Juvvala Sailaja, Kovvuri N. Bhargavi, G. L. Narasamba Vanguri, Nagireddi Suryakala · 5 authors
Crypto currencies have emerged as a popular investment option in recent years, with Ethereum being one of the most prominent ones.Accurate price prediction of Ethereum can provide valuable insights to investors and traders for making informed decisions.In this study, we utilized two time series prediction models, ARIMA (Auto Regressive Integrated Moving Average) and Facebook Prophet, to predict the price of Ethereum.This research focuses on collecting legacy price data of Ethereum from a reliable source.The data was preprocessed to handle missing values and outliers.ARIMA and Facebook Prophet models were then implemented on the preprocessed data to generate Ethereum price forecasts.The models were trained using a time period of historical data and validated using a hold-out set of data.The MSE, which measures the squared discrepancies between predicted and real Ethereum prices, was used to assess the models' performance.Lower MSE values indicate better model performance.The results revealed that Facebook Prophet outperformed ARIMA in terms of MSE, indicating superior accuracy in Ethereum price prediction.The higher accuracy of Facebook Prophet may be attributed to it's ability to handle seasonality, trend changes, and outliers, which are common characteristics of crypto currency price data.In conclusion, this study demonstrates the effectiveness of time series forecasting models, specifically ARIMA and Facebook Prophet, in predicting Ethereum prices.The findings suggest that Facebook Prophet may be a more accurate model compared to ARIMA for Ethereum price prediction, as evidenced by lower MSE values.The study provides valuable insights for investors and traders interested in utilizing forecasting models for Ethereum price prediction, and may serve as a basis for further research in this area.
Likhitha Amasala, Mahesh Datta Sai Ponnuru, P. Srideviponmalar
At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors.
In the Bitcoin trading landscape, predicting price movements is paramount. Our study focuses on identifying the key factors influencing these price fluctuations. Utilizing the Pearson correlation method, we extract essential data points from a comprehensive set of 14 data features. We consider historical Bitcoin prices, representing past market behavior; trading volumes, which highlight the level of trading activity; network metrics that provide insights into Bitcoin’s blockchain operations; and social indicators: analyzed sentiments from Twitter, tracked Bitcoin-related search trends on Google and on Twitter. These social indicators give us a more nuanced understanding of the digital community’s sentiment and interest levels. With this curated data, we forge ahead in developing a predictive model using Deep Q-Network (DQN). A defining aspect of our model is its innovative reward function, tailored for enhancing predicting Bitcoin price direction, distinguished by its multi-faceted reward function. This function is a blend of several critical factors: it rewards prediction accuracy, incorporates confidence scaling, applies an escalating penalty for consecutive incorrect predictions, and includes a time-based discounting to prioritize recent market trends. This composite approach ensures that the model’s performance is not only precise in its immediate predictions but also adaptable and responsive to the evolving patterns of the cryptocurrency market. Notably, in our tests, our model achieved an impressive F1-score of 95%, offering substantial promise for traders and investors.
Amaç–Bitcoin başta olmak üzere kripto varlık fiyatlarında meydana gelen hızlı değişimler gerek finansal yatırımcı gerekse medya tarafından ilgi görmektedir. Buna bağlı olarak kar elde etmek amacı başta olmak üzere pek çok farklı motivasyonla birçok araştırmacı ve finansal aktör, Bitcoin fiyatını etkileyen çeşitli faktörleri belirlemeye çalışmaktadır. Bitcoin fiyat hareketlerini etkilediği düşünülen Fed faiz oranı, altın ve Bitcoin’in farklı fiyat göstergeleri gibi öznitelikler üzerine detaylandırılan bir inceleme yürütülmektedir. Bu bağlamda fiyatları tahmin etmek için kullanılan çeşitli makine öğrenme algoritmaları üzerinde sistematik bir analiz yapılmaktadır.Yöntem –Farklı dört makine öğrenme modeli kullanılmış olup farklı tahmin hata oranları elde edilmiş ve her birinin çalışmada kullanılabileceği görülmüştür. Bulgular –Bitcoin veri seti için önerilen en iyi tahmin performansının sırasıyla Tesadüfi Ağaç (RF) %96,38, Karar Ağacı (DT) %96,28, Doğrusal Regresyon 95,06 ve Stokastik Gradient Descent(SGD) Doğrusal Regresyon %93,91 şeklinde olduğunu göstermektedir. Bitcoin fiyat değişimlerininFed faiz oranı ve altından ziyade kendi fiyat değişimlerinden daha yüksek oranda etkilendiği diğer sonuçlar arasında yer almaktadır. Tartışma –Tahmin modellemesinde en iyi sonuçları veren iki algoritmaya bakıldığında, gün içi en yüksek fiyatın son derece etkili olduğu söylenebilmektedir. En düşük fiyat ise ikinci derece en etkili özniteliktir. Söz konusu sonuç, Bitcoin’in en çok kendi fiyat dalgalanmalarından etkilendiğini göstermektedir.
<span>The majority of research on predicting the price of Bitcoin employs technical methods to enhance long short-term memory models' effectiveness. Although some studies employ different machine learning techniques, such as economic or technical indicators, their precision is inadequate. Thus, this research aims to introduce a model that predicts the price of Bitcoin by utilizing the long short-term memory (LSTM) technique and incorporating gold's economic and technical data as features. The research collected gold and Bitcoin price data from FinanceDataReader for around seven years, from January 1, 2016, to January 22, 2023, consisting of six categories: date, open, high, low, close, volume, and change (based on dollars). The normalized closing price data was trained for 50 epochs, resulting in the loss value reaching close to zero. The model's accuracy was measured by mean squared error, resulting in a score of 0.0004. This study's importance is two-fold: firstly, it can provide cryptocurrency-related businesses with more accurate predictions and improved risk management indicators. Secondly, incorporating economic metrics can address the limitations of overfitting and a single model's poor performance.</span>
Peter T. Yamak, Yujian Li, Ting Zhang, Kyefondeme C. Dakurah
This research proposes a novel approach for forecasting cryptocurrency prices, specifically Bitcoin which dominates the market. Accurately predicting cryptocurrency values is challenging due to their highly volatile nature. The proposed hybrid model uses ResNet Convolutional Neural Network to encode Bitcoin price time series data into discriminative representations. These representations capture long-range dependencies using XGBoost regression. Additionally, wavelet denoising is applied to filter noise from the price data. The combined ResNet-XGBoost-Wavelet model achieves satisfactory results for Bitcoin price forecasting and has practical applications for developing quantitative trading strategies. While incorporating sentiment analysis and additional influencing factors could further improve predictions, this work presents a competitive approach for minimizing investment risks and maximizing profits in the complex domain of cryptocurrency markets.
Guzalxon Belalova, Shakhida Gaybullaevna Mannanova, Botirjon Karimov
Over the past few decades, recurrent neural networks, particularly the Long Short-Term Memory (LSTM) architecture, have undergone several refinements. These networks have emerged as the go-to models for numerous machine learning challenges, especially those involving sequential data. One such application is the prediction of Bitcoin prices, a cryptocurrency that stands at the forefront of blockchain technology. This paper delves into the intricacies of forecasting Bitcoin prices using a suite of models, with a keen emphasis on the LSTM architecture, renowned for its prowess in handling tasks with long-term dependencies. Our exploration encompasses traditional time series models like ARIMA, neural network variants such as ANN and Transformer-based models, and even hybrid combinations. Specifically, our LSTM model, augmented with peephole connections, demonstrates its capability to learn and predict Bitcoin price fluctuations. We source our data from the Bitcoin Price Index and aim to gauge the accuracy with which these models can predict Bitcoin's price trajectory. Furthermore, our experiments involve the deployment of an "adam"-optimized Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network, revealing insights into their predictive performances.
Chibuzo Obi-Okoli, Olamide Jogunola, Bamidele Adebisi, Mohammad Ali A. Hammoudeh
The rapid growth and psudonomity inherent in blockchain technology such as in Bitcoin and Ethereum has marred its original intent to reduce dependant on centralised system, but created an avenue for illicit activities, including fraud, phishing, scams, etc. This undermines the reputation of blockchain network, giving rise to the need to identify these illicit activities within the blockchain network. This current work tackles this crucial problem by investigating and implementing six machine learning algorithms with a particular emphasis on striking a balance between accuracy, precision and recall. The novelty of the work lies in the utilising of the synthetic minority over-sampling technique to handle data imbalance. Thus, increasing the accuracy of the light gradient boosting machine classifier to 98.4%. The outcome of this work holds great potential for enhancing the security and credibility of blockchain ecosystems paving the way for a more secure and dependable digital future in the age of decentralised and trustless systems.
This paper focuses on the prediction of Bitcoin prices and returns based on the Long Short Term Memory (LSTM) neural network model, to better consider the impact of time factors. Since Bitcoin has long dominated the digital currency trading market, many researchers have completed many Bitcoin prediction results, including the screening of optimal features, comparison of prediction models and classification of prediction problems. Based on previous work, this article adds a Bitcoin revenue forecast section, presenting the results in the form of charts and data to provide more intuitive trends and more accurate performance. This paper uses LSTM as the experimental model, and uses the Bitcoin transaction history data set with timestamps as the original input. After a specific normalization method, the original model is trained, and then the subsequent transaction data is predicted. Compare it with the real value in the data set to get the final experimental results show that in this prediction problem, the performance of LSTM is slightly better than Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost); on the other hand, compared with price prediction based on real values for prediction, the prediction fluctuations of return are more obvious and more realistic, providing better reference value.
Non-fungible tokens (NFTs) have become an exciting technology that provides a fresh perspective on asset ownership, provenance, and value exchange. NFTs, a blockchain-based technology, are distinct and indivisible cryptographic tokens used to confirm and record the ownership of digital and physical assets in an immutable and transparent way. The fundamental block of NFT is a smart contract built on a blockchain network. This contract contains specific information about the asset it represents, such as its unique identifier, metadata, and ownership details. The information is kept private and tamper-proof due to the decentralized and distributed structure of the blockchain, boosting faith in the token’s authenticity. The NFT is gaining popularity, but it is still in the developing stage. There is a need for a comprehensive survey to guide future research and development in NFTs. Thus, this paper presents the technical components of NFTs, their features, and the minting process. Further, this survey paper describes different token standards for NFTs. It presents various applications of NFTs in healthcare, supply chain, gaming, identity verification, agriculture, intellectual property, smart cities, charity and donation, and education. The article also emphasizes the significant difficulties faced currently in implementing NFT technology from the viewpoints of ownership, governance, and property rights, as well as security, privacy, and environmental effects. This work also elucidates the future directions to overcome the challenges in adopting NFTs in various applications.
Decentralized Finance (DeFi), propelled by Blockchain technology, has revolutionized traditional financial systems, improving transparency, reducing costs, and fostering financial inclusion. However, transaction activities i n these systems fluctuate significantly and the throughput can be effected. To address this issue, we propose a Dynamic Mining Interval (DMI) mechanism that adjusts mining intervals in response to block size and trading volume to enhance the transaction throughput of Blockchain platforms. Besides, in the context of public Blockchains such as Bitcoin, Ethereum, and Litecoin, a shift towards transaction fees dominance over coin-based rewards is projected in near future. As a result, the ecosystem continues to face threats from deviant mining activities such as Undercutting Attacks, Selfish Mining, and Pool Hopping, among others. In recent years, Dynamic Transaction Storage (DTS) strategies were proposed to allocate transactions dynamically based on fees thereby stabilizing block incentives. However, DTS’ utilization of Merkle tree leaf nodes can reduce system throughput. To alleviate this problem, in this paper, we propose an approach for combining DMI and DTS. Besides, we also discuss the DMI selection mechanism for adjusting mining intervals based on various factors.
This study aims to analyze the effect of Bitcoin price spillover volatility on Altcoin prices (Ethereum, Tether, Binance Coin) and the price of the S&P 500 Index. The data used is weekly data with a research period from January 2018 to December 2022. The analysis used in this study is the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model. The results show a volatility spillover effect between Bitcoin and Binance Coin with more positive shocks than adverse shocks in Bitcoin price volatility on Binance Coin price. Meanwhile, the spillover volatility between Bitcoin and Ethereum, Tether, and the S&P 500 Index cannot be known because the price data is homoscedastic, so it cannot be continued with EGARCH modelling because the data needs to meet the modelling requirements.
Abstract: Certificates help students not only to prove their achieved goals and milestones but also ensure that he/she maintains a high level of knowledge in that particular field. An estimated total of 25.57 crore Indian students have been enrolled for primary to higher education in 2020–21 and nearly 65 lakhs of them graduate each year. Throughout this journey, a student generates a myriad number of certificates that may include results, transcripts, degrees, diplomas, etc. A student has to submit these certificates to apply for a job or seek higher admission in any particular organization. A major problem today is manually verifying and authenticating these certificates. Many hardworking people with genuine certificates get rejected and suffer because of the lack of a system that can differentiate original certificates from fake ones. With easy access to cheap and advanced software, document forgery has become a matter of concern nowadays. This scenario demands an updated system that could not only store documents safely but also help verify and authenticate them, their issuers, and holders in a way that is much simpler, effective, and secure. Blockchain technology comes up as a solution to all these problems. Blockchain has recently emerged as a potential means for the document-authentication process and can be easily used to tackle document forgery and counterfeiting as it follows a decentralized approach. Our proposed model includes several methods such as unique hash generation, key cryptography, digital ledgers, proof of work, digital signatures, and distributed storage which has made the document-verification process easier and more secure for both the certificate-generating organization and the holder of the certificate. The SHA-256 algorithm has been used to assign a unique hash to each uploaded document which can be used to validate its authenticity. Thus, this system meets up all the criteria for a document verification system by overcoming the drawbacks and difficulties currently faced in the traditional methods of document verification.
Hafiz Burhan Ul Haq, Minahil Irfan, Muhammad Saqlain
For the creation of cryptocurrencies like bitcoin, blockchain is the fundamental technology. Since the development of the steam engine, electricity, and computer technology, there has been a fourth industrial revolution. Blockchain technology is one of the components of this revolution, and it has been used in many sectors, including commerce, banking, and the legal system. In the beginning of this study, we talk about blocks and their many sorts. Following that, cutting-edge blockchain technology applications were covered. In addition, the benefits and drawbacks are also emphasized to help explain the blockchain idea. But there is also discussion on the use of blockchain in 5G.
The integrity of remote-sensing image data is susceptible to corruption during storage and transmission. Perceptual hashing is a non-destructive data integrity-protection technique suitable for high-accuracy requirements of remote-sensing image data. However, the existing remote-sensing image perceptual hash-authentication algorithms face security issues in storing and transmitting the original perceptual hash value. This paper proposes a remote-sensing image integrity authentication method based on blockchain and perceptual hash to address this problem. The proposed method comprises three parts: perceptual hash value generation, secure blockchain storage and transmission, and remote-sensing image integrity authentication. An NSCT-based perceptual hashing algorithm that considers the multi-band characteristics of remote-sensing images is proposed. A Perceptual Hash Secure Storage and Transmission Framework (PH-SSTF) is designed by combining Hyperledger Fabric and InterPlanetary File System (IPFS). The experimental results show that the method can effectively verify remote-sensing image integrity and tamper with the location. The perceptual hashing algorithm exhibits strong robustness and sensitivity. Meanwhile, the comparison results of data-tampering identification for multiple landscape types show that the algorithm has stronger stability and broader applicability compared with existing perceptual hash algorithms. Additionally, the proposed method provides secure storage, transmission, and privacy protection for the perceptual hash value.
Mohammed Balfaqih, Zain Balfagih, Miltiadis D. Lytras, Khaled Mofawiz Alfawaz · 6 authors
The concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.
Burak Öz, Filip Rezabek, Jonas Gebele, Felix Hoops · 5 authors
Maximal Extractable Value (MEV) has become a significant incentive on blockchain networks, referring to the value captured through the manipulation of transaction execution order and strategic issuance of profit-generation transactions. We argue that transaction ordering techniques used for MEV extraction in blockchains where fees can influence the execution order do not directly apply to blockchains where the order is determined based on transactions' arrival times. Such blockchains' First-Come-First-Served (FCFS) nature can yield different optimization strategies for entities seeking MEV, known as searchers, requiring further study. This paper explores the applicability of MEV extraction techniques observed on Ethereum, a fee-based blockchain, to Algorand, an FCFS blockchain. Our results show the prevalence of arbitrage MEV getting extracted through backruns on pending transactions in the network, uniformly distributed to block positions. However, on-chain data do not reveal latency optimizations between specific MEV searchers and Algorand block proposers. We also study network clogging attacks and argue how searchers can exploit them as a viable ordering technique for MEV extraction in FCFS networks.
Fake products are items that are marketed and sold as genuine, high-quality products but are counterfeit or low-quality knockoffs. These products are often designed to closely mimic the appearance and branding of the genuine product to deceive consumers into thinking they are purchasing the real thing. Fake products can range from clothing and accessories to electronics and other goods and can be found in a variety of settings, including online marketplaces and brick-and-mortar stores. Blockchain technology can be used to help detect fake products in a few different ways. One of the most common ways is through the use of smart contracts, which are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. This allows for a high level of transparency and traceability in supply chain transactions, making it easier to identify and prevent the sale of fake products and the use of unique product identifiers, such as serial numbers or QR codes, that are recorded on the blockchain. This allows consumers to easily verify the authenticity of a product by scanning the code and checking it against the information recorded on the blockchain. In this study, we will use smart contracts to detect fake products and will evaluate based on Gas cost and ethers used for each implementation.
Jorge Ceron, Cristian Tinipuclla, Pedro Shiguihara-Juárez
Blockchain has become an important alternative in plenty of industries due to its features such as traceability, transparency and data integrity. Blockchain is not only present in the management of structured data-like names, timestamps, addresses and so on, but also in the management of unstructured data such as videos or images. In the present work, we focus on videos because nowadays, they are potentially exposed to be modified or altered from plenty of sources and platforms. However, we found that there are hardly any surveys or systematic literature reviews containing applications of both blockchain and integrity of videos. Thus, in this survey, we state a literature review for primary articles about the use of blockchain for video integrity to update and provide a better comprehension about this topic. As results, we encountered Ethereum and internet of vehicles as the largest underlying blockchain and field of application, respectively. At the end, we explain our findings about blockchains for video integrity and why it is an emerging topic.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Due to many factors, Bitcoin has experienced huge price fluctuations since its emergence, and it has received extensive attention. Forecasting the price of bitcoin is of great significance for investors and for the country's future development. This paper collects the data of bitcoin price and indicator that may affect the price, and then use random forest algorithm for feature selection to remove all nonessential indicators. Then, CNN-Bi-LSTM-Attention model is built to train the data and predict the price of bitcoin. Finally, this model is compared with other models. It can be found that this model has higher prediction accuracy and better prediction effect than traditional models such as LSTM and CNN-LSTM.
This study focuses on using the Neural Prophet framework to forecast Bitcoin prices accurately. By analyzing historical Bitcoin price data, the study aims to capture patterns and dependencies to provide valuable insights and predictive models for investors, traders, and analysts in the volatile cryptocurrency market. The Neural Prophet framework, based on neural network principles, incorporates features such as automatic differencing, trend, seasonality considerations, and external variables to enhance forecasting accuracy. The model was trained and evaluated using performance metrics such as RMSE, MAE, and MAPE. The results demonstrate the model's effectiveness in capturing trends and predicting Bitcoin prices while acknowledging the challenges posed by the inherent volatility of the cryptocurrency market.
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.
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.