Mohammadhossein Homaei, Victor Gonzalez Morales, Oscar Mogollon Gutierrez, Ruben Molano Gomez · 5 authors
Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaWAN-based data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-Term Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and scalable for up to 1,000 smart meters. This work demonstrates a practical and secure architecture for decentralized water infrastructure in under-connected rural environments.
Aviation plays a crucial role in global transportation, providing an essential service for passengers and cargo and contributing significantly to economic growth and international connectivity. However, the increasing number of aircraft accidents and high repair costs emphasize the need for robust maintenance systems. With mechanical failures accounting for 36% of aviation accidents, a secure and efficient preventive maintenance system is essential to enhance safety and reduce downtime. Traditional methods suffer from delayed inspections, fragmented documentation, and inefficiencies in spare parts management, leading to increased operational costs and downtime. This paper proposes a blockchain-based solution integrating Ethereum smart contracts, Time Oracles, and InterPlanetary File System (IPFS) to automate maintenance scheduling, ensure data integrity, and enhance traceability. Time Oracles trigger maintenance at predefined intervals, ensuring timely inspections, while IPFS securely stores maintenance records, preventing data loss or tampering. Smart contracts automate maintenance workflows and spare parts procurement, improving efficiency and accountability. This paper details the system architecture, underlying algorithms, and rigorous testing and validation processes. Furthermore, a cost and security analysis evaluates the solution’s effectiveness, demonstrating its potential to significantly enhance safety, reduce operational costs, and improve transparency across aviation maintenance protocols.
This study examines the role of crypto funds (CFs) in enhancing the valuation and performance of decentralized digital platforms (DDPs) by mitigating coordination frictions and information asymmetries. Drawing on panel data from 1,200 Ethereum-based projects and event-study evidence around CF investment disclosures, we find that CF-backed DDPs achieve significantly higher token valuations in the primary market, experience positive cumulative abnormal returns (CARs) around investment announcements, and outperform non-CF-backed peers’ post-issuance. The impact of CFs is stronger when they hold central positions in investor networks and when token ownership is more decentralized. Robustness checks using alternative dependent variables, subsample analyses, and interaction terms confirm the validity of the findings. These results highlight the importance of institutional capital not only in financing but also in signaling quality and enhancing governance in decentralized ecosystems. Policy implications include the need for standard CF disclosure practices, token distribution guidelines, and improved audit standards for smart contracts. The findings contribute to emerging debates on institutional legitimacy, valuation dynamics, and governance in the digital asset economy.
Abstract While Ethereum smart contracts provide users with transfer and transaction services, vulnerabilities in smart contracts are constantly damaging users’ property and user experience. At present, many detection methods for smart contract vulnerabilities have been proposed, but these methods have not fully analyzed the information of multiple modalities of smart contracts, and their effectiveness in detecting long smart contracts is not ideal. We propose a lightweight Ethereum smart contract vulnerability detection method based on bimodal and hierarchical attention to address this issue. This method can combine the source code and opcode of smart contracts for analysis, and use a hierarchical attention network composed of bidirectional GRU and attention mechanism for vulnerability feature extraction. The experimental results show that in the task of detecting vulnerabilities in long smart contracts, this method has better detection capabilities for four types of vulnerabilities: Denial of Service, Reentrancy, Arithmetic, and Timestamp Dependency, compared to the most advanced deep learning smart contract vulnerability detection methods currently available.
Данная работа посвящена обзору автоматизированных инструментов безопасной разработки смарт-контрактов Ethereum. Рассматриваются актуальные уязвимости, характерные для смарт-контрактов, такие как уязвимость повторного входа, недостаточный контроль доступа, манипуляции с оракулом цены и другие. К каждой уязвимости приведена иллюстрация с уязвимым кодом. Далее рассмотрены разные типы существующих автоматизированных инструментов безопасной разработки смарт-контрактов: статический анализатор, линтер, символьный исполнитель, фаззинг и подходы на основе машинного обучения. Для каждого типа инструмента рассмотрено соответствующее реальное решение, которое является одним из лучших в своей категории. Это такие open-source решения как статический анализатор Slither, линтер Solhint, символьный исполнитель Mythril и фреймворк Foundry, который содержит в себе возможность фаззинга. Также рассмотрена текущая эффективность современных решений, которая показывает, что текущие угрозы плохо детектируется существующими инструментами. Исходя из этого предложены направления для дальнейшего развития новых инструментов безопасной разработки смарт-контрактов. Полученные результаты могут быть использованы для более глубокого понимания вопросов безопасности смарт-контрактов, а также для повышения безопасности децентрализованных приложений и развития методов автоматизированного аудита смарт-контрактов. This paper provides an overview of automated tools for secure development of Ethereum smart contracts. The article discusses current vulnerabilities specific to smart contracts, such as re-entrancy vulnerability, insufficient access control, price oracle manipulation, and others. Each vulnerability is accompanied by an illustration of the vulnerable code. Next, we discuss different types of existing automated tools for secure smart contract development: static analyzer, linter, symbolic executor, fuzzing, and machine learning-based approaches. For each type of tool, a corresponding real solution is considered, which is one of the best in its category. These are open-source solutions such as the Slither static analyzer, the Solhint linter, the Mythril symbolic executor, and the Foundry framework, which includes fuzzing capabilities. The current effectiveness of modern solutions is also considered, which shows that current threats are poorly detected by existing tools. Based on this, directions for the further development of new tools for the secure development of smart contracts are proposed. The obtained results can be used to gain a deeper understanding of smart contract security issues, as well as to enhance the security of decentralized applications and develop automated smart contract auditing methods.
Background: Solidity is the primary programming language used for developing smart contracts on Ethereum, representing a new generation of programming languages developed entirely in open environments. Objective: This longitudinal case study examines contribution patterns and emotional dynamics within the Solidity GitHub repository over a ten-year period (2014-2024). Method: We developed a contribution index combining metrics from developer activities (commits, pull requests, comments, and temporal engagement) and applied emotion detection to study communication patterns in a decade-long dataset of developer interactions. Results: The top 1 % of contributors are responsible for around 85 % of project contributions, yet the project exhibits dual paths to prominence: early contributors established technical foundations through code, while later contributors achieved influence through reviews and discussions. Emotional patterns show transitions from initial curiosity and confusion to eventual approval and gratitude. Conclusion: The project's recognition of diverse contribution types and evolving emotional dynamics enables sustainable growth despite concentrated contributions, demonstrating how open-source languages can evolve while maintaining both technical rigor and community engagement.
Blockchain technology establishes trust among participants through technical means. However, some malicious nodes may compromise this trust through short-range reorganization attacks for their interest. This paper develops an agent-based model to systematically analyze Proof-of-Stake short-range reorganization attacks, where three types of agents interact through distributed consensus mechanisms with ex-ante, fine-grained, and ex-post reorganization attack strategies. Through rigorous simulation of agent decision-making dynamics, we identify that: (1) Compared with ex-ante reorganization, the ratio of malicious nodes required for ex-post reorganization is much larger. (2) Increasing the node number increases the difficulty of ex-ante and ex-post reorganization. (3) The number of nodes affects ex-post reorganization attacks more significantly than ex-ante attacks. (4) Fine-grained reorganization significantly reduces attack difficulty
Abstract - In the rapidly advancing digital era, the requirement for secure, transparent, and reliable data-sharing mechanisms has become increasingly critical across various sectors. Traditional centralized data-sharing models suffer from inherent limitations, including vulnerability to data breaches, unauthorized access, single points of failure, and insufficient transparency in data access and audit trails. These challenges compromise not only the confidentiality and integrity of sensitive data but also erode stakeholder trust in digital systems. To overcome these issues, this paper presents BlockShare, a blockchain-powered, decentralized framework designed to facilitate secure, tamper-proof, and efficient data exchange. BlockShare leverages the foundational principles of blockchain technology—namely decentralization, immutability, and transparency—to enhance the robustness and reliability of data-sharing architectures. The proposed system eliminates central authority dependence by distributing data storage and control across a decentralized ledger, thereby minimizing potential attack vectors and ensuring continuous data availability. To regulate data access and maintain policy enforcement, smart contracts written in Solidity are integrated within the system. These smart contracts autonomously manage permissions and user authentication, ensuring that only verified and authorized parties can access specific datasets, with every action recorded immutably on the blockchain. Moreover, data confidentiality is preserved through the implementation of AES-256 encryption, a widely recognized standard for high-security data protection. Prior to storage, all data is encrypted and then uploaded to a decentralized file system, specifically the InterPlanetary File System (IPFS), which provides enhanced fault tolerance, redundancy, and distributed access. This dual-layered approach—combining blockchain for governance and IPFS for storage—ensures that data remains protected both in transit and at rest. By integrating smart contract-based automation, robust encryption protocols, and distributed storage solutions, BlockShare delivers a scalable and resilient infrastructure for data exchange. The system is particularly applicable in domains requiring stringent data protection and transparency, such as healthcare, finance, legal, and government sectors. Through this innovative approach, BlockShare aims to redefine trust in digital interactions and lay the groundwork for the next generation of secure data-sharing ecosystems. Keywords - Blockchain, Data Sharing, Decentralized Storage, Smart Contracts, Encryption, IPFS, Ethereum, Security, Data Privacy, AES-256, Web3, Authentication, DApp, Decentralization, Access Control.
Hassen Louati, Ali Louati, Elham Kariri, Abdulla Almekhlafi
Blockchain technology has transformed modern digital ecosystems by enabling secure, transparent, and automated transactions through smart contracts. However, the increasing complexity of these contracts introduces significant challenges, including high computational costs, scalability limitations, and difficulties in detecting anomalous behavior. In this study, we propose an AI-based optimization framework that enhances the efficiency and security of blockchain smart contracts. The framework integrates Neural Architecture Search (NAS) to automatically design optimal Convolutional Neural Network (CNN) architectures tailored to blockchain data, enabling effective anomaly detection. To address the challenge of limited labeled data, transfer learning is employed to adapt pre-trained CNN models to smart contract patterns, improving model generalization and reducing training time. Furthermore, Model Compression techniques, including filter pruning and quantization, are applied to minimize the computational load, making the framework suitable for deployment in resource-constrained blockchain environments. Experimental results on Ethereum transaction datasets demonstrate that the proposed method achieves significant improvements in anomaly detection accuracy and computational efficiency compared to conventional approaches, offering a practical and scalable solution for smart contract monitoring and optimization.
This study examines the impact of the coronavirus disease 2019 (COVID-19) pandemic on market efficiency by analyzing three time series -- price returns, absolute returns, and volatility increments -- in stock (Deutscher Aktienindex, Nikkei 225, Shanghai Stock Exchange (SSE), and Volatility Index) and cryptocurrency (Bitcoin and Ethereum) markets. The effect is found to vary by asset class and market. In the stock market, while the pandemic did not influence the Hurst exponent of volatility increments, it affected that of returns and absolute returns (except in the SSE, where returns remained unaffected). In the cryptocurrency market, the pandemic did not alter the Hurst exponent for any time series but influenced the strength of multifractality in returns and absolute returns. Some Hurst exponent time series exhibited a gradual decline over time, complicating the assessment of pandemic-related effects. Consequently, segmented analyses by pandemic periods may erroneously suggest an impact, warranting caution in period-based studies.
Chiara Oldani, Giovanni S. F. Bruno, Marcello Signorelli
This paper investigates the existence of bubbles in the daily prices of the most popular cryptocurrencies, Bitcoin (BTC), Ether (ETH), and Ripple (XRP), employing the recursive methods of Phillips et al. (2015) and Phillips et al. (2011) for testing and date-stamping episodes of exuberant behaviour over a period spanning seven years (2018–2024), including the COVID-19 pandemic crisis (2020–2021). The critical values of the tests are computed through the composite wild bootstrap technique by Phillips and Shi (2020) to make them robust to time-varying unconditional heteroscedasticity and the multiplicity issue in recursive tests. Results indicate that the prices of the most popular cryptocurrencies traded on decentralized ledgers, BTC and ETH, exhibited multiple episodes of exuberant behaviour, unambiguously for BTC and depending on the tests for ETH. Bubbles detected in the prices of BTC were due to the halving of the crypto, to market exuberance and to the pandemic crisis; bubbles detected on ETH prices were due to the launch of NFTs on the Ethereum blockchain, and to the change in investors’ expectations (from exuberant to pessimistic); the change in the stance of monetary policy burst the bubbles of BTC and ETH prices in 2024. No test supports the exuberance of XRP that is traded on a centralized ledger; weekly data confirm the absence of multiple bubbles. By looking at the presence of bubbles in these different digital ecosystems, we also consider how the technological differences can impact, possibly asymmetrically, bubbles' formation.
This study introduces a novel hybrid stochastic modeling framework for simulating Ethereum price dynamics by integrating Poisson and Gaussian processes. The model captures both abrupt price jumps, modeled using a Poisson process, and continuous price variations, represented by a Gaussian process. Our analysis reveals that significant price fluctuations occur approximately every 4.33 days, with an average daily return of 0.0041 and an annualized volatility of 0.8631, underscoring the extreme volatility inherent in Ethereum’s market behavior. By combining these processes, the model effectively encapsulates the intrinsic price patterns of Ethereum, including persistent oscillations and sudden surges. Simulations of future price trajectories demonstrate the model’s efficacy in replicating real world Ethereum price dynamics, offering valuable insights for traders and analysts in devising risk management strategies and making informed decisions in highly volatile cryptocurrency markets. The findings highlight the importance of hybrid models in addressing the unique challenges of modeling Ethereum’s price behavior.
Abstract- The evolution of blockchain and Web3 technologies has paved the way for decentralized application platforms that enable transparent, tamper-proof transactions without relying on centralized servers. However, existing solutions such as Gitcoin and Giveth are either too complex or unsuitable for modular deployment in educational and lightweight environments. This paper proposes MetaSuite, a role-based, blockchain-driven Software-as-a-Service (SaaS) platform that enables users to create, transfer, and donate tokens while allowing an administrator to securely withdraw funds. Built entirely on the Ethereum blockchain using Solidity smart contracts, MetaSuite operates without a backend and integrates wallet-based authentication via MetaMask and Ethers.js. The platform ensures transparent fund management through on-chain event logging and role-based access controls. Performance evaluations on the Ethereum HoleskyTestnet demonstrate the system’s reliability, gas-efficiency, and real-time responsiveness. By eliminating backend dependencies and emphasizing traceability, MetaSuite serves as a minimalistic yet scalable Web3 solution suitable for academic, experimental, and small-scale real-world deployments. Keywords—Blockchain, Web3, Smart Contracts, Ethereum, MetaMask, Tokenization, SaaS, Ethers.js, Decentralized Applications.
Sarthak Nimje, Rushab Taneja, Om Baviskar, Rachana Patil
Educational institutions face significant challenges with event attendance verification, including manual document validation, fraud risks, and delayed approval processes. This study introduces ElizaEdu, a novel decentralized AI agent system utilizing Ethereum blockchain and ElizaOS to automate and secure attendance verification workflows for academic events. The proposed system integrates autonomous AI agents to handle document validation, approval processes, and ERP integration, while utilizing blockchain technology for immutable record-keeping. The system employs four specialized agents: RequestBot for initial verification, VerifyBot for teacher validation, ApproveBot for department head confirmation, and ERPBot for automatic attendance updates. Through a 3-month pilot implementation with 120 students and 15 faculty members, ElizaEdu demonstrated an 85% reduction in verification time, complete elimination of document fraud, and 84% decrease in administrative workload. The system achieves 97.3% accuracy in document validation and 100% data integrity through blockchain verification. This study presents the architecture, implementation details, and evaluation results, demonstrating ElizaEdu’s effectiveness in transforming attendance management in educational institutions.
This study introduces the Multilayer Token Network (MLTN), a mathematical framework for analyzing Ethereum token transfers while capturing inter-token transformations crucial to Decentralized Finance (DeFi). Focusing on prominent fund accounts, we propose the PageRank-CheiRank Trade Balance (PCTB), an econometric measure inspired by balance sheet principles to quantify trade behavior over time. Applying MLTN to 2018–2024 transaction data, we reveal Alameda Research’s evolving trade strategies, fund interdependencies, and token-specific accumulation and distribution patterns, offering new insights into on-chain financial activities.
This paper explores the vulnerability of machine learning models to simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance metrics. Our findings, highlighting how prone those techniques are to simple attacks, are alarming, and the inconsistency in the attacks' effect on different algorithms promises ways for attack mitigation. We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness and show their effectiveness.
The increasing demand for scalable blockchain has driven research into parallel execution models for smart contracts. Crystality is a novel smart contract programming language designed for parallel Ethereum Virtual Machines (EVMs), enabling fine-grained concurrency through Programmable Contract Scopes and Asynchronous Functional Relay. This paper presents the first formal structural operational semantics for Crystality, providing a rigorous framework to reason about its execution. We mechanize the syntax and semantics of Crystality in the theorem-proving assistant Coq, enabling formal verification of correctness properties. As a case study, we verify a simplified token transfer function, demonstrating the applicability of our semantics in ensuring smart contract correctness. Our work lays the foundation for formally verified parallel smart contracts, contributing to the security and scalability of blockchain systems.
Phishing detection on Ethereum has increasingly leveraged advanced machine learning techniques to identify fraudulent transactions. However, limited attention has been given to understanding the effectiveness of feature selection strategies and the role of graph-based models in enhancing detection accuracy. In this paper, we systematically examine these issues by analyzing and contrasting explicit transactional features and implicit graph-based features, both experimentally and analytically. We explore how different feature sets impact the performance of phishing detection models, particularly in the context of Ethereum's transactional network. Additionally, we address key challenges such as class imbalance and dataset composition and their influence on the robustness and precision of detection methods. Our findings demonstrate the advantages and limitations of each feature type, while also providing a clearer understanding of how feature affect model resilience and generalization in adversarial environments.
Blockchain technology anchors decentralized systems by enabling secure, transparent data management across distributed networks, powering a wide range of applications from foundational cryptocurrencies like Bitcoin to the recently emerging tokenization of real-world assets (RWAs), such as property and commodities. However, its scalability and environmental sustainability depend on consensus mechanisms that maintain network integrity without imposing excessive computational or energy burdens. Proof of Work (PoW), a prevalent mechanism seen in Bitcoin, relies on miners performing energy-intensive cryptographic computations to ensure robust security, yet driving significant resource demands. In contrast, Proof of Stake (PoS) selects validators based on the amount of cryptocurrency they stake, as exemplified by Ethereum post-Merge, providing a markedly more energy-efficient option than PoW. While PoW excels in delivering decentralized security through computational effort, it does so at the cost of high energy consumption; PoS, meanwhile, enhances participation accessibility and reduces resource use but introduces potential centralization risks due to wealth concentration among larger stakers. The rapid rise of artificial intelligence (AI) models, with their substantial energy consumption, underscores a growing strain on computational resources. Hence, it inspires us to propose a new consensus mechanism, namely, Proof of Useful Intelligence (PoUI). PoUI is a hybrid consensus mechanism where workers execute AI-based tasks, such as natural language processing or image analysis, to earn coins, which are then staked to secure the network, seamlessly integrating security with real-world utility. This system leverages decentralized functional nodes, i.e., job posters who submit tasks, market coordinators who oversee jobs distribution, workers who perform computations, and validators who ensure accuracy, all orchestrated by smart contracts for task execution and reward allocation. Our energy analysis benchmarks PoW at$\mathbf{3. 5 1 ~ k W h} / \mathbf{m i n e r}$, PoS at$\mathbf{0. 1 ~ k W h} /$validator, and PoUI at$\mathbf{0. 6} \text{kWh} /$worker - yielding a 97 % energy reduction from PoW while adding value. Simulations further demonstrate that PoUI's dynamic reward adjustment regulates worker participation in the job market, which subsequently encourages a sufficient number of validators in the network.
The increasing interconnectivity of devices on the Internet of Things (IoT) introduces significant security challenges, particularly around authentication and data management. Traditional centralized approaches are not sufficient to address these risks, requiring more robust and decentralized solutions. This paper presents a decentralized authentication protocol leveraging blockchain technology and the IPFS data management framework to provide secure and real-time communication between IoT devices. Using the Ethereum blockchain, smart contracts, elliptic curve cryptography, and ASCON encryption, the proposed protocol ensures the confidentiality, integrity, and availability of sensitive IoT data. The mutual authentication process involves the use of asymmetric key pairs, public key registration on the blockchain, and the Diffie–Hellman key exchange algorithm to establish a shared secret that, combined with a unique identifier, enables secure device verification. Additionally, IPFS is used for secure data storage, with the content identifier (CID) encrypted using ASCON and integrated into the blockchain for traceability and authentication. This integrated approach addresses current IoT security challenges and provides a solid foundation for future applications in decentralized IoT environments.
Abstract-Non-Fungible Tokens (NFTs) have emerged as a transformative force in the digital economy, offering creators a novel and decentralized way to monetize their work. Powered by blockchain technology, NFTs ensure transparency, traceability, and ownership of digital assets—ranging from art and music to virtual real estate. By eliminating the dependency on traditional intermediaries such as galleries and auction houses, NFTs empower artists to connect directly with global audiences through dedicated marketplaces. This project explores the core concepts of NFTs, their evolution, and their underlying architecture, including blockchain, smart contracts, token standards, and NFT marketplaces. The work process involves the detailed study of how NFTs are minted by uploading digital assets onto a blockchain-supported marketplace, registered through smart contracts, and then traded securely between users. The project also outlines key components such as tokenization, metadata storage, and transaction validation through cryptographic proofs. Through a comprehensive timeline, technical breakdown, and real-world use cases, the paper emphasizes the growing significance of NFTs in redefining digital ownership, while also evaluating their future impact on the Indian market and beyond. Keywords – NFT, Token, Blockchain, Market, Asset, Ethereum, Fungible.
We introduce the DeTerministic Virtual Machine (DTVM) Stack, a next-generation smart contract execution framework designed to address critical performance, determinism, and ecosystem compatibility challenges in blockchain networks. Building upon WebAssembly (Wasm) while maintaining full Ethereum Virtual Machine (EVM) ABI compatibility, DTVM introduces a Deterministic Middle Intermediate Representation (dMIR) and a hybrid lazy-JIT compilation engine to balance compilation speed and execution efficiency. DTVM further accommodates diverse instruction set architectures (e.g., EVM, RISC-V) through modular adaptation layers. This enables seamless integration with DTVM's hybrid lazy-JIT compilation engine, which dynamically optimizes performance while preserving deterministic execution guarantees across heterogeneous environments. The key contributions including: 1). The framework achieves up to 2$\times$ acceleration over evmone in dominant Ethereum contract (e.g. ERC20/721/1155) execution and reduces fibonacci computation latency by 11.8$\sim$40.5% compared to Wasm based VMs. 2). A novel trampoline hot-switch mechanism enables sub-millisecond (0.95ms) post-deployment invocation times, outperforming up to about 23$\times$ in compilation and invocation efficiency. 3). It supports multi-language development (Solidity, C++, Rust, Java, Go, and AssemblyScript) through unified bytecode conversion while maintaining EVM ABI compatibility for seamless invocation. It reduces machine code object sizes by 30.0$\sim$72.6%, coupled with a minimized Trusted Computing Base. 4). It offers SmartCogent, an AI-driven full-stack development experience, leveraging fine-tuned LLMs and retrieval-augmented generation to automate tasks across the smart contract lifecycle: development, debugging, security auditing, and deployment. DTVM Stack has been open-sourced (https://github.com/DTVMStack).
Esam Mahdi, Carlos Martín-Barreiro, Xavier Cabezas
In this article, we introduce a novel deep learning hybrid model that integrates attention Transformer and Gated Recurrent Unit (GRU) architectures to improve the accuracy of cryptocurrency price predictions. By combining the Transformer's strength in capturing long-range patterns with the GRU's ability to model short-term and sequential trends, the hybrid model provides a well-rounded approach to time series forecasting. We apply the model to predict the daily closing prices of Bitcoin and Ethereum based on historical data that include past prices, trading volumes, and the Fear and Greed index. We evaluate the performance of our proposed model by comparing it with four other machine learning models: two are non-sequential feedforward models: Radial Basis Function Network (RBFN) and General Regression Neural Network (GRNN), and two are bidirectional sequential memory-based models: Bidirectional Long-Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The performance of the model is assessed using several metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), along with statistical validation through the nonparametric Friedman test followed by a post hoc Wilcoxon signed rank test. The results demonstrate that our hybrid model consistently achieves superior accuracy, highlighting its effectiveness for financial prediction tasks. These findings provide valuable insights for improving real-time decision making in cryptocurrency markets and support the growing use of hybrid deep learning models in financial analytics.