Xiangyu Bai, Jiali Hu, Xianming Liu, Yaqin Deng · 6 authors
The purpose of this paper is to discuss the design and implementation of the upgrade of encryption technology of the Grand Canal digital document system based on blockchain technology. Based on the core technologies of blockchain's distributed ledger, smart contract and encryption algorithm, this study proposes a novel upgrade scheme for the Grand Canal digital document system. The scheme realizes the non-tampering and full traceability of document data by constructing a decentralized document storage architecture, and optimizes the document management process by using smart contract technology and asymmetric encryption algorithm to ensure the security of document transmission and storage. As a modern technology with strong confidentiality and security, blockchain is extremely important for data protection, and is of great practical significance for promoting the digital inheritance and innovative development of the cultural heritage of the Grand Canal.
<p>Blockchain technology is the foundation of cryptocurrencies, which are virtual currencies. The decentralized nature of cryptocurrencies has resulted in a significant reduction of central authority over them, which has implications for global trade and relations. The need for an effective model to anticipate the price of cryptocurrencies is essential due to their wide variations in value. Due to the shortcomings of conventional production forecasting, in this work, four distinct models were used. The deep learning models are the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), and both the Facebook-Prophet and Silverkite support the exponential smoothing technique. Silverkite is designed to handle a wide range of time series forecasting tasks. Considering past bitcoin information from January 2012 to March 2021, a period of nine years, we looked at the models. The Bi-LSTM model yields a 7.073 mean absolute error (MAE) and a 3.639 root mean squared error (RMSE). The Bi-LSTM model identifies the deviations that might draw attention and avert any problems.</p>
Ni Wayan Wardani, Putu Gede Surya Cipta Nugraha, Kadek Nonik Erawati
The volatile nature of Bitcoin poses significant challenges for accurate price prediction, which is critical for informed decision-making by investors and policymakers. This study explores the application of decision tree algorithms to predict Bitcoin prices using a dataset comprising historical data on Bitcoin prices, market capitalization, and trading volumes. The research emphasizes feature engineering techniques, including derived metrics such as rolling averages and volatility indices, and integrates ensemble methods like Random Forest and Gradient Boosting to enhance predictive performance. The decision tree model achieved an accuracy of 53%, demonstrating its capability to capture general trends in Bitcoin price movements, particularly during high volatility periods. The study highlights the importance of key features such as the Relative Strength Index (RSI) and Moving Averages (MA14) while identifying limitations in predicting price decreases. Recommendations for future research include integrating external data sources, such as sentiment analysis and macroeconomic indicators, and exploring advanced modeling techniques to improve robustness and accuracy. This research contributes to the growing field of cryptocurrency price prediction by providing interpretable and actionable insights into market dynamics. The findings offer valuable tools for analysts and investors navigating the complexities of the cryptocurrency market.
Investment is an activity that is popular nowadays. Profitable investments are the hope of every investor. By investing. investors expect the invested assets to generate returns and to obtain profits for future life In investment studies. the most frequently discussed topic is the fluctuations. whether increases or decreases. of an asset's price (stocks). The risk of investment is loss in financial. The fluctuations of stock prices represent risks in the investment field. One measure used to determine gains and losses from stock prices is the return. To know return from data. we may use the compound return formula. Returns have empirical facts that require several tests. In this study. the empirical facts of returns are that the returns are not autocorrelated (autocorrelation function) and that the returns are leptokurtic distributed (thick-tailed distribution). We use the price data of Bitcoin (BTC) and Gasoline (UGA) from January 1. 2019. to December 31. 2023. The main of purpose of this research is to show empirical analysis of the Bitcoin and Gasoline return data. The results of the empirical analysis show that the return of stock price for Bitcoin (BTC) and Gasoline (UGA) meet the empirical properties of returns so that they can capture a good volatility model.
Elnaz Radmand, Jamshid Pirgazi, Ali Ghanbari Sorkhi
In the digital currency market, including Bitcoin, price prediction using artificial intelligence (AI) and machine learning (ML) is critical but challenging. Conventional methods such as technical analysis (based on historical market data) and fundamental analysis (based on economic variables) suffer from data noise, processing delays, and insufficient data. To make predictions more accurate, faster, and able to handle more data, the suggested method combines several steps: extracting important information, labeling it, choosing the best features, merging different models, and fine‐tuning the model settings. Based on the price data, this approach initially generates 5 labels with a new labeling method based on the percentage of average price changes in several days and generates signals (hold, buy, sell, strong sell, and strong buy). Thereafter, it extracts 768 features from technical studies using the TA‐Lib library and from an authoritative site. The TLBOA algorithm, which does not get stuck in the local optimum with two updates, was used to select and reduce features to 15 to avoid overfitting. A variety of ML models, including support vector machine and Naive Bayes, use these selected features for training. By using the evolutionary DE algorithm to optimize the XGBoost meta‐parameters, we increased the accuracy by 1%–4%. The proposed strategy has performed better than other models, such as XGBoost with 85.66% and gradient boosting with 84.15%, and has achieved an accuracy of 91%–92%.
Ch. V. Raghavendran, K. Chandra Mouli, Manu Hajari, A. Anil Kumar Reddy · 6 authors
Predictive modeling has emerged as a key focus for cryptocurrency market asset valuation due to its complex nature and high market volatility. The research looks into Ethereum price forecasting with the methods of autoregressive integrated moving average (ARIMA) and Facebook Prophet model and long short‐term memory (LSTM) networks. These models operate on historical Ethereum prices and show their efficiency regarding temporal pattern recognition and prediction accuracy. The ARIMA model helps reveal trends as well as seasonal patterns and irregularities within Ethereum price fluctuations. The Facebook Prophet model serves as a forecasting tool because it automatically handles peculiarities present within cryptocurrency price data. Time series forecasting with LSTMs becomes an advanced technique used to detect intricate patterns along with sustained dependency relationships between data points. The systematic process of preparing data and constructing models and assessing results enables proper utilization of LSTMs for predicting time series data with accuracy. Ethereum price datasets are applied to train the models which undergo performance evaluation using MPE alongside MAPE and RMSE along with MAE to reveal strengths and weaknesses during Ethereum price predictions. The evaluation shows that ARIMA and Facebook Prophet together with LSTM demonstrate success in modeling Ethereum price fluctuations. This research explores the effectiveness of time series forecasting methods for cryptocurrency price prediction yielding vital knowledge about reliable tools for financial market trend modeling. Current research findings will provide knowledge to investors and risk management professionals making decisions within the volatile digital asset space.
Incorporating blockchain technology into vehicle classification systems shows potential progress in data security, transparency, and decentralization. This work examines how blockchain technology can improve vehicle classification procedures, emphasizing the advantages and hurdles of various consensus mechanisms. Conventional methods of categorizing vehicles typically depend on centralized databases susceptible to data tampering and breaches. Using blockchain ensures data integrity by utilizing decentralized and immutable ledgers. We assess different consensus algorithms such as Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Practical Byzantine Fault Tolerance (PBFT), Federated Byzantine Agreement (FBA), and DAG (Directed Acyclic Graph) to determine their appropriateness for vehicle categorization. Our aim is to determine the most effective and secure method for incorporating blockchain technology into vehicle classification systems by analyzing these consensus mechanisms.
In the financial sector, as past economic and social events have shaken trust, this trust is being regained through the internet and computer technologies. Emerging in the 19th century, financial technology has led to a new economic understanding with digital money and especially bitcoin. The decentralized structure of bitcoin and the encryption systems used for security play an important role in preventing fraud and have become the center of attention of investors. As its value has increased, studies on price predictions have naturally increased. This study aims to predict the impact of data obtained from digital economy news sites on bitcoin price using natural language processing and machine learning techniques. In line with this goal, text vectorization was performed with the TF-IDF statistical method. Synthetic Minority Oversampling Technique (SMOTE) was applied to eliminate the imbalance in the vectorized data set. Classification models such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbor, Extra Trees, Bernoulli Naive Bayes and Multilayer Perceptron were applied to the obtained output.According to the results of the performance of different machine learning models in predicting the direction of bitcoin price fluctuation, the Extra Trees Classifier model showed the highest performance with an Accuracy of 86.71%, recall of 86.71%, precision of 86.99% and F1 score of 86.59%.
Bitcoin is the world's first decentralized cryptocurrency, using blockchain technology to secure and verify transactions. A hybrid model based on stochastic configuration network (SCN) with conformal prediction is proposed in this study. Initially, an SCN model is built, and predictions are generated by the model. Subsequently, the predicted values from SCN are fed into the conformal prediction model, resulting in the generation of confidence intervals that validate the reliability of these values. Finally, the dataset of historical Bitcoin prices sourced from Wikipedia have been utilized. The results indicate that the SCN-conformal prediction combination enhances prediction reliability.
This study examines the bitcoin price in USD in the world by developing a suitable time series model to identify its future trends. This data set consists of monthly bitcoin prices from August 2010 to July 2024. It was found that the original series is not stationary and not seasonality. The stationary was achieved by the first difference. Of the parsimonious models identified based on the Partial Autocorrelation Function (PACF) and Autocorrelation Function (ACF) of the stationary series, an auto-regressive integrated moving average (ARIMA) (2,1,2) model was identified as the best-fitt ed model. The significance of the model and its parameters and information criteria such as the Akaike Information Criterion (AIC), Schwarz Criterion, and log-likelihood was used to identify the best-fitted model. The model was trained using data from August 2010 to March 2024. The residuals of the model were found to be white noise. The mean absolute percentage error (MAPE) for validation data is 7.09%. The percentage errors for the validating set are all positive and varied from 3.5% to 12.9%. The predicted Bitcoin price (USD) from August to October 2024 are $59947.88, $60308.7, and $60669.53. Bitcoin price can be utilized by market demand and supply, regulatory environment, and technology development. Keywords: ACF; ARIMA models; Bitcoin price; Forecasting; PACF; Time series analysis
The world is witnessing a noticeable increase in financial exchange in digital currencies such as Bitcoin, Ethereum, and others, as transactions in electronic markets have begun to rise recently, which increases the difficulty of maintaining security and trust in decentralized financial systems that use distributed databases and the technologies that interact with them in Ethereum networks, blockchain, etc. This study presents a hybrid model based on the PyCaret library and includes 12 machine learning classifiers, with the aim of identifying fraudulent activities in Bitcoin transactions and enhancing the security of Ethereum networks and blockchain technology. The results reveal the effectiveness of different models in identifying fraudulent activities on the Ethereum network through a comprehensive performance comparison. The classifiers that showed the highest accuracy scores, which ranged from 0.9814 to 0.9862, were the Random Forest classifier, the visual gradient boosting machine, and the additive tree classifier. It is important to note that both Gradient Boosting Classifier and K Neighbors Classifier performed well, with accuracies above 0.96 and AUC scores above 0.99. However, some models, such as Naive Bayes, showed lower accuracy and AUC scores, suggesting that they have limitations in terms of accurately detecting fraudulent transactions. These results highlight the importance of choosing appropriate machine learning models for fraud detection tasks in general, with ensemble techniques such as Extra Trees and Random Forest showing great promise in this regard.
This publication focuses on the use of the artificial intelligence for detecting anomalies, especially in the blockchain network. The research methodology includes the selection of anomalies to be detected and the processing of blockchain data. Various artificial intelligence methods were implemented for anomaly detection as part of the tests, and one new solution—a Fuzzy Neural Network—was presented. The findings indicate the possibility of detecting selected anomalies in the blockchain using artificial intelligence, which is of significant importance for the security of this technology. The conclusions present a discussion on limitations, future research prospects, and guidelines for future work.
Nurfajar Iskandar, Yusmanizar Yusmanizar, Andi Vita Sukmarini
The emergence of Non-Fungible Tokens (NFTs) technology has led to a significant transformation in the creative industry and media content realm, introducing fundamental new opportunities for the media sector as well as for the conservation and preservation of newspaper content. This research aims to analyze the impact of implementing Blockchain NFTs technology on the conservation and preservation of print newspaper content. This is motivated by the fact that many print media outlets have not yet adopted NFT technology, despite its potential to secure and provide economic value to both old and new print news content. The adoption of Blockchain NFTs technology is considered a forward-looking technology that offers excellent opportunities for the preservation of print newspaper content. This research uses a qualitative method to explore the impact of Blockchain NFTs technology on the conservation and preservation of print newspaper content. The research methods include literature review, in-depth interviews, and secondary data analysis. The findings indicate that implementing Blockchain NFTs technology can provide benefits such as better data security, transparency, and the potential for new economic value through the creation and trading of NFTs from historically or otherwise significant print newspaper content. Keywords: Blockchain, Conservation, NFTs, Newspapers, Preservation
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Moiz Qureshi, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman · 5 authors
Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized clearing of transactions and money supply. This study attempts to accurately anticipate the BTC-USD prices (Close) using data from September 2023 to September 2024, comprising 390 observations. Four machine learning models—Multi-layer Perceptron, Extreme Learning Machine, Neural Network AutoRegression, and Extreme-Gradient Boost—as well as four time series models—Auto-Regressive Integrated Moving Average, Auto-Regressive, Non-Parametric Auto-Regressive, and Simple Exponential Smoothing models—are used to achieve this end. Various hybrid models are then proposed utilizing these models, which are based on simple averaging of these models. The data-splitting technique, commonly used in comparative analysis, splits the data into training and testing data sets. Through comparison testing with training data sets consisting of 30%, 20%, and 10%, the present work demonstrated that the suggested hybrid model outperforms the individual approaches in terms of error metrics, such as the MAE, RMSE, MAPE, SMAPE, and direction accuracy, such as correlation and the MDA of BTC. Furthermore, the DM test is utilized in this study to measure the differences in model performance, and a graphical evaluation of the models is also provided. The practical implication of this study is that financial analysts have a tool (the proposed model) that can yield insightful information about potential investments.
The proposed work builds upon the Random Forest machine learning algorithm to improve the process of digit forensic investigation in case of NFT. The following structure of this framework is aimed at identifying and disabling fraudulent or suspicious activities in NFT transactions by comparing different parameters like the Detection Time, False Positive Rate, the Total Transaction Volume Analyzed, the Anomalous Transaction Ratio, Clustering Accuracy, Data Utilization Efficiency, and Detection Sensitivity. Through using Random Forest, a solid ensemble learning technique that is well known for its on high accuracy as well as off overfitting tendency, it optimistically improves the identifying abilities of the framework in isolation of the false positives. The ability of the proposed system to deliver optimal results is further explained by line plots, area charts, histograms, and stem plots which all provide the variation of these metrics as the time proceeds. Not only does it enhance the effectiveness of detecting the fraudulent transactions, but it also enhances the application of data in the forensic analysis that creates a great advantage in the increasing realm of digital assets for investigators.
Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework. Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point. Our code and NFT datasets are publicly available at https://github.com/seferlab/deep_nft .
Udayveer Singh Virk, Devansh Verma, Gagandeep Singh, Prof. Sheetal Laroiya Prof. Sheetal Laroiya
Abstract—This project aims to develop a web3 platform that stores user credentials on the blockchain, providing high levels of security and privacy. Using a range of tools and technologies, including Metamask, RemixIDE, Ganache, Node.js, Solidity for smart contracts, HTML, and CSS, the platform offers a user-friendly interface that enhances the user experience. Smart contracts are used to ensure that user credentials are only visible to the individual user, providing a high level of security and privacy. This platform has the ability to revolutionize how users interact with online services and manage their digital identities, reducing costs, increasing trust, and improving expandability. The implementation of this project has demonstrated the overall benefits of blockchain and smart contracts in virtual identity management, including increased security, improved privacy, and enhanced user experience. The platform has the potential for further development and expansion, including the integration of biometric authentication, artificial intelligence and machine learning algorithms, and the expansion to include a range of online services. Overall, this project has demonstrated the significant potential of blockchain technology and smart contracts in digital identity management and has the ability to shift the way users communicate with online services, offering a one-stop-shop for their online needs. Keywords—Block chain, metamask, ganache, remix ide, solidity
In recent years, there has been a growing interest in probabilistic forecasting methods that offer more comprehensive insights by considering prediction uncertainties rather than point estimates. This paper introduces a novel variational autoencoder learning framework for multivariate distributional forecasting. Our approach employs distributional learning to directly estimate the cumulative distribution function of future time series conditional distributions using the continuous ranked probability score. By incorporating a temporal structure within the latent space and utilizing versatile quantile models, such as the generalized lambda distribution, we enable distributional forecasting by generating synthetic time series data for future time points. To assess the effectiveness of our method, we conduct experiments using a multivariate dataset of real cryptocurrency prices, demonstrating its superiority in forecasting high-volatility scenarios.
The cryptocurrency is the encrypted, digital and peer-to-peer currency invented using blockchain technology in 2009. It is implemented as medium of exchange between computers of the network without interference from any centralised authority. The Bitcoin is most widely used and valuable cryptocurrency across the world. In India, also many people prefer the Bitcoin for their investment. People want to be more aware of the possibilities and opportunities that cryptocurrencies can present, to maintain the confidence and trust rate of utilising cryptocurrencies. The goal of this paper is to predict the future value of Bitcoin cryptocurrency in Indian Rupees (INR), with machine learning using Python. The dataset of approximately past 768 days from current date is trained to predict the INR value of Bitcoin for next 10 days.
This study explores the integration of blockchain technology in wearable health devices through the design and development of a Smart Fidget Toy. We aimed to investigate design challenges and opportunities of blockchain-based health devices, examine the impact of blockchain integration user experience, and assess its potential to improve data control and user trust. Using an iterative user-centered design approach, we developed a mid-fidelity prototype of a physical fidget device with a blockchain-based web application. Our key contributions include the design of a fidget toy using blockchain for secure health data management, an iterative development process balancing user needs with blockchain integration challenges, and insights into user perceptions of blockchain wearables for health. We conducted user studies, including a survey (n = 28), focus group (n = 6), interactive wireframe testing (n = 7), and prototype testing (n = 10). Our study revealed high user interest (70%) in blockchain-based data control and sharing features and improved perceived security of data (90% of users) with blockchain integration. However, we also identified challenges in user understanding of blockchain concepts, necessitating additional support. Our smart contract, deployed on the Polygon zkEVM testnet, efficiently manages data storage and retrieval while maintaining user privacy. This research advances the understanding of blockchain applications in health wearables, offering valuable insights for the future development of this field.