In recent years, as a result of the increasing popularity of crypto-assets all over the world, an increasing number of innovative crypto assets are coming to the market. In this context, Fan Tokens, which is a type of "utility token", are also discussed in this research as a prominent crypto asset in recent years. As developments in both cryptocurrency and sports sector increase the interest in Fan Tokens day by day, the financial volume of the system is constantly growing. With the growing volume, the risks taken by those who buy Fan Tokens are also increasing. Although there are studies on the risks of crypto assets in the literature, since the issue is not addressed specifically for Fan Tokens, it is deemed worthy of our review. The relationship between the price movements of Fan Tokens and the movements of the dominant crypto assets has been examined. It has been determined by the regression and correlation analyzes in the research that the movements in the crypto money exchanges have an effect on the Fan Token exchanges. Therefore, the developments in the crypto money exchanges should be followed especially and carefully for those who see the Fan Tokens as an investment tool.
Bitcoin (BTC)-the first cryptocurrency-is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research's innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.
To tackle the problems of questioning the authenticity of products faced in e-commerce live streaming and the low data integrity in the process of product transactions, the concept of traceability and a product authentication scheme that integrates blockchain technology and traceability are proposed. The proposed scheme deploys the product authentication strategy on the Ethereum blockchain in the form of a smart contract and calls the product authentication method on the traceability body by executing the smart contract to realize the authentication of product quality. A combination of on-chain and off-chain is used to store private information to guarantee information integrity. Security analysis results show that the proposed scheme can make the risk of information leakage lower and data security higher under the premise of achieving product certification. A system simulation experiment shows that the solution can meet product certification requirements and have a favorable impact on the live commerce industry.
Among the new way of exchanging money, using crypto currency has been very popular. Its also an investment to get good returns over the period of time. Cryptocurrency has grown to more than 120 million investors around the world as per a survey of 2021.Its growing at the 15 to 20% ratio around the world every year. This fact leads to a serious consideration of security and its vulnerabilities in block chain. Apart from market risks, high volatility, lack of rules and regulations, cyber risks are one of the most required types which needs proper attention and technical understanding. Because the crypto currencies are fully decentralized the risk of attacks is exposed and in most of the cases defenseless. Proof of stake and proof of work are two major algorithms followed by almost all crypto currencies to allot stocks to the holders. In this paper, different types of risks and attacks with POS and POW are explained with its mitigation. The problems and outcomes are examined, reviewed and conferred in case of Ethereum and Bitcoin crypto currencies. These currencies decentralized frameworks and anonymity attracts unlawful activities. Recognizing and preventing them needs understanding of the mechanism of attacks which are discussed in easiest possible ways for even a new-bee or an outsider person.
Meiryani Meiryani, Caineth Delvin Tandyopranoto, Jason Emanuel, A. S. L. Lindawati · 7 authors
This study aims to determine the effect of global price movements for energy sector commodities, especially Crude Oil and Natural Gas Prices, on cryptocurrency price movements. This study focuses more on the Bitcoin cryptocurrency. This study uses quantitative methods, and the data collection used is secondary data with weekly data and the period from January 1, 2020-July 31, 2021. The number of observations used in this study amounted to 79 observations. Secondary data sources are obtained through the website finance.yahoo.com. The data processing technique will be carried out using Stata and SPSS software, the Multiple Linear Regression method, and the Classical Assumption Test. The results of this study show that global prices for energy sector commodities, especially Crude Oil, Natural Gas, have a positive effect on Bitcoin price movements. These results indicate a link between energy and Bitcoin caused by Bitcoin miners who are mining Bitcoin using energy so that when the price of Bitcoin rises, the price of energy will also increase.
Ethereum, a typical application of blockchain technology, has attracted extensive attention from all walks of life since its release. Owing to imperfections in existing supervision technology, illegal and criminal activities on blockchain platforms are becoming increasingly frequent. The most typical Ethereum fraud is the Ponzi scheme, which causes blockchain investors to lose millions of assets and severely impacts social development. Currently, Ponzi scheme detection primarily focuses on machine learning and data mining. However, existing detection methods still have two problems in data imbalance processing and feature extraction: (1) data enhancement using an oversampling algorithm produces noise and (2) feature redundancy existing in extracted feature data. The SMOTEENN algorithm is introduced to solve data imbalance. The PD-SECR method, the Convolutional Neural Network (CNN) feature extraction, and random forest (RF) classification models are used for detection, but the two models are independently trained. The results show that the detection method proposed in this study is more suitable for the Ethereum Ponzi scheme.
Moudher Kh. Abdal-Hammed, Afraa Ghazal, Hendra Ibrahim, Akhil Ahmed
redicting currency rates is important, for everyone who is trading and trying to build an investment portfolio from a range of crypto currencies. It is not subject to the same restrictions as fiat currencies. In this study, we seek to predict the exchange rate of BIT-COIN against the US dollar. The short-term data (365 observations) is processed using the LSTM model as one of the neural network models. Modeling is conducted by training a sample size of 67%, taking into account sharp fluctuations in the price of trade and a certain level of market efficiency. The GARCH model is used to select appropriate historical periods for how the LSTM model works and to test proficiency at the weak, semi-strong, and strong levels. The data series obtained from the website (Investing.com) have been processed. The researchers have found that the performance of the neural network improves as the EPOCH value increases with a training (research) period of 50 days before, which is consistent with the results of the proficiency test at the weak level. It agrees with the results of the sufficiency test at the weak level, which indicates that in the case under study (the Bitcoin market is effective at the weak level). It is advised that crypto-currency investors rely more on the historical trend of the price of the currency than on its current price, taking advantage of the artificial neural network model (LSTM) in dealing with little data of high volatility.
Thestudent academic records maintained by the university system manually arevulnerable to easily being modified or tampered with. These academic records ofstudents are proof of the student's performance for all semesters till thedegree is completed. They are supposed to be present at the time of theinterview or at the university if he wants to continue their studies further.The issue with the conventional approach is that it is simple for a maliciousperson to alter it so that it affects the student's grade in any way. This studyaims to use blockchain to create safe Smart Certificates. It offers a viablealternative for issuing, confirming, and exchanging certificates without fearof their integrity being compromised.
A food shortage, which has increased with the climate crisis, will be one of the biggest problems of the world, together with water scarcity, in the future and will damage the sustainability of the food supply system. With the effect of the COVID-19 pandemic, food resources are decreasing, and food prices are rising all over the world. The decrease in food sources increases the importance of food tracking even more. The exorbitant price increases after the COVID-19 pandemic are the most concrete indicators of this. Blockchain-based food tracking systems will be of critical importance because they will prevent exorbitant price increases with their contribution to food tracking processes, such as reliability and transparency. In this study, the establishment of a blockchain-based food tracking system in Turkey, its operation, and its results will be discussed. It was concluded that 97.54% of the participants using the established system found the application useful and wanted such an application to become widespread. In addition, comparing the performance data of the established blockchain-based system with other blockchain infrastructures, a value of 0.038 s for latency is 435 times better than Ethereum, one of the most popular blockchain infrastructures. A transmission per second value of 285, reception per second value of 335, and CPU load rate value of 19.22 are obtained with the proposed system.
Cryptocurrencies are now the most popular investment instruments among millenials. Crypto offers great returns in a short period of time. Prior to COVID-19, Crypto experienced significant price fluctuations accompanied by an increase in the number of high transaction volumes. This situation was disrupted by the presence of the COVID-19 which made the world economy devastated, marked by the decline of stock prices in the world, especially in Indonesia. A paired test was conducted in this study to compare the state of Crypto before and during COVID-19 with the variables of Risk, Transaction Volume, Return, and Sharpe Performance. The results showed that there was a significant difference in the variables of Transaction Volume and Return. However, there was no significant difference in the Risk and Sharpe performance before and during COVID-19. This study shows that despite the COVID-19 pandemic, the enthusiasm of investors who transact crypto assets is not affected and they still get returns in accordance with the investments made. The high risk will be followed by a high standard deviation, so that the Sharpe Performance is small. Cryptocurrencies still have many gaps to research, such as regulation, so that many countries have not legalized Crypto transactions. If there is no regulation for Crypto, it is certain that an increase in cybercrime harms crypto investors and threatens global financial stability. Nevertheles, with or without COVID-19, investment transactions gain and lose based on confidence in the limited market. Therefore, the success of confidence fluctuations in crypto encourages the emergence of alternative coins created by investors to conduct an Initial Coin Offering (ICO).
This review focuses on blockchain technology, and its application and common problem with reference solution. The blockchain technology is nascent and complex and involves many different fields, which leads to the development of cryptocurrency. However, the crptocurrency has high volatility that demands prompt solution. Deep learning technology is considered as a promising approach to address this issue. After research, this paper develops four models with high efficiency and accuracy, including NLANN, JNN. LSTM and GRN to realize prediction in crptocurrency.
To be or not to be is the question that Hamlet thinks about day and night. Gold or Bitcoins is an inescapable choice for investors. With the ever rising and falling price of gold and bitcoin, making good trading decisions is of paramount importance. In this paper, we systematically investigate how data can be used to quantify the factors that influence trading and make the final decision. We build time series with the prices of gold and bitcoin for the past five years. We obtained forecast curves with excellent fit by seasonality analysis and ARIMA time series model forecasts.
Cryptocurrencies are nowadays getting popular for investment due to its various benefits such as low transaction cost, blockchain secured platform, profit, etc. Bitcoin being top of the market capitalization currency, gained more popularity during covid-19 pandemic. This study focuses on bitcoin price prediction with covid-19 sentiment. Here Long Short Term Memory Deep learning model based on machine learning is used for price prediction. At the end both results i.e., with covid-19 sentiment and without it are compared which shows model performs better by adding sentiments.
In the context of COVID-19, the circulation of agricultural products is increasingly important for the nutrition and health of people. With the changing needs of society and the advancement of technology, the agricultural product circulation system needs to undergo corresponding changes to adapt to the modern fast-paced social system. Blockchain technology couples with the circulation of agricultural products, as its technical features, such as immutability and a distributed ledger database, ensures the speed and stability of the key information circulation process of agricultural products. The research goal of this paper was to clarify the influence of blockchain technology on the qualification rate and circulation efficiency for agricultural products. Based on the main characteristics of blockchain technology and a summary of domestic and foreign theoretical research, this paper simulated the impacts of blockchain technology on the agricultural product circulation system. The results revealed that blockchain technology can improve the qualification rate of agricultural products and thereby ensure their quality and safety. The introduction of blockchain increased the qualification rate by nearly 30%. Moreover, blockchain technology significantly enhanced the efficiency of the agricultural product circulation system, thereby greatly promoting economic benefits. The introduction of blockchain increased circulation efficiency by nearly 15%. Finally, the introduction of blockchain technology can effectively promote the governance level and reduce the supervision costs of the agricultural product circulation system. Through simulation analysis, we found that blockchain technology has a positive impact on both the qualification rate and circulation efficiency for agricultural products. These findings enrich research into the application of blockchain technology in the management and circulation of modern agricultural products.
Bitcoin is a type of Internet currency that is both a digital asset and a payment method. It enables for anonymous payment from one person to another, making it a popular payment mechanism for online illegal activity. Due to its recent price increase, Bitcoin has gotten a lot of attention from the media and the general public. The goal of this research is to discover the Bitcoin price's predictable price direction. Machine learning models are likely to provide us with the information we require to understand the future of cryptocurrency. It won't tell us what will happen in the future, but it might show us the overall trend and direction in which prices are likely to move. The proposed methodology aims to create a machine learning model that uses data to learn about the patterns in the dataset and then uses a machine learning algorithm to forecast the bitcoin price.
<p>Cybersecurity is an inherent characteristic that should be addressed before the large deployment of smart city applications. Recently, Blockchain appears as a promising technology to provide several cybersecurity aspects of smart city applications. This paper provides a comprehensive review of the existing blockchain-based solutions for the cybersecurity of the main smart city applications, namely smart healthcare, smart transportation, smart agriculture, supply chain management, smart grid, and smart homes. We describe the existing solutions and we discuss their merits and limits. Moreover, we define the security requirements of each smart city application and we give a mapping of the studied solutions to these defined requirements. Additionally, future directions are given. We believe that the present survey is a good starting point for every researcher in the fields of cybersecurity, blockchain, and smart cities.</p>
In recent years, popularity and use of cryptocurrencies has been rising along with their prices and Ethereum is the second most famous cryptocurrency after Bitcoin. Cryptocurrencies are based on blockchain, which is a distributed and empowered technology that has the power to transform any banking systems. It has become an attractive investment for traders as well as individuals looking to invest. The price of Ethereum varies and is controlled by different factors, such as the crypto market in which it is sold, supply and demand. Ethereum is so valuable because it could be used as cash, we could also pay a portion or part of Ethereum to someone in exchange and it is easily guaranteed by the blockchain. Unlike stocks, Ethereum price is much more variable, as it has a trading time of 24-hours a day without any close time. The paper compares the results of three different models, namely Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs) and Bi-directional Long Short-Term Memory (Bi-LSTMs). The dataset consists of the closing price for the last 2000 days that is used to predict both short-term (30 days) and long-term (90 days) Ethereum prices. These prices are being fetched from an API which is in JSON format and are updated every day.
Amaç: Bu çalışmanın amacı, Bitcoin ve Ons arasındaki volatilite aktarımını incelemektir. Bu nedenle, yatırımcılar riskten korunmak için portföylerinde Bitcoin’e yer vermeli mi ve Bitcoin Ons’a alternatif bir yatırım aracı mı konuları araştırılmıştır. Tasarım/Yöntem: Araştırmada öncelikle değişkenler getiri serisine çevrilmiş ve birim kök testleri sınanmıştır. Daha sonra, Bitcoin ve ONS arasındaki ilişki çok değişkenli stokasitik volatilite metodu ile incelenmiştir. Eviews9 ve WinBUGS14 paket programları yardımı ile analizler yapılmıştır. Bulgular: Analiz sonuçlarına göre, Bitcoin ve Ons değişkenlerinde meydana gelen şokların kalıcı etkiye sahip olduğu saptanmıştır. Bitcoin’den Ons’a doğru tek yönlü volatilite aktarımı olduğu tespit edilmiştir. Ayrıca Bitcoin’den Ons’a doğru gerçekleşen volatilite aktarımının pozitif olduğu belirlenmiştir. Sınırlılıklar: Çalışmada, 03.02.2012–13.01.2022 dönem aralığının alınması ve sadece iki değişkenin kullanılması araştırımın sınırlılıklarıdır. Ayrıca bu tarih aralığının alınmasının nedeni 2012 dönemi öncesi Bitcoin verisine ulaşılamaması ve analizlerin 2022 yılı Ocak ayında yapılmasıdır. Özgünlük/Değer: Çalışmanın diğer çalışmalardan ayrılan özelliği, Çok Değişkenli Stokastik Volatilite Metodu ile analizlerin yapılmasıdır. Ayrıca bu konuda literatürde çok çalışma olmaması ve literatüre katkı sunulması hedeflenmektedir.
The development of Internet technology provides a lot of convenience for the promotion of smart agriculture. At present, smart agriculture has gradually realized unmanned and automatic management, which can realize monitoring, supervision, and real-time image monitoring. However, the data in smart agriculture system cannot be guaranteed to be complete and vulnerable to attack. Based on this, this paper studies and analyzes the application of edge computing and blockchain in smart agriculture systems. Based on the simple analysis of the development of smart agriculture, the edge computing framework and the advantages of blockchain are used to build the framework system of smart agriculture. The classical architecture of edge computing and the confidentiality of blockchain are used to realize the analysis and storage of data. In view of the shortcomings of crop image overlap detection, it is proposed to detect the overlapping area and determine the feature points to analyze the image based on the edge computing and hash algorithm. In terms of data integrity, based on the advantages of blockchain, an edge data detection method based on short signature is proposed, and experiments are designed to analyze the accuracy and effectiveness of the algorithm. The simulation results show that the image mosaic algorithm can extract the contour information of the image and realize the fast image matching. The edge data integrity calculation based on short signature can meet the requirements and shorten the response time.
Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, “convolutional neural network” or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0–7), filters (10–40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10–12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.
This paper discusses, trying to accurately assess the price of Bitcoin by looking at different parameters affects the value of Bitcoin. In our work, we focus on understanding and seeing the evolution of Bitcoin daily market, a1 and gaining intuition in the most relevant aspects surrounding the Bitcoin price. In the meantime, market capitalization of publicly traded cryptocurrencies exceeds $ 230 billion. The most important cryptocurrency, Bitcoin, is used primarily as a digital value store, and its pricing opportunities have been extensively considered. These features are described in more detail in the following paragraph: details of the main Bitcoin, as described in the paper. Bitcoin is the most expensive digital currency in the market. However, Bitcoin prices have been highly volatile, making it difficult to forecast. As a result, the goal of this research is to find the most efficient and accurate model for predicting Bitcoin prices using various machine learning algorithms. Several regression models with scikit-learn and Keras libraries were tested using 1-minute interval trading data from the Bitcoin exchange website bit stamp from January 1. 2012 to January 8, 2018. The best results showed a Mean Squared Error (MSE) as low as 0.00002 and an R- Square (R2) as high as 99.2 percent.
With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors. In this paper, we focus on predicting the pump probability of all coins listed in the target exchange before a scheduled pump time, which we refer to as the target coin prediction task. Firstly, we conduct a comprehensive study of the latest 709 P&D events organized in Telegram from Jan. 2019 to Jan. 2022. Our empirical analysis reveals some interesting patterns of P&Ds, such as that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity. Here channel refers a form of group in Telegram that is frequently used to coordinate P&D events. This observation inspires us to develop a novel sequence-based neural network, dubbed SNN, which encodes a channel's P&D event history into a sequence representation via the positional attention mechanism to enhance the prediction accuracy. Positional attention helps to extract useful information and alleviates noise, especially when the sequence length is long. Extensive experiments verify the effectiveness and generalizability of proposed methods. Additionally, we release the code and P&D dataset on GitHub: https://github.com/Bayi-Hu/Pump-and-Dump-Detection-on-Cryptocurrency, and regularly update the dataset.
Since blockchain technology has proven to be effective in the development of a wide range of industries, its use in other fields is also being expanded. Agriculture is one such sector, where blockchain technology is being used to improve farm business operations. Today, several agribusiness firms are utilizing technology to improve food supply chain tracking. For example, Farmers Edge, the world’s leading company that revolutionized the field of digital agriculture through its work in providing advanced artificial intelligence solutions, as well as new opportunities that give agriculture a globally advanced future for all stakeholders, has taken a significant step forward. The issue of blockchain network technology and its applications in agriculture will be discussed in this study, as well as the key advantages that this technology can provide, when employed to make the lives of both producers and consumers easier. In addition, a total of 79 research papers were evaluated, with a focus on the state of blockchain technology in agriculture, related issues, and its future importance, as well as relevant contributions to this new technology and the distributions of this study by different countries.