The oil and gas industry involves a high level of operational expenditure and often faces high risks of asset safety and operational failures. It has never been so important to monitor and control the oil field operations remotely in real-time to ensure safety and efficiency. The traditional monitoring and control systems for oil field operations are typically centralized, prone to failure, and lack efficiency. Blockchain technology mitigates the centralization problem by creating a decentralized, immutable and transparent control environment for automatic monitoring and control of industrial operations. In this study, we propose a blockchain-based IoT framework for real-time monitoring and control to increase oil field operation and asset efficiency and safety. We present the key components of the framework, including the system architecture, operation flows, algorithms, and smart contracts. As a proof-of-concept modeling, a smart contract is developed and validated on a blockchain test platform. A comparative analysis shows the advantages of using blockchain technology and smart contract to provide trustworthy and automatic monitoring and control for oil field operations.
Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%.
With the development of information technology and network technology, digital archive management systems have been widely used in archive management. Different from the inherent uniqueness and strong tamper-proof modification of traditional paper archives, electronic archives are stored in centralized databases which face more risks of network attacks, data loss, or stealing through malicious software and are more likely to be forged and tampered by internal managers or external attackers. The management of intangible cultural heritage archives is an important part of intangible cultural heritage protection. Because intangible heritage archives are different from traditional official archives, traditional archive management methods cannot be fully applied to intangible heritage archives’ management. This study combines the characteristics of blockchain technology with distributed ledgers, consensus mechanisms, encryption algorithms, etc., and proposes intangible cultural heritage file management based on blockchain technology for the complex, highly dispersed, large quantity, and low quality of intangible cultural heritage files. Optimizing methods, applying blockchain technology to the authenticity protection of electronic archives and designing and developing an archive management system based on blockchain technology, help to solve a series of problems in the process of intangible cultural heritage archives management.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Recently, Bitcoin has gained great importance in the cryptocurrency market with the highest market capitalization. Investors and researchers have attempted to find out the drivers of Bitcoin prices and if they are predictable. However, there is only limited research in the literature that identifies the most effective economic and technical variables for predicting Bitcoin prices using machine learning models. Thus, in this study, the future Bitcoin prices utilizing several economic and technical factors using the ANFIS model are aimed to forecasted between 01.05.2013 - 26.02.2021 periods. The findings show that the ANFIS model produced accurate and consistent predicting results that are in line with the real data. As a result, investors who wish to make a profit by predicting future Bitcoin values might consider using the ANFIS approach as a forecasting tool.
Aboosaleh Mohammad Sharifi, Kaveh Khalili‐Damghani, Farshid Abdi, Soheila Sardar
Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly in recent years due to significant growth in its prices. It is important to create a prediction system which works well for investment management and business strategies due to the high chaos and volatility of Bitcoin prices. In this study, in order to improve predictive accuracy, Bitcoin price dataset is first divided into a time interval through time window, then propose a new model based on Long Short-Term Memory (LSTM) neural networks and Metaheuristic algorithms. Chaotic Dolphin Swarm Optimization algorithm is used to optimize the LSTM. Performance evaluation indicated that the proposed model can have more effective predictions and improve prediction accuracy. In addition, the performance of the optimized model is better and more reliable than other models.
This study aims to explore the potential use of the cryptocurrency bitcoin as an investment instrument in Indonesia. The return obtained from bitcoin cryptocurrency is compared to other investment instruments, namely stock returns, gold and the rupiah exchange rate. The research period was carried out based on research data from 2011 to 2020. This study employee compares means test (t test) and analysis of variance (F test) on rate of return of bitcoin investment. The bitcoin return compare to the rate of return form the others investments instruments namely exchange rate, gold and stock. The study collected 120 data of each investments instruments: bitcoin, exchange rate, gold and stock from various of sources during 2011–2020. Then, we calculate the return and risk of individual investment instruments. The results showed that the bitcoin currency had the highest rate of return 18% with a standard deviation of 61% compared to exchange rate, gold and stock returns. While the rate of return for the others investment instruments showed less than 0.5% with standard deviation less than 5%. The rate of return bitcoin has significance difference compare to the rate of return of exchange rate, gold and stock. The study contribute for the investors who would like to invest on bitcoin. The investors should understand the characteristic of bitcoin in term of rate of returns and also the risk. This study also contributes to government of Indonesia on crypto currency development. The Indonesia government should adopt and regulate on crypto currency in the future to secure the investor and economic growth.
K. Sri Lakshmi Sruthi, D. Ratnagiri, Rudru Jyothika, Salunkhe Sneha · 6 authors
Bitcoin is one of the most popular and valuable cryptocurrencies in the current financial market, attracting traders for investment and thereby opening new research opportunities for researchers. Countless research works have been performed on Bitcoin price prediction with different machine learning prediction algorithms. For the project: relevant features are taken from the dataset having strong correlation with Bitcoin prices and random data chunks are then selected to train and test the model. The random data which has been selected for model training, may cause unfitting outcomes thus reducing the price prediction accuracy. Here, a proper method to train a prediction model is being scrutinised. The proposed methodology is then applied to train a simple Long Short-Term Memory (LSTM) model to predict the bitcoin price for the upcoming 30 days. When the LSTM model is trained with a suitable data chunk, thus identified, sustainable results are found for the prediction. In the end of this project, the work culminates with future improvements. Bitcoin is a kind of Cryptocurrency and now is one of type of investment on the stock market. Stock markets are influenced by many risks of factor. And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes sudden fall without knowing influence behind it on the stock market. Because it's fluctuations, there's a need and automation tool to predict bitcoin on the stock market. This research study learns how to create model prediction bitcoin stock market prediction using LSTM, LSTM (Long Short-Term Memory) is another type of module provided for RNN later developed and popularized by many researchers, like RNN, the LSTM also consists of modules with recurrent consistency. The Method that we apply on this project, also technique and tools to predict Bitcoin on stock market yahoo finance can predict the result above $ 12600 USD for next days after prediction, in the last section we make conclusions and discuss future works.
Abstract Technological developments have always led to changes in all aspects of our lives. Crypto currency is one of those changes. As a result of those changes, thousands of currencies such as bitcoin, ripple, litecoin and ethereum have evolved and have found a use in business. The present study focuses upon Ripple and tries to explain its effects on banks and business theoretically. It has been stated that the money transfer performed through Ripple is faster and more economical when compared to present systems. Additionally, it has been realised that the present SWIFT system has been influenced by that speed and economy, and therefore taken considerable technologic steps with an effort to improve its system.
Marina Andreianova, Sebastian Sonntag, Teemu Antikainen, Cecilia Alho · 5 authors
Bitcoin usage has grown from zero to hundreds of billions of dollars in just over a decade. However, the research and studies of bitcoin usage fall short. This is mainly due to the nature of bitcoin, but also the myths of the early days. In this study, we go through the user data from LocalBitcoins, the largest peer-to-peer bitcoin marketplace in the world to study the bitcoin usage and needs. By analyzing and classifying major use cases for bitcoin through surveys and statistical analysis, this study will bring a better understanding of the different use cases for bitcoin and the role they play in bitcoin adoption. This research shows that the scope of bitcoin usage is noticeably more diverse and not merely limited to illegal usage or speculation.
Jay Mehta, Darsh Mehta, Jainam Jain, Surekha Dholay
Abstract— The technique of following a product or a batch of things throughout the supply chain to ensure that the products that reach clients are authentic and tamper-proof is known as asset tracking. The ultimate goal of an asset monitoring system is to track products along the supply chain, verifying that they haven't been tampered with and, if they have, pinpointing where the tampering took place. Traditional tracking technologies, such as BLE (Bluetooth Low Energy Beacon), which works within a limited range, RFID, and above-mentioned systems, are expensive and centralised. So, for this project, we'll use Blockchain Technology, which is an immutable, tamper-proof, decentralised distributed ledger with security features that allows us to establish an asset tracker that can follow our product along the supply chain. Ethereum is used to implement the system. Unlike other methods, there are no hardware components or large gadgets that may be removed from the original object and attached to the copy. Keywords— Blockchain, Supply Chain, Smart Contract, Keccak-256, Ethereum,GUI
Since the inauguration of cryptocurrencies, Bitcoin has been under pressure from competing tokens. As Bitcoin is a public open ledger blockchain coin, it has its weaknesses in privacy and anonymity. In the recent decade numerous coins have been initiated as privacy coins, which try to tackle these weaknesses. This research compares mostly mature privacy coins to Bitcoin, and comparison is made from a price perspective. It seems that Bitcoin is leading privacy coins in price terms, and correlation is typically high and positive. From the earlier crypto market peak of 2017–18, only a very small number of coins are showing positive returns in 2021. It is typical that many privacy coins have lost substantial amounts of their value (ranging 80–90%) or that they do not exist anymore at all. Only Horizen and Monero have shown long-term sustainability in their value; however, their price changes follow that of Bitcoin very closely. The role of privacy coins in the future remains as an open issue.
The gold mine has been a commodity used for thousands of years, today it is also an investment tool with the highest reliability. However; cryptocurrencies that are recently used are affecting our portfolio. Bitcoin is the most traded cryptocurrency. Since there are alternative investment instruments involved in portfolios, the relationship between these two independent values inspired the emergence of this study. The aim of this study was to investigate whether there is a causality-cointegration relationship between daily Bitcoin prices and gold prices for the periods between 10,01,2014 and 11,12,2020. In the application section, Toda Yamamoto causality and the Maki Cointegration test were applied. According to the results of the Toda Yamamoto causality test, there is a two-way causality relationship. According to the results of the Maki cointegration test, there was no long-term relationship between the series. As a result, it is expected that in the long term, investors will have a risk-reducing effect by including both investment instruments in the same portfolio.
A blockchain is a digital record of transactions. The name comes from its structure, in which individual records, called blocks, are linked together in single list, called a chain. It stands out with the rise of Bitcoin. Its popularity is increasing day by day. It provides anonymity, privacy and data integrity without any control organization. In this report, we will reveal the areas of research that have emerged. We will touch on what these research areas can bring forward. Articles are generally related to Bitcoin. But lately, this technology has entered many areas of our lives. Recommendations on future research directions are provided in this paper.
Data security is the key to the development of modern Internet technology. The distributed, decentralized, and secured hashed mechanism of the blockchain gives a complete new point of view for the evolution of data security technology. Block chain technology is one of the major technological innovations of this century. In the last couple of years, the interest around blockchain technologies is increasing. Many implementation of blockchain technology are widely available today. Blockchain,the foundation of Bitcoin, has gain much attention in this era. Blockchain is an encrypted, immutable, distributed ledger, which allows transactions take place in a decentralized manner. Blockchain based applications expected to alter numerous fields including financial services, health care, entertainment media, Internet of Things (IoT), and many more. The Blockchain technology plays important role in the process of data security. In this paper, we will discuss about the research being done on this new domain of Computer Science. It is not only the most popular topic to discuss about, but is the most technological innovation, that is all set to reform the entire world.
Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin. Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency. This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested. Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model. With the help of python libraries, the data filtration process was done. Python has provided with a best feature for data analysis and visualization. After the understanding of the data, we trim the data and use the features or attributes best suited for the model. Implementation of the model is done and the result is recorded. Finding: It was discovered that the linear regression model’s accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate. The LSTM model, on the other hand, shows a mini error rate of 0.08 percent. This, in turn, demonstrates that the neural network model is more optimized than the machine learning model. Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin. This paper compares the prediction outcomes of a machine learning model and an artificial neural network model. Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model. Keywords: Bitcoin; Block chain; Crypto currency; Machine Learning; Artificial Neural Network
Bitcoin and other digital currencies are financial assets with high volatility, which calls for an investigation of the factors that influence their prices and thus has led to a debate on whether they are reliable investment instruments or diversification tools. The present study aims to explore the impact upon Bitcoin prices of commodities such as gold and oil, the S&P 500 index, and the volatility index and financial stress index, which represent the financial risk environment. To this purpose, we analyze this relationship using the Autoregressive Distributed Lag (ARDL) approach based on the monthly data from the 2010-2021 period. The results suggest that both in the long and short run, gold price per ounce does not have a statistically significant effect on Bitcoin price. On the other hand, an increase in crude oil prices has a negative impact on Bitcoin price in the short run, with no significant effect in the long run. The S&P 500 stock market index positively affects the Bitcoin price both in the short and long run. In addition, our analysis results also demonstrate that developments indicating increased risk in the long run tend to reduce Bitcoin returns.Keywords: Bitcoin, gold, oil, volatility, ARDL.JEL Classifications: G11; B23DOI: https://doi.org/10.32479/ijefi.11602
This study examines the potential of cryptocurrencies such as Bitcoin, Ethereum, ripple, tether, and Bitcoin cash as hedging instruments and a safe haven for the Indonesian capital market, especially during the Covid-19 pandemic era. Now, Indonesia's capital market condition is in turbulence. The benefit of this research is to help the investors make decisions on which cryptocurrencies can be an instrument hedge and safe haven in this Covid-19 pandemic era for Indonesia Stock Exchange (IDX). The data used in this study are data on the closing price of the Composite Stock Price Index (CSPI), bitcoin (BTC), Ethereum (ETH), ripple (XRP), tether (USDT), and bitcoin cash (BCH) from January 3 to June 16, 2020. Data analysis used Generalized AutoregressiveConditional Heteroscedasticity (GARCH) and Quantile Regression (QREG). This study found that Bitcoin, Ethereum, tether, and Bitcoin cash can act as a hedge, but only the ripple cannot act as a hedge. Bitcoin, Ethereum, ripple, tether, and bitcoin cash cannot act as a safe haven when the Indonesian capital market was getting extreme, like during the Covid-19 pandemic era. The roles of Bitcoin, Ethereum, ripple, tether, and bitcoin cash as safe havens will fade when conditions in the Indonesian capital market become more extreme. This research can be used as a reference for investors for their investments by looking top four cryptocurrencies as a hedging instrument. However, in severe conditions such as during the Covid-19 Pandemic, the top five cryptocurrencies cannot be used as a safe haven, as revealed in this study.
Gausiya Momin, Trupti Ingle, Vaishnavi Mirajkar, Anand Magar
Bitcoin is the most profitable in the cryptocurrency market. However, the prices of Bitcoin have highly fluctuated which makes them very difficult to predict. This research aims to discover the most efficient accuracy model to predict Bitcoin prices from various machine learning algorithms. Using one-minute interval trading data on the exchange website name is bit stamp from January 1, 2012, to January 8, 2018, some different regression models with sci-kit- learn and Keras libraries had experimented. The best results showed that the Mean Squared Error (MSE) was as low as 0.00002 and the R-Square (R2) was as high as 99.2 Percentage.
In recent years, Bitcoin price prediction has attracted the interest of researchers and investors. However, the accuracy of previous studies is not well enough. Machine learning and deep learning methods have been proved to have strong prediction ability in this area. This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast.
As a part of food safety research, researches on food transactions safety has attracted increasing attention recently. Food choice is an important factor affecting food transactions safety: It can reflect consumer preferences and provide a basis for market regulation. Therefore, this paper proposes a food market regulation method based on blockchain and a deep learning model: Stacked autoencoders (SAEs). Blockchain is used to ensure the fairness of transactions and achieve transparency within the transaction process, thereby reducing the complexity of the trading environment. In order to enhance the usability, relevant Web pages have been developed to make it more friendly and conduct a security analysis for using blockchain. Consumers' reviews after the transactions are finished can be used to train SAEs in order to perform emotional tendencies predictions. Compared with different advanced models for predictions, the test results show that SAEs have a better performance. Furthermore, in order to provide a basis for the formulation of regulation strategies and its related policies, case studies of different traders and commodities have also been conducted, proving the effectiveness of the proposed method.
Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.
Since the invention of the Blockchain technology in 2008, it has been used in many domains to ensurehigh security and reliability of data, like from the use of Bitcoin to BaaS (Blockchain as a Service)which is a new blockchain trend and is a sort of cloud-based network for the organizations in thebusiness of building blockchain-based applications. This paper implements the combined approachof the decentralized Blockchain technology and the Supply Chain to establish that the end-users ina supply chain do not completely rely on the trader to establish that the product is counterfeited ornot and this can be done by authenticating the product at every stage in the Supply Chain by usingOne Time Passwords on the receiver’s mobile phone along with a deployed personnel who willbe responsible for assuring the quality of products. Furthermore, using this combined technical approachcan considerably lower down the cost of product quality assurance and this proposed systemwill track the authenticity of the product from its origin from the manufacturer to the end-user as well.
Central Bank Digital Currency (CBDC) is a digital currency issued by a central bank. Motivated by the financial crisis and prospect of a cashless society, countries are researching CBDC. Recently, global consideration has been given to paying basic income to avoid consumer sentiment shrinkage and recession due to epidemics. CBDC is coming into the spotlight as the way to manage the public finance policy of nations comprehensively. CBDC is studied by many countries. The bank of the Bahamas released Sand Dollar. Each country’s central bank should consider the situation in which CBDCs are exchanged. The transaction of the CDDB is open data. Transaction registers CBDC exchange information of the central bank in the blockchain. Open data on currency exchange between countries will provide information on the flow of money between countries. This paper proposes a blockchain system and management method based on the ISO/IEC 11179 metadata registry for exchange between CBDCs that records transactions between registered CBDCs. Each country’s CBDC will have a different implementation and time of publication. We implement the blockchain system and experiment with the operation method, measuring the block generation time of blockchains using the proposed method.