Blockchain Papers

Faisal Jamil, Muhammad Ibrahim, Israr Ullah, Suyeon Kim · 6 authors

No abstract is available for this record.

Büşra KUTLU KARABIYIK, Zeliha Can Ergün

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.

Aleksandar Petrović, Ivana Strumberger, Timea Bezdan, Hothefa Shaker Jassim · 5 authors

Cryptocurrencies are defined as digital mediums of exchange, that use strong cryptography for securing the transactions and verifying the ownership of the coins. Blockchain operates in the background to guarantee the security, transparency and traceability of the transactions. Consequently, cryptocurrencies became more and more popular and established their considerable presence in financial sector. However, one of the major drawbacks in the cryptocurrency market is the unreliability and unpredictability of their values, that poses a major risk for any kind of investment. Predicting the price of cryptocurrencies is therefore a hot research domain today. This paper proposes a novel method to predict the prices, that is based on a hybrid machine learning and swarm intelligence approach. The results of the conducted experiments suggest that the proposed model obtains higher accuracy than other recent similar approaches, and that it can be successfully applied for this important task.

Sincy Joseph, Smitha Karunan

Blockchain, the underlying technology behind Bit-coin, is an emerging technology in industry. Blockchain has the power to reform the existing business processes more democratic, transparent, secure, and efficient. Banking industries are the first movers that capitalize the disruptive potential of this technology. The indian banking system is one of the complex bank payment system in this world. The current infrastructure that is used by indian bank system is real time gross settlement system based and it follows a centralized architecture. Due to this centralized architecture the processing of transactions are slow and cum-bersome. It also causes large amount for security and recovery purposes. The real time gross settlement based system demands high need for security, resilience, and performance. Moving from traditional system to blockchain platform is not the prior concern but making a system that provide security, confidentiality, and decentralized money lending mechanism is the core idea. Here proposed a novel system that enable a decentralized Banking system and services based on Ethereum blockchain platform. The system support different services including money deposit, money transfer and loan checking etc. using the distributed ledger technology.

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.

Islam M. Momtaz A. Sadek, Muhammad Ilyas

The internet of things (IoT) implementation leads to an extended attack surface that requires continuous mitigation of security. Mission-critical situations (e.g., Smart Grid, Intelligent Transportation Systems, video surveillance, e-health) to business-oriented applications are all examples of IoT applications (e.g., banking, logistics, insurance, and contract law). IoT has made human existence considerably easier and more efficient. Because IoT devices are less expensive to build due to the use of various software, sensors, and the use of Artificial Intelligence, they violate several security regulations. That's why, in the IoT systems; extensive security support is required, particularly for mission-critical applications, but also for downstream commercial applications. There have been a variety of security strategies and approaches suggested and/or used. Blockchain is an amazing concept that manages a distributed ecosystem without the need for a third party. Researchers discovered that it has the ability to solve IoT software-related security problems. The biggest obstacle is combining blockchain with resource-constrained IoT computers. An experimental setup to incorporate blockchain on a smart laboratory IoT -based was completed, and then tested against Wireshark sniffing attacks, the system was more vulnerable to cyber-crimes attacks as we found out that when we employed blockchain technology as the communication medium of IoT devices, the no. of bytes needed for packet transmission was more than the bytes needed without applying Blockchain with about 959 bytes on average.

Nihar Ranjan Pradhan, Akhilendra Pratap Singh, Rajesh Mahule

Nowadays, smart applications are increasing day by day to improve the standard of agriculture in villages. A modern day agriculture is characterised by the presence of numerous smart Information and Communication Technology (ICT)-enabled services such as automated agriculture monitoring, data privacy, among others. Such agriculture monitoring employ multitudes of Internet of Things (IoT) devices to collect and share data between trusted users by means of a centralized intermediary for monitoring and control of the myriad automatic activities. However, a centralized intermediary is plagued by issues such as single point of failure, risk of data loss, man-in-the-middle attack and so forth. Blockchain based smart contracts for automated control in smart agriculture provides a decentralized and secure alternative. With the increasing population across the world, the demand for high-quality products which are fresh as well as healthy is increasing significantly and therefore in a very limited time, the farming and food production have to increase yield significantly. In the coming future, we need to produce double of what we are producing now. There is much research done in the field of smart farming but they don't tackle issues such as storing of data on a central server, complete details of the products from beginning to end which could satisfy a customer as well as stakeholders. In this paper, we presented a novel Blockchain based solutions for secure and smart monitoring of agriculture lands.

Hasan Kilimci, Mehmet Yigit Yildirim, Zeynep Hilal Kilimci

Bitcoin as a digital cryptocurrency interests the scientists substantially in the areas of computer science, cryptography and economics. In this work, we propose to forecast the last price of Bitcoin Dollar rate in short-term or frequent trading transactions known as day-trading. In addition to statistical indicators such as maximum, minimum, and average prices, technical indicators such as Bollinger band (BB), hour-based moving average (MA), Relative Strength Index (RSI) are also evaluated as a feature set. In order to estimate the price of Bitcoin, different deep and hybrid deep learning methodologies are employed, namely convolutional neural networks (CNNs), long short-term memory networks (LSTMs), convolutional long short-term memory networks (ConvLSTM), CNN Long Short-Term Memory Network (CNN-LSTM). Extensive experiment results exhibit that the usage of ConvLSTM hybrid deep learning model is capable to estimate the price of Bitcoin with 2.4076 of MAPE result.

Surachai Chancharat, Julaluk Butda

Abstract This chapter examines the dynamic linkages between the returns of Bitcoin, gold, and oil by using daily closing price data between July 17, 2010 and January 8, 2021. This study applies the diagonal BEKK–GARCH model for the purpose of analyzing a volatility spillover of variables in positive or negative ways. The empirical results show that the lagged returns inversely affect their current returns in oil. Based on the return spillovers between Bitcoin and gold, the empirical results indicate a unidirectional return spillover from Bitcoin to gold. Moreover, the authors found a unidirectional return transmission is observed from oil to Bitcoin, implying that oil returns are useful in forecasting Bitcoin returns. These findings are not only valuable for understanding of the interrelationships between the returns of Bitcoin, gold, and oil, but they are also of great interest to portfolio managers, investors, and investment funds that are actively dealing in Bitcoin, gold, and oil.

Cheng Yiyang, Kazunori Takashio

In today's era, the security and authenticity of information have always received extensive attention, especially when people have doubts about the authenticity of data. Hence, a traceable data storage platform with the feature that theoretically cannot be tampered with becomes extremely important. Under such demands, the deployment of smart contracts can effectively trace the source and discover problems. This article introduces an environmental data detection platform based on blockchain technology and smart contracts. It aims to record the data continuously and verify the authority of each transaction.

Melike Bildirici, Bahri Sonüstün

No abstract is available for this record.

Sunita Dasman

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.

Muhammed Miah, Shah Jahan Miah, Santosh Venkatraman

Blockchain, eventually block chain, (BC) is a distributed ledger system that processes and preserves data and their transactions using time-stamped blocks in decentralized method, which is able to address many traditional issues of information systems design. Since the introduction of the digital currency or cryptocurrency “bitcoin” in 2008, its foundation, the BC technology has gained tremendous interest, both in industry and academia. Over the past few years, many existing review articles generally focus on specific aspects of this rapidly growing disruptive technology, rather than purely investigating the BC to produce a whole landscape illustration, so that researchers may gain comprehensive knowledge on it. This paper presents a unique review on BC technology revealing the concepts, how it works, structure, types, components, characteristics, and applications. Sourcing from open-access online materials and academic outlets, we analyze sample articles concerning BC, associative technologies, their potential application and issues. A total of 56 contents on BC and related technologies were selected for relevant recent documents of the emerging development. The research team scrutinized the contents using established content analysis approach. It is anticipated that the findings would offer benefits in enhancing understanding to innovative solution for development researchers and professionals in industries.

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.

Zeyu Wang, Mingyu Li, Lu Jia, Cheng Xin

No abstract is available for this record.

Yiming Li

With the emergence of a large number of virtual currencies represented by Bitcoin and Ethereum and the continuous rise of their prices, a large number of investors have been attracted to invest in them. The attention and research based on virtual currencies have aroused the wide concern of the social public. The price prediction of the virtual currencies is one of the research hotspots, obtaining and analyzing the historical data of virtual currency price for the prediction of future price. We find that some commonly used machine learning algorithms have a great deviation in the price prediction of virtual currency. To solve this problem, we propose a novel support vector regression (SVR) based on data segmentation, which can significantly improve the accuracy and effectiveness of price prediction of virtual currency. The experimental results show that our algorithm has obvious advantages over the traditional SVR algorithm and other eight classical machine learning algorithms.

Nurhayati Buslim, Imam Lutfi Rahmatullah, Bayu Setyawan, Aryajaya Alamsyah

Being the most expensive and most popular cryptocurrency, both the business world and the research community have started to study bitcoin development. However, due to the absence of most government regulation, the price of bitcoin has become uncontrollable, resulting in frequent large fluctuations. Using a dataset from 17 August 2017 to 13 April 2021 the GRU, RNN, and LSTM methods will be compared and implement Grid Search and Random Search to find out which one will do better in this research. Those three methods are considered to be the best method to get a prediction, but it also depends on the model that computer could have. The best result is the GRU with Grid Search method with MAE (Mean Absolute Error) of training 0.0043 and testing about 0.0594.

Bhargavi K. Chauhan, Dhirenbhai B. Patel

No abstract is available for this record.

M. M. Fazle Rabbi, Prince Mahmud Hradoy, Md Mainul Islam, Md. Hsibul Islam · 6 authors

Blockchain technology is a decentralized ledger system that records immutable data. Nowadays blockchain is used to record, validate and secure peer-to-peer transactions. This paper focuses on how to use a blockchain network to make a partially decentralized system that we can use in banking agencies to sanction the loan. The consortium blockchain is maintained by pre-sets nodes that can improve transparency and security which is essential for the financial institution. This paper is a theoretical paper where we propose a model to validate loans and safely store the loans information in a blockchain network. The proposed model also uses the Hyperledger Fabric blockchain to implement the smart contracts method called ‘chaincodes’. The constructed system will consist of a blockchain network that can improve the reliability, efficiency, transparency, and safety of processing loans by the banking industry.

Jongyeop Kim, Seong-Soo Kim, Hayden Wimmer, Hong Liu

This study aims to predict cryptocurrency prices using Long Short-Term Memory(LSTM) and Gated Recurrent Unit(GRU) for three different coins: BitCoin, Ethereum, and Litecoin. For the training data for prediction, two data sets with different statistical characteristics in terms of Kurtosis and Skewness are used. LSTM and GRU models are trained and tested on the same hyperparameter configuration while increasing the number of epochs from 1 to 30. The accuracy of each model is measured by Root Mean Square Error (RMSE) and MAE (Mean Absolute Error). As a result of comparing GRU and LSTM, in BTC and ETH, the GRU was more advantageous for the downward stabilization trend, and the LSTM was suitable for the upward stabilization trend. However, in case of low-priced LTC, LSTM and GRU showed the same performance in sample type A, and in the case of type B, GRU was more accurate.

Henri Bouma, Armin Reuter, Patrick Brouwer, Martin George · 19 authors

Authentication of travel documents (e.g., passports) and breeder documents (e.g., birth certificates) is important to facilitate legal movement of passengers and to prevent cross-border crime, such as terrorism, smuggling, illegal migration and human trafficking. However, it is time consuming and difficult to verify all security features, the border guards differ in experience and expertise, and it is hard to stay alert every minute of a working day. New (artificial-intelligence based) technologies can assist in the automated fraud detection in travel and breeder documents, which may lead to faster and more consistent checks. This paper presents five categories of new technologies in automated document authentication to overcome the limitations of current document analysis systems in automated and non-automated border control scenarios. The first category consists of techniques related to the verification of visual security features on the holder page of travel documents. This category includes the verification of KINEGRAMs and other Optically Variable features under different light sources and lighting angles, and the analysis of printing techniques. The second category consists of techniques related to the analysis of breeder documents. This analysis can be at detail level (e.g., investigation of stamps) and at tactical level (e.g., verification of a check digit in a document number). The third category concerns the analysis of travel patterns, using information from the visa pages in passports. The stamps on these pages can be used to extract a travel pattern to support risk assessments and to detect anomalies. The fourth category is an analysis of the border-guard inspection history based upon a distributed ledger and blockchain technology that enables secure storage and prevents undesired manipulations. The last category analyzes the electronic chip of a passport. The software analyses document signer and country signer certificates on the chip to detect vulnerable cryptographic keys and tactical anomalies.

Erdoğan Kaygın, Yunus Zengin, Ethem Topçuoğlu, Serdal Özkes

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.

Chinya V. Ravishankar, K. S. Kavitha

No abstract is available for this record.