M. Ramalingam, G. Chemmalar Selvi, Nancy Victor, Rajeswari Chengoden · 11 authors
Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.
P. V. Nagamani, Gowri Anand, Srinivasa Prasanna, Basava Raju · 5 authors
The past several years have seen an increase in interest in trading that is supported by machine learning and artificial intelligence.Utilize automated trading with the aid of machine learning and artificial intelligence to reap the maximum rewards from the cryptocurrency market.For a specific time, we keep the daily data.We achieve excellent results by utilising tactics supported by cutting-edge algorithms.The results produced the expansion in the crypto currency industry with the aid of straight forward architecture and algorithms.The rise in market capitalization has led to a rise in popularity for the cryptocurrency in 2017.Today's market involves more than 1500 crypto currencies.For usage in online transactions, the crypto currency can be created.A crypto money technology is bitcoin.Bitcoin's value changes constantly, second by second.As a result, we apply machine learning architecture to forecast the value of the bitcoin price in this case.We are working to demonstrate that, in comparison to previous techniques and architectures, this ML architecture produces results that are more accurate.Our study use the Support Vector Machine(SVM) and K Nearest Neighbor(KNN)algorithms to successfully forecast bitcoin prices.The findings demonstrate that the Support Vector Machine(SVM) method outperforms the K Nearest Neighbor(KNN) method as it is currently being used.
Managing and storing data are crucial tasks for any industry, since they require accurate and secure record-keeping. Using blockchain technology has significantly increased recently due to its capability to address some of the challenges that come with traditional data storage and management systems. In this paper, we will explore the use of Ethereum smart contracts powered by the Solidity programming language using AES-CMAC.
M. Thamban Nair, Mohamed I. Marie, Laila A. Abd-Elmegid
One of the most significant and extensively utilized cryptocurrencies is Bitcoin (BTC). It is used in many different financial and business activities. Forecasting cryptocurrency prices are crucial for investors and academics in this industry because of the frequent volatility in the price of this currency. However, because of the nonlinearity of the cryptocurrency market, it is challenging to evaluate the unique character of time-series data, which makes it impossible to provide accurate price forecasts. Predicting cryptocurrency prices has been the subject of several research studies utilizing machine learning (ML) and deep learning (DL) based methods. This research suggests five different DL approaches. To forecast the price of the bitcoin cryptocurrency, recurrent neural networks (RNN), long short-term memories (LSTM), gated recurrent units (GRU), bidirectional long short-term memories (Bi-LSTM), and 1D convolutional neural networks (CONV1D) were used. The experimental findings demonstrate that the LSTM outperformed RNN, GRU, Bi-LSTM, and CONV1D in terms of prediction accuracy using measures such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score (R2). With RMSE= 1978.68268, MAE=1537.14424, MSE= 3915185.15068, and R2= 0.94383, it may be considered the best method.
Karamath Ateeq, Ahmed Abdelrahim Al Zarooni, Abdur Rehman, Muhammad Adnan Khan
Researchers and investors have recently become interested in forecasting the cryptocurrency price forecasting but the most important currency can take that it’s the bitcoin exchange rate. Some researchers have aimed at leveraging the technical and financial characteristics of Bitcoin to create predictive models, while others have utilized conventional statistical methods to explain these factors. This article explores the LSTM model for forecasting the value of bitcoins using historical bitcoin price series. Predict future bitcoin prices by developing the most accurate LSTM forecasting model, building an advanced LSTM forecasting model (LSTM-BTC), and comparing past bitcoin prices. This is the second step, if looking at the end of the model, it has very high accuracy in predicting future prices. The performance of the proposed model is evaluated using five different datasets with monthly, weekly, daily, hourly, and minute-by-minute bitcoin price data with total records from January 1, 2021, to March 31, 2022. The results confirm the better forecasting accuracy of the proposed model using LSTM-BTC. The analysis includes square error MSE, RMSE, MAPE, and MAE of bitcoin price forecasting. Compared to the conventional LSTM model, the suggested LSTM-BTC model performs better. The contribution made by this research is to present a new framework for predicting the price of Bitcoin that solves the issue of choosing and evaluating input variables in LSTM without making firm data assumptions. The outcomes demonstrate its potential use in applications for industry forecasting, including different cryptocurrencies, health data, and economic time.
Aleksandar Petrović, Luka Jovanović, Miodrag Živković, Nebojša Bačanin · 6 authors
The interest for cryptocurrencies is high and hence this work focuses on providing a practical real-world application of the swarm metaheuristics and long short term memory model (LSTM).The goal is price forecasting which is interesting due to the high volatility of the cryptocurrencies.The authors apply LSTM for the solution of the problem which has been proven to reap results with this type of problem.The LSTM is further optimized by a swarm metaheuristic -arithmetic optimization algorithm (AOA).The solution was tested alongside familiar high-performing competitors with the use of standard metrics mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).These metrics have been used for comparison between the solutions, upon which the proposed solution obtained overall best performance that testifies to the improvement of the solution.
Hae Sun Jung, Seon Hong Lee, Haein Lee, Jang Hyun Kim
Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market. As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices. Experiments have been conducted to predict Bitcoin prices using Twitter content. However, the amount of data was limited, and prices were predicted for only a short period (less than two years). In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected. These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts. An accuracy of 90.57% and an area under the receiver operating characteristic curve value (AUC) of 97.48% were obtained using the extreme gradient boosting (XGBoost). It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner (VADER) and 11 technical indicators utilizing moving average, relative strength index (RSI), stochastic oscillators in predicting Bitcoin price trends can produce significant results. Thus, the input features used in the paper can be applied on Bitcoin price prediction. Furthermore, this approach allows investors to make better decisions regarding Bitcoin-related investments.
The study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. The FB Prophet time series model and the LSTM recurrent neural network were selected to implement the study. Using the example of data from Binance (the most popular exchange in Ukraine) for the period from 06.07.2020 to 01.04.2023, prices for Bitcoin, Ethereum, Ripple, and Dogecoin were modeled and forecasted. The recurrent neural network of long-term memory showed significantly better results in forecasting according to the RMSE, MAE, and MAPE criteria, compared to the Naïve model, the traditional ARIMA model, and the FB Prophet results.
Çalışmanın amacı, 03.01.2020 ile 28.02.2022 dönemi için üretim araçlarındaki gelişmenin bir başka veçhesi olan dijitalleşme ile kripto paralara yönelimin hızlanmasının geleneksel borsalara alternatif olup olmayacağını simetrik ve asimetrik nedensellik test yöntemleriyle incelemektir. Bu çerçevede simetrik nedensellik analiz sonuçlarına göre, BTC ve ETH fiyatlarından SP500, NASDAQ ve DOWJ fiyatlarına doğru bir nedensellik ilişkisi saptanmış, aynı zamanda VIX’ten BTC ve ETH’ye doğru bir nedensellik ilişkisi bulunmuştur. Asimetrik nedensellik analizi sonuçlarına göre SP500, NASDAQ, DOWJ ve Altın fiyatlarındaki negatif değişmelerden, BTC fiyatlarındaki pozitif değişmelere doğru bir nedensellik ilişkisi tespit edilmişken, NASDAQ ve DOWJ fiyatlarındaki pozitif değişmelerden ETH fiyatlarının pozitif değişmelerine doğru bir nedensellik ilişkisinin varlığına ulaşılmıştır. Son olarak kripto paralar arasındaki nedensellik ilişkisi sınandığında BTC fiyatlarındaki negatif değişimlerden ETH fiyatlarındaki pozitif değişimlere, ETH fiyatlarındaki negatif değişimlerden BTC fiyatlarındaki pozitif değişimlere doğru bir nedensellik ilişkisi tespit edilmiştir.
Muhammad Zakhwan, Mohamed Rafik, Noraisyah Mohamed Shah, Anis Salwa Binti Mohd Khairuddin
Cryptocurrency is branded as a digital currency, an alternative exchange currency system with significant ramifications for the economies of rising nations and the global economy. In recent years, cryptocurrency has infiltrated almost all financial operations; hence, cryptocurrency trading is frequently recognised as one of the most popular and promising means of profitable investment. Lately, with the exponential growth of cryptocurrency in-vestments, many Alternative Coins (Altcoins) resurfaced as to mimic the fiat currency. Altcoins prediction, as the name suggests the alternative coins from the traditional cryptocurrency which is Bitcoin (BTC). There are several methods to forecast cryptocurrency prices namely Technical Analysis and Fundamental Analysis which has been widely used in forecasting fiat and stock prices. With the emergence of Artificial Intelligence (AI), Machine Learning and Deep Learning algorithms provide a different perspective on how investors can estimate the trend or the movement of prices. In this thesis, as cryptocurrency price are time-dependent, Recur-rent Neural Network (RNN) is presented due to RNN’s nature that is well suited for Time Series Analysis (TSA). The topology of proposed RNN model consists of 3 stages which are model groundwork, model development and testing and optimisation. The RNN architecture are extended to two different models specifically Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU). There are 4 hyperparameters that will affect the accuracy of the deep learning model in predicting cryptocurrency price. Hyperparameters tuning set the basis of optimising the model to improve the accuracy of cryptocurrency prediction. Hyperparameters listed in this project are limited to number of epochs, adaptive optimisation algorithm, dropout rate, and batch size. Next, the models are tested with data of different coins listed in the cryptocurrency market with different input features to find out the effect on the accuracy and robustness of the model in predicting the cryptocurrency price. This research demonstrates that GRU has the best accuracy in forecasting the cryptocurrency prices based on the values of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Executional Time, scoring 2.2201, 0.8076 and 200s using intra-day trading strategy Open, High, Low, Close Price (OHLC) as input features.
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.
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%.
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.
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.
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.
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.
Currently, the composition and structure of the production industry's supply chain is becoming increasingly complex. The loss and untimely transmission of supply chain information exacerbated the bullwhip effect. At the same time, due to the lack of a reliable repository of information, difficulties in traceability and accountability have also made supply chain management difficult. Blockchain has the characteristics of supporting distributed networks, synchronization of information between nodes, digital encryption, traceable information and unforgeable block content, which is suitable for use in supply chain and can provide a solution for it. In this paper, a design scheme of an integrated platform for information services provided by supply chain participants and based on the Ethereum blockchain is proposed. Using Ethereum smart contracts, the regular trade involved in the supply chain is realized using blockchain technology, and key information about the production and circulation of the supply chain is stored on the blockchain to ensure that the information cannot be falsified. At the same time, a reputation evaluation method based on smart contracts is used to evaluate the reputation of enterprises in the supply chain, which can provide references for supplier selection among enterprises.
Njoku ThankGod Anthony, Mahmoud Shafik, Fatih Kurugöllü, Hany F. Atlam
Over the past few years, Blockchain technology has been utilized in various applications to improve privacy and security. Although blockchain has proven its worth as a very powerful technology, research has shown that it is not entirely immune to security and privacy attacks. There was a successful 51% attack on Ethereum Classic back in January 2019 which shows that blockchain still facing security and privacy challenges. This paper aims to develop an anomaly detection solution for the Ethereum blockchain to overcome security challenges using Machine Learning (ML). The proposed solution focuses on using a dynamic approach where the normal operational behaviour of the Ethereum blockchain is used to train ML algorithms and any deviation will be tagged as an anomaly and will be detected by the system. Four ML algorithms including K-Nearest Neighbours (KNN), Gaussian Naive Bayes (GaussianNB), Random Forest, and Stochastic Gradient Descent (SDG) were utilized to train and verify the accuracy of the proposed solution. The experimental results demonstrated that the random forest algorithm provided the best accuracy of 99.84% over other ML algorithms.
In recent years, the bitcoin market has developed rapidly and has been recognized as a new type of gold by many investors. It may replace gold as a hedge against inflation and become a new investment asset for financial management. The investment relationship with gold has increasingly important research value and practical significance. This paper modeled daily price flow data from 11 September 2016 to 10 September 2021 to help market traders determine whether they need to buy, hold, or sell assets in their portfolios daily. The model predicts price fluctuations through linear regression prediction of machine learning, K-Nearest Neighbor (KNN) algorithm. In the linear regression prediction, the goodness of fit of gold is 89.44%, and the goodness of fit of Bitcoin is 98.43%. In the test set prediction of KNN algorithm, the goodness of fit of gold is 97.25%, and the goodness of fit of Bitcoin is 95.06%. Based on this, the optimal investment strategy and the initial investment value are obtained. Empirical analysis shows that bitcoin price volatility and gold price volatility have a strong substitution effect; gold and currency used will be a suitable combination of hedging, which will bring momentum for the development of the market economy and become an important force in the sustainable development of a high-quality-driven economy.