Currently bitcoin is considered an investment tools, the value of bitcoin itself is unstable so it is difficult to predict which can cause losses for bitcoin traders. Some previous research shows that Long Short-Term Memory (LSTM) which is a deep learning approach as an improvement of RNN has the best performance in predicting stocks and cryptocurrencies compared to Support Vector Machine (SVM), Exponential Moving Average (EMA), and Moving Average (MA), and Seasonal Autoregressive Integrated Moving Average (SARIMA). LSTM has the disadvantage that it is difficult to understand in determining the best parameters and to obtain good results it needs strict hyperparameter adjustment. This study aims to find the best parameters in LSTM by selecting the amount of data, training data composition, batch size, epoch and the amount of prediction time and analyzing prediction performance. In this study, data collection was carried out in real time and was able to provide predictions for the next few days. The test results of the LSTM algorithm have a performance with an average accuracy of 93.69% with the parameters of the amount of bitcoin price data used is 3 years, with a percentage of train data of 85%, using 10 batch sizes, with a number of epochs 125, and the highest average accuracy rate for 7 days of prediction.
In this paper, we performed bitcoin price prediction based on bitcoin price dataset using Support Vector Machine model, Random Forest model, Neural Network model, XGBoost model and LightGBM model and evaluated the performance of these models. We divided the Bitcoin price dataset into training and test sets in a ratio of 7:3, where 70 were used as the training set and 30 as the test set. The models were trained with the training set and tested with the test set using the stock price change (yield) as the target variable and other variables as input variables. By comparing the MSE, RMSE, MAE, MAPE and R² of the different models were evaluated and it was found that XGBoost has the best performance and the best prediction. The performance of the other four models ranged from good to poor, including LightGBM, Random Forest, Support Vector Machine and Neural Network. Among them, the neural network, whose MSE is tens of times higher than the other four models, performs the worst. The research results in this paper can provide reference value for future Bitcoin price prediction, and also provide some reference for choosing appropriate machine learning models.
Time Series forecasting has been approached by a multiplicity of techniques including deep learning methods of various degrees of sophistication, showcasing notable advancements and improved performance over the past few years. More recently, there has been a sustained interest in the study of Transformers, a class of models renowned for their remarkable capacity to capture intricate long-range dependencies and interactions. This ability is perceived as particularly relevant and impactful in the context of time series modeling, reflecting a growing recognition of their potential in enhancing forecasting accuracy and understanding of complex temporal patterns. However, taking advantage of this principle to deploy successful forecasting methods is not yet clearly understood, and requires significant experimentation or engineering. Therefore, in this paper, we compare multiple variations of the Transformer model (standard Transformer, Autoformer, Informer), coupled with diverse combinations of embedding data. In particular, as the emphasis of our work is on forecasting, we investigate the relationship between Transformers’ input segment length and prediction performance in a multi-step time intervals framework. Our results suggest that the Autoformer outperforms both standard Transformer and Informer across various prediction steps. We also observe that shorter input lengths and shorter prediction lengths generally produce better model performance.
This study provides a comprehensive analysis of the existing body of work on predicting the price of Bitcoin using deep learning techniques. It discusses the fundamental concepts behind deep learning and Bitcoin, including recurrent neural networks, convolutional neural networks, and long short-term memory networks. The study also examines the data sources used in training these models, including historical Blockchain transaction data, social media sentiments, and Bitcoin prices. The report also highlights the importance of metrics like mean absolute error, mean squared error, and root mean squared error for evaluating the effectiveness of various models. It also discusses future research topics, such as incorporating external factors into prediction models. The article offers valuable insights for academics, practitioners, and policymakers interested in cryptocurrency prediction.
Since cryptocurrencies are among the most extensively traded financial instruments globally, predicting their price has become a crucial topic for investors. Our dataset, which includes fluctuations in Bitcoin’s hourly prices from 15 May 2018 to 19 January 2024, was gathered from Crypto Data Download. It is made up of over 50,000 hourly data points that provide a detailed view of the price behavior of Bitcoin over a five-year period. In this study, we used potent algorithms, including gradient descent, attention mechanisms, long short-term memory (LSTM), and artificial neural networks (ANNs). Furthermore, to estimate the price of Bitcoin, we first merged two deep learning algorithms, LSTM and attention mechanisms, and then combined LSTM-Attention with gradient-specific optimization to increase our model’s performance. Then we integrated ANN-LSTM and included gradient-specific optimization for the same reason. Our results show that the hybrid model with gradient-specific optimization can be used to anticipate Bitcoin values with better accuracy. Indeed, the hybrid model combines the best features of both approaches, and gradient-specific optimization improves predictive performance through frequent analysis of pricing data changes.
Bitcoin has become a prominent financial instrument in recent years, attracting increasing attention as a digital currency. Accurately forecasting the valuation of a financial asset carries substantial significance for both retail and institutional investors. The aim of this study is to evaluate and compare the predictive capabilities of various models, namely Support Vector Regression (SVR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a hybrid model combining CNN and Bidirectional LSTM (CNN-BiLSTM), and XGBoost, in the context of forecasting Bitcoin price. The main aim of this study is to ascertain the algorithm that demonstrates the most efficacy in forecasting the price of Bitcoin. This study utilizes the S&P500 index, Gold/Dollar exchange rate, West Texas Spot Oil Price, and Dollar Index as exogenous factors in order to forecast the price of Bitcoin. The dataset encompasses a consecutive time span of 2191 days, commencing on January 1, 2015 and concluding on September 18, 2023. The models outlined in the study undergo a two-stage procedure, including of training and testing. The assessment of the models' performance was carried out by utilizing several statistical measures, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The results indicate that the XGBoost algorithm had greater performance in projecting the price of Bitcoin, as evidenced by its consistently higher performance metrics across all evaluated aspects. The XGBoost model was succeeded by the CNN-BiLSTM, CNN, and LSTM models, which are hybrid methodologies, resulting in the most advantageous results. The SVR model demonstrated the least favorable performance..
Counterfeit products have become a significant problem for small and medium-sized businesses (SMBs), with the estimated value of counterfeit goods worldwide reaching trillions of dollars. However, SMBs often lack the resources and technical expertise to implement sophisticated anti-counterfeiting measures. Towards this end, the work proposes a blockchain-based solution named as Fake Product Identification for Small and Medium Firms (FPISMF) using Hyperledger and AES encryption to enable SMBs to identify fake products and protect their brand reputation. The details of the products and the details of customers are encrypted using AES encryption and recorded on the blockchain. The application communicates with the blockchain network to validate the product and retrieve the details of the product. Chaincode is executed in a containerized environment, which provides isolation and security for the code and data being processed. An algorithm is also proposed to substitute the missing QR-code bits and data that helps reduce customer wait time. Experiments are conducted on synthesized data sets and results showing the effectiveness of the proposed FPISMF framework and reconciliation technique. It is observed from the results that though the time taken to replace a blurry bit is greatly reduced as compared to manual replacement of the product, there is an increase in this time when associated with encryption while extraction of the corresponding code from cloud database thereby achieving a time complexity of O(n), where ‘n’ is the number of scanned products. In addition, the AES SMB time complexity is approximately recoded as O(n/2) and the Cloud access and retrieval time is O(n) as compared to O(2n) in the existing work. This shows a significant improvement in the ability to replace missing bits and perform a secure analysis respectively.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
This project combines machine learning and conducted a systematic review, emphasizing blockchain’s blockchain technology to transform grain quality assessment and supply chain management. The machine learning layer uses Convolutional Neural Networks (CNN) and transfer learning (VGG16) to classify grains with high accuracy. The rice dataset was first processed and optimized, where the CNN model achieved a validation accuracy. The blockchain layer uses Ethereum smart contracts for decentralized business management, ensuring transparency and security in the rice supply chain through the use of Truffle, Ganache, and Metamask. The project aims to improve efficiency and transparency, and help farmers and consumers in the grain industry.
Maruf Farhan, Rejwan Bin Sulaiman, Abdullah Hafez Nur
Consumer confidence, business credibility, and economic development are all negatively impacted by the prevalence of counterfeit items in circulation. The potential role of blockchain technology in finding a solution to this issue is explored in this chapter. For product authentication, it employs Ethereum, smart contracts, and QR codes to create a transparent and secure system. A permanent record of a product's origin, ownership, and authenticity can be created using the proposed method by utilising the immutability and traceability of blockchain technology. By streamlining and automating the verification process, smart contracts guarantee fast and accurate validation. Conversely, quick response (QR) codes facilitate consumers' ability to verify the authenticity of a product and acquire more information about it. This chapter details the system's design, implementation, and testing findings, demonstrating that it effectively detects counterfeit items and opens up the supply chain.
Cryptocurrency is a novel form of digital or virtual currency that employs cryptographic techniques to guarantee secure financial transactions, control the creation of new units, and verify the transfer of assets. Cryptocurrency signifies a fundamental change in the understanding of currency and financial transactions. The fundamental tenets of decentralisation, cryptographic security, and limited supply seek to revolutionise the conventional financial environment, providing fresh opportunities for financial inclusion, transparency, and innovation. Nevertheless, the path of cryptocurrencies is being influenced by ongoing challenges, such as regulatory uncertainties and market volatility, as they progressively establish themselves as a vital component of the global economy. It can be deduced that Dogecoin lacks the ability to supplant Bitcoin. Ethereum and Bitcoin exhibit a notably higher level of security compared to Dogecoin and Bit connect. That is the rationale behind their ability to withstand the decline in 2018 and also endure the current decrease in price. The depreciation of dogecoin is inevitable. Dogecoin is a valid and authentic form of digital currency. Nonetheless, the cultural structure of dogecoin ultimately undermines its own triumph.
Block chain technology, non-fungible tokens (NFTs), and cryptocurrency have all contributed to the explosive rise of the digital assets sector. The dynamic and cutting-edge digital asset marketplace shown in this abstract is intended to satisfy the various demands of traders, investors, and enthusiasts in the digital asset market. The marketplace offers a user-friendly and safe platform for purchasing, selling, and managing different digital assets, and it is distinguished by its user-centric approach. It offers real-time market data, order book information, and robust analytical capabilities for well-informed decision-making, all with an intuitive interface that streamlines asset transactions. The ability to move assets seamlessly between blockchain networks, multi factor authentication, cold storage, and complex trading algorithms that optimise trading tactics are some of the key characteristics. ----------------------------------------------------------------------------------------------------------------------------------- Keywords Blockchain, Smart Contract, Ethereum, Transparency, Data sharing, Privacy, Byzantine fault tolerance, decentralized systems.
Technological advances in the last decades have led to the realization of the concept of a smart environment. A significant part of this development is decentralized ledger technology and its variant, a blockchain. The blockchain database is immutable, open to all stakeholders, secure by architecture and robust. Applying a blockchain in water supply sanitary control creates opportunities for optimization, higher quality of service, cost reduction, better sanitary standards and public control. Physical, chemical, and biological water supply contamination is a great source of public health hazards. Implementation of a blockchain for water supply IoT, from the source point to the consumption point, enables effective response to changing environments, possible cross-contamination, stormwater management or disaster and emergency action. The chapter encompasses all fundamental elements and principles of water collection, distribution and consumption, with a focus on the health hazards and sanitary requirements for potable water. The chapter listed the main contaminants, methods of their registration and elimination, and requirements for drinking water in accordance with WHO, EU Drinking Water Directive and EPA standards. Blockchain technology solutions are described for smart water supply, including smart supply management, smart contracts, tokenization, smart compliance systems, and, most importantly, effective utilization of distributed ledger technologies for sanitary monitoring of water sources, water treatment, and water distribution systems.
The exponential growth of the Internet of Things (IoT) alongside the increasing significance of cryptocurrencies has unveiled critical security challenges in digital transactions. This study uses a comprehensive method to design, create, and assess secure and efficient cryptocurrency wallets specifically designed for the Internet of Things (IoT) environment. The study presents the Enhanced Elliptic Curve Digital Signature Algorithm (EECDSA), which integrates sophisticated ECDSA, blockchain technology, Golang programming language, and JSON data exchange. The design methodically emphasizes scalability, interoperability, and strict adherence to security protocols for various IoT devices and networks. The study thoroughly analyzes ECDSA and introduces EECDSA, highlighting the double-and-add algorithm to enhance efficiency. Comparative analyses show that EECDSA outperforms RSA, ECDSA, and multi-signature algorithms in terms of execution time and memory usage on different CPUs, particularly on ARM-based architectures. The results highlight EECDSA’s capacity for efficient cryptographic operations, making it suitable for IoT devices with constrained resources.
Digitalization has led to new investments in information including advance administration, storage, and gathering of corporate information. Blockchain technology has enabled this basic change, and it is becoming a feasible way to manage digital assets in various areas. These assets have enormous value and can be traded in a separate market from traditional assets. Non-fungible tokens (NFTs) are a prime example of this progress. NFTs have created a data-based digital asset market and are safely kept on blockchain network. Unlike fungible digital assets, NFTs are irreplaceable. Gaming, health care, real estate, metaverse and finance could benefit from it. NFTs are more than just digital files and can be crucial to digital finance and its integration in BFSI is evolving due to blockchain and cryptocurrency. NFTs have fundamental obstacles that must be solved before they are accepted. Usability, privacy, governance, security, extensibility, environmental effect and intellectual property are some challenges faced by NFTs. This study explores the opportunities and challenges created by NFTs.
Cryptocurrency is a virtual currency that can be used as a financial or economic standard, foreign currency reserve, and as a means of payment in some countries. The value that goes up and down every time is not easy to predict using logic. This is a problem for investors, besides that investors lack knowledge about the direction of crypto money movement. In addition, there is no system that can predict the price of Bitcoin, so this can cause investors to take the wrong steps in transactions and can cause losses. To avoid this risk, a system is needed that can predict bitcoin prices using data mining techniques, namely forecasting, the algorithms used are CNN and LSTM. The data used is Bitcoin closing price data from January 1, 2017, to April 26, 2023. The data is divided into 80% training data and 20% testing data. The prediction results are evaluated using MAPE which gets a MAPE value of 0.037 or 3.7% in the CNN algorithm, while the LSTM algorithm gets a value of 0.065 or 6.5%. The MAPE results of the two algorithms are in the MAPE range <10%, so it can be said that the ability of the forecasting model is very good so that it can be used as a reference to determine the prediction of bitcoin prices in the next few periods.
Abstract Compares ARMA models, boosting, neural network models, HAR_RV models and proposes a new method for predicting one day ahead realized volatility of financial series. HAR_RV models are taken as compared classical volatility prediction models. In addition, the phenomenon of transfer learning for boosting and neural network models is investigated. Bitcoin and E-mini S&P500 are chosen as examples. The realized volatility is calculated based on intraday (intraday—24 hours) data. The calculation is based on the closing values of the internal five-minute intervals. Comparisons are made both within and between the two intervals. The intervals considered are January 1, 2018–January 1, 2022 and January 1, 2018–April 2, 2023. Since there were structural changes in the markets during these intervals, the models are estimated in sliding windows of 399 days length. For each time series, we compare three-parameter enumeration boosting, about 10 different neural network architectures, ARMA models, the newly proposed CTCM method, and various training transfer and training sample expansion options. It is shown that ARMA and HAR_RV models are generally inferior to other listed methods and models. The CTCM model and neural networks of CNN architecture are the most suitable for financial time series forecasting and show the best results. Although transfer learning shows no improvement in terms of forecast precision and yields little decline. It requires more extensive and detailed study. The smallest MAPEs for Bitcoin and E-mini S&P500 realized volatility forecasts are achieved by the newly proposed CTCM model and are 21.075%, 25.311% on the first interval and 21.996%, 26.549% on the second interval, respectively.
Bahman Jafari Tabaghsar, Reza Tavoli, Mohammad Mahdi Alizadeh Toosi
Anomaly detection is an important technique for recognizing fraudulent activities, suspicious activities, network intrusions, and other unusual events that may be of great importance but difficult to detect. Therefore, the purpose of this research is to investigate and detect anomalies in Bitcoin transactions on the blockchain platform with a machine learning approach, to extract effective features based on Relief algorithm. The steps of the proposed method are as follows: The Bitcoin data set is collected from the Bitcoin Chart site and clustered with cumulative hierarchical clustering, and then feature selection is done using Relief algorithm. In the next step, the data set is analyzed by the method of experimental data analysis and anomalies are detected. In the next step, the data is labeled as normal and abnormal, and a class record is formed. Then, with the support vector machine algorithm classification, the classification and model error is estimated. The evaluation criterion in this research is accuracy and mean square error. The results show that the error of the model with the MSE of the support vector machine was equal to 0.0065. The MSE error value of the proposed model without feature extraction is equal to 0.049, which indicates that feature extraction plays an important role in this model and is one of the new and innovative aspects of this research. Therefore, the proposed method has performed much better by extracting useful features and support vector machine, and the accuracy rate of the model was 91.11%. The accuracy of the proposed method has been compared with other methods, which shows the higher accuracy and better efficiency of this method.
Shimal Sh. Taher, Siddeeq Y. Ameen, Jihan A. Ahmed
Scalability remains a critical challenge for blockchain technology, limiting its potential for widespread adoption in high-demand transactional systems. This paper proposes an innovative solution to this challenge by applying the Snake Optimization Algorithm (SOA) to a blockchain framework, aimed at enhancing transaction throughput and reducing latency. A thorough literature review contextualizes our work within the current state of blockchain scalability efforts. We introduce a methodology that integrates SOA into the transaction validation process of a blockchain network. The effectiveness of this approach is empirically evaluated by comparing transaction processing times before and after the implementation of SOA. The results show a substantial reduction in latency, with the optimized system achieving lower average transaction times across various transaction volumes. Notably, the latency for processing batches of 10 and 100 transactions decreased from 30.29 ms to 155.66 ms–0.42 ms and 0.37 ms, respectively, post optimization. These findings indicate that SOA is exceptionally efficient in batch transaction scenarios, presenting an inverse scalability behavior that defies typical system performance degradation with increased load. Our research contributes a significant advancement in blockchain scalability, with implications for the development of more efficient and adaptable blockchain systems suitable for high throughput enterprise applications.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Amit Kumar Tyagi, Swetta Kukreja, Richa Richa, P. Sivakumar
By the end of 2021, the worldwide cryptocurrency market valuation had hit a whole high of $3 trillion. Blockchain technology underpins cryptocurrencies such as Bitcoin and Ethereum. The adoption of blockchain, as well as the technology and products it enables, will continue to have a significant influence on company operations. However, blockchain technology is much more than a secure cryptocurrency transfer method. It may be utilised in areas other than finance, including as healthcare, insurance, voting, welfare benefits, gaming and artist royalties. The global economy is prepared for the blockchain revolution, with the technology already having an influence on business and society on many levels. If the term “revolution” seems extreme, consider that eight of the world’s ten largest corporations are developing a variety of blockchain-based solutions. Any enterprise or organisation that is engaged in the recording and oversight of any type of transaction stands to profit from shifting its operations to a blockchain-based platform. This paper discusses about the various roles blockchain has taken up over the years in several different industries along with the future opportunities and scope of expansion to numerous other professional sectors.