Cheri Venkata Sai, Gurijela Pavan, Pittala Abhirameshwar, S. Suma
These come hand in hand with unprecedented levels of complexity in copyrighting and mon- etizing creations. In general, this protects the copyrights under the existing framework, which are cen- tralized, expensive, and beyond the reach of any independent creator. This paper presents an innovative blockchain-based framework for image copyrighting and social crypto monetization by using blockchain technologies such as Ethereum smart contracts and the InterPlanetary File System (IPFS). The proposed framework enables creators to publish digital images, calculate cryptographic proofs of image ownership with the SHA-256 hashing algorithm, store images in IPFS, and record metadata into the blockchain with unchanged timestamps. In addition, the platform supports “Like to Earn”, where public engagement for viewing is translated directly into rewarding creators with cryptocurrencies via smart contracts. The proposed framework adopts Web3 technologies to enable secure signing of all transactions with fraud prevention using the Elliptic Curve Digital Signature Algorithm (ECDSA) technique through MetaMask wallet authentication. Experimental evaluation of the proposed framework confirms that it can remove duplicate uploads, promptly verify image ownership, and enable social monetization of cryptocurrencies in a secured way.
Open access
2 source records
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Abstract— The rapid growth of cryptocurrency and blockchain technology has significantly increased the demand for platforms that enable users to efficiently explore and verify blockchain transactions. This paper presents the design and development of an Ethereum Blockchain Explorer (Multi-Chain), a web-based platform that provides a centralized interface for accessing blockchain data such as transactions, wallet addresses, and block details. The system is implemented using modern web technologies, particularly React.js, to ensure a responsive and intuitive interface. It allows users to search blockchain records using transaction hashes, wallet addresses, or block numbers. By integrating multiple blockchain networks into a single platform, the system enhances accessibility and usability of blockchain information for developers, researchers, and general users. The proposed system demonstrates improved multi-chain support and provides a more user-friendly experience compared to traditional single-chain blockchain explorers. Keywords— Blockchain Explorer, Ethereum, Multi-Chain, Smart Contracts, RPC, Web3, React.js
Type of the article: Research ArticleAbstractCryptocurrency markets are highly volatile, making price prediction a complex yet essential task for investors, financial engineers, and institutions. The purpose of this study is to evaluate whether Bayesian optimization of technical indicator parameters significantly improves the forecasting performance of Long Short-Term Memory (LSTM) models compared to baseline configurations. The study used daily Bitcoin and Ethereum price data from January 2016 to September 2025. Six technical indicators representing trend, momentum, volatility, and volume-based technical indicators are constructed and dynamically optimized through Bayesian optimization. The optimized indicators are then used as inputs to an LSTM forecasting framework. The study found that the baseline LSTM model achieved moderate predictive accuracy, where Ethereum outperformed Bitcoin. After optimization, both models exhibited improved performance, reducing the forecasting error for Bitcoin by 36.4% and for Ethereum by 12.2%. LSTM model with Bayesian optimized indicators showed a higher forecasting accuracy as compared to the baseline model, with 32% and 18.6% improvements for Bitcoin and Ethereum, respectively. These findings suggest that combining optimized technical indicators with LSTM models enhances predictive power in cryptocurrency markets. The approach offers a robust forecasting framework for traders, analysts, and algorithmic systems in high-volatility environments.Acknowledgment“This work was funded by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. KFU261690].”
P. Subramanya Sai, Bhukya Niranjan, Akula Tejaswini, A Varshitha · 5 authors
The exponential increase in digital data exchange and online communication has intensified the need for secure, transparent, and dependable file-sharing systems. Critical information such as financial records, healthcare data, confidential documents, and research outputs is frequently transmitted across distributed networks, where conventional centralized storage models introduce significant vulnerabilities. These traditional systems rely on single-point control, making them prone to data breaches, unauthorized access, service disruptions, and integrity violations. Furthermore, they lack transparency and robust audit mechanisms, raising concerns about data reliability and trustworthiness during storage and transmission. To overcome these limitations, this work proposes a decentralized and secure file-sharing framework that integrates blockchain technology, the Inter-Planetary File System (IPFS), and cryptographic techniques. The system ensures data confidentiality by encrypting files using the Elliptic Curve Integrated Encryption Scheme (ECIES) before storing them in the distributed IPFS network. Instead of placing the actual data on-chain, only essential metadata including file hash, ownership details, timestamps, and access permissions is maintained within a smart contract on the Ethereum blockchain. This design guarantees immutability, traceability, and protection against tampering, while enabling fine-grained access control. In addition to secure storage, the framework incorporates ChaCha20-based symmetric encryption to evaluate and compare computational performance with asymmetric methods. The combination of decentralized storage, cryptographic security, and immutable ledger technology eliminates reliance on centralized authorities, thereby reducing single points of failure and enhancing system resilience. The proposed approach ensures that only authorized entities can access and decrypt shared content while maintaining transparency of file transactions.
Peer-to-peer (P2P) payments facilitate rapid direct transactions but are frequently compromised by trust asymmetry, leading to substantial risks of non-delivery or non-payment. This study addresses these vulnerabilities by introducing a lightweight, deterministic escrow mechanism based on Ethereum smart contracts, specifically designed to bridge the regulatory gap in consumer protection. Unlike conventional escrow systems that rely on costly human intermediaries or complex decentralized autonomous organization (DAO) structures, the proposed "FairPay" model advances the state-of-the-art by offering a streamlined five-state lifecycle architecture comprising Created, Funded, WorkSubmitted, Released, and Refunded stages. The research prioritizes an analytical problem-solution flow, focusing on a state-machine design that enforces automated role-based restrictions. Methodological evaluation conducted on the Ethereum Sepolia testnet demonstrates a 100% functional success rate across all unit test scenarios. Furthermore, gas cost analysis reveals that the system is economically viable for granular transactions, with core operational functions maintaining a low execution overhead. Beyond operational success, the primary scholarly contribution lies in the design insight of balancing high cryptographic security with granular transaction accessibility, providing a scalable framework for the modern digital economy. However, the system currently assumes binary participant decisions for work verification, representing a transparency-oriented limitation in handling highly subjective service deliverables. Ultimately, this study demonstrates that algorithmic trust, mediated through a simplified state-machine, offers a more efficient and transparent alternative to existing high-complexity blockchain models, effectively resolving the tension between decentralized security and practical usability in P2P digital interactions.
Rizqi Akbar Makarim, Desinta Maheswari, Aqila Dina Pramustiwi, Kartika Ayu Rahmawati · 5 authors
The volatility of cryptocurrency markets has increased substantially in recent years, particularly for Ethereum (ETH), which exhibits fat-tailed distributions and persistent volatility clustering that traditional linear models are unable to capture. This study aims to analyze and model the volatility of ETH/USD using high-frequency hourly data to determine the most appropriate volatility model for describing Ethereum’s intraday market dynamics. The dataset consists of 8,760 hourly closing prices from October 31, 2024 to October 31, 2025, obtained through the CryptoCompare API. The methodological framework includes data preprocessing, log-return transformation, stationarity analysis using the Augmented Dickey–Fuller test, detection of heteroskedasticity via the ARCH–LM test, and estimation of several ARCH and GARCH model specifications. The results show that ETH/USD returns are stationary, non-normally distributed, and exhibit clear volatility clustering. Among the ARCH models, only ARCH(1) adequately captures short-term fluctuations, while ARCH(2) provides no additional benefit. In contrast, GARCH models demonstrate superior performance in capturing both short-term shocks and long-term persistence. Based on AIC, BIC, and log-likelihood values, GARCH(1,2) emerges as the best-performing model, offering the highest flexibility in representing Ethereum’s persistent and reactive volatility patterns. These findings confirm that ETH/USD volatility is predictable and can be modeled statistically. Future research may incorporate asymmetric GARCH extensions or external explanatory variables to improve predictive performance.
The rapid advancement of digital technologies in healthcare has increased the need for secure, transparent, and efficient management of medical data. However, most existing systems rely on centralized architectures, where sensitive patient information is controlled by a single authority. This creates vulnerabilities such as data breaches, unauthorized access, and single points of failure, which can compromise data integrity and patient privacy. The core problem addressed in this research is the lack of a decentralized and tamper-resistant mechanism for managing Electronic Health Records (EHR). Current solutions often suffer from limited transparency, inefficient data sharing between patients and doctors, risks of data manipulation, dependency on intermediaries, and scalability issues due to large medical files like reports and prescriptions. To address these challenges, this research proposes a blockchain-based healthcare management system integrated with the Inter-Planetary File System (IPFS). Blockchain technology, implemented using Ethereum and Web3, ensures secure, immutable, and transparent transaction handling through smart contracts. IPFS is used for decentralized storage of medical files, with only cryptographic hashes stored on the blockchain to reduce storage overhead while maintaining data integrity. The system enables patients to book appointments, upload medical reports, and securely share them with doctors. Doctors can access records, provide diagnoses, and generate prescriptions, which are also stored via IPFS and linked to the blockchain. This ensures that data cannot be altered without detection, enhancing trust. The proposed system improves data security, privacy, reliability, and scalability in healthcare data management.
Prof. M. S. Bhosale, Abhishek Kangude, Vedant Khandare, Sunil Kajave
Abstract: In modern digital governance, ensuring secure, transparent, and tamper-proof elections remains a critical challenge. Traditional voting systems, whether paper-based or electronic, suffer from issues such as centralized control, lack of transparency, and vulnerability to tampering. This paper proposes a Blockchain-Based Decentralized E-Voting System that utilizes Ethereum smart contracts, MetaMask authentication, and a Flutter-based frontend to provide a secure and transparent voting mechanism. Each vote is recorded as an immutable blockchain transaction, ensuring integrity, verifiability, and prevention of double voting. The system eliminates the need for third-party intervention and enables real-time result verification. Experimental results demonstrate improved security, reliability, and scalability, making the system suitable for modern digital election processes. Keywords: Blockchain, E-Voting, Smart Contracts, Ethereum, MetaMask, Decentralization, Web3
Jiahao Qi, Dian Ding, Jie Li, Jiannong Cao · 7 authors
Account migration in sharded blockchains presents a critical trade-off between optimization effectiveness and system availability. While dynamically reallocating accounts across shards can significantly reduce cross-shard transaction overhead, existing migration mechanisms cause service disruptions that intensify as state data volumes grow. To address this challenge, we propose BIND, a batch-wise account migration protocol that eliminates service interruptions by enabling continuous transaction processing throughout migration. BIND introduces a dual transaction pool architecture that isolates transactions involving migrating accounts while allowing non-migrating accounts to operate uninterrupted. To optimize migration efficiency, we design a reverse greedy heuristic algorithm that partitions accounts into batches based on community cohesion, maximizing intra-batch connectivity to front-load cross-shard communication reduction. We evaluate BIND using real Ethereum transactions, demonstrating superior performance over existing mechanisms. BIND achieves 12% higher overall throughput, reduces migration time to 23.6%-39.3% of the one-shot baseline (across 1-10Gbps bandwidth), and lowers cross-shard transaction rates by 24.1% compared to random batching. These results confirm BIND as a practical solution for large-scale, non-disruptive account migration in production sharded blockchains.
Fraudulent activities on blockchain networks threaten the integrity and reliability of decentralized finance ecosystems. Accurately identifying malicious nodes such as phishing or ransomware addresses, within large-scale blockchain transaction graphs remains a critical challenge due to their dynamic, sparse, and continuously evolving topologies. Transfer learning offers a powerful paradigm for fraud detection because many fraudulent schemes, including ransomware and phishing, are often orchestrated by overlapping actor groups that share behavioral and structural patterns across networks. Leveraging these shared representations enables knowledge transfer from previously observed fraud types to emerging ones. However, the complex and multi-modal nature of digital financial systems introduces substantial challenges for graph-based transfer learning. Fraudulent activities are shaped by diverse modalities including graph structure, transaction sequences, temporal price dynamics, and textual metadata, while distributional shifts frequently occur across time and platforms. Existing graph transfer learning methods struggle to model such multi-modal dependencies and to align divergent feature distributions. To tackle these challenges, we develop a Multi-mOdal Enhanced Graph Transfer Learning (MOE-GTL) framework which incorporates graph, temporal, and textual modalities for fraudulent node detection. We further introduce Temporal-aware Maximum Mean Discrepancy (TMMD), a regularization mechanism that explicitly aligns multi-modal feature distributions between source and target graphs over time. Extensive experiments reveal that our MOE-GTL model notably improves the accuracy of fraudulent node classifications on Ethereum and Solana transaction graphs.
Against the complex characteristics of the Ethereum transaction network and the limitations of existing graph embedding methods based on random walks, which fail to effectively capture transaction temporal dynamics and the flow of funds, we propose a fraud detection algorithm for Ethereum, ETX2Vec (Ethereum Transactions (TX) to Vector), which improves upon transaction subgraph construction and random walk strategies. First, in terms of transaction subgraph construction, we extract the first-order predecessor and successor neighboring nodes of the target node to reconstruct the transaction subgraph, enabling the random walk to effectively capture the complete flow of funds. Second, in the design of the random walk strategy, we introduce two key improvements: (1) the next node is selected based on the non-decreasing principle of transaction timestamps, effectively capturing the temporal dynamics of transactions within the network, and (2) a biased random walk strategy is designed based on both transaction timestamps and amounts, with a parameter α introduced to control the weighting of these factors when calculating transition probabilities. Experimental results show that ETX2Vec achieves an average performance of 96.04% in downstream node classification tasks, outperforming the best model in similar studies by 3.74%, and even surpassing neural network models such as GAT and GCN. This demonstrates that ETX2Vec is more effective at understanding and processing the Ethereum transaction network, leading to the learning of high-quality node embedding vectors.
ABSTRACT This paper explores how financial innovation and environmental sustainability intersect by analyzing spillovers between FinTech, blockchain energy use, and green finance. Using a Quantile Vector Autoregression (QVAR) framework, we examine weekly data from 2018 to 2024 across 11 digital, environmental, and macro‐financial indices. Our findings reveal a striking asymmetry: FinTech and equity markets consistently act as systemic shock transmitters, especially during crises and booms, while blockchain energy consumption behaves as a passive shock absorber. Notably, Ethereum's energy profile remains sensitive to market exuberance even after its transition to proof‐of‐stake. Connectedness weakens markedly in tranquil regimes but resurges sharply at market extremes, underscoring the fragility of digital–green linkages. These results advance the literature on climate‐FinTech integration by showing how digital finance volatility propagates to sustainability assets. We call for targeted policy interventions that align blockchain development with climate goals and promote transparency and resilience in digital financial markets.
Money laundering enables malicious actors to integrate illegal profits into the legitimate economy and has long been a central concern in financial regulation. Blockchain systems introduce new channels for laundering through decentralized, pseudonymous, and cross-border asset transfers. In this context, blockchain exploiters often rely on laundering to conceal fund origins and enable cash-out.
In decentralized finance (DeFi), accidental cryptocurrency transfers to incorrect wallet addresses are a large usability and security issue, frequently causing permanent loss of funds. We present CryptoSafeSend, a smart contract-based safety protocol for transactions featuring a cryptographically secure passcode verification scheme supporting conditional transfers. This work addresses higher-level security issues by introducing a PBKDF2-based key derivation function, which generates strong encryption keys based on Firebase's Firebase Unique Identifier. Secret passcodes are encrypted in Advanced Encryption Standard operating in Galois/Counter Mode functions deployed underneath the Web Crypto API, and the initialization vector and ciphertext are Base64 encoded for off-chain reliable storage and Firebase Firestore-based messaging. The protocol prevents unauthorized exploitation by safely binding off-chain passcode transmission to a matching on-chain verification, reinforcing user trust without undermining decentralization. Also, to ensure recoverability, CryptoSafeSend integrates a 7-day on-chain escrow lock, after which unclaimed funds become permissionlessly refundable to the sender, preserving decentralization while eliminating reliance on trusted intermediaries. Testing on an Ethereum testnet confirms negligible gas overhead, immunity against double claims, and strong security guarantees, qualifying CryptoSafeSend as a valuable constituent in next-generation secure digital asset protocols.
Luigi Crisci, Lorenz Schuler, Herbert Jordan, Bernhard Scholz
The Ethereum state database uses Merkle Patricia Trie (MPT), which suffers from large witness proof sizes and high storage overhead. Verkle Tries have been proposed as a replacement, offering witness proofs below 150 bytes through vector commitments and Inner Product Argument aggregation. However, deploying a Verkle Trie in a high-throughput, short block-time blockchain such as Sonic, which produces a block every 300 milliseconds, introduces substantial engineering challenges related to storage efficiency, commitment computation costs, and the need to serve both live and historical state queries in real time. We present SonicDB S6, a production-grade Rust Verkle Trie database for the Sonic blockchain, which leverages its non-forking property to enable aggressive storage optimizations. Occupancy-aware node specializations, selected via an $\mathcal{O}(k n^2)$ dynamic program, reduce live storage by 97.8\%. Delta nodes that record only changed slots reduce archive storage by 95\%. Batched updates, multi-threaded commitment computation, and homomorphic Pedersen caching yield $3.2\times$ higher throughput than a persistent Geth Verkle baseline while sustaining production block-rate performance.
Recent advancements, specifically the 2026 whitepaper by Google Quantum AI, Stanford University, and the Ethereum Foundation (arXiv:2603.28846), have demonstrated the resource feasibility of breaking secp256k1 elliptic curve cryptography using fault-tolerant quantum computation (≤ 1200 logical qubits and ≤ 90 million Toffoli gates). While their work validates this capability via zero-knowledge STARK proofs without disclosing explicit circuits, we provide the continuous operator-theoretic framework that explains the exact physical collapse mechanism underlying their discrete resource results. By modeling cryptographic hardness as a stable, invariant computational manifold, we show that quantum vulnerability is a manifestation of a Birman-Schwinger instability. We prove that, within this model, the introduction of a transverse quantum operator (e.g., Shor's algorithm implemented via Quantum Phase Estimation) forces a resolvent singularity in the classical generator when the resource perturbation parameter crosses a critical threshold (μ_c). We establish a strict Hardness Phase Transition, demonstrating that cryptographic security is equivalent to the point 1 remaining outside the spectrum of the Birman-Schwinger kernel. Furthermore, we formalize zero-knowledge proofs (such as the Groth16-wrapped STARK artifacts published by Babbush et al.) as highly constrained Boolean projectors. We show that these proofs trigger an epistemic spectral collapse via Zeno stabilization, certifying the non-invertible regime without decohering the raw computational state into the public domain. The manuscript includes an exact analytic toy model demonstrating bound-state collapse into the continuum, explicitly mapping the destruction of exponential cryptographic isolation to a polynomial scattering state. This formalization transitions cryptographic failure from a domain of discrete computational estimates to a continuous framework of operator-theoretic necessity.
This paper analyses MicroMix, a noncustodial Ethereum mixer that unlinks deposits from withdrawals using browser-side zkSNARKs, a centralised relayer, and on-chain enforcement via Semaphore and Mixer contracts. The study formalises core acceptance conditions—value conservation, nullifier uniqueness, external‑nullifier scoping, and signal binding—and evaluates risks that persist despite sound cryptography, including timing correlation in small anonymity sets, Sybil pool distortion, single‑relayer censorship, ETH payout liveness under gas‑stipend limits, ERC‑20 heterogeneity, circuit–verifier input/order mismatches, and cross‑chain replay. The work proposes concrete mitigations: randomised scheduling and probabilistic batching, multi‑denomination support, decentralised relayer participation with user-paid fallbacks, guarded call patterns with reentrancy protection, SafeERC20 enforcement and token whitelisting, strict public‑input ordering and signal‑to‑field mapping, a fixed mixer-scoped external nullifier, and chain-bound proofs. With these measures, MicroMix can preserve unlinkability while improving liveness and correctness in adversarial environments, advancing practical, privacy‑preserving withdrawals on Ethereum.
Este artigo discute como padrões ERC e mecanismos de comunicação cross‑contract sustentam a interoperabilidade entre contratos inteligentes em Ethereum e EVM‑chains. Padrões de tokens como ERC‑20, ERC‑721, ERC‑777 e ERC‑1155 definem interfaces mínimas para transferência, consulta de saldo e eventos, permitindo que carteiras, DEXs, marketplaces e outras aplicações interajam de forma uniforme com ativos fungíveis e não fungíveis, enquanto extensões multi‑token como o ERC‑1155 combinam características de ERC‑20 e ERC‑721 e suportam transferências em lote. A interoperabilidade é reforçada pelo padrão ERC‑165, que introduz um mecanismo padronizado de detecção de interfaces via supportsInterface(bytes4), permitindo que contratos verifiquem, on‑chain, se outros contratos implementam interfaces específicas antes de interagir com eles. Na prática, a comunicação cross‑contract em Ethereum é implementada via opcodes de chamada (CALL, DELEGATECALL, STATICCALL), que permitem compor funcionalidades entre contratos, mas introduzem riscos de segurança como reentrância cross‑contract, em que contratos mal projetados são reentrados por outros antes de atualizar seu estado, abrindo espaço para exploração. Estudos e guias de segurança catalogam padrões de ataques de reentrância, incluindo reentrância entre funções e entre contratos, e recomendam padrões como checks‑effects‑interactions, uso de mutexes e desenho criterioso de callbacks em tokens com hooks (como ERC‑777) para mitigar esses riscos. Conclui‑se que a interoperabilidade de contratos na Web3 depende tanto de padrões de interface bem definidos (ERCs, ERC‑165) quanto de práticas seguras de comunicação cross‑contract, com impacto direto na liquidez, composabilidade DeFi e governança de protocolos.<br>
Este artigo apresenta um esboço estruturado sobre “Análise de Custo de Deploy em Diferentes EVMs.”. O objetivo é analisar os fundamentos técnicos e econômicos do custo de implantação de contratos inteligentes em Ethereum L1, diversas Layer‑2 (rollups) e outras chains EVM‑compatíveis, discutindo implicações para o ecossistema Web3 e tendências de mercado. A metodologia baseia‑se em revisão bibliográfica e análise de casos práticos, com foco na decomposição do custo de deploy em componentes de gas (execução, armazenamento de código, dados de transação) e em como upgrades recentes – como Cancun/Deneb e a introdução de blobs de dados – alteram a estrutura de custos, especialmente para rollups que publicam dados em L1. Estudos mostram que, enquanto o gas é uma unidade abstrata consistente, o custo econômico por byte de código e por transação varia significativamente entre L1 (onde picos históricos chegaram a dezenas de dólares por transação) e L2s, onde taxas médias frequentemente ficam abaixo de centavos, especialmente após a redução em até 94% do custo por byte de dados com blobs. Ao mesmo tempo, análises de mercado indicam que L2 fees são estruturalmente compostas por uma parcela L1 (custo de dados e liquidação) mais uma parcela L2 (execução local), de modo que mudanças na economia de gas da L1 impactam indiretamente o custo de deploy e operação nas L2s. Conclui‑se que decisões de arquitetura e de escolha de EVM para deploy devem considerar não apenas o custo imediato de gas, mas também a herança de segurança, a volatilidade das taxas e a dependência em upgrades de protocolo que alteram a economia de dados e execução.<br>
Este artigo analisa o design de contratos inteligentes orientados a eventos como estratégia para maximizar a eficiência de indexação off‑chain em protocolos Web3. Em plataformas como Ethereum, eventos são registrados como logs com topics indexados, permitindo que indexadores filtrem rapidamente transações relevantes sem precisar reexecutar o estado completo do contrato, o que torna a indexação por eventos uma abordagem muito mais eficiente do que consultas frequentes ao estado on‑chain via eth_call. Frameworks de indexação como The Graph organizam esses eventos em subgraphs, definidos por manifests que especificam quais contratos e eventos acompanhar, e por esquemas que mapeiam logs para entidades armazenadas em bancos de dados otimizados para consulta via GraphQL. Boas práticas de desenvolvimento orientado a eventos recomendam projetar contratos de modo que toda transição de estado relevante para a aplicação gere um evento completo (com dados suficientes para reconstruir o contexto off‑chain), reduzindo a necessidade de chamadas adicionais à cadeia, o que melhora desempenho de indexing e diminui custos de infraestrutura. Técnicas como uso de entidades imutáveis em subgraphs, cache inteligente de eth_call e cálculo de derivadas off‑chain a partir de eventos (em vez de leitura repetida de storage) podem acelerar indexação e consultas em ordens de grandeza, especialmente em cenários de histórico extenso e baixa densidade de eventos relevantes. Conclui‑se que contratos orientados a eventos, aliados a arquiteturas de indexação especializadas, são fundamentais para tornar dados on‑chain consultáveis em escala, suportando experiências de usuário ricas e composabilidade DeFi com menor custo operacional.<br>
Bobba Pavan Santosh, Bommeneni Pavan Madhav, Dr. J. R. Jayavelu, Dr. P. Dhivya
Cryptocurrencies have found their way into contemporary financial systems as a significant component of modern-day financial systems because of their decentralized nature, their ease of adoption and uptake. Ether is considered to be one of the most actively traded currencies and its value tends to be highly volatile. It is not easy to forecast the market price trend of Ethereum due to the influence that technical trends, investor behavior, and external factors have over the market. In this project, the researcher will use machine learning to assess the future price direction of Ethereum the following day through the use of Python. The past trends of prices are analyzed and augmented with various technical indicators in order to reflect the market trends and momentum. The best potential machine learning model was selected after training and evaluating many models using Logistic Regression. The results demonstrate that machine learning may be used to provide rational insights into the price movement of Ethereum and to aid in decision-making using these insights.