In recent years, cryptocurrencies have received much attention due to their recent price surge and crash. In fact, their prices have been volatile, making them very difficult to predict. Accordingly, various machine learning methods have been used by researchers to investigate factors that affect cryptocurrencies prices and the patterns behind their fluctuations. From various machine learning and deep learning methods, this study aims to find an efficient and accurate model for predicting Bitcoin, Ethereum, and Binance Coin prices. Our experiments show that the Ridge regression model outperforms more complicated prediction models, such as RNNs and LSTM, in predicting the exact closing price. On the other hand, LSTM can anticipate the direction of the cryptocurrency price better than others.
Emergence and development of the blockchain technology, which is able to transform into “a most powerful disruptive innovation”, shall definitely concern universities. Moreover, nowadays the blockchain technology meets the challenges that both the system of higher education and the entire society are currently facing. Advantages of the blockchain technology are decentralized open data, absence of forgeries, safe storage of information, and reduction of transaction expenses related to data checkup, control, and verification. As of right moment, blockchain is considered to be a widely applicable technology with huge potential. In addition, information stored on a Blockchain record can be accessed from anywhere at any time. The potential for blockchain to manage and maintain student records is enormous. Blockchain is the key piece of technology used to create digital currencies like bit coins. Blockchain technology is a component of the fourth modern revolution after the development of the steam engine, the electric motor, and the computer. It has been used in many fields, including finance, law, and trade. The current article focused on its potential educational applications and looked into how blockchain technology might be used to address various challenges with education.
Asha Rani Borah, Annamalai Senthil Kumar, S V Kasish, M S Jashwanth · 5 authors
The financial ecology has changed thanks to blockchain technology. The first known instance of blockchain may be found in a 2008 whitepaper written by a person using the alias Satoshi Nakamoto. Non-Fungible Tokens, or NFTs, are a blockchain product that has attracted a lot of public interest. A NFT is a digital property that is implemented via blockchain. It is used to prove ownership and authenticity as it cannot be duplicated, replaced, or divided. An NFT's ownership is documented in the blockchain and is transferrable by the owner, enabling the sale and trading of NFTs. Through this paper we try to establish a relationship between NFT value and various factors such as social media, OpenSea data and so on. We aim to achieve this by using Support Vector Machine, Recurrent Neural Networks, Regression for accurate results. Thus, predicting the value of the NFT precisely.
M Mallegowda, Anita Kanavalli, M. N. Thippeswamy, K. P. Gupta · 7 authors
The immutable decentralised and fast nature of blockchain has given rise to numerous applications based on this technology. The integrity of its data is one of the primary features of blockchain technology. By utilising blockchain technology, the system becomes decentralised, enabling the consumer to independently check the accuracy of the data and the product. We suggest a decentralised Blockchain system to stop the fabrication of fake products so that both the supplier and the consumer may use it to trade real goods without having to supervise directly owned stores, which can significantly reduce the cost of product quality assurance. In this project, Quick Response (QR) codes offer a powerful method to tackle the practice of product counterfeiting thanks to new developments in wireless and mobile technologies. Counterfeit goods may be found using a QR code scanner, which connects the product's QR code to a Blockchain. Therefore, this technique might be used to construct blocks in the database using the product's unique codes and record the product's details. It gathers the user's individual code and runs it against records in the Blockchain database. The consumer will be informed if the code matches, or if the product is fake, in which case they will receive the message.
As a great innovation in virtual currency, bitcoins have the possibility to survive perpetually, although they are like gigantic bubbles. However, no matter whether bitcoins could survive or not, the technology used by bitcoins will exist and develop. There is a great possibility for bitcoins to be served in the intending currency, being issued, supported, and controlled by the government. Consequently, the research for bitcoins is meaningful. To explore the time relationship of bitcoins and give a prediction about the future price based on the given data, ARIMA and GARCH models are used in this paper. Although both of the two models failed to provide the accurate forecasts at the end of this research, they still proved the correlation within time series of bitcoins.
Bitcoin establishes itself as an investment asset and is often named the New Gold. This study, however, shows that the two assets are different in univariate and multivariate aspects. First, we construct GARCH, APARCH and APARCH-in-Mean models to analyze and compare conditional variance properties of Bitcoin and Gold, and find Bitcoin does not have the significant inverse leverage effect as Gold. Then we apply the BEKK-GARCH model to estimate time-varying conditional correlations between Bitcoin and Gold with other major market indexes. The results show that Bitcoin can not hedge the market risk, especially when a crash occurs. So we conclude that Bitcoin and Gold feature fundamentally different properties as assets and linkages to equity markets.
M. M. Rakibul Hasan, Md. Mahinur Alam, Kanita Jerin Tanha
The present land document reservation process that is done manually provides a lot of insecurity, unsafely, and many scopes for land deed fraud in terms of storing land documents and maintaining the details of ownership of specific land property. So, the current land deed reservation and verification method don’t seem reliable and efficient. To make it a reliable and safe transaction, we will use blockchain technology. We have created a blockchain system for land deed authentication utilizing the data encryption algorithm SHA-256. Using this system, land deed transactions will be safer and can store, verify, and preserve all of the relevant information of a land deed document. This proposed architecture store retrieves and detects attempts to modify copies using a variety of approaches, including several efficient technologies like Zero Knowledge proof, Public-key cryptography, and IPFS; It generates far more efficient solutions than the other systems. This method has been put out as a potential means of thwarting fraudulent land deeds and bringing delight to the public.
Kowshik Sankar Roy, Md. Ebtidaul Karim, Pritom Biswas Udas
The extensive usage of the Blockchain technology as one of the most popular forms of decentralized platform has been spread across a numerous field over the recent years. From financial sectors like banking industry to the supply chain management of multiple large corporate farms, blockchain technology has been proven its productivity across different communities. However, the reliability of the blockchain system has often been compromised with the introduction of various scams and fraudulent activity within the system. Due to the absence of a comprehensive and definitive dataset, the challenges of building an effective fraud detection model becomes even more sever in this particular field. Thus, in our work, we propose a deep learning based blockchain fraud detection model based on the Ethereum blockchain transaction data. With the association of a reliable dataset in this field, we build a deep learning-based detection model to classify the fraudulent activities within the system. The proposed classification model is comprised of a Long Short-Term Memory (LSTM) unit and a dense unit to detect the fraudulent transactions. For the sake of reducing down the complexity and avoiding the unnecessary transactional features, Information Gain has been utilized as the feature selection unit of the model. When compared to the corresponding values of different models on the same dataset, experimental results show a significant improved results in different aspects using the proposed approach.
Vishal Suthar, Vipul Bansal, Ch.Srinivasa Reddy, José L. Gonzáles · 6 authors
The development of blockchain technology (BT) in recent years has made it a distinctive, revolutionary, and popular innovation. Information security and confidentiality are prioritised by the decentralised database in BT. Additionally, the consensus process in it ensures the validity and security of the data. However, it brings up fresh security concerns including majority assault and the double expenditures. Data analytics using cryptocurrency sensitive data are needed to address the aforementioned problems. These dataset' analytics highlight the value of recently developed techniques such as machine learning (ML). ML uses a reasonable quantity of data to generate accurate predictions. In ML, data exchange and dependability are essential to enhancing the precision of outcomes. Results from the fusion of these two technologies (ML and BT) may be quite exact. In this research, we give a thorough investigation into the use of machine learning (ML) to strengthen the security of BT-based intelligent systems. The assaults on a blockchain-based network may be analysed using a variety of classic machine learning (ML) approaches, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Clustering, Bagging, and Support Vector Machines (SVM) (LSTM).We also discuss how the two technologies may be used together in a number of advanced areas, including smart urban, the national grid, medicine, and autonomous aerial vehicles (UAVs). The difficulties and concerns facing future research are then examined. Finally, a study based with a thorough analysis is offered.
With the rapid growth of technology, cryptocurrency like Bitcoin is attracting more and more attention. Its high volatility in prices creates many difficulties for predicting and there has been much work on this. This paper aims to provide a comparison of various machine learning models like linear regression, SVM, random forest, and neural networks for predicting the directions for Bitcoin close prices. The dataset used is from Jan 2012 to March 2021 and all four prices are used for predictions: Close, Open, High, and Low. Two different methods are used to fit the different types of machine learning algorithms: for regressors, close price predictions are first done and then construct in the predicted direction; for classifiers, direction predictions are done directly. Accuracy is used to do the comparison, which is the percentage of correct direction predictions made via the algorithm. It is shown that LSTM, a neural network algorithm generates the highest accuracy of about 58% and the random forest classifier has the lowest accuracy of about 55.47%.
This paper uses ARIMA-GARCH model to predict the prices of gold and bitcoin from 9/10/2016 to 9/11/2021, and fully analyzes the transaction date and constructs a price prediction model based on ARIMA-GARCH model, which can accurately predict the short-term price fluctuations in the future. When the transaction commission increases, the investor’s return decreases, but the gap between the investor’s return and the basic investment return gradually shortens. According to the official data in this thesis, the simulated transaction finally changed 1000 dollars into more than 9700 dollars, which performs well in the actual market.
The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain's entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.
Due to Bitcoin's innovative block structure, it is both immutable and decentralized, making it a valuable tool or instrument for changing current financial systems. However, the appealing features of Bitcoin have also drawn the attention of cybercriminals. The Bitcoin scripting system allows users to include up to 80 bytes of arbitrary data in Bitcoin transactions, making it possible to store illegal information in the blockchain. This makes Bitcoin a powerful tool for obfuscating information and using it as the command-and-control infrastructure for blockchain-based botnets. On the other hand, Blockchain offers an intriguing solution for IoT security. Blockchain provides strong protection against data tampering, locks Internet of Things devices, and enables the shutdown of compromised devices within an IoT network. Thus, blockchain could be used both to attack and defend IoT networks and communications.
Bitcoin is one of the most successful cryptocurrencies, and research on Bitcoin price prediction is getting more and more attention. Previous studies have used traditional statistical methods and machine learning models to predict Bitcoin prices. However, previous studies also have many problems, such as too few influencing factors, lack of model optimization, and poor prediction effect. This paper selects 27 factors related to Bitcoin price changes and screens the features through the XGBoost algorithm and the Random Forest algorithm (RF). In this study, combined forecasting models based on Support Vector Regression (SVR), Least Squares Support Vector Regression (LSSVR) and Twin Support Vector Regression (TWSVR) are used to predict Bitcoin price, separately. In addition, the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) are applied for parameter tuning of the models. Expected Variance Score (EVS), Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used to measure the prediction accuracy of the combined models. The CPU time is used to measure the operation speed of the combined models. The experimental results show that the combined model XGBoost-WOA-TWSVR has the best prediction effect, and the EVS score of this model is 0.9547. In addition, our research verifies that Twin Support Vector Regression has advantages in both prediction effect and computational speed.
Adedeji Daniel Gbadebo, Joseph Olorunfemi Akande, Ahmed Oluwatobi Adekunle
Purpose: A major challenge traders, speculators and investors are grappling with is how to accurately forecast Bitcoin price in the cryptocurrency market. This study is aimed to uncover the best model for the forecasts of Bitcoin price as well as to verify the price series that offers the best predictions performance under different periodicity of datasets. Design/methodology/approach: The study adopts three different data periods to verify whether frequency matters in forecasting Bitcoin price. The Bitcoin price, from 01/01/15 to 11/01/2021, is trained and validated on selected forecast models, including the Naïve, Linear, Exponential Smoothing Model, ARIMA, Neural Network, STL and Holt-Winters filters. Five forecast accuracy measures (RSME, MAE, MPE, MAPE and MASE) are applied to confirm the best performing model. The Diebold‐Mariano test is used to compare the forecasts based on the daily price with those based on the weekly and monthly. Findings: Based on the accuracy measures, the results indicate that the Naïve model provides more accurate performance for the daily series, while the linear model outperforms others for the weekly and monthly series. Using the Diebold‐Mariano statistics, there is evidence that forecasting Bitcoin price is not sensitive to the data periodicity. Research limitations/implications: The study has a major limitation, which is the shared sentiment to apply actual Bitcoin price series, and not the returns or log transformation for the forecast models. Notably, actual data may sometimes be loud, hence increasing the possibility of over predictions. Originality/value: In forecasting, different approaches have been used, this paper compares outputs of both statistical and machine learning methods in order to arrive at the best option for the Bitcoin price forecasts. Hence, we investigate whether the machine learning tools offer better forecasts in terms of lower error and higher model’s accuracy relative to the traditional models.
Smart contracts are applications running on the blockchain which control many virtual currencies. Since smart contracts are composed of code, they inevitably have defects. In recent years, many smart contract defects have caused lots of economic losses and harmful impacts. A contract that has defects may have some errors that cause unwanted results. As smart contracts cannot be modified once deployed, it is necessary to ensure that they are free from defects. In this paper, we focus on eleven defects of smart contracts and construct a deep learning-based model to detect these contract defects more accurately. Our model regards the smart contract’s operation codes as a sequential sentence and uses an Attention-based bidirectional long short term memory (BiLSTM-Attention) model to find smart contract defects. We evaluate our model’s and other models’ performance on 45622 real-world smart contracts. The experimental results show that our model can achieve higher accuracy (95.40%) and F1-score (95.38%). In addition, our model is highly efficient and can quickly detect large numbers of contracts.
Pedro Raffy Vartanian, Álvaro Alves de Moura, Joaquim Carlos Racy, Roberto Simioni Neto
In May 2014, the animation “Quantum” was the first work to be associated with a non-fungible token (NFT) type certificate. As of 2020, the market has evolved considerably, with the millionaire figures and exponential growth typical of new disruptive technologies. Considering the recent rise of the NFT market, it is important to understand how it works and, above all, the determinants of the prices of NFTs are highlighted. Based on a detailed analysis of this new market, a GARCH multivariate econometric model is applied in order to assess whether it is possible to identify the price determinants of NFTs, based on the behavior of the prices of cryptocurrencies (Bitcoin and Ethereum), the US interest rate and the price of gold. The research is based on the study by Dowling (2022a), which sought to analyze relations between the prices of NFTs and cryptocurrencies. The results found coincide with the prices of NFTs that are similar and independent of cryptocurrencies, the interest rate and the price of gold, some specific differences to identify a determined period.
Akif Akgül, Eyyüp Ensari Şahin, Fatma Yıldız ŞENOL
Crypto assets succeeded in making their name known to large masses with Bitcoin, which emerged as a result of the creation of the first genesis block in 2008. Until 2010, the aforementioned recognition showed itself mostly in areas such as games, but over time it managed to enter the portfolios of individual investors. Especially as of end of 2017, the rapid increases in monetary value quickly attracted the attention of corporate companies and then the (Central Banks). These assets have created different alternatives (also know as altcoins) by working and have managed to become one of the important financial instruments today. This study has examined in detail the techniques (Chaos theory, Onchain analysis and Sentiment analysis) developed on the price predictions of crypto assets, which are very important in terms of the number and quality of investors. In the study, findings were obtained that new techniques such as onchain and sentiment are more prominent in estimating crypto asset prices compared to traditional asset price estimation methods of crypto assets and that these techniques can make consistent estimations.
Blockchain network is defined as interconnection of many computers, and each and every computer holds the copy of the ledger. It can be observed as continuously budding chain of blocks, and blocks are interconnected with the support of hash function. Validating of new blocks is followed by a set of protocols and consensus mechanism from every node in the network. The records are kept and arranged in linear fashion chain. The main feature of the Blockchain technology is that it allows secure communication between untrusted parties without the involvement of any third party authority. Artificial intelligence, which emulates the human intelligence, is impacting heavily on the business and social media applications nowadays. Machine learning which is the subset AI, automatically learns and improve based on input data. Whereas deep learning which is subset of machine learning uses networks to identify complex patterns in data. The basic approach of machine learning is to collect and analyze the data at central location like server. But in today’s scenario the data is decentralized and emerges from multiple sources. Hence the need of distributed machine learning algorithms in many applications is required. ML can be used to make chain smarter than before. By making use of decentralized data architecture of Blockchain we can build good models of machine learning. This paper investigates the possibility of integrating Blockchain Technology and Machine learning for optimization and improvement of Warehouse operations at data and transactions levels by providing security processes needed for smart and secure warehouse system.
In this paper, we investigate how to forecast Non-Fungible Token (NFT) sale prices by using multiple multivariate time series datasets containing features related to the NFT market space. We examined eight recent studies regarding the forecasting and valuation of NFTs and compared their most important findings. This laid the fundamental work for two separate machine learning prototypes based on Long Short-Term Memory (LSTM) which are able to forecast the sale price history of an individual NFT asset. Root Mean Squared Errors (RMSE) of 0.2975 and 0.24 were obtained which appears to be promising.
Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning-blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Bitcoin is yet to be assumed as a worthy cryptocurrency and rewarding asset in the global market. As polynomial-based neural networks (PBNNs) are very robust and more accurate in modeling stock price prediction, their advantage in Bitcoin pricing needs to be analyzed. In this study, the robustness of PBNNs, based on Chebyshev (CPBNN) and Legendre (LPBNN), is blended with the proposed algorithm, coined as the mutated climb monkey algorithm (MCMA), to control the estimation of network parameters to accurately predict the one-day-ahead Bitcoin price. The performance was evaluated by a comparative analysis of the testing of both CPBNN and LPBNN with each of the six algorithms under consideration on three different datasets collected within the same time interval. As the use of a few evaluation criteria will not be able to identify an efficient predictor model, this study also proposes the use of a Multi-Criteria Decision-Making (MCDM) framework to rank all models using 15 different evaluation criteria. The ranking of the models clearly indicates that the proposed MCMA algorithm outperforms all other algorithms under study. The convergence plots of the top two models for the datasets also indicate that the PBNN using MCMA for learning predicts better results.
This work explains the role of tree regressions, long-term short-term memory models and ARIMA models in predicting Bitcoin values. In this study, we used Bitcoin Sets data to test and train the Ml and the Ai models. The data filtration process was completed with the help of the Python Library. When we understand the data, we adjust it for using those characteristics or attributes that are best suited for model. Here this model was implemented by recording the results. The accuracy of the decision tree regression model was found to be high when comparing with other machine learning models; R flat is found that is equal to 0.67.On the other hand, the R of the LSTM model is flat 0.49, the R of the ARIMA model is flat. Ninety-four. By this, we can see that deep learning model is highly improved model when compared with machine learning model.
Kritika M. Garg, Nidhi Sharma, Shriya Sharma, Chetna Monga
The term cryptocurrency refers to a digital currency based on cryptographic concepts that have become popular in recent years. Bitcoin is a decentralized cryptocurrency that uses the distributed append-only public database known as blockchain to record every transaction. The incentive-compatible Proof-of-Work (PoW)-centered decentralized consensus procedure, which is upheld by the network's nodes known as miners, is essential to the safety of bitcoin. Interest in Bitcoin appears to be growing as the market continues to rise. Bitcoins and Blockchains have identical fundamental ideas, which are briefly discussed in this paper. Various studies discuss blockchain as a revolutionary innovation that has various applications, spanning from bitcoins to smart contracts, and also about it being a solution to many issues. Furthermore, many papers are reviewed here that not only look at Bitcoin’s fundamental underpinning technologies, such as Mixing and the Bitcoin Wallets but also at the flaws in it.