The volatility and complexity of Bitcoin make it a challenging task to accurately predict its price. While past research has implemented machine learning to enhance the precision of Bitcoin price prediction, limited attention has been given to examining the viability of employing diverse modeling techniques to datasets with varying data structures and dimensional attributes. In order to forecast Bitcoin prices using machine learning techniques at different intervals, this study initiates by categorizing Bitcoin prices into daily prices and highfrequency prices. This project aims to predict the price of Bitcoin using machine learning techniques, specifically the Random Forest Classifier algorithm.
Abstract In the present paper, the Granger causality test is used to study the causality relationships between Bitcoin and some of the most highly traded currencies, including euro, Japanese yen, British pound, Chinese yuan, and Indian rupee. To this purpose, the daily exchange rates of Bitcoin and the selected currencies to USD between 2014 and 2018 were used. Different from findings in existing literature, our study shows that there are no Granger causalities between Bitcoin and Euro, Japanese yen, British pound, and Indian rupee. A Granger causality is found in the direction from the Chinese yuan to Bitcoin.
Kiseok Jeon, Junghee Lee, Bumsoo Kim, James J. Kim
As the value of Bitcoin increases, the difficulty level of mining keeps increasing. This is generally addressed with application-specific integrated circuits (ASIC), but block candidates are still created by the software. The overhead of block candidate generation is relatively growing because the hash computation is boosted by ASIC. Additionally, it is getting harder to find the target nonce; If it is not found for a block candidate, a new block candidate must be generated. A new candidate can be generated to reduce the overhead of block candidate generation by modifying the coinbase without selecting and verifying transactions again. To this end, we propose a hardware accelerator for generating Merkle trees efficiently. The hash computation for Merkle tree generation is conducted with ASIC to reduce the overhead of block candidate generation, and the tree with only the modified coinbase is rapidly regenerated by reusing the intermediate results of the previously generated tree. Our simulation results demonstrate that the execution time can be reduced by up to 98.92% and power consumption by up to 99.73% when the number of transactions in a tree is 2048.
Cryptocurrencies have emerged as a popular investment option, characterized by their high volatility and potential for significant price fluctuations.The ability to accurately predict cryptocurrency prices is crucial for investors to make informed decisions.In this research paper, we conduct a comprehensive comparative analysis of three widely used forecasting models -Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Linear Regression -for cryptocurrency price prediction.We evaluate and compare the performance of these models using historical cryptocurrency data, considering various evaluation metrics and scenarios.
Zainab Khalid Mohammed, A. A. Zaidan, Hazleen Aris, Hassan A. Alsattar · 7 authors
Abstract Metaverse is a new technology expected to generate economic growth in Industry 5.0. Numerous studies have shown that current bitcoin networks offer remarkable prospects for future developments involving metaverse with anonymity and privacy. Hence, modelling effective Industry 5.0 platforms for the bitcoin network is crucial for the future metaverse environment. This modelling process can be classified as multiple-attribute decision-making given three issues: the existence of multiple anonymity and privacy attributes, the uncertainty related to the relative importance of these attributes and the variability of data. The present study endeavours to combine the fuzzy weighted with zero inconsistency method and Diophantine linear fuzzy sets with multiobjective optimisation based on ratio analysis plus the multiplicative form (MULTIMOORA) to determine the ideal approach for metaverse implementation in Industry 5.0. The decision matrix for the study is built by intersecting 22 bitcoin networks to support Industry 5.0's metaverse environment with 24 anonymity and privacy evaluation attributes. The proposed method is further developed to ascertain the importance level of the anonymity and privacy evaluation attributes. These data are used in MULTIMOORA. A sensitivity analysis, correlation coefficient test and comparative analysis are performed to assess the robustness of the proposed method.
Purpose Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of bitcoin (BTC) on gold, the volatility index (VIX) and the dollar index (USDX). Design/methodology/approach The series used are weekly and cover the period from January 2016 to November 2022. To generate the results, the unrestricted vector autoregression (VAR), structural vector autoregression (SVAR) and wavelet coherence were performed. Findings The findings are mixed as not all tests show the exact effects of BTC in the three asset classes. However, common to all the tests is the significant influence that BTC maintains on gold and vice versa. The positive shock in BTC significantly increases the gold prices, confirmed in three different tests. The effects on the VIX and USDX are still being determined, where in some tests, it appears to be influential while in others not. Originality/value BTC’s diversification potential with equity stocks and USDX makes it a valuable security for portfolio managers. Furthermore, regulatory authorities should consider that BTC is not an isolated phenomenon and can significantly influence other asset classes such as gold.
This research paper presents a stacked ensemble model for next day Bitcoin price prediction, incorporating diverse look-back windows and evaluating the performance of various models within the ensemble framework using metrics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The base layer, layer-0, comprises LSTM and GRU models with different look-back windows. The layer-1 models, including CNN, SVR, Linear Regression, Random Forest Regressor, LSTM, and KNN, are tested individually in conjunction with the base layer models. Extensive experiments demonstrate the effectiveness of the stacked ensemble approach, improving prediction accuracy. The comparative analysis provides insights into the strengths and weaknesses of each model, aiding in the identification of optimized combinations for Bitcoin price prediction. This research contributes to the field by showcasing the value of diverse look-back windows and evaluating models in a stacked ensemble framework, enhancing the accuracy of Bitcoin price forecasting.
Crypto currency is an immerging field for investments and trading which attracts many businessmen, investors, and most importantly a generation of aspiring youth which understands the future of money transactions and its security. Cryptocurrency price prediction problem solutions can provide extremely useful information which will prevent investors from losing money invested on cryptocurrency. The ability to forecast the price of an asset such as crypto offers the opportunity for profit by trading it. The goal of this study is to use LSTM (Long-Short Term Memory) and RNN (Recurrent Neural Network) with Sentiment Analysis to create an algorithm model that can accurately predict the price of cryptocurrency the following day.
Parth Daxesh Modi, Kamyar Arshi, Pertami J. Kunz, Abdelhak M. Zoubir
Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality data to leverage their power. There are some techniques such as augmentation that can help us with increasing the dataset size, but we cannot exploit them on historical bitcoin data. As a result, we propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.
Gelişen teknolojinin sağladığı olanaklar sayesinde internet kullanımıyla gerçekleştirilen işlemlerde artış olmuş ve bu da verilerde artışa neden olmuştur. Bu durum işletmeler için verilerin güvenli bir şekilde saklanması, paylaşılması, kontrolünün sağlaması ve yönetilmesine yönelik yeni teknoloji ihtiyacı doğurmuştur. Bu kapsamda faydalanılabilecek güncel teknolojilerden birisi de blok zinciri (Blockchain) yapısıdır. Blok zinciri yapısı birçok alanda kullanılabilecek bir teknoloji olup günümüzde en popüler kullanım alanı kripto paralar üzerinde olmaktadır. Bu çalışmada önemli alt kripto para birimlerinden biri olan Polkadot kripto para birimi için tahminleme işlemi yapılması amaçlanmıştır. Yapılan çalışmada 20.08.2020 ve 27.02.2023 tarihleri arasındaki veriler kullanılmış olup, bu verilere göre çıktı değer olarak günlük ortalama Polkadot değerinin tahmin edilmesi amaçlanmıştır. Girdi değerleri için kümeler iki farklı şekilde oluşturulmuştur. İlk girdi değerlerinde; Polkadot YouTube arama sayısı, Polkadot Google arama sayısı ve Polkadot hacmi kullanılmıştır. İkinci girdi değerlerinde ise ilk girdi değerlerinden farklı olarak alt kripto paraların lideri Ethereum eklenmiştir. İki farklı girdi yapısından oluşan bu çalışmada Polkadot para birimi günlük ortalama değerlerinin tahminlenebilmesi için yapay sinir ağlarında çok katmanlı algılayıcılar ile derin öğrenme yöntemlerinden olan uzun kısa süreli bellek yapısı kullanılarak tahminleme çalışması yapılmıştır. Sonuçlar incelendiğinde elde edilen yapay sinir ağlarında 4 girdi kümesinden oluşan değerlerin 0,93 korelasyon katsayısı ile daha iyi sonuç verdiği belirlenmiştir.
Open access
Currency Recognition and Detection
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Non-fungible token (NFT) is a tradable unit of data stored on the blockchain which can be associated with some digital asset as a certification of ownership. The past several years have witnessed the exponential growth of the NFT market. In 2021, the NFT market reached its peak with more than $40 billion trades. Despite the booming NFT market, most NFT-related studies focus on its technical aspect, such as standards, protocols, and security, while our study aims at developing a pioneering recommender system for NFT buyers. In this paper, we introduce an extreme deep factorization machine (xDeepFM)-based recommender system, NFT.mine, which achieves real-time data collection, data cleaning, feature extraction, training, and inference. We used data from OpenSea, the most influential NFT trading platform, to testify the performance of NFT.mine. As a result, experiments showed that compared to traditional models such as logistic regression, naive Bayes, random forest, etc., NFT.mine outperforms them with higher AUC and lower cross entropy loss and outputs personalized recommendations for NFT buyers.
As crypto market knowledge is not widely available to new investors; this problem may lead to wrong investments and financial losses. Inadequate understanding of the elements that have a significant impact on the price of bitcoin might potentially result in poor investing decisions. Now, there are more than $230 billion worth of openly traded cryptocurrencies on the market. The main purpose of Bitcoin, the most valuable cryptocurrency, which also has the best price predictability, is to act as a digital store of wealth. Bitcoin is a digital currency that is used all over the world for advanced payments or mostly for speculation. For instance, Bitcoin is decentralized because no one owns it. Bitcoin exchanges are easy since they are not tied to any one country. This research study employs a variety of machine learning approaches to predict Bitcoin values. Accurate price prediction is crucial for making wise investing decisions nowadays because of its high volatility feature. The price of bitcoin is initially divided into daily and high-frequency categories in this study. A collection of high-dimensional characteristics and fundamental trading features are used, respectively, for its daily and price forecast. Next, we see that while sophisticated machine learning algorithms like SVM accurately forecast pricing, statistical approaches like linear regression do not. The significance of sample dimensions in machine learning techniques is acknowledged in this study on predicting Bitcoin values.
Abstract To measure the diversification capability of Bitcoin, this study employs wavelet analysis to investigate the coherence of Bitcoin price with the equity markets of both the emerging and developed economies, considering the COVID-19 pandemic and the recent Russia-Ukraine war. The results based on the data from January 9, 2014 to May 31, 2022 reveal that compared with gold, Bitcoin consistently provides diversification opportunities with all six representative market indices examined, specifically under the normal market condition. In particular, for short-term horizons, Bitcoin shows favorably low correlation with each index for all years, whereas exception is observed for gold. In addition, diversification between Bitcoin and gold is demonstrated as well, mainly for short-term investments. However, the diversification benefit is conditional for both Bitcoin and gold under the recent pandemic and war crises. The findings remind investors and portfolio managers planning to incorporate Bitcoin into their portfolios as a diversification tool to be aware of the global geopolitical conditions and other uncertainty in considering their investment tools and durations.
Ayesha Kalhoro, Asif Ali Wagan, Abdullah Ayub Khan, Jim‐Min Lin · 7 authors
Non-fungible tokens (NFTs) are individual tokens with valuable information stored inside them over blockchain technology. They can be purchased and sold like other physical and virtual art pieces because their worth is mostly determined by the market and demand. The unique data of NFTs render it simple to verify and authenticate their ownership and transfer of tokens between owners. However, in Pakistan, developers cannot acquire different licences to accomplish their projects not because they cannot afford it, but because they cannot invest in every piece of software to accomplish each new sensitive task. Rather, they can render the product platform independent. Considering this technology, this paper provides IT professionals with a new NFT approach and business policies that solely belong to the information technology domain. In addition, this paper also introduces how NFT tokens can hold software applications. Since we can store files, we can let NFTs also store complete applications to help developers in further utilising virtuality and having the metaverse at their fingertips. Whenever they succeed in a project, they never receive rewards, and their skills only pay the bills. In a nutshell, this paper presents a prototype of NFTs that would be further polished to save and utilise applications in a decentralised manner while rewarding the developers.
S. Shalini, S Sheela, S Abhishek, P Bhavyashree · 6 authors
The current pharmaceutical sector is dealing with several issues. The pharmaceutical sector nowadays is dominated by counterfeit medications. Health research funding groups estimate that between 10 and 30 percent of the pharmaceuticals in the market are counterfeited. Globally, this fatal issue is creating several serious health risks. WHO estimates that 30% of all medications worldwide are fake. In practically every region of the world, this issue is becoming worse by the second. As drugs travel across several scattered networks, it is challenging to identify fakes. By integrating Blockchain technology into the network, the pharmaceutical supply chain may be made safer. Every area of the network keeps track of the medications through a record, making it possible to identify any introduction of fake medications. System transparency and dependability are improved with Blockchain security. By employing Blockchain technology, the initiative seeks to guarantee medicine quality, transaction security, and data protection. By eliminating information about expired and fake medications from the system, this effort increases system dependability and helps to make the Blockchain a stable and widely used technology. To determine whether or not counterfeit medications have been distributed, IOT technology is also employed.
Cryptocurrencies have established their identity as a healthy alternative to the maintenance of digital assets. Their applications include low-cost money transfers and yield farming. Ethereum is a blockchain that provides the functionality of doing more than a transaction regarding cryptocurrency. Ether is the default cryptocurrency of Ethereum, which is issued to the miners after the successful completion of the consensus mechanism to avoid fraudulent miners gaining profits. Transactions in Ether require that the user should include what is called a “fee” besides the amount that is sent by the user. The EIP-1559 (Ethereum Improvement Proposals) upgrade to the Ethereum protocol has substantially changed how the transaction fee is calculated. Since this transaction data can be considered time-series data, many prior approaches have been proposed to forecast such a transaction fee using suitable methods effectively. One-dimensional Convolutional Neural Networks have recently been successfully applied to time-series forecasting problems, showing promising results. This paper proposes a univariate 1-D CNN for an effective forecast of transaction fees in the new Ethereum protocol. Furthermore, this paper also compares the proposed method with existing standard approaches, and the results show the superior performance of simple 1-dimensional convolutional neural networks over existing hybrid models.
Raden Aditya Kristamtomo Putra, Mita Wahidiyat, Donna Carollina, Fairuz Iqbal Maulana
Non-Fungible Tokens (NFTs) now becomes the latest digital currency phenomenon. This phenomenon began in 2014 and widely used in nowadays. Based on this phenomenon, this research was carried out. The research conducted to do overview research related to Non-Fungible Tokens based on the SCOPUS database from 2017-2021. From the search results of the SCOPUS database, it was found that there were 68 studies related to Non-Fungible Tokens from 2017-2021. The most numerous documents are conference papers (N=37) and the publication source with the most documents (N=5) is Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics. The country with the most Non-Fungible Token keyword research is the United States (N=14). While the subject area of research that discusses the most Non-Fungble Token is Computer Science (N = 54). Based on the results, the research trend over Non Fungible Token is related to 2 cluster which is about Blockchain and Technology. So, there is there are still many opportunities for other research to be carried out outside the two clusters and their relationships.
Amogh Shukla, Tapan Kumar Das, Sanjiban Sekhar Roy
TRON is a decentralized digital platform that provides a reliable way to transact in cryptocurrencies within a decentralized ecosystem. Thanks to its success, TRON’s native token, TRX, has been widely adopted by a large audience. To facilitate easy management of digital assets with TRON Wallet, users can securely store and manage their digital assets with ease. Our goal is first to develop a methodology to predict the future price using regression and then move on to build an effective classifier to predict whether a profit or loss is made the next day and then make a prediction of the transaction success rate. Our framework is capable of predicting whether there will be a profit in the future based on price prediction and forecasting results using regressors such as XGBoost, LightGBM, and CatBoost with R2 values of 0.9820, 0.9825 and 0.9858, respectively. In this work, an ensemble-based stacking classifier with the Whale optimization approach has been proposed which achieves the highest accuracy of 89.05 percent to predict if there will be a profit or loss the next day and an accuracy of 98.88 percent of TRX transaction success rate prediction which is higher than accuracies obtained by standard machine learning models. An effective framework will be useful for better decision-making and management of risks in a cryptocurrency.
Blockchain technology has become popular in the recent years due to Bitcoin and Ethereum which are potential in terms of security, transparency and sharing in the same network by Peer-To-Peer network. Though this technology has high costing, it is worth to use in the businesses to reduce fraudulences from inside and outside of organization. Invoice system is a technology that all companies invest to protect the data of invoice management because it is important for analysis by the trends of business each year. In this paper the use of blockchain technology to protect the data of invoice by developing the invoice management system with smart contract for invoice rules to approve, reject, and to protect the data is proposed to find out changes of invoice in blockchain network system by a web application in intranet to manage the limitation of small business. The conceptual framework and the output obtained by the developed system shows the feasibility to develop a private blockchain system for invoice management.
With the rapid developments in today's technologies, people can now perform their payment and shopping transactions through digital platforms. However, payment security problems in e-services have led people to seek alternative payment methods. Thanks to blockchain technology, cryptocurrencies that are not dependent on the central authority and can be paid in a completely secure way have been developed. Bitcoin is a digital currency that is not tied to a central authority or bank, introduced in Satoshi Nakamoto's 2008 article entitled "Bitcoin: The Peer-to-Peer Electronic Money System". Bitcoin, which attracts the attention of investors in the financial world, especially during the pandemic process, is traded in a market with high volatility. For this reason, it is of great importance for those who want to make forward price predictions. In this study, it is aimed to develop a price prediction method that will contribute positively to the profit share of Bitcoin investors. With Bitcoin, the data belongs to a time series, and Random Forest Regression, a model used to predict time series, was used. The model is trained on two years of Bitcoin data for the years 2020-2022. The statistical error measures of the model were calculated as MSE, R2, MAE and RMSE as 0.031%, 99.39%, 31.16% and 55.33%, respectively.
K. Kowsalya, R. Paritala Jhansi Rani, Ritu Ritu, Malika Bhiyana · 6 authors
The Iot devices (IoT) and cryptocurrencies may be combined to create a platform that promotes improved and sustainable and credibility. The resulting innovations have been used in a number of industries, most notably in the monitoring of farm products. Particularly Ai devices (such as RFID, GIS, Geolocation, and others) have the capacity to streamline the collection of data pertaining to crucial traceable features. To handle, store, and search for information, data is acquired and put into the public ledger. The data security that enters the system may be ensured via a dispersed, randomized, and noninflatable cryptocurrency. Nonetheless, IoT devices could produce aberrant data as they gather data.This study evaluates the entire beverage distribution network from sowing to sales, develops the network topology and each operate effectively, and patterns and enforces a computer vision (Fluid ounces) proof - of - stake tea reputable monitoring system, which considers literally the entire record keeping chain of food goods (MBITTS). This article presents a unique approach based on Internet of Things (IoT) technologies, such as RFID sensors, for enhancing the precision of cryptocurrency source data.
Currently, the two developments that are most obvious to the public are machine learning (ML) and blockchain technology. The first is the development of AI and big data, and the financial sector has been substantially impacted by the second. Recently, the advancement of blockchain technology has converted a spectacular, frequently challenging, and moving innovation. The blockchain's decentralized database emphasizes information security and protection. The agreement tool also makes sure that data is reliable and true. In any event, it brings up a few security concerns; to address these concerns, data analytics on blockchain-based safe information is required. Analysis of this data highlights the importance of the newly-emerging invention, machine learning (ML). The substantial amount of data used by machine learning technology allows it to provide precise conclusions. In ML, reliability and data exchange are crucial to enhancing the accuracy of results. ML and blockchain technology combined can produce incredibly precise results. Since the two innovations are information-driven, there is a growing interest in implementing them for more secure and useful information analysis and sharing. In this article, research is done to demonstrate the viability and productivity of merging blockchain with machine learning advances. First, examine the fundamental language and structure of blockchain and machine learning. Different ML techniques that can be applied to blockchain framework are presented in the next section. Additionally, consider how the fusion of these two concepts advances data security and cybersecurity. The study is ended in the final section, which discusses future prospects for research and challenges brought on by the ongoing and possible integration of ML and blockchain.
With the rise of Internet finance and big data, blockchain technology is expected to propose solutions to the challenges faced by agricultural supply chain finance in recent years. This paper will study the problems of food and safety and the low level of technology in rural areas through literature research. There is a gap between China's grain production rate and that of developed countries. Because of its decentralization and precise traceability characteristics, blockchain technology helps to build a distinctive regulatory and accountability system for food and agricultural safety in China. At the same time, blockchain technology with intelligent contract can effectively simplify the business process of agricultural supply chain finance, and reduce the threshold and cost of rural technology promotion, and increase security because of its features that cannot be changed artificially. It can be seen that the blockchain has practical significance to the challenges faced agricultural supply chain finance.