Zehra Kadiroğlu, Beyza Nur Akılotu, Abdulkadir Şengür
Bitcoin is a digital currency that uses cryptographic rules to regulation and generation units of currencies. In 2009, it was introduced by a person (or a group of people) using the name Satoshi Nakamoto before putting on the market as open source software. Bitcoin is a new payment system and investment tool for the purchase, storage and use of money as digitally. However, when people want to invest in bitcoin, the biggest factor they have to consider is how the price will change in the future. Recent developments in the field of machine learning have shown that some algorithms can provide appropriate solutions to predict future prices of crypto currencies. In this study, a detailed literature review is made about the studies using machine learning methods to estimate the future price of bitcoin. Basic and current information about bitcoin was included in this study. In addition, the theory of machine learning methods used is explained. The findings of the literature review show that machine learning methods can be successful in predicting bitcoin price.
Abdul Ghaffar Khan, Amjad Hussain Zahid, Muzammil Hussain, Muhammad Shoaib Farooq · 6 authors
Today the WEB is growing and expanding with the unexpected high speed and recently it gained extensive attention. The world is focusing and working on the web so that it could make more ease in our life. The web start from static pages and goes towards the smart industries and factories. All the industrial machines are augmented and directly connected to the web which leads to connect with different systems. This type of systems visualizes the whole production process and these systems are autonomous to take decisions on its own. Forth industrial revolution is the second name of industry 4.0 which includes the Internet of Things (IoT), Cognitive Computing (CC), Big Data, AI and it is fostering Industrial Internet of Things (IIoT). Advanced web is covering the maximum aspects to encourage the businessman community to make trading using technology. The blockchain version 4.0 is helping the industry to ensure the transparency of supply, immutability of records and it ensures the trust of end to end trading organizations. Supply chain is now using the blockchain technology.
Asli Demir, Beyza Nur Akılotu, Zehra Kadiroğlu, Abdulkadir Şengür
Bitcoin is a new currency that is recognized as a creative payment network. The operating system functions independently of a central structure or bank. Bitcoin is managed by the developed network. Bitcoin's open source code structure allows it to be uncontrolled and uncontrollable by an individual. The use of bitcoin requires virtual wallet. Verification of all payments is secured using public key encryption. Fluctuate in Bitcoin prices at a high rate, making it difficult to predict. For this reason, new cryptology methods related to bitcoin price estimation and security are tried in the literature. In our study, bitcoin price estimation was made by using machine learning methods using KAGGLE Bitcoin Dataset 2010-2019 data set. The methods used are long-short term memory networks, support vector machines, artificial neural networks, Naive Bayes, decision trees and the nearest neighbor algorithm. Obtained accuracy rates are 97.2%, 91.8%, 86.6%, 85%, 81.2% respectively.
Yasin Akın, Caner Dikkollu, Bekir Baran Kaplan, Uğur Yayan · 5 authors
The following topics are dealt with: learning (artificial intelligence); data mining; pattern classification; feature extraction; social networking (online); neural nets; security of data; convolutional neural nets; Big Data; evolutionary computation.
The Blockchain technology provides a way to record transactions or any digital interaction that is designed to be secure, tamper-proof, transparent, highly resistant to outages, traceable and auditable. The Internet of Things (IoT) technology is able to link computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and provides the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. These features encourage us to explore the combined application of IoT and Blockchain-based technology. In this paper, we propose a system architecture of blockchain and IoT based intellectual property protection system, which can process three types of intellectual property: 1) Patents, Copyrights, Trademarks etc.; 2) Industrial design, Trade dress, Craft works, Trade secrets etc.; and 3) Plant variety rights, Geographical indications etc. Using blockchain P2P network and IoT devices, the system can help us to establish a trusted, self-organized, open and ecological intellectual property protection system. To the best of our knowledge, this is the first work that applying blockchain technology and IoT technology on traditional intellectual property protection and trade ecosystem.
The encrypted money market has attracted the attention of investors all over the world. Among the encrypted currency, Bitcoin is undoubtedly the most popular. Because blockchain technology is the crucial support of bitcoin, exploring the relationship between bitcoin and the Blockchain Index is necessary. In addition, the volatility of bitcoin and the Blockchain Index is crucial for investors. Therefore, this paper uses the Granger causality test to explore the correlation between bitcoin and the Blockchain Index. Furthermore, their volatility is analyzed by a GARCH-class model.
Nowadays blockchain technology is a subject of continuous interest both of scientists and practitioners in order to expand its implementation in other sectors of economy and finance. Still the most popular application of blockchain technology rests bitcoins. The present paper proposes several possible applications of blockchain in the sphere of Cultural and Creative Industries based on the proven and accepted so far strengths of the technology.
Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with their many variations that can effectively forecast. However, with the recent advancement in the computational capacity of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms have been developed to forecast time series data. This article compares different methodologies such as ARIMA, Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM) and WaveNets for estimating the future price of Bitcoin.
In the modernized world like digital world, traditional way of payments through banks and other third parties are out of sphere. To meet the digital competency digital token like bitcoin based crypto currency payment is required. Lots of business persons are moving towards the digital way of secure payment. Intruders like hackers hamper the digital token and make immortality in the transaction which in turn create the double spend. Double spend is a serious threat in the Bitcoin network. Our research work focuses on double spend detection of transaction before it gets confirmed and added to the block by the miners. The proposed new architecture for detecting double spend using Dual Payout based on Lost Agreement Amount (DPL2A) will identify one of the ways that double spend attack occur before it is added to the blockchain. This architecture gives the clear identification of double spend attack and their full details of transaction occurrence so that when it is broadcasted into the peer-to-peer network, the network nodes will use this architecture to detect double spend, its occurrence is fully prevented and only the genuine transaction will be added to the blockchain.
The red-hot crypto currency is a bitcoin which occupies first position in the capital investment of financial world which is assaulted by various factors like wallet attacks, network attacks, mining attacks and double spending attacks. Double spending is the major attack in which the attacker tries to cheat the network nodes and use the same coin for more than one set of transactions. Of this the original transaction identification from the set of transactions is a challenging one. In this paper we propose a solution for identifying the primary transaction from the set of double spended or multi spended transactions. The proposed approach finds the authentic transaction from the list of double spended transactions using transaction hash value, which is primarily used for every transaction in the Bitcoin network. Transaction hash value is used as transaction identifier for each bitcoin transaction. By comparing the transaction hash value with the existing pool of unconfirmed input pool, transhash pool and utxopool one can identify the genuine transaction from the flawed transaction list. The firsthand transaction is then added to the Confirmed input pool which is then entered into the newly added block of the blockchain. This architecture will prevent the double spend of bitcoin further in the network which facilitates the network nodes as well as minimize the miners task for verification and validation of transaction.
Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable.
L. B. Krithika, Abhisek Mazumdar, Rajesh Kaluri, Wang Jing
Blockchain technology is very trending and promising. It can revolutionize the traditional way of manipulation of data in many industries. There are industries which blockchain can disrupt: banking, cyber security, smart contract, insurance, cloud storage, government, healthcare, media streaming. The decentralized approach of blockchain using peer-to-peer system to verify the correct record of the ledger, which builds a trust in the system. A system can be compiled and made to get adopted with the concept of smart contract. The aim of the work is to develop a system that is flexible enough to get implemented in the industries like finance, cyber security, data storage, buying and selling of properties, healthcare, etc. This will use a one-way encryption method known as SHA-256. A block with the 256-character code bind with the other metadata of the block will be termed as a smart contract for the item.
Blockchains were initially introduced in 2009, and since then, they have evolved into a revolutionary technology that has affected, not only the digital world but also our everyday lives and activities. Blockchain systems and architectures continue to grow at a fast rate, constantly introducing innovative solutions across multiple research topics. However, despite their wide adoption by the research community, the technology is still not mature enough to address the problems of business use cases. Issues like scalability, complexity, cryptocurrencies' market fluctuations etc. prevent the proposed solutions from being widely accepted. Based on this observation this paper introduces an innovative solution to enable Big Data analysis of private information using blockchains. This solution is based on the Hyperledger Fabric blockchain and is able support the strict privacy and security requirements of operational and business environments.
Cryptocurrency made on the basis of block-chain technology Bitcoin is drawing the attention of individuals, corporations, governments and financial institutions today. As the number of Bitcoin transactions increases over the past years, the scale of the Bitcoin market has been increasing day by day. Predicting the number of transactions contained in a Bitcoin block is important in a Bitcoin network. The aim of this paper is to propose a learning feature selection method for designing a machine learning model that predicts the number of transactions contained in the Bitcoin block by applying the machine learning algorithm. Selecting the appropriate feature to design a machine learning model is crucial things to the performance of the model. We apply correlation analysis to select the appropriate learning feature of the transaction count prediction model in the Bitcoin block and verify the validity of the proposed method through experiments.
I Made Wirawan, Triyanna Widiyaningtyas, Muchammad Maulana Hasan
Bitcoin is currently the most widely used digital currency. The popularity of bitcoin continues to increase and become an asset of investment. To handle with the erratic bitcoin price changes, a prediction method is needed to help its users in predicting the price in the future. By utilizing a method that is able to recognize the pattern of change in the data time series in a certain period of time can be known bitcoin price for several days ahead with a high degree of accuracy. This research uses experimental methods. Data obtained from www.coingecko.com on May 1, 2013 to June 7, 2019. Preprocessing stage includes attribute removal, stationary test, and differencing. Determination of the model candidate using correlogram method. The predictions are done using the Autoregressive Integrated Moving Average (ARIMA) method, which is capable of generating high accuracy in short-term predictions. Evaluating the prediction results using Mean Absolute Percentage Error (MAPE). The results showed that ARIMA (4,1,4) models resulted in predictions with the smallest MAPE, 0.87 for the next one-day prediction and 5.98 for the next seven days. Thus the ARIMA (4,1,4) model is feasible to be used as a predictive method of Bitcoin for one to seven days ahead.
Based on daily data about Bitcoin and six other major financial assets (stocks, commodity futures (commodities), gold, foreign exchange (FX), monetary assets, and bonds) in China from 2013 to 2017, we use a VAR-GARCH-BEKK model to investigate mean and volatility spillover effects between Bitcoin and other major assets and explore whether Bitcoin can be used either as a hedging asset or a safe haven. Our empirical results show that (i) only the monetary market, i.e., the Shanghai Interbank Offered Rate (SHIIBOR) has a mean spillover effect on Bitcoin and (ii) gold, monetary, and bond markets have volatility spillover effects on Bitcoin, while Bitcoin has a volatility spillover effect only on the gold market. We further find that Bitcoin can be hedged against stocks, bonds and SHIBOR and is a safe haven when extreme price changes occur in the monetary market. Our findings provide useful information for investors and portfolio risk managers who have invested or hedged with Bitcoin.
After Bitcoin Introduced around the world, many Cryptocurrencies was created that followed the standard of bitcoin. The use of Bitcoin or other Cryptocurrency as a currency is also an interesting study from an Islamic economic perspective. They tried to use gold with value back by gold , which gold itself is famous for its exchange rate stability. From abu bakar There is a need for monitoring organization of the cryptocurrency, to controlling from Riba (Interest), Maysir (gambling) and ghahar (Uncertainty). To solve this problem there is a need a tool that can predict with certainty based on valid historical data, to produce accurate prediction results and produce Economic value estimations that are close to Gold real value. With the results we can monitoring day by day, see next day value and continuously based on Cryptocurrency with value back by gold, and see what other impact influences the value by looking the factor negative or positive with sentiment analysis. In the last section we discuss and provide method that we analyse from previous work to produce method to estimate value cryptocurrency value back by gold.
Lukman Adewale Ajao, James Agajo, Emmanuel Adewale Adedokun, Loveth Karngong
This research work proposes a method for the securing and monitoring of petroleum product distribution records in a decentralized ledger database using blockchain technology. The aim of using this technique is to secure the transaction of distributed ledgers in a database and to protect records from tampering, fraudulent activity, and corruption by the chain participants. The blockchain technology approach offers an efficient security measure and novel advantages, such as in the transaction existence and distribution ledger management between the depot, transporter, and retailing filling station. Others advantages are transparency, immunity to fraud, insusceptibility to tampering, and maintaining record order. The technique adopted for this secure distributed ledger database is crypto hash algorithm-1 (SHA-1)-based public permissioned blockchain and telematics, while this telematics approach is an embedded system integrated into an in-vehicle model for remote tracking of geolocation (using Global Positioning System (GPS)), monitoring, and far-off data acquisition in a real-time. The scope of the data in the secure distributed ledger database (using blockchain) developed are identification (ID) of the tanker operator, Depot name, Source station ID, Destination station ID, Petroleum product volume, Transporter ID, and Geographic automobiles location. This system proved to be efficient, secure, and easy to maintain as it does not permit any individual for records tampering, but supports agreement of ~75% of participants in the chain to make changes.
La blockchain propose un système d'enregistrement décentralisé, immuable et transparent. Elle offre un réseau de nœuds sans entité de gouvernance centralisée, ce qui la rend "indéchiffrable" et donc plus sûr que le système d'enregistrement centralisé sur papier ou centralisé telles que les banques. L’approche traditionnelle basée sur l’enregistrement ne fonctionne pas bien avec les relations numériques où les données changent constamment. Contrairement aux canaux traditionnels, régis par des entités centralisées, blockchain offre à ses utilisateurs un certain niveau d'anonymat en leur permettant d'interagir sans divulguer leur identité personnelle et en leur permettant de gagner la confiance sans passer par une entité tierce. En raison des caractéristiques susmentionnées de la blockchain, de plus en plus d'utilisateurs dans le monde sont enclins à effectuer une transaction numérique via blockchain plutôt que par des canaux rudimentaires. Par conséquent, nous devons de toute urgence mieux comprendre comment ces opérations sont gérées par la blockchain et combien de temps cela prend à un nœud du réseau pour confirmer une transaction et l’ajouter au réseau de la blockchain. Dans cette thèse, nous visons à introduire une nouvelle approche qui permettrait d'estimer le temps il faudrait à un nœud de la blockchain Ethereum pour accepter et confirmer une transaction sur un bloc tout en utilisant l'apprentissage automatique. Nous explorons deux des approches les plus fondamentales de l’apprentissage automatique, soit la classification et la régression, afin de déterminer lequel des deux offrirait l’outil le plus efficace pour effectuer la prévision du temps de confirmation dans la blockchain Ethereum. Nous explorons le classificateur Naïve Bayes, le classificateur Random Forest et le classificateur Multilayer Perceptron pour l’approche de la classification. Comme la plupart des transactions sur Ethereum sont confirmées dans le délai de confirmation moyen (15 secondes) de deux confirmations de bloc, nous discutons également des moyens pour résoudre le problème asymétrique du jeu de données rencontré avec l’approche de la classification. Nous visons également à comparer la précision prédictive de deux modèles de régression d’apprentissage automatique, soit le Random Forest Regressor et le Multilayer Perceptron, par rapport à des modèles de régression statistique, précédemment proposés, avec un critère d’évaluation défini, afin de déterminer si l’apprentissage automatique offre un modèle prédictif plus précis que les modèles statistiques conventionnels.
Investment in cryptocurrency has been in trend from last many years. Bitcoin is one of the most popular and valuable cryptocurrency. Many studies have been done on bitcoin price prediction using various parameters which includes bitcoin factors, social media etc. In this paper, a comparative study of the various parameters affecting bitcoin price prediction is done based on Root Mean Square Error (RMSE) using various deep learning models like Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). We have studied the effect of Gold price on the price of bitcoin.
Jul 17, 2019·Proceedings of the International Conferences ICT, Society, and Human Beings 2019; Connected Smart Cities 2019; and Web Based Communities and Social Media 2019
In the University setting, part of management recorded all education transaction since the students register until theygraduate. In this point, the transaction raises several new and crucial challenges. At a closer look, the concerns appeareddue
The blockchain is the innovative technology in recent year. It brings a revolution to both industrial and academic area. In fact, it is a distributed storage system based on cryptography and consensus algorithms. Currently, many applications have been implemented in finance, agriculture, payment, and medicine area. We proposed a platform called High-value Commodities as a Service (HCS) to combine blockchain and high-value assets together. Traditionally, real estates, jewelry and artworks are main luxuries word-widely. We provide a sound and reliable environment for those investors to trade. Due to characteristics of the blockchain, users on our platform can track the source for each item. And all private data would be pushed on chains which can avoid some privacy-related problems.