptocurrencies within a decentralized ecosystem. Thanks to its success, TRON’s native token, TRX, has been widely adopted by a large audience. To facilitate easy management of digital assets with TRON Wallet, users can securely store and manage their digital assets with ease. Our goal is first to develop a methodology to predict the future price using regression and then move on to build an effective classifier to predict whether a profit or loss is made the next day and then make a prediction of the transaction success rate. Our framework is capable of predicting whether there will be a profit in the future based on price prediction and forecasting results using regressors such as XGBoost, LightGBM, and CatBoost with R2 values of 0.9820, 0.9825 and 0.9858, respectively. In this work, an ensemble-based stacking classifier with the Whale optimization approach has been proposed which achieves the highest accuracy of 89.05 percent to predict if there will be a profit or loss the next day and an accuracy of 98.88 percent of TRX transaction success rate prediction which is higher than accuracies obtained by standard machine learning models. An effective framework will be useful for better decision-making and management of risks in a cryptocurrency.
With the rapid developments in today's technologies, people can now perform their payment and shopping transactions through digital platforms. However, payment security problems in e-services have led people to seek alternative payment methods. Thanks to blockchain technology, cryptocurrencies that are not dependent on the central authority and can be paid in a completely secure way have been developed. Bitcoin is a digital currency that is not tied to a central authority or bank, introduced in Satoshi Nakamoto's 2008 article entitled "Bitcoin: The Peer-to-Peer Electronic Money System". Bitcoin, which attracts the attention of investors in the financial world, especially during the pandemic process, is traded in a market with high volatility. For this reason, it is of great importance for those who want to make forward price predictions. In this study, it is aimed to develop a price prediction method that will contribute positively to the profit share of Bitcoin investors. With Bitcoin, the data belongs to a time series, and Random Forest Regression, a model used to predict time series, was used. The model is trained on two years of Bitcoin data for the years 2020-2022. The statistical error measures of the model were calculated as MSE, R2, MAE and RMSE as 0.031%, 99.39%, 31.16% and 55.33%, respectively.
With the rise of Internet finance and big data, blockchain technology is expected to propose solutions to the challenges faced by agricultural supply chain finance in recent years. This paper will study the problems of food and safety and the low level of technology in rural areas through literature research. There is a gap between China's grain production rate and that of developed countries. Because of its decentralization and precise traceability characteristics, blockchain technology helps to build a distinctive regulatory and accountability system for food and agricultural safety in China. At the same time, blockchain technology with intelligent contract can effectively simplify the business process of agricultural supply chain finance, and reduce the threshold and cost of rural technology promotion, and increase security because of its features that cannot be changed artificially. It can be seen that the blockchain has practical significance to the challenges faced agricultural supply chain finance.
This paper aims to analyze cryptocurrency volatility by examining the effect of Gold, Dollar Index, and Composite Stock Price Index (IHSG) as independent variables and on Bitcoin and Ethereum as dependent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum, which have the largest market capitalization. The data in this study used the period January 1, 2018, to December 31, 2021. This study used GARCH analysis. This study's results indicate that Bitcoin's volatility is influenced by the price of Bitcoin itself, gold, and the stock exchange index, and Ethereum and the stock exchange index influence Ethereum. This shows that the cryptocurrency market is inefficient as the prices are also affected by past prices.
Nicolás López, Alexander Agbu, Adamson Oloyede, Emmanuel E. Essien · 6 authors
Certain IoT (Internet of Things) device data such as meteorological data are public goods, which are by definition, in high demand by a large user base. Accessibility to these datasets thrives with the use of blockchain technology. However, research has shown that due to the consensus mechanism of existing blockchain platforms, transaction approval delays and high transaction (gas) fees have been a challenge. This paper presents a software tool to integrate and store IoT device data onto a resource-efficient blockchain, with a decentralized proof of stake consensus mechanism, for faster and scalable application, with zero to near-zero transaction fees. • Some small Internet of Things (IoT) data like temperature, pressure, and humidity weather data can be stored on a blockchain. • Node-RED is a tool used by millions of users from large companies like IBM to small hobbyists. • The eosio-push node is custom built to take in IoT and transmit to a blockchain. • It can be run on raspberry pi. • It allows for customization of endpoints and message types. • The software is publicly available on NPM and receives between 10 and 50 downloads per week.
Mr. R. Arunachalam, Myana Santhoshini, R. Tamil Prabha, R. Tamil Prabha
In this paper, we tried to estimate the Bitcoin price precisely taking into consideration various parameters that affect the Bitcoin value. In our work, we pointed to understand and identify daily changes in the Bitcoin market while obtaining insight into most appropriate features surrounding Bitcoin price. We will predict the daily price change with highest possible accuracy. The market capitalization of publicly traded cryptocurrencies is currently above $230 billion. Bitcoin, the most valuable cryptocurrency, serves primarily as a digital store of value, and its price predictability has been well-studied. For the first phase of our investigation, we aim to understand and identify daily trends in the Bitcoin market while gaining insight into optimal features surrounding Bitcoin price. Our data set consists of various features relating to the Bitcoin price and payment network over the course of five years, recorded daily. For the second phase of our investigation, using the available information, we will predict the sign of the daily price change with highest possible accuracy with deep learning algorithm such as long short term memory for greater accuracy. Compared with benchmark results for daily price prediction, we achieve a better performance, with the highest accuracies of the statistical methods and deep learning algorithms. Deep Learning models includes Long Short-Term Memory in RNN for Bitcoin price prediction are superior to statistical methods
B. Subashini, Hemavathi Devarajan, Venkatesh Kaliamoorthy
Blockchains typically employ IPFS for off-chain storage of user information.Centralized management, muddled data, inaccurate data, and the simplicity of building information enclaves plague traditional traceability systems.In this research, blockchain technology is used to record and access data on Non-Perishable (NP) agricultural commodities in the distribution chain to solve the challenges above.The blockchain and IPFS both store public and private data encrypted.This lessens the burden on the blockchain and enhances information search.Blockchain technology enhances farmer-customer relationships and food supply chains by tracking food back to its source.Its secure data storage enables datadriven farming.By storing encrypted files IPFS hashes in smart contracts, IPFS secures agricultural data and addresses the blockchain storage problem.Being deployed in association with connects makes it possible for rapid financial transactions to occur with any changes made to the blockchain's data.This article analyses performance and simulates implementation in Ethereum testnets.The results show that our system protects sensitive data, supply chain data, and real-world applications by increasing the throughput and latency.
Aims: This article investigates recent advancements in machine learning and blockchain technology for cryptocurrency price prediction. The study presents a ML system using various techniques applied to six different datasets. The findings highlight that simpler models can outperform complex ones in predicting cryptocurrency prices. Methods: The methods used in this study include applying diverse ML techniques such as LSTM, CNN, SVM, KNN, XGBoost, Astro ML, LASSO, RIDGE, linear regression, DT, and GP on six cryptocurrency datasets to predict prices. Results: The research evaluated various machine learning techniques for predicting cryptocurrency prices and reported the following RMSE values: Bitcoin prediction using Nadaraya-Watson kernel regression yielded an RMSE of 0.17, while Dogecoin prediction with linear regression resulted in an RMSE of 0.032. Ethereum price prediction using Gaussian regression achieved an RMSE of 0.02. For USD Coin, a combination of XGBoost, Gaussian regression, and Ridge techniques led to an RMSE of 0.014. Binance Coin price prediction using Gaussian regression had an RMSE of 0.032, and finally, Cardano Coin prediction employing LSTM reached an RMSE of 0.059. Conclusion: This study demonstrated the effectiveness of various machine learning techniques in predicting cryptocurrency prices. It revealed that simpler models can outperform complex ones in certain cases. The research contributes valuable insights to the field and can guide future work in cryptocurrency price prediction. The proposed model achieved promising results as evaluated by the RMSE metric.
The success of blockchain technology in cryptocurrencies reveals its potential in the data management field. Recently, there is a trend in the database community to integrate blockchains and traditional databases to obtain security, efficiency, and privacy from the two distinctive but related systems. In this survey, we discuss the use of blockchain technology in the data management field and focus on the fusion system of blockchains and databases. We first classify existing blockchain-related data management technologies by their locations on the blockchain-database spectrum. Based on the taxonomy, we discuss three types of fusion systems and analyze their design spaces and trade-offs. Then, by further investigating the typical systems and techniques of each type of fusion system and comparing the solutions, we provide insights of each fusion model. Finally, we outline the unsolved challenges and promising directions in this field and believe that fusion systems will take a more important role in data management tasks. We hope this survey can help both academia and industry to better understand the advantages and limitations of blockchain-related data management systems and develop fusion systems that meet various requirements in practice.
As the price of virtual currency fluctuates greatly, precise prediction and appropriate trading strategies can bring investors best returns. This paper predicted the price of Ethereum and Bitcoin in the light of autoregressive integrated moving average model (ARIMA) and get a R2 of 0.995 and 0.993 respectively, which indicates the model can yield reasonable predictions. Then their investment ratios are set to 0.88 and 1.12 respectively by analytic hierarchy process (AHP). Particle swarm optimization (PSO) is used to solve the daily revenue function formed by the predicted price and the current price. Finally, the paper compared the returns yielded by the PSO trading strategy optimized by AHP and the strategy without optimization. It can be concluded that the AHP has a possibility of 64.66 per cent to yield more returns when used.
Jay Joshi -, Shivani More -, Vineet Kunder -, Karan Patel - · 5 authors
Non Fungible Tokens which are commonly known as NFT’s are digital items such as audios, Videos, Photographs etc. NFT’s are unique cryptographic tokens that exist on a blockchain and cannot be replicated. All Non Fungible Tokens have unique identification codes and metadata that differentiate each token from each other.
In the past few decades, there has been an increasing demand for assets trading with help of machine learning. Contemporarily, the cryptocurrency and gold market has become prosperous with extremely dramatical fluctuations. This paper aims to study the trading price laws based on machine learning scenarios of Bitcoin and Gold to predict the price of the two currencies. To be specific, this study gives an inside view of the application of a method combined three algorithms (i.e., KNN, XGBoost and LightGBM) to predict the future Gold and Bitcoin price browser based on past data from 2017 to 2022. According to the analysis, the study shows the difference of three models, the accuracy of the combined algorithms and proves the related metrics to predict the price of the Gold and Bitcoin. Overall, these results give a guideline for the investor to make sensible decisions about Bitcoin and Gold price and shed light on guiding further exploration of price forecasting in terms of machine learning approaches.
This study investigates the important role that the blockchain plays to manage the information about who did what and when and hence provides a strong base for any legal potential conflicts. Blockchain technology permits you to distribute, encrypt, and secure the records of digital transactions. In addition, bitcoin and other cryptocurrencies are encompassed in it. Even though the construction industry has traditionally been a late user of innovative technology compared to other sectors of the economy, it faces various hurdles in terms of trust, accessibility, information sharing, and process automation. As a result, stakeholders, clients, subcontractors, contractors, and suppliers have been unable to work together effectively. Even if building information modeling is employed, which envisions a centralized building, the primary benefit of blockchain is the secure storage of sensitive sensor data.
Monika di Angelo, Thomas Durieux, João F. Ferreira, Gernot Salzer
Abstract Blockchain programs (also known as smart contracts) manage valuable assets like cryptocurrencies and tokens, and implement protocols in domains like decentralized finance (DeFi) and supply-chain management. These types of applications require a high level of security that is hard to achieve due to the transparency of public blockchains. Numerous tools support developers and auditors in the task of detecting weaknesses. As a young technology, blockchains and utilities evolve fast, making it challenging for tools and developers to keep up with the pace. In this work, we study the robustness of code analysis tools and the evolution of weakness detection on a dataset representing six years of blockchain activity. We focus on Ethereum as the crypto ecosystem with the largest number of developers and deployed programs. We investigate the behavior of single tools as well as the agreement of several tools addressing similar weaknesses. Our study is the first that is based on the entire body of deployed bytecode on Ethereum’s main chain. We achieve this coverage by considering bytecodes as equivalent if they share the same skeleton. The skeleton of a bytecode is obtained by omitting functionally irrelevant parts. This reduces the 48 million contracts deployed on Ethereum up to January 2022 to 248 328 contracts with distinct skeletons. For bulk execution, we utilize the open-source framework SmartBugs that facilitates the analysis of Solidity smart contracts, and enhance it to accept also bytecode as the only input. Moreover, we integrate six further tools for bytecode analysis. The execution of the 12 tools included in our study on the dataset took 30 CPU years. While the tools report a total of 1 307 486 potential weaknesses, we observe a decrease in reported weaknesses over time, as well as a degradation of tools to varying degrees.
Blockchain technology is becoming widely popular nowadays along with their decentralized peer- to-peer network and its privacy. Bitcoin is also widely storming in the world. Blockchain technology changes the lifestyle of people and business views on many fields through its privacy and security. Many researches were done on this technology because of its security and requirements in various fields of life. In the current era major issues are security on online translation, cloud computing, large data and Blockchain more focus on designing secure service. The objective of writing this review paper is to summarize what Blockchain is and spread awareness about its usage, security and how it works. Key Words: Blockchain, Ethereum, Cryptocurrency, Bitcoin, Consensus Methodor Algorithm, consensus rules, Hash,Genesis Block, Applications of Blockchain, Booming Domain of Blockchain
NFT, or non-fungible tokens, are online certificates of ownership that can be traded based on data units stored in digital ledgers belonging to blockchain technology.This is non-fungible, meaning that it cannot be exchanged and is unique.NFT has been around since 2014.But now, it is increasingly being considered as a practical method for trading digital artwork or art.To buy NFT assets, you require special coins in the form of NFT coins, which consist of various types, such as mana coins, sand, axes, and other NFT coins.The NFT coins are used to process NFT purchase transactions.The movement of NFT coins over time is relatively erratic and uncertain.This NFT coin price prediction will be very useful for investors to know how the investment flow of each price works because the price of each NFT coin will change from time to time.through the literature study stage, interviews, and viewing daily NFT coin price data where the attributes used are date, open, high, low, close, and volume.The method used in this research is k-Nearest Neighbours.Dataset collection through the website www.coinmarketcap.comfor the period January 1, 2019 to December 31, 2021.Then the data processing is carried out.An accurate NFT coin price prediction model can help investors in considering transaction decisions because NFT coin prices, which tend to be non-linear, will allow investors to make predictions.This study aims to obtain the predicted value of NFT coins using the k-Nearest Neighbours algorithm.