Cryptocurrencies have gained immense significance and popularity in recent times. With thousands of digital currencies available, selecting the right one can be challenging for users. In the financial sector, accurately predicting future prices is crucial for profitable investments in digital currencies. However, price prediction in this realm poses unique challenges, as it lacks physical goods or services as the basis, unlike stock prices. Machine learning emerges as a pivotal tool for addressing this challenge and plays a vital role in price prediction. This research analyzes five prominent currencies - Monero, Bitcoin, Ethereum, IOTA, and Zcash - employing five models: SVR, LRG, Huber, RANSAC, MLP, and AdaBoost. The experimental results demonstrate promising outcomes, showcasing the ability to predict digital currency prices with an impressive R2 score of 1.0 for specific machine learning algorithms. This advancement opens new avenues for informed decision-making and profitable ventures in the dynamic world of digital currencies
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.
Any organisation that saves structured data has the potential to transform as a result of blockchain technology. Blockchain is a system of shared distributed ledgers used to store internet data. The way the information is handled makes it special. It is enduring, decentralised, open, and safe. Given that the blockchain's initial foray into the finance sector is regarded as largely successful with bitcoin and cryptocurrencies, think tanks and technocrats are looking into the potential applications of this revolutionary technology in a variety of industrial sectors, including health care, retail, real estate, and others. Some governments' goals and ambitions have already been altered by blockchain technology. Countries like the US, UK, China, Australia, and Singapore are investing in blockchain.
As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision.
Parvataneni Rajendra Kumar, S. Meenakshi, S. Shalini, S. Devi · 5 authors
The integration of deep learning and blockchain technologies has the potential to revolutionize soil quality prediction in smart agriculture. Deep learning models, like neural networks and convolutional neural networks, enable accurate predictions of soil properties by considering intricate relationships within data. Contextual learning approaches, including embeddings and data fusion, enrich the prediction process by incorporating external factors like weather conditions and land management practices. Blockchain technology ensures secure storage of predictions and data, while smart contracts facilitate automated model execution. This integrated system empowers farmers with accurate predictions for optimal resource allocation and fosters collaboration through decentralized data sharing. Future directions include advancements in deep learning algorithms, blockchain applications, and potential integration with IoT and remote sensing technologies.
Smart Agriculture and AI
Artificial Intelligence and Decision Support Systems
The value of bitcoin as a financial asset is rising and due to its extreme volatility, accurate forecasts are necessary to guide investment choices. Adoption, regulatory developments, geopolitical events, and macroeconomic factors all have an impact on its price. Accordingly, a lot of researchers have looked into a variety of factors that influence the value of bitcoin including the trends that underlie its fluctuations, but limited studies have focused on applying various machine learning techniques in this domain. Therefore, the objective of this study is to analyze multiple algorithms used for machine learning regression model in order to identify the system which can estimate bitcoin values most effectively and accurately based on multiple attributes. To forecast the bitcoin price, the dataset has been analyzed and preprocessed meticulously and then multiple machine learning regression models such as XGBoosting, Gradient Boosting Regressor, Hist Gradient Boosting Regressor, Random Forest, Linear Regression, Support Vector Regressor, Neural Network Regressor, Decision Tree, Gaussian Process Regressor, and K-Nearest Neighbors Regressor. The top findings were 99.497 percent (almost 99.5 percent) R-squared (R2), 0.01281 (RMSE), and 0.005755 (MAE) scores employing gradient boosting regressor model.
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.
Kafila, R. Appavu Raj, P Pavithra, Soyabkhan Mehbubkhan Baloch · 6 authors
Blockchain technology dependent on cryptocurrency have lately excited the curiosity of capitalists. They focused on forecasting the financial item's risk and return ratios. As a result, financial items require an autonomous algorithm to anticipate the return percentage of cryptocurrency. Deep learning (DL) algorithms that were lately developed lay the path for the return percentage forecasting procedure. This paper proposes a blockchain financial product utilizing DL for a smart return rate prediction (RRP-DLBFP) method. The suggested RRP-DLBFP method entails creating a long short-term memory (LSTM) framework for return percentage forecasting. Furthermore, the Adam optimization is used to effectively change the LSTM algorithm's hyperparameters, resulting in improved forecasting accuracy. The Ethereum rate of return has been selected as the aim of guaranteeing the RRP-DLBFP method's superior performance, and its outcomes are studied in various metrics. In terms of several assessment variables, the model's results demonstrated the superiority of the RRP-DLBFP method over the present latest methods. The suggested RRP-DLBFP exhibits MSE values of 0.0435 & 0.0655, accordingly, contrasted with a mean of 0.6139 & 0.723 for comparing techniques in both training and evaluation.
Swarup Yeole, Mohammad S. Obaidat, Aditya Goel, Sanskar Chandra · 5 authors
Blockchain-based e-commerce NFT warranty system is a new solution that allows customers to receive digital warranty for their purchases on e-commerce platforms. The system is built using the Ethereum blockchain and uses non-fiscal tokens (NFTs) to set up and execute transactions. This project aims to solve the physical certificate management problem and the challenge of warranty tracking for customers and companies. With the NFT warranty system, customers can store the warranty in their connected digital wallet and easily send it to other users when selling. Also, companies do not need to manage services separately, as authentication and authorization are tracked by a blockchain-based system. The advantage of this project is that it provides a secure and transparent warranty system that prevents fraud and counterfeit products. Additionally, the use of NFTs ensures the authenticity and authenticity of all warranties, thus preventing competition or abuse of warranties. The project works by allowing companies to join e-commerce sites and add their vendors to generate NFT security tokens. When customers make a purchase, they receive a digital NFT warranty that they can use for warranty service. Warranty claims are recorded on the blockchain and can be tracked by both the customer and the company. The system also adds value to customers' purchases by allowing them to resell NFT-warrantied products. All in all, the blockchain-based e-commerce NFT warranty system is a cutting-edge solution that will simplify the warranty process for clients and customers. company. It offers an easy-to-use, secure and transparent warranty system and provides added value to customers.
The most emerging new technology of trust is the concept of blockchain technology, which is a combined ecosystem of entwined dependencies of agricultural supply chain and agricultural produce. A sufficiently high transparency is the most essential requirement where agricultural produce and supply chain are to be implemented in a combined manner. Thus, blockchain is emerging as the most reliable and secure way to ensure consumer benefits. The ledger of records, usually maintained in the form of blocks, are encapsulated and maintained in a network. The newer records are stored in the next block and each of the existing block is connected to the newer block so that the sequence of blocks in maintained. Each block leads to the next block in the chain and the distributed nature of the network ensures transparency and integrity of each block. Any alteration in any block by any agent can be easily detected and the integrity can be maintained. Cryptography is another major concept that plays a pivot role in ensuring security and integrity of the blocks formed. Various types of online transactions, documentations, or records can be managed using blockchain technology. In particular, agriculture is a sector which fulfils the demand supply chain of food and the demand for better quality food is having the highest demand.
B. Murali Krishna, I. Sapthami, Venkata Ramana Banka, Chittibabu Ravela
People are now investing more on crypto currency and Bit coin. Hence, predicting the price of a bit coin can save people from financial loss. Moreover, the price of bit coin also fluctuates based on the stock market prices. In order to predict the price of the crypto currency, this research study has used LSTM (Long Short Term Memory) to generate assessments based on bitcoin stock queries. Based on stock demand, the proposed LSTM model integrates with the Yahoo Finance to predict the price of the Bitcoin.
With the increasing reliance on digital infrastructure and the transmission of sensitive information online, the necessity for robust cybersecurity measures has become urgent. This research paper explores the effectiveness of employing honeypots and the MITRE ATT&CK framework to detect adversary behaviors in the context of email and cryptocurrency attacks. Through the deployment of honeypots, a diverse range of attack data was captured, enabling the identification of recurring patterns such as phishing and scamming in email, as well as account hijacking in cryptocurrency. By mapping this data to the MITRE ATT&CK framework, we were able to identify the Tactics, Techniques, and Procedures (TTP) utilized by adversaries, which can guide security strategies and mitigate future attacks. Our analysis underscores the value of honeypots in detecting and analyzing adversaries’ TTP, emphasizing the need for ongoing research to enhance our comprehension of emerging threats.
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.
Trading in Digital currency is an opportunity for an alternate investment option and getting popularity day by day. Bitcoin is one of the most popular digital currencies based on technology implementation. Though it operates free from any central control, still many investors trade in bitcoin and also contribute to the economy. The objective of this research paper is to implement and analyse five different statistical and machine learning algorithms in next day bitcoin price prediction. The different algorithms implemented in this work are Random Forest, Support Vector Regression (SVR), Ridge regression, Lasso regression and Long Short Term Memory (LSTM) models. The data used in this work is the daily traded data for the period from November 2021 to February 2023. From the experiments, it is concluded that LSTM model and Lasso regression predict the same value for the next day bitcoin price with an accuracy of 97.88%followed by Random Forest and Support vector model with accuracy of 94.65% and 94.40% respectively. However, the highest accuracy is observed by the Ridge regression which is 98.02%.
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.
K. Swetha, Courtney Shareef, G. Sreenivasulu, K. K. Baseer · 5 authors
The recent technological innovation, Blockchain system is used to store data in a way that makes it difficult or impossible to edit, hack, or steal the data. It is an important part of technology with a decentralized architecture, distributed database, and continuously expanding records. There are many other technologies that are used along in blockchain. After doing critical study on many documents and understanding the technologies used by various studies, its necessities, and the issues encountered by it while building their respective models. The purpose of this study is to examine various tools and technologies that can be used in blockchain. Around 200 research works have been collected and up to 40 research studies are filtered. Research studies are filtered based on various factors. Some research studies were excluded based on title, few were excluded based on abstract and titles. This study is categorized in terms of Blockchain using Ethereum, Electronic Health records, Smart Contracts and Health monitoring.
Jai Kishan Karahyla, Neelam Sharma, Sushant Chamoli, Dr Anil Shirgire · 6 authors
Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin’s pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN.
Bitcoin is the very first digital or cryptocurrency based on Block chain concept. The objective of this paper is to forecast the price of Bitcoin using various Machine Learning Time Series models like: Moving Averages (MA), Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM). As we know that the price of Bitcoin is very volatile in nature so producing appropriate predictions is difficult. Also we know that Bitcoin Price nature is not stationary, so we have converted our non-stationary data to stationary for models like ARIMA which works properly on stationary data only. At last, we have compared the results of MA, ARIMA, XGBoost and LSTM for Bitcoin prediction based on RMSE and we found that first three models have given somewhere similar results whereas LSTM has given different.
Cryptocurrency, a digital form of currency, has emerged as a prominent asset class with unique characteristics such as decentralization, security, and global accessibility. As the adoption of cryptocurrencies continues to grow, the need for accurate price prediction using machine learning (ML) algorithms becomes crucial for various stakeholders in the financial ecosystem. This paper presents a comprehensive approach to cryptocurrency price prediction, focusing on the following key aspects: The data for this study is collected from the Binance platform, a leading cryptocurrency exchange known for its extensive market data and liquidity. The dataset includes historical price data across different time frames, including 1-hour, 4-hour, and daily intervals. Prior to analysis, the collected data undergoes thorough preprocessing steps to ensure data quality and consistency. This process includes handling missing values, removing outliers, and standardizing data formats for further analysis. Long Short-Term machine learning model algorithm is employed for price prediction. This model is chosen for its ability to capture complex patterns and dynamics in cryptocurrency price movements. The prediction phase involves training and testing the ML model using the preprocessed data. Performance evaluation metrics such as R-squared, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean absolute percentage error (MAPE) are utilized to assess the accuracy and robustness of the prediction model across different time frames. Accurate cryptocurrency price prediction is essential for various stakeholders, including investors, traders, businesses, and regulators. It facilitates informed decision- making, risk management, market analysis, trading strategy development, business planning, and regulatory compliance in the dynamic cryptocurrency market. By addressing these key elements, this study aims to contribute to the advancement of cryptocurrency price prediction methodologies using ML techniques, thereby enhancing decision-making processes and fostering a more efficient and transparent digital asset market ecosystem.
Cryptocurrencies have become a major element in enterprises and financial market showing promising potential during the past ten years. Predictions that are accurate can help cryptocurrencies investors to make the best decisions and possibly enhance their earnings. For this work 4 cryptocurrencies namely Bitcoin, Binance Coin, Ethereum and Tether (USDT) are considered. The considered dataset is for around 5 years, which has a total of 9 columns, One Date Column ranging from year 2017 to 2022 and for each of the Coin there is the Closing price and the Volume. The Dataset is collected from Kaggle. Attempts are made to build the models in various ways, by dealing with certain features or by taking a subset of the dataset. All the models, according to the way the dataset was dealt were evaluated based on certain evaluation metrics like R-squared, mean square error and root mean square error. The models used are KNN, Decision Tree, Random Forest, XGBoost and CatBoost. Visualized and understood how the prices of those cryptocurrencies were impacted by other features of the dataset. The models that outperformed are Random Forest and CatBoost. Achieved RMSE was 365, 2.99, 29.99 and 0.0023 for Bitcoin, Binance, Ethereum and USDT respectively.
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.