Patrick Spiesberger, Jan Droll, Hannes Hartenstein
In the Ethereum system, the exclusion of specific transactions is currently feasible with minimal effort due to a power imbalance among entities. This censorship opportunity threatens the dependability of time-sensitive services deployable on Ethereum. In this paper, we look at this threat from an access control perspective and attribute it to a lack of accountability for censorship, a lack of policy definition and enforcement, as well as to the lack of disincentivization of policy violation. We propose an approach to enforceable policies in Ethereum. Furthermore, we demonstrate how a specific policy can address the shortcomings of existing censorship mitigation techniques, particularly Inclusion Lists. Under the assumption that block assemblers are unwilling to incur significant financial penalties as well as that the local view on the network messages is sufficiently consistent, the proposed approach guarantees the inclusion of a transaction in a block within 27 seconds in a non-saturated network. The empirical validation of sufficiently consistent views on outstanding transactions is currently in progress.
Ala’a Shamaseen, Mohammad Qatawneh, Basima Elshqeirat
Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in the community. With the decentralization and digitization of energy transactions in smart grids, security, integrity, and fraud prevention concerns have increased. The main problem addressed in this study is the lack of a secure, tamper-resistant, and decentralized mechanism to facilitate direct consumer-to-prosumer energy transactions. Thus, this is a major challenge in the smart grid. In the blockchain, current consensus algorithms may limit the scalability of smart grids, especially when depending on popular algorithms such as Proof of Work, due to their high energy consumption, which is incompatible with the characteristics of the smart grid. Meanwhile, Proof of Stake algorithms rely on energy or cryptocurrency stake ownership, which may make the smart grid environment in blockchain technology vulnerable to control by the many owning nodes, which is incompatible with the purpose and objective of this study. This study addresses these issues by proposing and implementing a hybrid framework that combines the features of private and public blockchains across three integrated layers: user interface, application, and blockchain. A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. This makes it more suitable for smart grid environments. The entire framework was developed without relying on existing decentralized platforms such as Ethereum. The system was evaluated through comprehensive experiments on performance and security. Performance results show a throughput of up to 60.86 transactions per second and an average latency of 3.40 s under a load of 10,000 transactions. Security validation confirmed resistance against digital signature forgery, invalid smart contracts, race conditions, and double-spending attacks. Despite the promising performance, several limitations remain. The current system was developed and tested on a single machine as a simulation-based study using transaction logs without integration of real smart meters or actual energy tokenization in real-time scenarios. In future work, we will focus on integrating real-time smart meters and implementing full energy tokenization to achieve a complete and autonomous smart grid platform. Overall, the proposed system significantly enhances data integrity, trust, and resistance to counterfeiting in smart grids.
While Ethereum's discovery protocols (Discv4/ Discv5) incorporate robust cryptographic designs to protect user privacy, real-world deployment reveals critical vulnerabilities when users deviate from security guidelines. In this paper, we design a system called EGNInfoLeaker. Our study is the first work that uncovers widespread public key reuse across Ethereum's peer-to-peer networks - a practice that fundamentally undermines the protocol's privacy guarantees. Through systematic analysis of 300 real-world network snapshots, we identify 83 users controlling 483 service nodes via public key reuse, enabling precise de-anonymization through IP correlation. Using evidence collected by EGNInfoLeaker, our Graph-Based Identity Association Algorithm links users to network entities and generates comprehensive user profiles. For User27, it exposes the public key, IP, network ID, location (country/region/city), and ISP/ORG details. The EGNInfoLeaker system demonstrates how such cryptographic misuse transforms theoretical anonymity into practical identity leakage, exposing users to surveillance and targeted attacks. These findings establish that protocol security depends not only on sound design but also on strict user compliance. Going forward, our detection framework provides a foundation for enhancing real-world privacy preservation in decentralized networks.
S Almeida, Washington Luiz de L. Praxedes, Leone B. Hollanda, Cidcley Teixeira de Souza · 5 authors
The purpose of this work is to analyze the performance of two different blockchain networks in the context of healthcare applications. Blockchain networks are known for their decentralized structure, resiliency, and tamperproofness. Hyperledger Fabric and Ethereum were used to benchmark and compare its performance in two scenarios for a simple blockchain smart contract that stores patient image data. The results showed a slight advantage for Ethereum in increased load tests and it also obtained a lower degree of variation during these tests.
Yusuf Oli Rahat, Md Kamrul Islam, Shah Farhan Rabbani
Blockchain-based financial systems are increasingly evaluated not only as speculative infrastructures but as operational payment, settlement, and record-keeping networks that can be benchmarked against incumbent financial rails. This paper develops a quantitative, data-driven framework for assessing blockchain-based financial systems across four dimensions that matter in global markets: transaction efficiency, transparency, cost optimization, and overall performance resilience. Drawing on evidence from public blockchain networks, payment and remittance statistics, policy experiments, and institutional distributed-ledger pilots, the study synthesizes academic literature with world data from the World Bank, the Federal Reserve Bank of New York, the Bank for International Settlements, Visa, Bitcoin, Ethereum, and Solana documentation and analytics. The paper proposes a metrics architecture that combines latency, throughput, fee burden, settlement certainty, auditability, availability, governance quality, and interoperability into a unified comparative scorecard. It then applies the framework to three categories of blockchain-based finance: public permissionless chains, permissioned institutional distributed ledgers, and hybrid tokenized payment systems. The evidence suggests that blockchain systems create measurable gains in traceability, programmability, and atomic settlement, especially in cross-border and multi-party workflows where reconciliation frictions are costly. However, these gains are uneven. Public chains often face volatility in fees, congestion risk, and governance externalities, while permissioned systems improve control and compliance at the expense of openness and composability. The paper argues that the relevant policy and managerial question is therefore not whether blockchain is universally superior, but under which transaction environments it dominates legacy systems on speed, transparency, cost, and operational risk. The study concludes with a research agenda for standardized blockchain performance metrics, institution-grade benchmarking, and explainable analytics for digital financial infrastructure.
Abdullah Alsaleh, Ghanshyam G. Tejani, Shailendra Mishra, Sunil Kumar Sharma · 5 authors
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, complying with regulations and the different types of data used by various institutions. We introduce the anisotropic-residual capsule hybrid Gorilla Badger optimized network (Aniso-ResCapHGBO-Net) framework for detecting brain tumors in a privacy-preserving, decentralized system used by many healthcare institutions. ResNet-50 and capsule networks are incorporated to achieve better feature extraction and maintain the structure of images' spatial data. To get the best results, the hybrid Gorilla Badger optimization algorithm (HGBOA) is applied for selecting the key features. Preprocessing techniques include anisotropic diffusion filtering, morphological operations, and mutual information-based image registration. Updates to the model are made secure and tamper-evident on the Ethereum network with its private blockchain and SHA-256 hashing scheme. The project is built using Python, TensorFlow and PyTorch. The model displays 99.07% accuracy, 98.54% precision and 99.82% sensitivity on assessments from benchmark CT imaging of brain tumors. This approach also helps to reduce the number of cases where no disease is found when there is one and vice versa. The framework ensures that patients' data is protected and does not decrease the accuracy of brain tumor detection.
Cryptocurrencies, particularly Bitcoin and Ethereum, have reshaped global conceptions of money, ownership, and exchange. With the rise of blockchain technology—distributed, immutable digital ledgers—applications have expanded into areas such as smart contracts, asset tokenization, and non-fungible tokens (NFTs). These shifts present pressing challenges to traditional Islamic legal structures, which have historically grounded financial rulings in well-defined principles such as prohibition of riba (intere... In this context, Islamic jurisprudence must critically engage with these technologies—not by rejection or blind acceptance—but through a measured analysis rooted in legal maxims and maqāṣid al-sharīʿah (the higher objectives of Islamic law). This study explores the Islamic legal perspective on cryptocurrencies and blockchain technologies, providing an analytical review of scholarly opinions, regulatory frameworks, and economic realities across Muslim and global contexts.
Patients and healthcare authorities frequently lack confidence in one another when it comes to the security of their medical records in healthcare settings. Particularly when it comes to patient data management, hospitals are infamous for having inadequate security and have long been the target of cyberattacks. Using blockchain technology to store medical records has drawbacks, including an excessive dependence on centralised cloud servers for key storage, privacy concerns and the potential for attackers to deduce personal information about patients based on their blockchain activity. A system where patients have autonomy over their medical records and who can view them is a promising scenario. This article provides a framework for indexing and securing a user’s medical records, with emphasis placed on the healthcare setting using an Ethereum blockchain. The records are secured using biometric authentication and the patient’s Personal Identifiable Information (PII). The patient can grant and revoke access to their records to individual healthcare authorities, and the Interplanetary Name System (IPNS) is used for off-chain record storage. The framework is modular and can be adapted for use in other environments, such as proof of ownership of tickets, and storing travel documents for verification by border control. A smart contract is used to store the hashes of the patient’s iris scans on an Ethereum Virtual Machine (EVM) compatible blockchain. Privacy-preserving identifiers are used to anonymise the patient and where their records are stored on the blockchain. Our approach is to the best of our knowledge the only one that simultaneously offers encryption, anonymity, unlinkability and efficient off-chain storage. Additionally, our approach is the only approach we are aware of that provides record revocability.
Custom tokens are fundamental in decentralized applications (dApps) operating on Ethereum and other Blockchain platforms. Ethereum, in particular, relies on the ERC-20 standard as a widely accepted token interface, facilitating seamless integration with numerous pre-existing dApps, user interface platforms, and popular web applications like exchange services. A notable security challenge within the ERC-20 framework is the “lost token problem”. This problem arises because users occasionally send tokens to the wrong addresses, and it has caused more than $27 million in damage. In this paper, we evaluate three existing solutions to this issue. Through the utilization of formal modeling, property specification, and the TLC model checker. Most importantly, we propose a novel double-layer solution to remedy the ERC-20 vulnerability. Our formal verification and experimental results indicate our approach encompasses the protection of the already deployed smart contracts, which is a critical aspect that has never been addressed in the existing mitigation techniques.
Open access
Security and Verification in Computing
Radiation Effects in Electronics
Physical Unclonable Functions (PUFs) and Hardware Security
Shahida Hafeezan Qureshi, Saif Ur Rehman Malik, Junaid Haseeb, Syed Atif Moqurrab · 6 authors
ABSTRACT Federated Learning (FL) is emerging as a premier paradigm for privacy‐preserved Machine Learning (ML), enabling devices to train models without central data pooling collaboratively. In the contemporary Internet of Things (IoT) landscape, characterized by escalating energy consumption and associated carbon footprint, FL is recognized not merely for its privacy features. Intrinsic to decentralized architectures such as FL, secure communication is based on digital signatures to guarantee integrity. This is particularly evident in sensitive sectors such as the Internet of Vehicles (IoV), banking, and healthcare. Integrating FL becomes imperative and intricate as these sectors are intertwined with the IoT fabric. Our study unveils “Secure Federated Learning Framework (SecFL),” a pioneering decentralized framework combining FL and sustainable computing. SecFL offers defences against adversarial attacks such as data poisoning and label flipping. Utilizing the Rivest‐Shamir‐Adleman (RSA) asymmetric encryption algorithm for securing digital communications and transactions, combined with ElGamal encryption and a private Ethereum blockchain, ensures enhanced client‐specific security. Our research emphasizes the formal modeling of adversarial dynamics using High‐Level Petri nets (HLPN) within the FL‐IoT ecosystem, balancing system dynamics and energy conservation. Our model consistently outperforms contemporary solutions in accuracy and time efficiency after validation. As IoT burgeons into domains like environmental monitoring, smart cities, and energy grids, the SecFL framework, fostering FL, optimizes energy utilization and bolsters resource efficiency. In our comparative analysis, the Elliptic Curve Digital Signature Algorithm (ECDSA) algorithm demonstrates superior transaction latency and verification time compared to RSA and Elliptic Curve Cryptography (ECC).
Michael Smith, Valerie Kilders, Todd Kuethe, Nicole Olynk Widmar
We examine the relationship between market performance of leading cryptocurrencies (Bitcoin and Ethereum), meme-stocks (AMC, GameStop), and subjects of corporate boycotts (Bud Light) using weekly market price and volume data along with social media data of weekly mentions (which total 337 million in this dataset) and net sentiment. Using vector autoregression (VAR) time series analysis along with Granger causality testing and structural breaks, we successfully predict trade volume of these various assets using social media data and price data. We also find that closing price data and trade volume are reliable predictors of net sentiment about crypto in online and social media. However, we struggle to predict the closing price for the group of assets studied. We also employ impulse response functions, finding evidence of a dynamic relationship occurring between online and social media net sentiment and online media volume with closing price and trade volume. These functions show that investor sentiment operates with a short memory lasting around 3 weeks, additionally these functions show that price generates a shock on trade volume but that crypto and meme-stock markets experience this differently. Our findings reinforce the notion that meme-stock traders and herd investors do not trade on market fundamentals but are instead sensitive to herding (or sentiment) movements. Our findings also suggest that compared to these meme-stock investors, crypto markets have more traditional motivations of loss aversion.
This study examines the market impact of China's comprehensive cryptocurrency ban announced in May 2021, employing an event study methodology. The investigation is motivated by the need to understand how major regulatory interventions affect cryptocurrency markets, given their growing significance in the global financial ecosystem. Using daily price data for Bitcoin and Ethereum, we analyze abnormal returns (AR) and cumulative abnormal returns (CAR) around the announcement date. The analysis reveals significant negative market reactions, with Bitcoin experiencing a CAR of -70% and Ethereum -87% during the 30-day post-event window. Our findings suggest that this ban had a more severe and persistent impact compared to previous regulatory actions, reflecting the market's heightened sensitivity to comprehensive regulatory measures. The results demonstrate the substantial influence of major regulatory interventions on cryptocurrency market stability and provide important implications for policymakers considering cryptocurrency regulations. Furthermore, the study highlights how regulatory actions in one jurisdiction can generate significant spillover effects across global financial markets.
Siang-Li Jheng, Alexandra Conda, Daniel Traian Pele, Wolfgang Karl Härdle
Abstract 2024 marks a significant milestone in integrating digital finance into the global financial landscape. The U.S. Securities and Exchange Commission’s approval of Bitcoin and Ethereum ETFs signaled wider mainstream adoption. Shortly thereafter, Donald Trump’s return to the presidency drove Bitcoin prices beyond $100,000. In light of these developments, we observe the rapid changes in cryptocurrency market prices, trends, and regulatory policies, which drive us to conduct a comprehensive review of cryptocurrencies asset’s literature and examine its robustness. Our study covers several themes: how cryptocurrencies fit into broader asset allocation strategies, techniques to create crypto-based indexes, current debates over speculative bubbles, and the evolution of valuation models to highlight the dual aspects of market opportunities and risks. Throughout our review, we compare previous studies with the latest data, seeking to determine which arguments continue to hold up and which require adjustment. Although digital assets have experienced multiple crashes, they often rebound more strongly than expected, making them a topic of intense debate among academics, regulators, and investors. We aim to assemble an organized summary of research findings, providing a comprehensive framework that unites historical evolution with recent shifts and future perspectives.
Layer-2 protocols can assist Ethereum's limited throughput, but globally broadcasting layer-2 data limits their scalability. The Danksharding evolution of Ethereum aims to support the selective distribution of layer-2 data, whose availability in the network is verified using randomized data availability sampling (DAS). Integrating DAS into Ethereum's consensus process is challenging, as pieces of layer-2 data must be disseminated and sampled within four seconds of the beginning of each consensus slot. No existing solution can support dissemination and sampling under such strict time bounds. We propose PANDAS, a practical approach to integrate DAS with Ethereum under Danksharding's requirements without modifying its protocols for consensus and node discovery. PANDAS disseminates layer-2 data and samples its availability using lightweight, direct exchanges. Its design accounts for message loss, node failures, and unresponsive participants while anticipating the need to scale out the Ethereum network. Our evaluation of PANDAS's prototype in a 1,000-node cluster and simulations for up to 20,000 peers shows that it allows layer-2 data dissemination and sampling under planetary-scale latencies within the 4-second deadline.
The recent blockchain policy pronouncement in Nigeria gave rise to examine the effect on digital currency market performance. Knowing that policy statements played a dynamic role on market performances, and for the fact that digital currency is link to market volatility, this work analyzed the impact of Nigerian blockchain policy on digital currency market performance during the short-term periods. Based on the exigencies, the research covered a period of 23 weeks using a-weekly data between May 3, 2023 and October 4, 2023. The study employed selected top-five digital currencies including Bitcoin, Ethereum, Tether, BNB, and XRP of their market performances extracted from crypto database. The generalized autoregressive conditional heteroskedasticity (GARCH) least squares analytical tool was applied to ascertain how Bitcoin, Ethereum, Tether, BNB, and XRP digital currencies market performance responded to Nigerian blockchain policy in the short-term. The findings showed that Nigerian blockchain policy impacted negatively on Bitcoin, XRP, and BNB market performance in the short-term. However, Nigerian blockchain policy impacted positively on Tether, and Ethereum market performance in the short-term. The research further revealed Ethereum, and BNB digital currencies constituted significant variables of study. Finally, Nigerian blockchain policymakers were recommended to revised and address the diverse impacts on digital currencies with tailored regulations to enhance investors protection, and support the positive trends for a balance-support of the digital currency market.
Hameer Hussain Shah, Asra Shaikh, Muhammad Mujtaba, Tanveer Hussain Shah
Purpose: The purpose of this study is to examine how the adoption of major cryptocurrencies affects the financial markets of Pakistan. It focuses on three key areas: the gold market, the stock market (KSE-100 index), and the bond market. Design: This research employs a quantitative approach, utilizing regression analysis to investigate the relationship between the adoption of five major cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple) and their impact on gold prices, stock market performance, and bond price movements in Pakistan. Findings: The results show that cryptocurrencies do affect financial markets, but each coin has a different impact. Bitcoin and Binance Coin increase gold prices, while Cardano and Ripple decrease them. In the stock market, Bitcoin lowers the index, but Ethereum and Ripple increase it. Cardano and Binance Coin have little to no impact on stocks. For bonds, Cardano and Ripple lower prices, while Binance Coin and Ethereum raise them. Bitcoin has a small negative effect on bonds. Originality and Value: This study contribute significantly to reshaping the investment culture in Pakistan, particularly in the context of evolving regulatory frameworks, creating awareness about digital currencies especially cryptocurrencies so that financial investors, portfolio and fund managers can take better informed decisions. Keywords: Cryptocurrency, Blockchain Technology, Financial Inclusion, Bitcoin, Ethereum, Gold Market, Stock Market, and Bond Market JEL Classification codes: G0, G1, G2, O3
Sustainable industrial development depends on optimizing resource and energy integration within Eco-industrial parks (EIPs), combined with stringent carbon emissions reduction policies. The main challenge is ensuring transparency, accountability, and data privacy while optimizing the conversion of raw materials and energy into valuable products and controlling emissions within EIPs. This research introduces an innovative framework to design optimized EIPs and deploy a blockchain-enabled trading platform for resources and emissions management, tackling these key issues. The proposed framework integrates EIPs with emission control policies, supported by two distinct smart contracts: one dedicated to blockchain-based resource trading and another handling financial transactions related to emission control policies, including other regulations such as income tax. The resource trading platform fosters transparency, enabling accurate tracking of material and energy flows. Furthermore, the framework integrates an off-chain Mixed-Integer linear Programming model (MILP) to optimize EIP design and operations, which is seamlessly integrated with smart contracts on the Ethereum blockchain (BC) to ensure data privacy and traceability among processes to meet environmental targets. The model also determines emission reductions and investments in carbon capture technology, promoting operational efficiency. By incorporating identity verification and external entities for compliance, the framework ensures secure and regulated operations. Offering a powerful tool to decision-makers and authorities, this framework enhances comprehension of resource and emissions tracking, paving the way for the development of innovative policies and fostering regulatory compliance. This development promotes sustainable industrial activities and supports environmental goals.
Revolutionizing distributed agile software testing, we propose BCTestingPlus, a groundbreaking blockchain-based platform. In the traditional distributed agile software testing lifecycle, software testing has suffered from a lack of trust, traceability, and security in communication and collaboration. Furthermore, developers’ failure to complete unit testing has been a significant bottleneck, causing delays and contributing to project failures. Introducing BCTestingPlus, a transformative blockchain-based architecture engineered to overcome these challenges. This framework integrates blockchain technology to establish an inherently transparent and secure environment for software testing. BCTestingPlus operates on a private Ethereum blockchain network, offering superior control and privacy. By implementing smart contracts on this network, BCTestingPlus ensures secure payment verification and efficient acceptance testing. Crucially, it aligns development and testing teams toward shared objectives and guarantees equitable compensation for their efforts. The experimental results and findings conclusively show that this innovative approach demonstrates that BCTestingPlus significantly enhances transparency, bolsters trust, streamlines coordination, accelerates testing, and secures communication channels for all parties involved in the distributed agile software testing lifecycle. It delivers robust security for both development and testing teams, ultimately transforming the efficiency and reliability of distributed agile software testing.
Cryptocurrency price prediction has become crucial for informed trading decisions due to the volatile nature of assets like Bitcoin, Ethereum, Ripple, and Litecoin. Traditional methods like ARIMA and GARCH struggle with this volatility, while modern approaches such as machine learning and deep learning provide better accuracy. This study evaluates advanced models, including LSTM, GRU, and Light GBM, to predict cryptocurrency prices and assess trading strategies before and after the COVID-19 pandemic. GRU and LSTM excel at identifying patterns in price data, with GRU performing best for Ripple. Ensemble methods like Light GBM proved highly accurate for Bitcoin and Ethereum across time periods. Simpler models like RNN were sufficient for Ripple and Litecoin. The COVID-19 pandemic significantly impacted market dynamics, emphasizing the importance of precise predictions. Trading strategies based on model predictions showed that ensemble methods like Light GBM yielded the highest profitability post-pandemic. The findings highlight the need to tailor models to specific cryptocurrencies and market conditions. Improved deep learning tools can enhance trading efficiency and provide actionable insights for investors and policymakers. Future research could focus on predicting multiple cryptocurrencies simultaneously and optimizing portfolio-based trading strategies. Key Words: LSTM, ARIMA, GARCH, RNN
Sunil P. Chinte, Prof. S. A. Thakare, Aarti R. Jaiswal, Nikunj Hasmukhrai Raja · 5 authors
The rapid expansion of Internet of Things (IoT) ecosystems has resulted in an unprecedented surge in data generation, necessitating reliable, scalable, and secure storage mechanisms. Traditional centralized storage systems suffer from inherent limitations such as single points of failure, limited scalability, and vulnerability to cyberattacks, which compromise the confidentiality and availability of critical IoT data. This study introduces a blockchain- based decentralized storage framework aimed at addressing these critical issues. By leveraging the distributed and immutable characteristics of blockchain technology, the proposed system enhances data integrity, ensures transparency, and facilitates trustless data exchange among heterogeneous IoT devices. The methodology includes mathematical modeling of key performance parameters such as latency, throughput, storage efficiency, and consensus delay. Smart contracts are integrated to automate validation and enforce rules among interconnected devices, while redundancy mechanisms like replication and erasure coding improve storage reliability and efficiency. The framework’s effectiveness is evaluated using simulation tools including Hyperledger Caliper and Ethereum Testnets for blockchain behavior, and NS-3 and OMNeT++ for modeling dynamic IoT network environments. Experimental results reveal a 30% improvement in data retrieval time, 25% gain in storage efficiency, 40% enhancement in system resilience, and a 50% increase in transaction throughput over conventional approaches. These metrics highlight the suitability of the proposed model for real-world applications requiring scalable and secure IoT data management, such as healthcare monitoring, smart cities, and industrial automation. The model’s reproducibility and modularity make it a robust solution for future research and deployment. Overall, this work demonstrates that blockchain-integrated decentralized storage frameworks present a transformative step toward resilient and scalable IoT infrastructures.
Juan de Anda-Suárez, José Luis López-Ramírez, Daniel Jiménez-Mendoza, José Manuel Benitez-Quintero · 7 authors
Autonomous Vehicles (AV) have been extensively studied in both scientific and social contexts. Over the past two decades, there has been a significant rise in their real-world applications, including neural networks, Blockchain, Internet of Things, autonomous navigation, computer vision, automation processes, and various other areas. Hence, it is imperative to investigate the interplay between software, hardware, and individuals. To guarantee secure and unaffected interactions within autonomous vehicle devices and networks, decentralized Blockchain technology is proposed. This study presents the introduction of a framework we named “DEMU-NAV” for an ecosystem that includes Artificial Intelligence (AI), humans, and robots. The framework makes use of a decentralized Blockchain, Smart-Contract (SC), and Internet of things (IoT) network. Our framework was implemented using Ethereum and Python, enabling us to oversee Blockchain, Smart-Contracts, and the IoT for the facilitation of autonomous vehicle navigation.
Bu çalışmada, kripto para piyasasında en yüksek işlem hacmine sahip varlıklar arasında yer alan Bitcoin, Ethereum ve Binance Coin’in günlük kapanış fiyatları incelenmiştir. Çalışmada ele alınan kripto para getirilerinin uzun hafıza özelliği gösterip göstermediğinin belirlenmesi ve uygun model tahmininin yapılması amaçlanmıştır. Getiri serilerinin uzun hafıza özelliğinin tespit edilmesi için literatürde sıklıkla ele alınan Hurst katsayısı, Geweke ve Porter-Hudak testi ve Yerel Whittle Tahmincisi kullanılmıştır. Ayrıca uzun hafızanın tespitine yönelik klasik testlerin yanı sıra, çok değişkenli yerel Whittle Skor tipi test ve Qu (2011) tarafından önerilen uzun hafıza testleri de yapılmıştır. Elde edilen bulgulara göre üç kripto para biriminin de uzun hafıza özelliği gösterdiği tespit edilmiştir. Ardından uzun hafızanın varlığı dikkate alınarak getiri ve koşullu varyansın modellenmesi için Otoregresif Kesirli Bütünleşik Hareketli Ortalama (ARFIMA) ve Kesirli Bütünleşik Genelleştirilmiş Otoregresif Koşullu Değişen Varyans (FIGARCH) modelleri tahmin edilmiştir. Tahmin edilen FIGARCH modellerinde kesirli fark parametresi 0
This study investigates the effectiveness of a hybrid forecasting model that combines Generalized Autoregressive Conditional Heteroskedasticity (GARCH) with Long Short-Term Memory (LSTM) neural networks, specifically applied to the cryptocurrency market, focusing on Bitcoin and Ethereum.The inherent volatility of cryptocurrencies presents substantial challenges for accurate price prediction, necessitating advanced methodologies that can adapt to fluctuating market conditions.We first utilize GARCH models to analyze and capture the time-varying volatility in the returns of Bitcoin and Ethereum, enabling a comprehensive understanding of the underlying market dynamics.Following this, we implement LSTM networks to exploit their capability to model complex, non-linear relationships in sequential data, enhancing the predictive power of the model.The performance of the GARCH-LSTM framework is rigorously evaluated using historical price data for Bitcoin and Ethereum, employing key metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to assess forecasting accuracy.The results demonstrate that the hybrid approach significantly outperforms traditional forecasting methods, providing more reliable predictions and insights into market trends.This study contributes to the growing body of literature on cryptocurrency forecasting by illustrating the potential of combining econometric techniques with advanced machine learning methods, offering valuable implications for traders and investors in the cryptocurrency ecosystem.However, the experimental results revealed that the LSTM model outperformed the other eight methods in terms of forecasting performance measures, the RMSPE validation is 0.112561, and the RMSE validation is 0.011456.