Bitcoin is invented in 2009 by the pseudonymous Satoshi Nakamoto. Bitcoin is a decentralized digital currency system [1]. Bitcoin is the most acknowledged cryptocurrency in the world, which provide it interesting for financier. The cryptocurrency market capitalization on date 22nd July 2020 value represents roughly USD 277 billion of dollars, bitcoin representing 62% of it. However, a disadvantage for investors is the difficulty of predicting the price of bitcoin due to the high volatility of the bitcoin exchange rate. Measurement, estimation, and modeling of currency exchange rate volatility compose a significant research area. For this reason, a lot of studies done about bitcoin price prediction both Machine Learning (ML) and Statistical Methods. In comparison studies, ML methods perform better in general. This review is a comprehensive study on how we can better predict bitcoin prices by grouping previously done studies. The presentation of Bitcoin price prediction studies in groups reveals, the difference from other review studies. These are statistical methods, ML and statistical methods, ML-ML, frequency effect of selected time, effect of social media and web search engine, causality, optimization of hyperparameters methods.
There are large numbers of vehicles in the populated country like India. It's a very common scenario that traffic police came across some vehicle random vehicle and had some doubt in mind but do not have in hand information about that vehicle and end up leaving that thought. Sometimes this may result in some disaster. With the advent of technology, there are mobile applications and web based systems are available to ease up the process by which traffic police can fine the vehicle owner or people can pay the fine online. But yet there is no system is available through which traffic police can get all the details about the particular vehicle. This motivated us to design and developed an application thorough which traffic police can get all the information right from owner of the vehicle to its RC book and insurance status on just one click. Looking at the chances of data tampering, we have also played an attention to the data security and used blockchain for creating distributed, robust and tempered proof system. In this paper we have discussed traffic police assistance system, which can scan the vehicle number plate, identify the number and provide the all the information and documents stored against that vehicle number. To address the issue of data security and alteration of sensitive data blockchain is used so that any alteration can be monitored. As the complete information process is dependent on how correctly the vehicle number is identified, so the number plate recognition module is tested thoroughly under various conditions. Finally user feedback is taken and analyzed to evaluate the feasibility and usability of the proposed application.
This paper is discusses the problems of the short-term forecasting of financial time series using supervised machine learning (ML) approach. For this goal, we applied several the most powerful methods including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). As dataset were selected the daily close prices of two stock index: SP 500 and NASDAQ, two the most capitalized cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and exchange rate of EUR-USD. As features we used only the past price information. To check the efficiency of these models we made out-of-sample forecast for selected time series by using one step ahead technique. The accuracy rates of the forecasted prices by using ML models were calculated. The results verify the applicability of the ML approach for the forecasting of financial time series. The best out of sample accuracy of short-term prediction daily close prices for selected time series obtained by SGBM and MLP in terms of Mean Absolute Percentage Error (MAPE) was within 0.46-3.71 %. Our results are comparable with accuracy obtained by Deep learning approaches.
Blockchain is a digital ledger in which each record known as blocks and that are combined in a single list known as a chain. It is regarded as Bitcoin's backbone technology. It is also regarded as cohesive collections of digital wallets. Blockchains are primarily used by cryptocurrencies such as Bitcoin and other applications to record these transactions. A blockchain is commonly referred to as a collection of distributed databases that consists of all public transactions, records and digital events then that information is shared among the participants. Every transaction is verified and it cannot be removed. The main features of this technology are reliable, efficient operation, fault tolerance and scalability. Some of the applications are manufacturing, government and finance when the three properties met together (i.e., Efficiency, Scalability and Security). By using several computers, each transaction that is applied to a blockchain is validated. A peer-to-peer network is developed by these systems that are used to validate these forms of blockchain transactions. They work together to ensure that any transaction is legitimate until it is added to the blockchain, and invalid blocks cannot be added to the chain by these systems. When a new block is added, it can be connected to a previous block using a cryptographic hash and the chain cannot be broken and each block is recorded permanently. Blockchain can be used for an exchanging the transaction securely without an intermediate. It enables customer relationship and agile chain values and thereby integrating with IoT and Cloud technology. The functionality of distributed ledger is combined with blockchain security to solve the financial and non-financial industry problems. This paper proposes the blockchain technology with devices and creates a common platform and secure data communication.
Ricardo Carreño Aguilera, Miguel Patiño-Ortiz, Julián Patiño-Ortiz, ADAN ACOSTA BANDA
Blockchain technology apparently is a trivial innovation, but this technology has attracted huge investors in a very short period compared to other technologies, and it is still having a lot of potential applications. Smart contracts are making possible execution in an automated and safe way by using blockchain technology. Therefore, smart contracts are applied in this research for the expert system. This paper is about an expert system working with smart contracts and neural networks as the inference machine to decide on the sensors optimal distribution and taking actions when sensor readings are out of range: control lights, activating fire alarms, temperature alarms, etc. for all spaces (parks, schools, hospitals, etc.) in a smart city based on the needs, and likes of the expert system user. This expert system works using a blockchain structure on the EOSIO ecosystem with all data gathered by the sensors being saved in cloud online making internet of things environment and essential data saved in a blockchain node.
Subhi Alrubei, Edward A. Ball, Jonathan Rigelsford
The increased implementation of Edge Computing technology has provided The Internet of Things (IoT) with the ability of real-time data processing and tasks execution requested by smart devices. To support this processing the integration of Artificial Intelligence (AI) into IoT is considered one of the most promising approach. While AI helps in the analyses of the data, blockchain technology provides a robust environment within which to create a secure, distributed way to share and store data. This paper proposes an architecture that combines the strengths provided by edge computing, AI, and blockchain technologies to provide robust, secure, and intelligent solutions for secure and faster data processing and sharing. The pandemic created by the rapid spread of the novel Coronavirus COVID-19, as well as the tracking of viruses in water sewage to help control the spread of such viruses, were used as our case study for exploring this architecture. To secure the proposed architecture a new concept for consensus mechanism based on Honesty-Based Distributed Proof of Work (DPOW) were devised and tested.
Sazeen Taha Abdulrazzaq, Farooq Safauldeen Omar, Maral A. Mustafa
Since the introduction of blockchain, cryptocurrencies have become very attractive as an alternative digital payment method and a highly speculative investment.With the rise in computational power and the growth of available data, the artificial intelligence concept of deep neural networks had a surge of popularity over the last years as well.With the introduction of the long short-term memory (LSTM) architecture, neural networks became more efficient in understanding long-term dependencies in data such as time series.In this research paper, we combine these two topics, by using LSTM networks to make a prognosis of decentralized blockchain security.In particular, we test if LSTM based neural networks can produce profitable trading signals for different blockchains.We experiment with different preprocessing techniques and different targets, both for security regression and trading signal classification.We evaluate LSTM based networks.As data for training we use historical security data in one-minute intervals from August 2019 to August 2020.We measure the performance of the models via back testing, where we simulate trading on historic data not used for training based on the model's predictions.We analyze that performance and compare it with the buy and hold strategy.The simulation is carried out on bullish, bearish and stagnating time periods.In the evaluation, we find the best performing target and pinpoint two preprocessing combinations that are most suitable for this task.We conclude that the CNN LSTM hybrid is capable of profitably forecasting trading signals for securing blockchain, outperforming the buy and hold strategy by roughly 30%, while the performance was better.The LTSM method used by current system for encrypting passwords is efficient enough to mitigate modern attacks like man in the middle attack (MITM) and DDOS attack with 95.85% accuracy
In the past eight years of Bitcoin's history, the economy has seen the price of Bitcoin rapidly grow due to its promising outlook on the future for crypto currencies. Investors have taken note of several advantages Bitcoin provides over the traditional banking system. One such trait is that Bitcoin allows for decentralized banking, meaning that Bitcoin cannot be regulated by powerful banks. There is also a market cap of 21 million Bitcoins that can be in circulation, therefore a surplus of Bitcoins cannot be "printed" which would result in inflation. Bitcoin resolves the issue with transaction security by using a block chain, or a ledger, which records the history of every transaction ever made into one long hexadecimal "chain" of anonymous transactions, which keeps transaction history transparent, but also confidential. Bitcoin as a result has become a very bullish investing opportunity, and due to the huge volatility of the Bitcoin market price, this paper attempts to aid in investment decision making by providing Bitcoin market price prediction. Our team explored several Machine Learning algorithms by using Supervised Learning to train a prediction model and provide informative analysis of future market prices. We start with Linear Regression models, and train on several important features, then We proceed with the implementation of Recurrent Neural Networks (RNN) with Long Short Term Memory (LSTM) cells. All code is written in Python using Google's Tensor Flow software library. We show that the price of Bitcoin can be predicted with Machine Learning with high degree of accuracy.
Alberto Femenias-Hermida, Cristian R. Munteanu, José M. Vázquez-Naya
One of the major factors hindering the adoption of crypto assets in general, and Bitcoin in particular, is the high level of complexity they present to the common user. Although physical coins are a possible solution, the need to place trust in the manufacturers (so that they throw away the private key) is a big drawback that has hampered their widespread use. The recent boom of the maker movement has brought in a significant number of users with access to 3D printing devices, as well as the supporting electronic and computing resources. We have taken advantage of these capabilities to develop an open source project that interested parties can use to easily print a physical model of a Bitcoin coin, along with the necessary software that allows the creation and validation of keys and addresses.
Uğur Kaya, Fırat Akba, İ̇hsan Tolga Medeni, Tunç D. Medeni
Son zamanlarda kullanımı oldukça yaygınlaşan blokzinciri teknolojisinin, İnternet teknolojisi ile beraber adı sıkça anılır olmaya başlamıştır. Blokzinciri teknolojisiyle geliştirilen Bitcoin, sanal para birimleri arasında en çok piyasa hacmini elinde bulunduran sanal para birimidir. Sanal para piyasalarının kontrolünü elinde bulunduran bir merkezi otoritenin olmaması sebebiyle fiyat manipülasyonlarına ve dışarıdan müdahalelere açık olan bu pazarda, en uçtaki yatırımcının yatırım yapabilmesi açısından yol gösterimine ihtiyaç duyulmaktadır. Son zamanlarda bu ihtiyacı karşılamak amacıyla birtakım yöntemler kullanılmaya başlanmıştır. Bu çalışmada makine öğrenmesi, zaman serileri analizi ve derin öğrenme yöntemleri kullanılarak Bitcoin fiyatlarındaki dalgalanma hakkında çeşitli tahminleme ve sınıflama yöntemleri beraber olarak değerlendirilmiştir. Bu bağlamda, koronavirüs pandemisi öncesi ve sonrasındaki Bitcoin kapanış fiyatları ve düşüş-yükseliş eğilimleri baz alınarak iki ayrı veri kümesi oluşturulmuştur. Bu veri kümeleri üzerinde tahmin ve sınıflama yöntemleri değerlendirilerek, başarıları karşılaştırılmıştır. Karşılaştırmalar sonucunda, pandemi öncesi verilerle yapılan çalışmada Destek Vektör Makineleri, pandemi sonrası verilerle yapılan çalışmada ise ARIMA en başarılı sonuçları vermiştir.
The provision of electric vehicles (EVs) is increasing due to the need for ecological green energy. The increment in EVs leads to an intelligent electric vehicle transportation system's need instead of cloud-based systems to manage privacy and security issues. Collecting and delivering the data to current transportation systems means disclosing personal information about vehicles and drivers. We have proposed a secure and intelligent electric vehicle transportation system based on blockchain and machine learning. The proposed method utilizes the state of the art smart contract module of blockchain to build an inference engine. This system takes the sensors' data from the vehicle control unit of EV, stores it in the blockchain, makes decisions using an inference engine, and executes those decisions using actuators and user interface. We have utilized a double-layer optimized long short term memory (LSTM) algorithm to predict EV's stator temperature. We have also performed an informal analysis to demonstrate the proposed system's robustness and reliability. This system will resolve the security issues for both information and energy interactions in EVs.
The top priority of today's healthcare system is delivering medicine directly from the manufacturer to end-user. The pharmaceutical supply chain involves some level of commingling of a collection of stakeholders such as distributors, manufacturers, wholesalers, and customers. The biggest challenge associated with this supply chain is temperature monitoring as well as counterfeit drug prevention. Many drugs and vaccines remain viable within a specific range of temperatures. If exposed beyond this temperature range, the medicine no longer works as intended. In this paper, an Internet of Things (IoT) sensor-based blockchain framework is proposed that tracks and traces drugs as they pass slowly through the entire supply chain. On the one hand, these new technologies of blockchain and IoT sensors play an essential role in supply chain management. On the other hand, they also pose new challenges of security for resource-constrained IoT devices and blockchain scalability issues to handle this IoT sensor-based information. In this paper, our primary focus is on improving classic blockchain systems to make it suitable for IoT based supply chain management, and as a secondary focus, applying these new promising technologies to enable a viable smart healthcare ecosystem through a drug supply chain.
Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine (SVM) and Random Forest (RF) are proposed and analyzed for modelling and forecasting the Bitcoin price. While some of the proposed models are univariate, the other models are multivariate and as a result, the maximum, minimum and the opening daily price of Bitcoin are also used in these models. The proposed models are applied on the Bitcoin price from December 18, 2019 to March 1, 2020 and their performances are compared in terms of the performance measures of RMSE and MAPE by Diebold-Mariano statistical test. Based on RMSE and MAPE measures, the results show that SVM provides the best performance among all the models. In addition, ARIMA and Bayesian approaches outperform other univariate models where they provide smaller values for RMSE and MAPE.
Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern for many people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile applications. They generate a large amount of data that can be optimized and used efficiently by advanced deep learning (ADL) techniques. The importance of such innovations from the viewpoint of supply chain management is significant in different processes such as for broadened visibility, provenance, digitalization, disintermediation, and smart contracts. This article takes the secure IoT-blockchain data of Industry 4.0 in the food sector as a research object. Using ADL techniques, we propose a hybrid model based on recurrent neural networks (RNN). Therefore, we used long short-term memory (LSTM) and gated recurrent units (GRU) as a prediction model and genetic algorithm (GA) optimization jointly to optimize the parameters of the hybrid model. We select the optimal training parameters by GA and finally cascade LSTM with GRU. We evaluated the performance of the proposed system for a different number of users. This paper aims to help supply chain practitioners to take advantage of the state-of-the-art technologies; it will also help the industry to make policies according to the predictions of ADL.
Haotian Yang, Shuming Xiong, Samuel Akwasi Frimpong, Mingzheng Zhang
The introduction of a consortium blockchain-based agricultural machinery scheduling system will help improve the transparency and efficiency of the data flow within the sector. Currently, the traditional agricultural machinery centralized scheduling systems suffer when there is a failure of the single point control system, and it also comes with high cost managing with little transparency, not leaving out the wastage of resources. This paper proposes a consortium blockchain-based agricultural machinery scheduling system for solving the problems of single point of failure, high-cost, low transparency, and waste of resources. The consortium blockchain-based system eliminates the central server in the traditional way, optimizes the matching function and scheduling algorithm in the smart contract, and improves the scheduling efficiency. The data in the system can be traced, which increases transparency and improves the efficiency of decision-making in the process of scheduling. In addition, this system adopts a crowdsourcing scheduling mode, making full use of idle agricultural machinery in the society, which can effectively solve the problem of resource waste. Then, the proposed system implements authentication access mechanisms, and allows only authorized users into the system. It includes transactions based on digital currency and eliminates third-party platform to charge service fees. Moreover, participating organizations have the opportunity to obtain benefits and reduce transaction costs. Finally, the upper layers supervision improves the efficiency and security of consensus algorithm, allows supervisors to block users with malicious motives, and always ensures system security.
Purpose 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. Design/methodology/approach 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. Findings The results show that no significant correlation exists between bitcoin and the blockchain index; external shocks aggravate the volatility of bitcoin and the blockchain index, and the volatility has a certain degree of sustainability; and blockchain index has obvious leverage, namely, its decline has a stronger impact. Originality/value The volatility of bitcoin and the blockchain index is crucial for investors.
Eng Chuen Loh, Shuhaida Ismail, Azme Khamis, Aida Mustapha
Bitcoin is the most popular cryptocurrency with the highest market value. It was said to have potential in changing the way of trading in future. However, Bitcoin price prediction is a hard task and difficult for investors to make decision. This is caused by nonlinearity property of the Bitcoin price. Hence, a better forecasting method are essential to minimize the risk from inaccuracy decision. The aim of this paper is to compare two different training algorithms which are Levenberg-Marquardt (LM) backpropagation algorithm and Scaled Conjugate Gradient (SCG) backpropagation algorithm using Feedforward Neural Network (FNN) to forecast the Bitcoin price. After obtaining the forecasting result, forecast accuracy measurement will be carried out to identify the best model to forecast Bitcoin price. The result showed that the performance of Bitcoin price forecasting increased after the application of FNN – LM model. It is proven that Levenberg-Marquardt backpropagation algorithm is better compared to Scaled Conjugate Gradient backpropagation when forecasting Bitcoin price using FNN. The resulting model provides new insights into Bitcoin forecasting using FNN – LM model which directly benefits the investors and economists in lowering the risk of making wrong decision when it comes to invest in Bitcoin. Keywords: Bitcoin Price; Artificial Neural Network; Forecasting
Blokzincir, bitcoin dijital para biriminin de alt yapısını oluşturan yeni bir teknolojidir. Popüler ve yaygın yatırımlar sayesinde günlük gerçekleştirilen bitcoin işlem sayısı gün geçtikçe artmaktadır. Bitcoin verisi her geçen gün artmakta ve dolayısıyla artan büyük bitcoin verisinin analizi ve madenciliği için yeni veri madenciliği yöntemlerine ihtiyaç duyulmaktadır. Buna ek olarak, Bitcoin fiyatındaki dalgalanmalar ve anormal fiyat değişimleri ve bu değişimlerdeki anormalliklerin keşfi ekonomistler için büyük önem taşımaktadır. Bu çalışmada, 2012-2019 yıllarına ait 8 yıllık bitcoin fiyat veri kümesi kullanılarak bitcoin fiyat farkı ve bitcoin fiyatı yüzdesel farkı olmak üzere iki farklı veri kümesi oluşturulup, anormallik tespiti gerçekleştirilmiştir. Öncelikle veri kümesi ön işlem aşamasından geçirilerek gereksiz sütunlar çıkarılmıştır ve daha sonra günlük fiyat farkları kullanılarak veri setleri oluşturulup, DBSCAN algoritması ile anormallik tespiti yapılmıştır. Ayrıca bu çalışmada DBSCAN aloritmasının sonuçları istatistiksel yöntemin sonuçları ile karşılaştırılıp, tartışılmıştır. Sonuçlar incelendiğinde, DBSCAN algoritması ve istatistiksel metodun bitcoin fiyatlarındaki anormallikleri her iki veri kümesinde de başarıyla tespit edebildiği görülmüştür. Bununla birlikte DBSCAN algoritması normal günlük fiyat değişimlerine yakın olan anormak fiyat değişimlerini de keşfedebildiği için istatistiksel metottan daha iyi performans göstermiştir. Ayrıca, bu çalışmada bitcoin fiyat farkı veri kümesi ve bitcoin fiyatı yüzdesel farlı veri kümesi karşılaştırılmış ve her bir veri kümesi için olan sonuçlar ve sebepleri tartışılmıştır. NOT: Makalenin düzeltilmiş haline Alper Ecemis - Düzeltme ulaşabilirsiniz.
Bitcoin en popüler ve yaygın olarak kullanılan dijital para birimidir. Bu nedenle, Bitcoin fiyat hareketinin tahmini finansal piyasalar için büyük önem taşımaktadır. Bitcoin fiyat tahmininde ekonometrik modellerin yanında veri madenciliği yöntemlerinden de faydalanılmaktadır. Veri madenciliğinde kullanılan araç ve yöntemler yardımıyla veriler modellenerek yararlanılacak bilgilere dönüştürülürler. K-Star algoritması veri madenciliği, obje tanımlama ve kontrol sistemleri gibi birçok alanda kullanılmakta olan örnek tabanlı bir yaklaşımdır. Bu çalışmada Makroekonomik değişkenlerin Bitcoin fiyatlarını etkileme seviyeleri, Makine Öğrenme yöntemlerinden Lazy Learning Öğrenmeye Dayalı K-Star Algoritması kullanılarak analiz edilmiştir. Çalışmanın veri seti, bağımlı ve bağımsız değişkenlerin 3 Ocak 2017 - 30 Ocak 2019 yılları arasındaki iş günü bazında 510 adet gözlem değerini içermektedir. Bu gözlemlerin 474 adedi (%93’ü) algoritmanın modellenmesi (eğitim) için, 36 adedi (%7’si) ise sınıflandırma (test) için kullanılmıştır. Modelin Bitcoin fiyatlarını gelecek dönem “yükseliş” mi yoksa “düşüş” mü göstereceğine ilişkin sınıflandırma başarısının %61,1 oranında olduğu, Bitcoin fiyatlarının “yükseliş” göstereceğine ilişkin doğru sınıflandırma başarısının %71,42, “düşüş” göstereceğine ilişkin doğru sınıflandırma başarısının ise %46,66 olduğu tespit edilmiştir. Sonuç olarak Makine Öğrenme Tekniğinin belli bir performans gösterdiği ancak Bitcoin fiyatlarının öngörülebilirliğinin henüz beklentinin altında olduğu ortaya çıkmıştır.
This research was conducted to analyze cryptocurrency volatility. Gold, Dollar Index, and Composite Stock Prices Index in the Indonesia Stock Exchange (IDX) variable are used as independent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum which have the largest market capitalization. The data used in this study is from 1st January 2017 to 31st December 2019. This study uses GARCH analysis. The result of this study indicates that the volatility of Bitcoin and Ethereum is not influenced by other variables, but it is influenced by the prices of each Bitcoin and Ethereum at past prices. This shows that the cryptocurrency market is an inefficient market.
The video created by a surveillance cameras plays a crucial role in crime prevention and examinations in smart cities. The closed-circuit television camera (CCTV) is essential for a range of public uses in a smart city; combined with Internet of Things (IoT) technologies they can turn into smart sensors that help to ensure safety and security. However, the authenticity of the camera itself raises issues of building up integrity and suitability of data. In this paper, we present a blockchain-based system to guarantee the trustworthiness of the stored recordings, allowing authorities to validate whether or not a video has been altered. It helps to discriminate fake videos from original ones and to make sure that surveillance cameras are authentic. Since the distributed ledger of the blockchain records the metadata of the CCTV video as well, it is obstructing the chance of forgery of the data. This immutable ledger diminishes the risk of copyright encroachment for law enforcement agencies and clients users by securing possession and identity.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Blockchain and cryptocurrencies have risen to popularity in the recent years to a great extent due to its increasing trading volumes and huge capitalization in the market. These cryptocurrencies are being used not only for trading but are being accepted for monetary transactions as well these days. As the prices fluctuate and return on investment increases investors, traders and general public are showing increased interest towards bitcoin and altcoins. This research focuses on implementing forecasting models that will return accurate price predictions for cryptocurrencies. Prices for Bitcoin, Ethereum and Litecoin are predicted using the traditional forecasting model for timeseries ARIMA, the Prophet Model and deep learning algorithm LSTM. The results of the three models were evaluated and the LSTM Model was found to outperform the Prophet as well as the ARIMA model.
Jan 1, 2020·Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia
In recent years, Bitcoin has attracted a lot of attention because of its nature that supports encryption technology and monetary units. For traders, Bitcoin becomes a promising investment since its fluctuating prices potentially draw high profit (the higher the risk the higher the return). Unlike co