P. M. Yawalkar, Deepak Narayan Paithankar, Abhijeet Rajendra Pabale, Rushikesh Vilas Kolhe · 5 authors
An integrated identity is a centralized identifier that makes it possible for customers to get access to a variety of business services from a single network. The risks and assaults include identity leaks, centralized management, auditing restrictions, and lengthy breach investigation procedures. The article presents a technique for creating a blockchain-based, integrated identification system in a marketplace by automating and decentralizing the creation and auditing of strong and secure attributes. When individuals engage in market transactions, they act as nodes in a distributed blockchain network, contributing to the development of federated identities. Using a single federated identity, members of this network are able to use any of the participating companies' services. In this, IoT sensors and wearables can automatically log real-time data for you while identifying patterns and flagging problems. The total transparency of all blockchain transactions provides participants the ability to see which services they're using and their users the ability to track the usage of their identities. To test the proposed architecture, implementation done on a public blockchain and a permissioned blockchain (Ethereum and Hyperledger Fabric).
Stale blocks are not avoidable in blockchain, such as the Bitcoin network, when proof-of-work is used as the consensus protocol. However, as the economic loss to the miners and the security risk to the network cannot be ignored, research is needed to identify and analyse stale blocks. By analysing the factors influencing the generation of stale blocks, the authors propose a new machine learning model based on XGBoost. They propose a new data collection method for bitcoin nodes to obtain real data for training prediction model. Then, based on the model, they generate optimal mining strategies and analyse the economic benefits. The experimental data and application cases show that the real-time data detection and machine learning model that they propose can accurately identify and predict the generation of stale blocks and generate an economically optimal mining strategy in the Bitcoin network with the presence of stale blocks.
Contemporarily, the popularity and use of cryptocurrencies has risen along with their prices and Ethereum is the second most popular and largest cryptocurrency after Bitcoin. Cryptocurrencies are based on the blockchain, which is a decentralized technology that has the power to change any banking system. They have become an attractive investment for both traders and individuals looking to invest. The price of Ethereum fluctuates and is affected by various factors, e.g., the crypto trading exchange as well as supply and demand. Ethereum is so valuable because it can be used as cash and one also pay Ethereum in full or in part to someone in exchange. Besides, it is easily guaranteed by the blockchain. Unlike stocks, the price of Ethereum is much more variable because it is traded 24 hours a day and there are no closing times. On this basis, this paper compares the results of two different models, namely linear regression and Long Short-Term Memory networks (LSTM). The dataset comprised in the closing prices of the last 372 days for Ethereum. The performance of the obtained models is critically evaluated using statistical indicators Root Mean Squared Error (RMSE) and the study have drawn our conclusions based on the RMSE result. The paper demonstrates a technique for using time series data in both models and determining each model's RMSE. These results shed light on guiding further exploration of prediction Ethereum prices and trends.
Contemporarily, cryptocurrency has a high market value, and the price of cryptocurrency fluctuates dramatically. This article analyzes the parameters effects of the LSTM model on Bitcoin price prediction accuracy based on Python and modules of Numpy, Pandas, Keras, Tensorflow, and Sklern. The analysis clarifies the relationship between the accuracy of Bitcoin price prediction and different parameters in the LSTM model. It is discovered that when larger batch sizes are supplied at minor epochs, the accuracy of Bitcoin price prediction declines. Meanwhile, the number of neurons affects the accuracy. In addition, compared to lengths of 14, 30, and 60, the prediction error grows greater when a single time sequence is 7 in length. Apart from that, at present, using closing prices from the past two years rather than the past 1 year, 3 years, or 5 years can make predictions more accurate. These findings shed light on recommendations for adjusting various parameters in the development of the LSTM model for Bitcoin price prediction.
Predicting Bitcoin price is a universal research area as it attains significance in predicting the market way of its rate so that, investors could procure profits. Concurrently, with the evolution of Machine Learning (ML), researchers attempted to use ML based algorithms for forecasting the Bitcoin price. However, these researches have resulted in inefficient prediction due to error rate. For alleviating such pitfalls, this study intends to forecast the Bitcoin price by comparing its deviations pre and post Covid using suitable ML algorithms. To achieve this, the study proposes Auto Regressive Integrated Moving Average (ARIMA) with Optimized Genetic Algorithm (OGA). In this case, ARIMA model is considered as it possess the innate ability in capturing standard temporal reliances which is distinct to time-series data. Further, hyperparameters are selected by GA based on the fitness function. Based on this, hyperparameter tuning is performed which assist to improvise the model performance. For determining if there exists any deviations in Bitcoin price (pre and post Covid), Augmented Dickey Fuller (ADF) test is considered. Further, comparative analysis is regarded in accordance with performance metrics to validate the performance of the proposed system which proves its effectiveness in predicting Bitcoin price.
There has been a lot of buzz about blockchain technology lately, thanks to the success of digital currencies like Bitcoin and Ethereum. All of these applications of blockchain technology have received a lot of attention. Bitcoin and Ethereum were able to process 7 and 15 TPS, respectively, when they were launched, whereas VISA and PayPal were able to process 1700 and 193 TPS. Scalability, which may be described as the capability to modify the block size to accommodate the rising traffic, is the most significant barrier to the widespread adoption of blockchain technology. This article aims to examine blockchain applications, including their applications, smart contracts, and the solutions to those problems. It is vital to scale blockchains while maintaining their fundamentally decentralised nature to appreciate these technologies' promise fully. The successful implementation of scalability solutions is necessary for several reasons, including the provision of services whose performance is on par with that of prevalent technologies, the support of innovative applications, and the maintenance of the workload produced by an expanding user base. In the future, this study will be extended by considering the options that contributors provide as an initiative towards resolving these issues.
In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combina-tion of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The sec-ond key discovery was that a strong combination forecasting model that responds to the multiple fluctua-tions that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction accuracy criterion and matching curve-fitting in this work demonstrated that if the residuals of the revised model are white noise, the forecasts are unbiased. Future work investigating robust hybrid model forecasting using fuzzy neural networks would be very interesting.
Blockchain technology is propelled by distributed ledger technology (DLT). Due to its natural and unfalsified attributes (reliable, efficient, resilient and transparent) pushing it to a prime choice in several monetary related applications and financial services. With the amalgamation of artificial intelligence, various blockchain systems-based limitations can be resolved easily and rapidly in order to provide high-performing and useful results. This paper means to present a through survey on the topical developments and solutions in blockchain technology based on artificial intelligence for resolving its problems. The findings of this study will conclude the important knowledge and direction on the idea of proposing blockchain-based systems to support time-sensitive and real-time-specific applications.
As cryptocurrencies develop, it cannot be denied that crypto prices are volatile. One of the influencing factors is the increasing volume of transactions which attracts the interest of researchers to conduct research in developing coin price predictions from cryptocurrencies. The method, algorithm and amount of data affect the prediction results. In this study, prediction modelling will be carried out using the LSTM method and short-term data. This study will conduct two experiments using the simple LSTM method and utilising multivariate time series with LSTM. The smallest predicted value is obtained using an 80/20 data allocation distribution scenario, input layer LSTM = 360, Epoch = 500, a Solana coin with RMSE = 0.111, R2 = 0.9962. It can be interpreted that short-term data can be used in making predictive models. Still, special attention needs to be paid to the characteristics of the dataset used and the modelling methodology, and it is hoped that the results of this study can be used in further research.
Abdul Quadir, Santhanakrishnan Narayanan, H. Sabireen, Arun Kumar Sivaraman · 5 authors
Abstract Ever since Ether is launched as a digital currency, its rise has been rapid. It is currently the second most valuable digital currency in the world. There are more than 1 million transactions happening on the Ethereum network every day, and this number is expected to continue to increase. Due to the increasing number of transactions, fraudulent transactions have also increased, which has resulted in a large amount of money being lost and has also destroyed the livelihoods of many individuals. Due to their similarity to valid transactions, it is extremely difficult to distinguish between them. Additionally, Ethereum's pseudo‐anonymity adds to the difficulty of identifying the parties involved. Since there are millions of transactions every day, it would be difficult to manually verify each one. Therefore, a mechanism for validating these transactions is needed. In this context, this paper proposes a novel approach to detecting fraudulent accounts associated with these transactions by implementing machine learning algorithms among the given set of transactions. We propose a framework for creating a stacking classifier by combining several standalone classification algorithms and creating a meta‐learner based on the output of each base algorithm. The algorithms include Logistic Regression, Naive Bayes, Decision Trees, Random Forests, AdaBoosts, KNNs, SVMs, and Gradient Boosts. As a result of combining these algorithms, a powerful classifier with the ability to detect fraudulent transactions. A variety of machine learning models were trained and evaluated on the test set using various metrics. Based on the results of the individual algorithm the Random Forest algorithm achieved the highest accuracy of 95.47%, followed by Gradient Boosting at 94.61% which is an ensemble algorithm using the boosting technique. The Stacking classifier that combines Multinomial Naive Bayes and Random Forest as the base learners and logistic regression as the Meta learner achieved the highest accuracy of 97.18% with an F1 score of 97.02%. Based on the results of all the stacking models developed, it is concluded that algorithms tend to perform better when combined properly. When compared to the other approaches, the proposed approach has outperformed the others, making it feasible in the real world to detect fraudulent transactions.
Indri Tri Julianto, Dede Kurniadi, Fakhrun Mahda Khoiriyyah
Non-Fungible Tokens (NFTs) experienced a peak of popularity in Indonesia through content created and sold by an account at OpenSea called Ghozali Everyday in early 2022. Ghozali reportedly earned ± Rp. 1.3 billion from the content he has created. This sparked the curiosity of the Indonesian people to imitate what Ghozali Everyday did in the hope of getting similar benefits. The market price of NFTs is the same as stock prices, which will fluctuate depending on the price of the cryptocurrency because these NFTs can generally be purchased with the cryptocurrency, namely Ethereum. This research was conducted to predict the price of NFTs using the Data Mining Prediction Algorithm. Five algorithms are compared to find the best algorithm: Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, and Generalized Linear Model. The methodology used is Knowledge Discovery in Databases. The NFTs price dataset is taken from the page coinmarketcap.com from 16 November 2021 to 16 November 2022. The results show that the best Data Mining Prediction Algorithm is a Neural Network with a value of The lowest Root Mean Square Error (RMSE) compared to other algorithms, namely 83.617 +/- 18.853 (micro average: 85.590 +/- 0.000). After the Neural Network is used in the Dataset, the graph results show no significant difference between the Closing Price and the Predicted Price.
M Vanditha, Surendra R Hegde, K Snehith, Anitha S Prasad · 5 authors
Blockchain is a revolutionary technology where information is exchanged between different entities in a chain by using distributed software design and powerful computing. The Novel Method uses smart contract and Ethereum blockchain for tracing and tracking crops efficiently with complete security and business operations. The Technology terminates the requirement for an intermediary, transaction records and trusted centralized authority. Hence, Blockchain improves safety and efficiency by maintaining high reliability and integrity. Smart contracts are used in the proposed system to regulate and oversee all communications and transactions between the participants in the ecosystem of the agricultural supply chain. All transactions are preserved and recorded in the immutable ledger of the blockchain, which is linked to a decentralized file system called as IPFS. Data about images and locations are submitted by various supply chain participants which are then are utilized to ensure the transparency and traceability of the crops using IPFS.
Bitcoin is a type of Cryptocurrency that relies on Blockchain technology and its growing popularity is leading to its acceptance as an alternative investment. However, the future value of Bitcoin is difficult to predict due to its significant volatility and speculative behavior. Considering this, the key objective of this research is to assess Bitcoins’ explosive behavior during 2013–2022 including the most volatile COVID-19 pandemic and Russia–Ukraine war period and to forecast its price by comparing the predictive abilities offive different econometric, machine learning and artificial Intelligence methods namely, ARIMA, Decision Tree, Random Forest, SVM, and Artificial Intelligence Long Short-Term Memory Network (AI-LSTM). The precision of such methodologies has been assessed using root mean square error (RMSE) and mean average per cent error (MAPE) values. The findings confirmed that the AI-LSTM model performs better than other forecast models in predicting Bitcoins’ opening price on the following working day. Therefore, Bitcoin traders, policymakers, and financial institutions can use the model effectively to better forecast the next day’s opening price.
Blockchain technology is frequently referred to as the fourth industrial revolution, which will change the world. Blockchain technology sometimes referred to as distributed ledger technology creates an ecosystem that is decentralized, dispersed, and devoid of central authority. Since Bitcoin's introduction, research into non-financial use cases has continued to expand the technology's usefulness. Through categorization and integrity verification in both the industry and processing terminals, the blockchain paradigm regulates data collection and dissemination instances. From the Bitcoin digital currency system to more contemporary uses, the origin of this technology is traced. This paper presents essential ideas about Blockchain and offers our perspective on the challenges, the future changes, and the predictable effect of Blockchain. Blockchain has created an immense pool of opportunities for every field of life, e-commerce, supply chain, sustainable smart cities, or adoption of e-governance. The impact on various fields by use of Blockchain is discussed, also raising the research investigation questions.
Indranil Ghosh, Esteban Alfaro, Matías Gámez, Noelia García
Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement.
Minh Hoang Nguyen, Phuong Duy Huynh, Son Hoang Dau, Xiaodong Li
The rapid development of blockchain and cryptocurrency in the past decade has created a huge demand for digital trading platforms. Popular decentralised exchanges (DEXs) such as Uniswap and PancakeSwap were created to address this market gap, facilitating cryptocurrency exchange without intermediaries and hence eliminating security and privacy issues associated with traditional centralised platforms. This, however, due to lack of regulation, results in the emergence of a host of damaging investment fraudulent schemes, including Ponzi, honey pot, pump-and-dump, and rug-pull.In this study, we aim to investigate the problem of detecting rug-pull on Uniswap using supervised learning. We aggregate a list of 23 features and propose the use of a hybrid feature selection technique to find the most relevant features for rug-pull. The classifier, using this refined set of features, outperforms the classifier in the previous studies and achieves an f1-score of 99%, a precision of 97% on non-malicious tokens, and a recall of 99% on malicious tokens. Additionally, we show that the XGBoost classifier, built using these proposed features, can distinguish scam tokens and newly listed tokens, which are often harder to differentiate as they have similar characteristics, and also propose a validation method.
Irfan Abdul Karim Shaikh, P.Vamsi Krishna, Swagat Gourav Biswal, A. Sasi Kumar · 6 authors
Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique.
As a new type of digital currency, Bitcoin is considered as “future gold” by various scholars. Therefore, this study considers Bitcoin and gold as a group of hedging assets to conduct investment research and it also discusses the investment rules between Bitcoin and gold: prediction of the rise and fall of Bitcoin, comparison of the characteristics of Bitcoin and gold, and the impact of the transaction procedures of Bitcoin and gold on the final trading results, and formulates trading strategies through optimization algorithms. Then, four machine learning algorithms, i.e., LSTM, BP neural network, Adaboost, and Bagging, are introduced to predict the rise and fall of gold and Bitcoin the next day, and then, the entropy weight method is used to synthesize four predicted results to ensure the robustness of the predicted results. To establish the optimal trading strategy, this study considers the maximum expected return as the goal to develop a single-objective optimization model and historical five-day price volatility as a risk factor. In this study, ant colony, simulated annealing, and genetic algorithms are used to solve the single-objective optimization model. Finally, we conclude that Bitcoin, similar to other financial assets, e.g., gold, is sensitive to shocks and volatile and possesses a relatively quiet cycle. When Bitcoin has an asymmetric impact, Bitcoin and gold can equally treat transactions.
Puja Das, Moutushi Singh, D.A. Karras, Deepsubhra Guha Roy
Conventional lifestyles and working methods are motivated by rising trends and new technologies to form smart cities. Distributed chains are growing to computerize with a highly complicated framework. It is becoming an essential source of additional advantages in today's digital and smart city. Customers or users of smart cities are highly concerned about the quality of food and farm products. On the other hand, tracking the source information is challenging and, preserving its provenance throughout the supply chain network. Traditional supply chain networks are centralized, where a third party does the trading. These centralized systems lack of quality transparency and distribution accountability. Therefore, we provide a promising approach with a unique strategy for converting conventional agriculture to smart agriculture, concerning smart cities, considering blockchain technology and the Internet of Things (IoT) features. The blockchain technique is responsible for maintaining the immutability of the existing network. Still, it faces problems resolving fundamental issues in the distribution chain, like the trustworthiness of the different involved members, transaction responsibility, and real-time tracking. As a result, a trustworthy organization with traceability, conviction, confirmed transportation, and transparent mechanisms in the e-farm supply chain is proposed. Ethereum is used to deploy the proposed smart contract. Testing with the Hyperledger caliper measuring tool shows performance evaluation of several factors such as latency, throughput, resource consumption transaction per second, and so on are assessed. The findings support the effectiveness of the recommended strategy.
Machine learning and deep learning algorithms produce very different results with different examples of their hyperparameters. Algorithm parameters require optimization because they aren't specific for all problems. In this paper Long Short-Term Memory (LSTM), eight different hyperparameters (go-backward, epoch, batch size, dropout, activation function, optimizer, learning rate and, number of layers) were used to examine to daily and hourly Bitcoin datasets. The effects of each parameter on the daily dataset on the results were evaluated and explained These parameters were examined with hparam properties of Tensorboard. As a result, it was seen that examining all combinations of parameters with hparam produced the best test Mean Square Error (MSE) values with hourly dataset 0.000043633 and daily dataset 0.00073843. Both datasets produced better results with the tanh activation function. Finally, when the results are interpreted, the daily dataset produces better results with a small learning rate and small dropout values, whereas the hourly dataset produces better results with a large learning rate and large dropout values.
Bitcoin was the first cryptocurrency introduced as a cryptographic proof-based electronic payment system in 2009. Till now approximately more than 10,000 digital coins are active in the crypto market. Cryptocurrency is a virtual digital asset that uses cryptography and blockchain technology for transaction verification and records maintenance. Its trading is gaining attention due to volatile behavior, decentralized nature, and liquidity in this digital asset. Trading this digital asset provides anonymity and security in transactions. Groundless fluctuations in its price contribute to making its trade risky. Market Prediction of the cryptocurrency is trending because it can reduce the trade loss risk. Data related to this market is vast and publicly available on the internet. It is nearly impossible to infer the market by simple data analysis. Statistical price prediction approaches are less effective due to the absence of seasonality in cryptocurrency market data. Therefore researchers proposed efficient price prediction techniques utilizing statistical, algorithmic, and neural network-based Machine Learning models. This paper provides a detailed literature survey related to the state-of-the-art Machine learning-based prediction methodologies for the market prediction of the digital asset from 2014 to 2022. This research will categorize, summarize, and review the existing research in cryptocurrency market prediction using Machine Learning classifiers. This paper will benefit researchers to be productive in the right direction in the future.
This study aimed to explain the relationship between bitcoin and nonfungible tokens (NFTs) to determine if the NFT is an alternative investment to bitcoin or a complement during oil price uncertainty. The results showed a comovement between NFT and bitcoin prices. However, after excluding the effect of oil prices and using the partial wavelet coherence test, the results changed and the comovements disappeared: bitcoin and NFT became two separate assets that are affected by different variables. Moreover, oil price has more impact on bitcoin than NFT in the medium and long run. However, these results indicate that the change in oil prices, to some extent, is not considered a strong influence on the crypto market. Nevertheless, a significant rise in crude oil prices leads to a significant change in the comovement between crypto assets and they become interrelated.