Cryptocurrencies are digital assets that have attracted a lot of investment and attention. It is challenging and essential for investors and traders to predict their stock price movements. Making accurate predictions about cryptocurrency prices is crucial for avoiding losses and gaining profits. Our research proposes a novel method for predicting the stock closed prices of three popular cryptocurrencies: Bitcoin, Ethereum and Polkadot. The SVR (Support vector regression) machine learning method can provide robust and accurate predictions for nonlinear and nonstationary data. This paper compares SVR radial basis functions (RBFs) and hybrid kernels based on cryptocurrency data characteristics. SVR parameters such as regularization, gamma, and epsilon can also be tuned using grid search. Our approach is tested on real-world cryptocurrency stock prices collected from Yahoo Finance. Prediction performance is measured using regression metrics like MAPE (Mean absolute percentage error) and R2 score. In our work, a MAPE value of 0.07772 and an R2 score of 0.9999 have been obtained. The results of our experiments indicate that our approach is significantly more accurate and reliable than existing methods.
Blockchain technology faces significant challenges related to sustainability, including issues with optimisation, as well as high energy and gas consumption—factors that developers may sometimes neglect. We introduce a methodology to analyse the key sustainability topics discussed by Go-Ethereum developers, using thematic analysis of their issues and comments from Github. Our approach uses the BERT model to conduct an in-depth topic analysis, enabling us to study the underlying themes and trends in developer’s conversations regarding energy use and sustainability. We assess the sustainability of the identified topics using the five dimensions outlined in the Sustainability Awareness Framework (SusAF): economic, social, individual, environmental, and technical. Our goal is to shed light on how much attention developers pay to sustainability and energy consumption issues. The findings from this qualitative analysis aim to encourage technologists to incorporate these considerations into their future projects, in order to achieve better outcomes in terms of sustainability and reduced consumption.
Davide Mancino, Alberto Leporati, Marco Viviani, Giovanni Denaro
Recently, the Ethereum blockchain changed its consensus algorithm from Proof-of-Work (PoW) to Proof-of-Stake (PoS). This change has greatly reduced overall energy consumption but has paved the way for the profiling of numerous mechanisms and actors that can operate off-chain to achieve Maximal Extractable Value (MEV). This raises questions about how transparent such a scenario is, both from the point of view of block validation power and from the point of view of the rewards achieved by distinct actors within the platform. To address this concern and to mitigate potential negative externalities, a permissionless ecosystem has recently been proposed, which should be transparent and fair for the extraction of MEV. In this article, after briefly describing the new ecosystem, we conduct an in-depth analysis of Ethereum blocks and other information about the actors operating in the ecosystem, to highlight potential critical situations.
Chris Natoli, Parinya Ekparinya, Guillaume Jourjon, Vincent Gramoli
Traditional blockchain systems offer a secure way of tracking the ownership of digital assets as long as the attacker does not control a large portion of the overall computational or mining power. They typically require participants to generate a proof-of-work before proposing a block at a given index of the chain. To choose one block among the candidate blocks at the same index, Nakamoto’s consensus, Ghost , and the original Ethereum’s consensus select, respectively, the longest branch, the heaviest subtree and the branch with the most difficult crypto-puzzles. This allows an attacker who can generate proofs-of-work faster than others to double spend by overwriting any given branch. In this article, we present a double spending attack, called the Balance attack, that simply needs to delay some messages. This result sheds new lights on an important, often implicit, assumption of the blockchain, synchrony , under which the transmission delay of any message should be within a known upper bound. We show that the attack succeeds with high probability on the protocols of the two largest blockchain systems in market capitalization, Bitcoin and Ethereum. To quantify the impact of our attack, we replicated the blockchain network run by 50 financial institutions and achieved double spending in less than 20 minutes. Finally, we demonstrate the success of the attack empirically by modifying the geth software and hijacking BGP in a controlled distributed system whose distribution of mining power is set to the distribution observed on the Ethereum main blockchain.
Land Administration is crucial for effective governance.However, in many developing countries, including Indonesia, traditional land administration systems often suffer from inefficiencies, corruption, and lack of transparency, leading to disputes and conflicting claims over land ownership.Blockchain technology has offered a promising solution to address the challenges by providing a decentralized, immutable, and transparent recording of land information.Provides encryption cryptography and descriptions to validate data.This research paper explores the implementation of an Ethereum-based private blockchain to build a land administration system DaPPS where users can sell and buy land certificates in Indonesia.A prototype land administration system was built with Ethereum and land certificates as NFT was built.This study focuses on adopting blockchain to help in the land registration system.Transaction performance testing is conducted, it shows between 6-15 seconds.While promising, more research is needed to address scalability and integration with legacy systems.
Jawaid Iqbal, Hasnain Raza, Reqad Ali, Muhammad Awais
The purpose of this research is to analyze whether Blockchain technology can affect the share-economy. Apart from that, blockchain technology has been innovating the whole of the industries and so the academics are discovering the possibilities and starting to incorporate them in order to provide additional tech possibilities. The sharing economic system is the system which enables to share asset among the one person to the other person. It has seen the remarkable growth in the last few years, Uber, Careem, Airbnb, Zostel, Hostel World are some companies to mention which have fueled this growth. Yet, the majority of the transactions through the sharing economy system are facilitated by a centralized infrastructure executing an intermediary role that might be vulnerable to issues of hacking and data breach and such operations come at a high cost and expending more effort in keeping the system active is also a factor worth mentioning. A different method which is free of control centers such as the peer-to-peer sharing and smart service model which is being implemented in the Hospitality industry can overcome those obstacles. Through the use of a blockchain-backed payment system based on an accommodation-sharing structure, the research will develop a prototype of the proposed system in the form of a DApp on the Ethereum blockchain. The aim of these studies and research is to inform the public about the revolution that is blockchain and its benefits for trade, technology, business, and daily life.
Non-fungible tokens (NFTs) are a type of digital asset based on blockchain that contain unique codes verifying the authenticity and ownership of different assets such as art pieces, music, gaming items, collections, and so on. This phenomenon and its markets have grown significantly since the beginning of 2021. This study, using daily data between November 2017 and November 2022, predicts the volume of NFT sales by utilising Random Forest (RF), GBM, XGBoost, and LightGBM methods from the community machine learning methods. In the predictions, several financial variables, including Gold, Bitcoin/USD, Ethereum/USD, S&P 500 index, Nasdaq 100, Oil/USD, Euro/USD, and CDS data, are treated as independent variables. According to the results, XGBoost is found to be the best prediction method for NFT market volume estimation concerning several statistical criteria, e.g., MAE, MAPE, and RMSE, and the most significant influential feature in determining prices is the Ethereum/USD exchange rate.
S. B. Goyal, Anand Singh Rajawat, Ritu Shandilya, Varun Malik
Industrial Internet of Things (IIoT) solutions have transformed industrial productivity and operations. The incorporation of Ethereum blockchain technology into IIoT creates new weaknesses, exposing industrial systems to several cyberattacks. An unique IIoT framework mitigates Ethereum-based attacks in industrial applications to solve these vulnerabilities. This system uses supervised learning and quantum classifiers to detect and fix fraudulent Ethereum transaction patterns in real time. Our methodology has lower false positive rates and higher detection accuracy than conventional methods, according to first trials. This study shows that quantum computing and machine learning (ML) can improve the security of Ethereum-enabled IIoT devices in industry.
This work proposes a novel methodology to help in decision making in the cryptocurrency market. Two investment strategies have been designed for Ethereum (ETH), based on predictions of the price and trend of this cryptocurrency using real data. The two Ethereum cryptocurrency prediction systems rely solely on past values of other contextual stock indices, market indicators and online trends, and ignore any technical indicators of price evolution. Real data from cryptocurrency market has been collected and processed with different feature selection methods. Applying a regression approach with Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, prediction models for the ETH price for 1, 7 and 15 days are obtained and compared. Also, support vector machine (SVM) is applied to predict the ETH price trend by applying a classification approach. In both approaches, sentiment analysis has been included to check its effect on the prediction results. The reliability of these prediction models in the current market has been evaluated by designing two original knowledge-based investment strategies. They are tested over two different time periods with real cryptocurrency market data. The results show that it is possible to generate up to 5.16 profit factor with few operations using these models. Furthermore, adding sentiment analysis has shown to have little influence. In this way, we contribute to the advancement of our knowledge of this volatile and still young cryptocurrency market, and specifically of the evolution of Ethereum and the factors that can influence its behavior.
In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively.
Khulekani Sibanda, Patrick Ndayizigamiye, Hossana Twinomurinzi
Introduction: In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. Methods: This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Results: Analysis of 19 selected papers revealed three primary use cases: patient-centric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. Conclusion: NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions.
Open access
2 source records
Blockchain Technology Applications and Security
Biomedical and Engineering Education
Artificial Intelligence in Healthcare and Education
Context: Scientific research, increasingly reliant on data and computational analysis, confronts the challenge of integrating collaboration and data sharing across disciplines. Collaborative frameworks that support decentralized decision-making and knowledge-sharing are essential, yet integrating them into computational environments presents technical challenges, such as decentralized identity, user-centered policy-making, flexible asset management, automated provenance, and distributed collaborative workflow management. Solution: This study introduces a conceptual framework and its prototype implementation called Decentralized Virtual Research Environment (D-VRE). This approach enhances seamless, trusted data sharing and collaboration within research lifecycles. It incorporates custom sharing policies, secure asset management, collaborative workflows, and research activity tracking, all without centralized oversight. Evaluation: Demonstrated through a real-world case study in the CLARIFY project, the prototype of the decentralized virtual research environment proved effective in enabling advanced data sharing and collaborative scenarios, showcasing its adaptability in scientific research. Results: Integrated into JupyterLab, D-VRE supports custom collaboration agreements and smart contract-based automated execution on the Ethereum blockchain. This ensures secure, verifiable transactions and promotes trust and reliability in shared research findings. Contribution: D-VRE addresses barriers to scientific research collaboration and data sharing, offering a scalable and adaptable decentralized model. This model promotes a more inclusive, efficient, and trustworthy research ecosystem, paving the way for future advancements in virtual research environments.
Caroline Ribeiro dos Santos, Luiz Rzezak, Pablo Augusto Barranjard Rocha, Rodrigo de Oliveira · 6 authors
O artigo visa analisar o impacto comportamental da pandemia do Coronavírus nos mercados de criptomoedas, com foco nas moedas Bitcoin e Ethereum. Utilizando métodos tradicionais de análise financeira e técnicas avançadas de computação, o estudo investiga a volatilidade dessas criptomoedas durante o período da pandemia, identificando padrões e tendências de preço. Por meio da linguagem Python e ferramentas de análise de dados, foram aplicadas regressões LOWESS para suavizar a tendência dos preços e identificar possíveis influências da pandemia nos mercados de criptomoedas através de gráficos. A geração de gráficos para visualização dos resultados foi uma parte crucial do estudo, pois permitiu uma análise mais intuitiva e acessível das tendências e padrões identificados. Por meio desses gráficos, foi possível visualizar de forma clara e concisa como o preço das criptomoedas Bitcoin e Ethereum se comportou durante o período da pandemia, destacando momentos de volatilidade, tendências de alta ou baixa e possíveis correlações com eventos específicos. Essa visualização facilita a interpretação dos resultados e a comunicação das conclusões do estudo para diferentes públicos, incluindo investidores, acadêmicos e profissionais do mercado financeiro.
The gas fee, paid for inclusion in the blockchain, is analyzed in two parts. First, we consider how effort in terms of resources required to process and store a transaction turns into a gas limit, which, through a fee, comprised of the base and priority fee in the current version of Ethereum, is converted into the cost paid by the user. We adhere closely to the Ethereum protocol to simplify the analysis and to constrain the design choices when considering multidimensional gas. Second, we assume that the gas price is given deus ex machina by a fractional Ornstein-Uhlenbeck process and evaluate various derivatives. These contracts can, for example, mitigate gas cost volatility. The ability to price and trade forwards besides the existing spot inclusion into the blockchain could enable users to hedge against future cost fluctuations. Overall, this paper offers a comprehensive analysis of gas fee dynamics on the Ethereum blockchain, integrating supply-side constraints with demand-side modelling to enhance the predictability and stability of transaction costs.
Upgradable smart contracts play an important role in the decentralized application ecosystem, to support routine maintenance, security patching, and feature additions. In this paper, we conduct an empirical study on proxy-based upgradable smart contracts to understand the characteristics of contract upgrading. Through our study on 57,118 open source proxy contracts, we found that 583 contracts have ever been upgraded on Ethereum, involving 973 unique implementation contract versions. The results show that developers often intend to improve usability of contracts if upgrading, where functionality addition and update are the most frequent upgrade intentions. We investigated the practical impacts of contract upgrades, e.g., breaking changes causing compatibility issues, storage collisions and initialization risks leading to security vulnerabilities. The results demonstrate that there are 4,334 ABI breaking changes due to the upgrades of 276 proxies, causing real-world broken usages within 584 transactions witnessed by the blockchain; 36 contract upgrades had storage collisions and five proxies with 59 implementation contracts are vulnerable to initialization attacks.
The emergence of non-fungible tokens (NFTs) in the blockchain environment has prompted many intriguing questions for private law scholars around the world. A question as basic as whether NFTs can be owned has proven difficult in many countries. This is the first research question of our article, which focuses on NFTs created in the Ethereum system by utilizing standard ERC-721. Because these NFTs are identifiable and distinguishable from all other tokens, the notion of owning an NFT is not unthinkable. Yet no universal answer can be offered. Whether NFTs qualify as objects of ownership must be studied at the level of individual legal systems. We argue that NFTs can be owned under Finnish law, with the same probably applying to many other legal systems. Starting with this notion, we pose two further research questions. As the second research question, we ask what problems of a patrimonial law nature may arise in attempts to connect different kinds of rights, even irrevocably, to owning or holding an NFT. Creditor rights seem relatively easy in this respect because most legal systems allow prospective debtors to obligate themselves as they wish. We also study whether a limited liability company could issue an NFT as a share certificate with legal effects corresponding to those of a physical (paper) share certificate. While an affirmative answer could be justified in some legal systems, Finnish law makes it difficult to tokenize a company's shares other than in the framework of a settlement system within the meaning of the European Union's DLT Pilot Regulation. Even greater difficulties arise in attempts to connect the ownership of a (material) thing and of an NFT so that a person who owns a token also owns the thing. Our third and final research question addresses tokenization of digital art, which gives rise to some special questions. We ask what rights the transferee of an NFT can receive in connection with tokenization of digital art. Here, our main finding is that digital art can be meaningfully tokenized even though digital copies are not regarded as possible objects of ownership.
David Melo, Saúl E. Pomares Hernández, Lil María Rodríguez-Henríquez, Julio César Pérez-Sansalvador
Blockchain technology ensures record-keeping by redundantly storing and verifying transactions on a distributed network of nodes. Permissionless blockchains have pushed the development of decentralized applications (DApps) characterized by distributed business logic, resilience to centralized failures, and data immutability. However, storage scalability without sacrificing throughput is one of the remaining open challenges in permissionless blockchains. Enhancing throughput often compromises storage, as seen in projects such as Elastico, OmniLedger, and RapidChain. On the other hand, solutions seeking to save storage, such as CUB, Jidar, SASLedger, and SE-Chain, reduce the transactional throughput. To our knowledge, no analysis has been performed that relates storage growth to transactional throughput. In this article, we delve into the execution of the Bitcoin and Ethereum transactional models, unlocking patterns that represent any transaction on the blockchain. We reveal the trade-off between transactional throughput and storage. To achieve this, we introduce the spent-by relation, a new abstraction of the UTXO model that utilizes a directed acyclic graph (DAG) to reveal the patterns and allows for a graph with granular information. We then analyze the transactional patterns to identify the most storage-intensive ones and those that offer greater flexibility in the throughput/storage trade-off. Finally, we present an analytical study showing that the UTXO model is more storage-intensive than the account model but scales better in transactional throughput.
Die Masterarbeit untersucht die Speicherung und Performanzanalyse von Provenance-Daten mithilfe einer Blockchain. Der Fokus liegt auf der Nutzung einer privaten Blockchain zur Speicherung von Provenance-Graphen und deren Effizienzbewertung unter verschiedenen Bedingungen.
Kausthav Pratim Kalita, Debojit Boro, Dhruba K. Bhattacharyya
Abstract Electronic medical records are a patient's digital asset that enhances the information available to doctors for tracking their patients' health. When this information is stored in a secure environment, health examination reports can serve as a dependable repository for thorough observation of a patient's well‐being. However, it is crucial for the owner to have control over access to these repositories. In this scenario, a blockchain ecosystem with appropriate access control mechanisms can help create a distributed and decentralized storage platform to ensure the safety and security of data. Developing cost‐effective smart contracts and creating clear design diagrams to represent them are essential for establishing such an ecosystem. This paper introduces a smart contract for the Ethereum blockchain that allows an owner to maintain control over their data. The paper presents a diagram for visually representing the modules within our smart contract, providing readers with a clearer understanding of the access control techniques utilized in implementing our strategies. Our smart contract offers clinicians a valuable means of accessing historical data to promptly evaluate a patient's health in emergency situations. We showcase its efficacy by illustrating how it streamlines insurance claims, where it verifies the patient's coverage and automatically authorizes medical expense payments. Lastly, a study is presented to showcase an effective method of storing the ingested data within the Ethereum network. The suggested approach allows restrictions on data visibility based on the viewer's accessibility through identity‐based access control achieved using additional structures in smart contracts. These structures store filtered records accessible to users based on their viewing privileges. The simulated test bed results support the efficiency of using smart contracts with additional structures in terms of gas consumption when compared to those that use a single structure for read and write operations.
This research aims to scrutinise the Layer 2 ecosystem within the Ethereum blockchain, providing a basis for informed decision-making by users with limited technical expertise when selecting Layer 2 projects. The research is based on data collected from the L2BEAT community. The article outlines the key technical concepts underlying various Layer 2 projects on the Ethereum blockchain, and describes the characteristics of projects such as ZK Rollup, Validium, Optimistic Rollup and Optimium. It juxtaposes the project types identified by the L2BEAT team with a simplified framework of the Ethereum Layer 2 ecosystem proposed by Vitalik Buterin. Ethereum blockchain Layer 2 scaling projects are audited, focusing on the indicators "total value locked in escrow agreements" and "share of value locked in total project value". The technologies used to verify the authenticity of the system state in Ethereum Blockchain Layer 2 scaling projects are explained. An analysis of fraud detection approaches in Layer 2 blockchain scaling projects is undertaken. Techniques for validating messages transmitted over Layer 2 blockchain bridges are described. An assessment of the security of the Layer 2 blockchain ecosystem is presented, along with an evaluation of the popularity of such projects among users. The study concludes that for users, pricing in the Ethereum Layer 2 blockchain ecosystem is key, with Arbitrum One emerging as the undisputed leader in scaling projects. In terms of bridges, user preference leans towards those that facilitate the withdrawal of funds in the event of dishonest or malicious behaviour by the bridge operator.
Do Hai Son, Nguyen Danh Hao, Tran Thi Thuy Quynh, Le Quang Minh
Decentralized applications (DApps) have gained prominence with the advent of blockchain technology, particularly Ethereum, providing trust, transparency, and traceability. However, challenges such as rising transaction costs and block confirmation delays hinder their widespread adoption. In this paper, we present our DApp named W2E - Workout to Earn, a mobile DApp incentivizing exercise through tokens and NFT awards. This application leverages the well-known ERC-20 and ERC-721 token standards of Ethereum. Additionally, we deploy W2E into various Ethereum-based networks, including Ethereum testnets, Layer 2 networks, and private networks, to survey gas efficiency and execution time. Our findings highlight the importance of network selection for DApp deployment, offering insights for developers and businesses seeking efficient blockchain solutions. This is because our experimental results are not only specific for W2E but also for other ERC-20 and ERC-721-based DApps.
Eduardo Jorge Lira Antunes da Silva, Cristina Souza de Araújo, Jucimar Maia da Silva, Roberto Junio Rodrigues Gomes · 7 authors
The development of software aligned with Web3 innovations is essential to stimulate discussions on the application of these technologies in the scientific realm. This study presents the development of a 'cryptogame,' a game that integrates blockchain technology to incorporate Non-Fungible Tokens (NFTs) into its functionalities. The paper explores fundamental Web3 concepts, with references to relevant literature, and details the development process, including the methodologies and models adopted. As a result, a puzzle game was created that utilizes NFTs as playable characters, allowing for the transfer of these assets between digital wallets. Furthermore, the study investigates the application of these concepts in adapting a traditional mobile game for the use of NFTs, demonstrating the necessary changes in user interface and game mechanics to support the integration with digital assets.
Bitcoin price volatility fascinates both researchers and investors, studying features that influence its movement. This paper expends on previous research and examines time series data of various exogenous and endogenous factors: Bitcoin, Ethereum, S&P 500, and VIX closing prices; exchange rates of the Euro and GPB to USD; and the number of Bitcoin-related tweets per day. A period of three years (from September 2019 to September 2022) is covered by the research dataset. A two-layer framework is introduced tasked with accurately forecasting Bitcoin price. In the first layer, to account for complexities in the analyzed data, variational mode decomposition (VMD) extracts trends from the time series. In the second layer, Long short-term memory and hybrid Bidirectional long short-term memory networks were used to forecast prices several steps ahead. This work also introduced an enhanced variant of the sine cosine algorithm to tune the control parameters of VMD and both neural networks for attaining the best possible performance. The main focus is on combining VMD with modified metaheuristics to improve cryptocurrency closing value forecast. Two sets of experiments were conducted, with and without VMD. The results have been contrasted with models tuned by seven other cutting-edge optimizers. Extensive experimental outcomes indicate that Bitcoin price can be forecasted with great accuracy using selected features and time series decomposition. Additionally, the best model was analyzed, and Shapley values indicated that features such as EUR/USD exchange rates, Ethereum closing prices, and GBP/USD exchange rates, have a significant impact on forecasts.
Aman Dwivedi, Divyansh Sharma, Sharad Gumber, Satyam Gupta
Abstract: Carpooling has seen a significant upsurge in popularity in recent years since it relieves commuters of the burden of driving a car and later spares them from the mad dash to locate a parking space. The industry now uses centralised database servers, which puts the system at risk of hacking, causes data leaks, and undermines security within the system. Additionally, there is a chance that the owner will misuse the data in a centralised system. Since the newly developed Blockchain technology provides complete anonymity and database transparency, data security and traceability may be guaranteed across the entire server. The goal of the suggested solution is to create and implement a blockchain-based, smart contract-based peer-to-peer vehicle sharing system. The Solidity programming language is utilised to carry out the smart contracts. Ethereum blockchain is used in the ecosystem's construction. Eliminating the middleman would result in a peer-to- peer (P2P) car-sharing programme, which would reflect decreased costs and exposure to data theft.