Melvin M. Vopson, Serban Lepadatu, Anna Vopson, Szymon Łukaszyk
The storage, transmission, and processing of data become significant problems when large digital data files or databases are involved, as in the case of decentralized online global databases such as blockchain. Here, we propose a novel method that allows for the scalability of digital assets, including blockchain databases in the download, validation, and confidentiality processes, by developing a lightweight blockchain technology called Entropic Blockchain. This is a computer-implemented mathematical method by which to generate an information-entropic numerical barcode representation of a digital asset. Using this technique, a 1–2 Mb block of digital data can be represented by a few bytes, significantly reducing the size of a blockchain. The entropic barcode file can be utilized on its own or as an optically machine-readable entropic barcode for secure data transmission, processing, labeling, identification, and one-way encryption, as well as for compression, validation, and digital tamper-proof checks. The mathematics of this process and all the steps involved in its implementation are discussed in detail in this article.
Abdelatif Hafid, Maad Ebrahim, Ali Alfatemi, Mohamed Rahouti · 5 authors
The rapid growth of the stock market has attracted many investors due to its potential for significant profits. However, predicting stock prices accurately is difficult because financial markets are complex and constantly changing. This is especially true for the cryptocurrency market, which is known for its extreme volatility, making it challenging for traders and investors to make wise and profitable decisions. This study introduces a machine learning approach to predict cryptocurrency prices. Specifically, we make use of important technical indicators such as Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) to train and feed the XGBoost regressor model. We demonstrate our approach through an analysis focusing on the closing prices of Bitcoin cryptocurrency. We evaluate the model's performance through various simulations, showing promising results that suggest its usefulness in aiding/guiding cryptocurrency traders and investors in dynamic market conditions.
Melike Bildirici, Yasemen Uçan, Ramazan Tekercioğlu
This paper introduces hybrid models designed to analyze daily and weekly bitcoin return spanning the periods from 18 July 2010 to 28 December 2023 for daily data, and from 18 July 2010 to 24 December 2023 for weekly data. Firstly, the fractal and chaotic structure of the selected variables was explored. Asymmetric Cantor set, Boundary of the Dragon curve, Julia set z2 −1, Boundary of the Lévy C curve, von Koch curve, and Brownian function (Wiener process) tests were applied. The R/S and Mandelbrot–Wallis tests confirmed long-term dependence and fractionality. The largest Lyapunov test, the Rosenstein, Collins and DeLuca, and Kantz methods of Lyapunov exponents, and the HCT and Shannon entropy tests tracked by the Kolmogorov–Sinai (KS) complexity test determined the evidence of chaos, entropy, and complexity. The BDS test of independence test approved nonlinearity, and the TeraesvirtaNW and WhiteNW tests, the Tsay test for nonlinearity, the LR test for threshold nonlinearity, and White’s test and Engle test confirmed nonlinearity and heteroskedasticity, in addition to fractionality and chaos. In the second stage, the standard ARFIMA method was applied, and its results were compared to the LieNLS and LieOLS methods. The results showed that, under conditions of chaos, entropy, and complexity, the ARFIMA method did not yield successful results. Both baseline models, LieNLS and LieOLS, are enhanced by integrating them with deep learning methods. The models, LieLSTMOLS and LieLSTMNLS, leverage manifold-based approaches, opting for matrix representations over traditional differential operator representations of Lie algebras were employed. The parameters and coefficients obtained from LieNLS and LieOLS, and the LieLSTMOLS and LieLSTMNLS methods were compared. And the forecasting capabilities of these hybrid models, particularly LieLSTMOLS and LieLSTMNLS, were compared with those of the main models. The in-sample and out-of-sample analyses demonstrated that the LieLSTMOLS and LieLSTMNLS methods outperform the others in terms of MAE and RMSE, thereby offering a more reliable means of assessing the selected data. Our study underscores the importance of employing the LieLSTM method for analyzing the dynamics of bitcoin. Our findings have significant implications for investors, traders, and policymakers.
Neural networks (NNs) are well established and widely used in time series forecasting due to their frequent dominance over other linear and nonlinear models. Thus, this paper does not question their appropriateness in forecasting cryptocurrency prices; rather, it compares the most commonly used NNs, i.e. feedforward neural networks (FFNNs), long short-term memory (LSTM) and convolutional neural networks (CNNs). This paper contributes to the existing literature by defining the appropriate NN structure comparable across different NN architectures, which yields the optimal NN model for Bitcoin return forecasting. Moreover, by incorporating turbulent events such as COVID and war, this paper emerges as a stress test for NNs. Finally, inputs are carefully selected, mostly covering macroeconomic and market variables, as well as different attractiveness measures, the importance of which in cryptocurrency forecasting is tested. The main results indicate that all NNs perform the best in an environment of bullish market, where CNNs stand out as the optimal models for continuous dataset, and LSTMs emerge as optimal in direction forecasting. In the downturn periods, CNNs stand out as the best models. Additionally, Tweets, as an attractiveness measure, enabled the models to attain superior performance.
Mamun Ahmed, Sayma Alam Suha, Fahamida Hossain Mahi, Forhad Uddin Ahmed
Since entering the market in 2009, Bitcoin has had a price that is extremely erratic. Its price is influenced by factors such as adoption rates, regulatory changes, geopolitical occurrences, and macroeconomic developments. Experts believe that Bitcoin's price will rise in the long run due to limited supply and rising demand. Therefore, the aim of this study is to propose an ensemble feature selection and machine learning-based approach to predict bitcoin price. For this research purpose, the cryptocurrency-based dataset has been used, visualized, and preprocessed. Five different feature selection approaches (Pearson, RFE, Embedded Random Forest, Tree-based and Light GBM) are followed by ensemble methodology, with the maximum voting approach to extract the most significant features and generate a dataset with reduced attributes. Then the dataset with or without feature selection is used for bitcoin price prediction by applying ten different machine learning regressing models, which includes six traditional, four bagging and boosting ensemble techniques. The comparative result analysis through multiple performance parameters reveals that the decreased number of features improves the performance for each of the models and the ensemble models outperform other types of models. Therefore, Random Forest regression ensemble ML model can get the best prediction accuracy with 0.036018 RMSE, 0.029470 MAE and 0.934512 R2 employing the dataset with reduced features for estimating the value of bitcoin.
A capacity of foreseeing price fluctuations in bitcoin with exceptionally precise is very worthwhile to investigators and funding sources. However, as the cryptocurrency market is nonlinear, it can be challenging to determine the distinctive features of time-series data, which renders it challenging to forecast accurate price estimations. Massive oscillations in non-stationary cryptocurrency values underscore the pressing necessity to precise forecasting models. The most effective methodology for cryptocurrency price forecasting is machine learning, foremost ensemble and deep learning. Traditional statistical methods are difficult to execute accurately because to the lack of seasonal variations and the need to meet a number of naive requirements. The suggested methodology builds upon the random walk theory, commonly utilized in financial markets to model stock values. To simulate market volatility, this methodology utilizes randomization into the observed feature activations of neural networks at a layer-wise level. Moreover, a mechanism to assess the market’s reaction pattern is incorporated into the prediction model. Training was conducted on ARIMA and LSTM, short for Long Short-Term Memory models using Ripple, Ethereum, and Tron as illustrative examples.
The recurring neural network is a deep learning algorithm that is commonly used to develop prediction systems. There are many variants of RNN such as RNN itself, long-short-term memory (LSTM), and gated recurring unit, so it is frequently debatable which algorithm from the RNN family has the most optimal efficiency and computation time. When developing a prediction system, sequential or time series data is required so that an accurate prediction can be made. Sequential or time series data involve data arranged in a time sequence, such as weather data, financial data, carbon emission data, and traffic data recorded over time. This research will be carried out by predicting the three RNN models against historical Bitcoin value data. The research method used is Experimental Design by comparing the performance between the three models on bitcoin value time series data, testing is done by involving hyperparameters such as Tanh, Sigmoid, and ReLU activation functions, batch size, and epochs. The aim of this research is to find out which RNN model can produce the most optimal performance and find out what performance measures can be used to evaluate and compare the performance between the three models. The results of the study show that LSTM is the most effective model with RMSE 0.012441 and MSE 0.000155 but inefficient because it takes 3 minutes 24 seconds to run the computation; in the meantime, the Tanh activation function gives the most optimal prediction than Sigmoid and RelU and therefore should be the main candidate to be used with RNN models when predicting Bitcoin prices.
This paper examines the correlation between three prospective investing options: the Bitcoin cryptocurrency price, gold, and the Dow Jones stock index. The main research question is whether there is a causal effect of gold and the DWJ on Bitcoin and how this effect varies on time. The study begins with a background analysis that explains the definitions and operation of cryptocurrencies, followed by a brief overview of gold and its derivatives. In addition, a historical review of stock markets is provided, with a focus on the Dow Jones index. Then, a literature review follows. Daily data from three separate periods are used, each spanning four years. The first period, running from October 2014 to September 2018, provides an overview of the introduction of official cryptocurrency price data. The second period, running from Oct 2018 to Sept 2022, captures more recent trends preceding COVID-19. The third period, from January 2020 to December 2023, is the whole COVID-19 period with the initiation, embedded, and terminal phases. Classical inductive statistical methods (descriptive, correlations, multiple linear regression) as well as time series analysis methods (autocorrelation, cross-correlation, Granger causality tests, and ARIMA modeling) are used to analyze the data. Rigorous testing for autocorrelation, multicollinearity, and homoskedasticity is performed on the estimated models. The results show a correlation of Bitcoin with gold and the DWJ. This correlation varies over time, as in the first period the correlation mainly concerns the DWJ and in the second it mainly concerns gold. By using ARIMA models, it was possible to make a forecast in a time horizon of a few days. In addition, the structure of the forecasting mechanism of gold and DWJ on Bitcoin seems to have changed during the COVID-19 crisis. The findings suggest that future research should encompass a broader dataset, facilitating comprehensive comparisons and enhancing the reliability of the conclusions drawn.
Cryptocurrencies are digital assets that have attracted a lot of investment and attention. It is challenging and essential for investors and traders to predict their stock price movements. Making accurate predictions about cryptocurrency prices is crucial for avoiding losses and gaining profits. Our research proposes a novel method for predicting the stock closed prices of three popular cryptocurrencies: Bitcoin, Ethereum and Polkadot. The SVR (Support vector regression) machine learning method can provide robust and accurate predictions for nonlinear and nonstationary data. This paper compares SVR radial basis functions (RBFs) and hybrid kernels based on cryptocurrency data characteristics. SVR parameters such as regularization, gamma, and epsilon can also be tuned using grid search. Our approach is tested on real-world cryptocurrency stock prices collected from Yahoo Finance. Prediction performance is measured using regression metrics like MAPE (Mean absolute percentage error) and R2 score. In our work, a MAPE value of 0.07772 and an R2 score of 0.9999 have been obtained. The results of our experiments indicate that our approach is significantly more accurate and reliable than existing methods.
Cryptocurrencies are a revolution in the domain of economics, currency, and trade, Bitcoin being the prime among them. With the ever increasing demand and limited number of bitcoins available, bitcoin as well as other cryptocurrencies has been a hot topic among economists, traders, inverters, and researchers. Although these coins based on blockchain are phenomenal, the volatile nature of the cryptocurrency markets has spurred significant interest in developing accurate prediction models to aid investors and market participants. This research paper examines the application of the Bidirectional Long Short-Term Memory model for predicting Bitcoin prices. This study uses historical Bitcoin price data and features such as opening prices, closing prices, and trading volumes to predict the target closing prices for Bitcoins. The study evaluates the performance of the Bi-LSTM model against comparative models, considering various relevant metrics. It was observed that Bi-LSTM performs significantly better among other regression models in capturing the inherent volatility and non-linearity of cryptocurrency markets. Our proposed model outperformed previous studies in various reported metrics. Additionally, we explored the impact of various hyperparameters and input features on the model’s performance to achieve an ideal state. Insights obtained by conducting this study would eventually contribute to an extensive understanding of the potential of deep learning techniques in forecasting cryptocurrency prices, offering valuable implications for investors and risk management strategies in the evolving landscape of digital assets.
Non-fungible tokens (NFTs) are a type of digital asset based on blockchain that contain unique codes verifying the authenticity and ownership of different assets such as art pieces, music, gaming items, collections, and so on. This phenomenon and its markets have grown significantly since the beginning of 2021. This study, using daily data between November 2017 and November 2022, predicts the volume of NFT sales by utilising Random Forest (RF), GBM, XGBoost, and LightGBM methods from the community machine learning methods. In the predictions, several financial variables, including Gold, Bitcoin/USD, Ethereum/USD, S&P 500 index, Nasdaq 100, Oil/USD, Euro/USD, and CDS data, are treated as independent variables. According to the results, XGBoost is found to be the best prediction method for NFT market volume estimation concerning several statistical criteria, e.g., MAE, MAPE, and RMSE, and the most significant influential feature in determining prices is the Ethereum/USD exchange rate.
Bechir Alaya, Tarek Moulahi, Salim El Khediri, Suliman Aladhadh
Machine Learning (ML) is receiving unprecedented hype and attention. However, the ML runtime environment is still at risk from threats, such as manipulation of model parameters or contradictory poisoning of training datasets. A blockchain is a technology that combines a set of existing techniques, protocols, and tools to form a distributed and secure ledger of all transactions. This article examines and proposes a way of integrating ML suitable for Blockchain to protect the training dataset and model parameters. Another major contribution of this work is the deployment and securing of the decision process of ML, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models. This smart contract-based deployment has equipped the Blockchain-based system to detect toxic recordings intelligently. The effectiveness of this proposed approach is measured in both its detection capabilities and its operational efficiency, by applying a case study of medical records as a sensitive area that tested the performance of this approach.
This study investigates the efficacy of ARIMA and LSTM models in predicting Bitcoin prices, emphasizing the importance of accurate price prediction for trading, risk management, and investment strategies in the volatile cryptocurrency market. The objectives are to analyze Bitcoin prices to identify underlying patterns and trends, compare the predictive performance of ARIMA and LSTM models, and provide insights into their practical applications for Bitcoin price prediction. A comprehensive dataset of Bitcoin prices from January 1, 2011, to December 31, 2023, sourced from CoinMarketCap, was used. Data preprocessing included handling missing values, removing duplicates, achieving stationarity through differencing, and normalizing data using MinMaxScaler. The ARIMA model's best-fitting parameters were identified using ACF and PACF plots, and it was trained with the statsmodels library. The LSTM model involved data preparation through windowing and train-test splitting, constructing a neural network with LSTM layers, and training using TensorFlow/Keras. Evaluation metrics included Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), with comparisons based on accuracy and computational efficiency. The ARIMA model demonstrated impressive performance with an MAE of 2.308392356829177e-215 and an RMSE of 0.0, indicating a near-perfect fit to the training data. The LSTM model achieved an MAE of 0.00021804577826689423 and an RMSE of 0.00021916977109865863, showing robust performance in handling nonlinear and long-term dependencies. The ARIMA model excelled in computational efficiency with a training time of 2.548070192337036 seconds and a prediction time of 0.0009970664978027344 seconds, while the LSTM model required 378.69622468948364 seconds for training and 0.6859967708587646 seconds for prediction. The results highlight ARIMA's effectiveness in capturing linear trends and its suitability for short-term trading strategies, while LSTM is better for long-term investment strategies due to its ability to model complex patterns. Despite potential overfitting in ARIMA and high computational demands for LSTM, the study suggests exploring hybrid models, incorporating additional data sources, and developing advanced techniques to enhance predictive accuracy in future research.
Matthew I. Swindall, Kritagya Upadhyay, James H. Brusuelas, G West · 5 authors
The study and preservation of ancient texts presents unique challenges due to the degradation and damage these manuscripts often exhibit. Papyrology, specifically, relies on meticulous study and reconstruction of fragmented texts by experts. Current digital platforms for editing and publishing papyrological texts lack the ability to manage the complex components of critical editions while facilitating peer review. In our work we propose a novel framework utilizing blockchain and smart contracts to automate the storage and retrieval of multiple related editions of a text while ensuring contributions by multiple authors are recorded. The system architecture consists of a user interface for submitting editions, a smart contract that manages storage, the blockchain ledger which stores data locations, and the use of the decentralized storage platform, the Interplanetary Files System (IPFS). Experiments demonstrated the feasibility of the framework by storing 501 synthetic editions on IPFS and recording metrics for encoding, transmission time, blockchain transaction time, and transaction costs. We believe this novel framework could enable the advancement of digital papyrology through distributed peer review and allow for the integration of AI agents into papyrology. Future development of the proposed platform could significantly modernize digital edition management for papyrology and the humanities in general.
These days, there is a lot of demand for cryptocurrencies, and investors are essentially investing in them. The fact that there are already over 6,000 cryptocurrencies in use worldwide because of this, investors with regular incomes put money into promising cryptocurrencies that have low market values. Accurate pricing forecasting is necessary to build profitable trading strategies because of the unique characteristics and volatility of cryptocurrencies. For consistent forecasting accuracy in an unknown price range, a variation point detection technique is employed. Due to its bidirectional nature, a Bi-LSTM appropriate for recording long-term dependencies in data that is sequential. Accurate forecasting in the cryptocurrency space depends on identifying these connections, since values are subject to change over time due to a variety of causes. In this work, we employ four deep learning-based models that are LSTM, FB-Prophet, LSTM-GRU and Bidirectional-LSTM(Bi-LSTM) and these four models are compared with Silverkite. Silverkite is the main algorithm of the Python library Graykite by LinkedIn. Using historical bitcoin data from 2012 to 2021, we utilized to analyse the models’ mean absolute error (MAE) and root mean square error (RMSE). The Bi-LSTM model performs better than others, with a mean absolute error (MAE) of 0.633 and a root mean square error (RMSE) of 0.815. The conclusion has significant ramifications for bitcoin investors and industry experts.
Madhu B K, D Sowmya, N Spoorthi, Umme Kulsum · 5 authors
In this technology counterfeiting is very common and dangerous, also another consequence of counterfeiting is that a company’s reputation suffers. There are several methods such as RFID tags artificial intelligence blockchain and QR based systems etc. In our survey paper we are focusing mainly on blockchain techonlogy. Blockchain typically managed by peer-to-peer computer network for use as a public distributed ledger. The blockchain technology ensures identification and traceability of original product through the supply chain
This chater delves into the synergistic convergence of two transformative technologies reshaping the business landscape. Commencing with an exploration of fundamental blockchain principles, the segment elucidates its decentralized nature, distributed ledger, and smart contract capabilities. Simultaneously, it navigates the intricacies of IoT, unraveling interconnected devices, data communication, and practical applications spanning diverse sectors. Acknowledging the challenges inherent in IoT, such as security concerns and data integrity, the chapter strategically positions blockchain as a viable solution to address these issues. A comprehensive examination of the fusion of blockchain and IoT is undertaken, emphasizing the heightened security, transparency, and efficiency resulting from their integration. Through a series of real-world applications, the segment illustrates how this amalgamation transforms supply chain management, legal and financial processes, healthcare, and the energy sector.
Abstract Nowadays, the prediction of cryptocurrency side effects on the critical aspects of the exchange rates in intelligent business is one of the main challenges in the financial market. Cryptocurrency is defined as a set of digital information concerning internal financial protocols of digital marketing, such as blockchain, which operates according to a decentralized architecture. On the other hand, fraud activities in Ethereum transfer and management of cryptocurrency now increase and affect safe transactional processes. This article presents a new machine‐learning approach to Ethereum fraud Detection based on Bayesian Optimizable Ensemble Bagged Trees (BOEBT) algorithm. Moreover, the main goal of this study is to derive the accuracy of the cryptocurrency prediction model using different machine‐learning algorithms and compare their evaluation parameters together. The performance of the proposed prediction model using the machine learning algorithms was evaluated by the MATLAB tool. The experimental results show that the proposed BOEBT algorithm merits achieving 99.21% accuracy and 99.14% F1‐Score to other machine learning algorithms for cryptocurrency fraud prediction.
In the volatile cryptocurrency market, accurately forecasting Bitcoin prices is crucial yet challenging, carrying significant economic implications. This paper presents a novel hybrid model that merges the predictive capabilities of Long Short-Term Memory (LSTM) networks with the computational efficiency of Gated Recurrent Units (GRU). This integration is designed to simultaneously capture long-term dependencies and short-term fluctuations inherent in Bitcoin price dynamics, thus providing a comprehensive analysis framework. The model utilizes a meticulous architecture starting with an input layer that normalizes data to address price variability, followed by LSTM layers that interpret long-term trends, and GRU layers that refine insights based on short-term variations. Evaluated using a dataset divided into training, validation, and testing phases and optimized with the Adam algorithm, the model’s performance surpasses traditional forecasting methods and standalone neural networks. Metrics such as RMSE, MAE, and R2confirm its superior predictive accuracy, with significant improvements over benchmarks like ARIMA, standalone LSTM, and GRU models. This breakthrough highlights the Hybrid LSTM-GRU model’s potential as a transformative tool for investors and analysts navigating the complexities of the cryptocurrency market.
This paper presents a Deep Reinforcement Learning (DRL) based Internet of Things (IoT)-enabled Prism blockchain. The recent advancements in the field of IoT motivate the development of a secure infrastructure for storing and sharing vast amounts of data. Blockchain, a distributed and immutable ledger, is best known as a potential solution to data security and privacy for the IoT. The scalability of blockchain, which should optimize the throughput and handle the dynamics of the IoT environment, becomes a challenge due to the enormous amount of IoT data. The critical challenge in scaling blockchain is to guarantee decentralization, latency, and security of the system while optimizing the transaction throughput. This paper presents a DRL-based performance optimization for blockchain-enabled IoT. We consider one of the recent promising blockchains, Prism, as the underlying blockchain system because of its good performance guarantees. We integrate the IoT data into Prism blockchain and optimize the performance of the system by leveraging the Proximal Policy Optimization (PPO) method. The DRL method helps to optimize the blockchain parameters like mining rate and mined blocks to adapt to the environment dynamics of the IoT system. Our results show that the proposed method can improve the throughput of Prism blockchain-based IoT systems while preserving Prism performance guarantees. Our scheme can achieve 1.5 times more system rewards than IoT-integrated Prism. In our experimental setup, the proposed scheme could improve the average throughput of the system by about 6,000 transactions per second compared to Prism.
Peter T. Yamak, Yujian Li, Ting Zhang, Pius Kwao Gadosey
In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a type of convolutional neural network (CNN). We propose a tripartite classification system to accurately represent Bitcoin price trends. In addition, we demonstrate the effectiveness of Wide-TSNet through various experiments, in which it achieves an Accuracy of approximately 94% and an F1 score of 90%. It is also shown that lightweight CNN models, such as SqueezeNet and EfficientNet, can be as effective as complex models under certain conditions. Furthermore, we investigate the efficacy of other image transformation methods, such as Gramian angular fields, in capturing the trends and volatility of Bitcoin prices and revealing patterns that are not visible in the raw data. Moreover, we assess the effect of image resolution on model performance, emphasizing the importance of this factor in image-based time-series classification. Our findings explore the intersection between finance, image processing, and deep learning, providing a robust methodology for financial time-series classification.