Abstract A Non-Fungible Token (NFT) is a digital asset representing ownership or proof of authenticity of a unique digital item. NFTs are used for various purposes, including digital art, collectibles, virtual real estate, and tokenizing unique digital or physical items, and have introduced new dimensions to digital ownership and enabled individuals to tokenize unique digital assets using blockchain technology. Although NFTs offer exciting opportunities, they suffer from interoperability, high energy consumption, piracy, ownership control, and security issues. In this paper, an idea has been proposed in which any image, pdf file, or video file can be converted to an NFT and owned. We have used the ERC-721 standards, the PoS consensus protocol, and a smart contract to address the challenges. The proposed framework provides a step-by-step guide to create, list NFTs and maintain secure ownership where metadata are stored on IPFS, which generates a unique URL. This URL is then logged on the blockchain, saving time and costs. The created NFTs are interoperable among various applications and frameworks. Smart contract has been formally verified using Slither and tested against vulnerability using Smart Contract Weakness Classification (SWC) standards. Performance of the proposed system has been measured in terms of execution cost, latency, and throughput including statistical indicators like variance and confidence intervals. Minting cost has been compared with the similar network condition.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
D. A. Vidhate, Prajesh Gaikwad, Aditya Gadge, Abhijay Jadhav · 5 authors
The growth of financial technology has introduced Central Bank Digital Currency (CBDC), which is basically a digital version of money issued by central banks. In this work, a blockchain-based system is proposed that uses QR codes and UID numbers to make transactions easier and more secure. Blockchain helps keep a proper record of transactions so they cannot be easily changed or tampered with. Using QR codes makes payments quick and simple, especially for everyday use. The system also uses smart contracts to handle processes automatically. Since everything runs on a decentralized network, it reduces dependency on a single authority and lowers the chances of fraud. At the same time, user privacy is maintained by storing only encrypted verification data instead of actual personal details.
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.
Ammar Ahmed Othman, Seddiq Hassan Al-Banna Ali, Mohammed Bakr Youssef
In the digital currency, Bitcoin (BTC) is called the gold of the digital currency. It is possible to make some profits in trading of bitcoins, though this market is a very illiquid market and it is very challenging to determine the price of a bitcoin. The current work uses historical data and technical indicators to predict Bitcoin prices in a broad approach. BTC-USD price data were obtained using Yahoo Finance API and covered from 01/01/2015 till 07/01/2024. The concept of feature engineering was applied to improve the dataset by including vital financial characteristics, including Moving Averages, RSI, and Bollinger Bands for higher forecasting precision. The forward-looking model for the Bitcoin price was developed using machine learning and deep learning algorithms. The efficiency of the model was assessed with the help of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The overall values of Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error were 0.0062, 8.39e-05 and 0.0092 respectively which suggest that the proposed model is accurate in forecasting the future prices.
Deep learning has emerged as a widely applied approach across various fields, with finance and forecasting being among its most prominent areas of use. Within this domain, different deep learning architectures have been developed to address specific prediction problems. This study compares the performance of ARIMAX and several deep learning models—including LSTM, BILSTM, CNN-LSTM, GRU, and TFT—in forecasting Bitcoin prices. The dataset consists of daily values from January 2014 to January 2025. The dependent variable is the daily Bitcoin closing price ($), while the independent variables include oil price (USD/barrel), gold price (USD/ounce), platinum price ($/XPT), and the USD/TRY exchange rate. All analyses were conducted in Python using Google Colab, with the Keras library employed for model implementation. Root Mean Square Error (RMSE) was selected as the evaluation metric for predictive accuracy. The results indicate that the TFT model achieved the highest predictive performance, followed closely by the GRU model. LSTM, BILSTM, and ARIMAX models showed similar yet weaker performance, while the CNN-LSTM model produced the least accurate forecasts, with significantly higher RMSE values compared to the other models.
In the rapidly evolving landscape of cryptocurrency in India, a powerful new contender has emerged—Jaimax Coin, often hailed as the best presale crypto coin in India. As blockchain technology reshapes finance, digital ownership, and decentralized ecosystems, we stand at the forefront of innovation, introducing Jaimax, the next-generation crypto pre-sale coin designed for long-term scalability, high utility, and mass adoption. As a revolutionary new crypto coin India trusts for transparent and secure financial growth, Jaimax is strategically engineered to transform both the DeFi (Decentralized Finance) and NFT (Non-Fungible Token) markets. With advanced tokenomics, unmatched security standards, and a visionary roadmap, Jaimax is positioned to become one of the most influential digital assets in India’s expanding blockchain environment.
In India’s rapidly expanding digital finance ecosystem, Jaimax Coin has emerged as one of the most reliable, transparent, and growth-oriented blockchain assets. As the demand for secure crypto coin options increases, Jaimax is positioned as one of the best presale crypto coin in India, offering visionary investors unmatched opportunities in the evolving world of decentralized finance, NFTs, and digital currency innovation. Backed by advanced blockchain architecture, transparent tokenomics, and a strong mission toward accessible financial technology, Jaimax stands apart from the countless new crypto coins in India.
This study compares classical, deep learning, and hybrid approaches for cryptocurrency price forecasting across Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), and Cardano (ADA) using daily data from 15 September 2022 to 15 September 2025. We implement an ARIMA baseline, a stacked LSTM network, and an inverse-error-weighted ARIMA- LSTM ensemble. Feature engineering includes trend, momentum, volatility, and volume indicators; models are evaluated with expanding walk-forward validation and multiple metrics (R2, RMSE, MAE, MAPE, sMAPE, MASE). Statistical significance is assessed via Diebold-Mariano tests. Results indicate that ARIMA consistently outperforms LSTM across all assets, with average performance of R2= 0.924 ± 0.051, MAPE = 2.19 ± 0.81%, and MASE = 0.95 ± 0.30, compared with LSTM’s R2= 0.527 ± 0.709, MAPE = 4.36 ± 0.26%, and MASE = 1.91 ± 1.25. The ensemble attains R2= 0.902 ± 0.084 and MAPE = 2.36 ± 0.58%, retaining ~97% of ARIMA’s explanatory power while reducing volatility relative to LSTM. Asset-specific analyses show strong ARIMA performance for ETH (R2= 0.972 ± 0.038) and BNB (R2= 0.964 ± 0.045), and LSTM failure on BTC (R2= -0.534 ± 0.214). Cross-asset dispersion in R2is markedly lower for ARIMA than for LSTM, indicating superior generalization. DM tests confirm ARIMA’s advantage over LSTM (p < 0.001) across assets, with ARIMA versus ensemble differences nonsignificant for BNB. Under rigorous out-of-sample evaluation, the parsimonious ARIMA model provides the most accurate and stable forecasts, while the ensemble offers a robust alternative when cross-asset stability is prioritized.
Counterfeit consumer products have become a serious global issue effecting several industries like medicines, electronics, luxury goods and fast moving consumer goods. Existing supply chain management systems offer no transparency and traceability creating vulnerabilities for counterfeit goods to access legitimate marketplaces. This paper proposes a complete blockchain-based framework for counterfeit goods detection and authentication. Our proposed framework uses Ethereum blockchain technology, smart contracts programmed in Solidity and a user-friendly ReactJS UI to create product authentication system that is immutable and transparent. The software allows producers to register authentic products with unique identifiers (UIDs) such as QR codes or serial numbers on the blockchain along with product metadata such as manufacturer, manufacture date, batch data, and product specifications. Consumers and vendors can utilize the UI to authenticate product claims for trust verification. As consumer and vendor users are using the UI, they can even verify authenticity with the real-time capability to interact with the blockchain, while also ensuring data integrity through user tampering and illegal changes. The UI is utilizing the Truffle framework for creating and deploying a smart contract on the Ethereum blockchain, Ganache local blockchain simulator, and web3.js for being able to connect from frontend to the blockchain. Experimental results show a significant improvement in authenticating goods, decreased verification time and increased transparency through the use of our product authentication system. The proposed framework represents a viable solution to significant challenges in product authentication while providing scalability, security and cost effectiveness for global acceptance and use.
Rashed Alnuman, Tayyab Sajid, Wesam Almobaideen, Qusai Hasan
Abstract Blockchain technology has revolutionized digital financial transactions and asset ownership by enabling decentralized and automated operations through smart contracts. Solidity smart contracts, used in the Ethereum blockchain network, facilitate secure and trustless execution of agreements. However, like any code, smart contracts are prone to vulnerabilities. Considering the assets and value of currency these smart contracts handle, their exploitation leads to severe financial losses and loss of operations. Such exploits have resulted in billions of dollars in stolen or locked assets. In this paper, we present an ensemble multilabel classifier model approach for the automated detection of vulnerabilities in Solidity smart contracts using a real smart contract dataset, with a detailed methodological process that includes processing the dataset. The proposed model stack achieves excellent results with F1 scores ranging from 82.0% to 99.9% for each vulnerability dataset. The proposed model is also compared with common static analyzer tools and models proposed in the literature following a similar approach. Moreover, we package the models into a web application, demonstrating deployment and functionality.
Lingling Xia, Tao Zhu, Zhengjun Jing, Qun Wang · 7 authors
Digital currencies, led by Bitcoin and USDT, are characterized by decentralization and anonymity, which obscure the identities of traders and create a conducive environment for illicit activities such as drug trafficking, money laundering, cyber fraud, and terrorism financing. Focusing on the USDT-TRC20 token on the Tron blockchain, we propose a two-layer transaction network-based approach for virtual currency address identity recognition for digging out hidden relationships and encrypted assets. Specifically, a two-layer transaction network is constructed: Layer A describes the flow of USDT-TRC20 between on-chain addresses over time, while Layer B represents the flow of TRX between on-chain addresses over time. Subsequently, an identity metric is proposed to determine whether a pair of addresses belongs to the same user or group. Furthermore, transaction records are systematically acquired through blockchain explorers, and the efficacy of the proposed recognition method is empirically validated using dataset from the Key Laboratory of Digital Forensics. Finally, the transaction topology is visualized using Neo4j, providing a comprehensive and intuitive representation of the traced transaction pathways.
Samsudin Samsudin, Muhammad Dedi Irawan, Muhammad Irwan Padli Nasution, Raissa Amanda Putri
Bitcoin’s extreme price volatility has long posed challenges for both investors and researchers seeking reliable forecasting models. Conventional financial approaches often fail to capture the highly complex, nonlinear, and fast-moving nature of cryptocurrency markets. To address this gap, this study develops a Bitcoin price prediction model using Random Forest Regression based on on-chain market data. The dataset was obtained from publicly available historical Bitcoin daily trading records spanning more than five years. Key features include opening price, daily high and low ranges, trading volume, and percentage change. The research was carried out in several stages. First, data preprocessing was conducted through normalization, handling of missing values, and feature engineering. Second, model training was performed with Random Forest, including parameter tuning to optimize predictive accuracy. Third, model evaluation employed R² and Mean Absolute Percentage Error (MAPE) as primary performance indicators. Fourth, visualization was implemented using interactive charts to allow users to observe short-term price fluctuations and long-term market patterns. The system development followed an iterative methodology inspired by the Streamlit Framework, which is an open-source Python library that simplifies building interactive web applications for data science and machine learning. This approach provides flexibility, enabling rapid experimentation and adaptation to evolving market conditions. The results show that the proposed model achieves near-perfect R² values (approaching 1.0) with consistently low MAPE, highlighting its reliability. Beyond predictive performance, the framework is designed to be scalable, supporting future integration with deep learning methods such as LSTM and external macroeconomic indicators, thus offering both practical utility for investors and academic contributions to decentralized finance research.
Muhammad Haroon Tariq, Uswa Ihsan, Zaenal Alamsyah
Ownership rights related to land and property represent a highly contentious matter in areas across Pakistan because female inheritors struggle to assert their property rights due to cultural practices along with unclear procedures and traditional document systems. The present government-controlled systems demonstrate inadequate proficiency along with safety protocols to execute fair inheritance distribution, mainly impacting marginalized populations. This research introduces a blockchain system known as the Land Registration and Inheritance Automation System (LRIAS) which prioritizes the female protection of inheritance privileges. The proposed system includes digitalizing the traditional paper-based land registration and inheritance process. The system ensures blockchain security through the implementation of MetaMask together with Web3.js for Ethereum transactions. The blockchain system distributes inheritances through programmed agreements which follow Shariah validation rules. The LRIAS establishes permanent and free-version records that show who owns land and who the legal heirs are. The system enables women to access their inheritance records through verifiable reliable data which cannot be altered. Through the system, authorities can verify inheritance claims and execute them without bureaucratic interference, which minimizes both legal disputes and family conflicts. Experimental tests show that the LRIAS succeeds in safeguarding women’s land inheritance claims and increasing confidence in legal inheritance procedures.
Hakam Dzakwan Diash, Vannesa Nathania, Mohammad Idhom, Trimono Trimono
The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself. This study aims to develop an ETH price prediction model using a combined architecture of Convolutional Neural Network (CNN) and also Bidirectional Long Short-Term Memory (BiLSTM). CNN is used to extract local features from historical ETH closing price data, while BiLSTM models bidirectional temporal patterns. The dataset used includes ETH daily price from January 2020 to January 2025, which are obtained from Yahoo Finance and have gone through a normalization process and transformation into sequential form. The model is trained for 100 epochs with an early stopping mechanism to prevent overfitting and evaluated using the MAPE and coefficient of determination (R²) metrics. The evaluation results show that the CNN-BiLSTM model is able to predict ETH prices with a MAPE value of 2.8546% and an R² of 0.9415, indicating high performance in capturing actual data trends. This study shows that the hybrid CNN-BiLSTM approach is effective for Ethereum price prediction.
ABSTRACT The rapid development of synthetic media tools has blurred the lines between human‐created and AI‐generated content, which has been exacerbated by overfitted detection models. This has put the authentication of digital media at risk, raising concerns about media credibility and trustworthiness due to the deception presented by synthetic media. Furthermore, a separation between artificial creativity and human creativity means that current ownership laws cannot provide sufficient authentication for digital media. This paper proposes an authentication detection model for artwork by combining a neural network and blockchain technology. Once an artwork has been detected as human‐created, its image hash is stored on the blockchain, providing a solution for preserving digital artwork authenticity. The model was trained using a combined dataset composed of both human‐created artwork and synthetic artwork generated by the Midjourney and Stable Diffusion tools, resulting in an increase in accuracy of almost 20% for detecting synthetic artwork. By introducing doubt in less confident outputs, the model achieved an accuracy of over 92% when tested against independent datasets. This is a significant improvement over detection models that experience a deterioration in accuracy when faced with independent datasets. Additionally, using the Polygon blockchain instead of Ethereum reduced the time to store authentic artwork on the blockchain from 21 s to 10 s, and the interquartile range of the cost of writing to the blockchain was reduced by 97.4%, improving the scalability of the model. The results of this paper contribute to knowledge by showing how the detection of synthetic artwork can be improved by using multiple datasets for training models, as well as providing long‐term preservation of digital artwork authenticity by using blockchain.
Currency Recognition and Detection
Generative Adversarial Networks and Image Synthesis
Adnan Sagheer, Ali Raza, Muhammad Rizwan Rashid Rana, Faiza Kiran
The highly stochastic, nonlinear, and volatile nature of Bitcoin prices poses significant challenges for accurate forecasting using traditional statistical models. To address this, we propose a hybrid deep learning architecture that combines the strengths of Convolutional Neural Networks (CNNs) for spatial feature extraction with Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), for capturing long-term temporal dependencies. This integrated framework effectively models both spatial and temporal patterns from historical Bitcoin price data. The model was trained and evaluated using real-world Bitcoin datasets.Experimental results demonstrate that the proposed CNN+LSTM model outperforms traditional machine learning and standalone deep learning approaches. Specifically, it achieves a Root Mean Square Error (RMSE) of 245.76, a Mean Absolute Error (MAE) of 11.45, a Mean Absolute Percentage Error (MAPE) of 15.68%, an R² score of 0.92, and a Mean Bias Error (MBE) of 6.94. These results highlight the effectiveness and reliability of the proposed hybrid model in enhancing the accuracy and stability of financial time series forecasting, providing valuable insights for traders, investors, and financial analysts.
Halilibrahim Gökgöz, Aamir Aijaz Syed, Hind Alnafisah, Ahmed Jeribi
The recent economic turmoil and the increasing volatility of bitcoins have necessitated the need for exploring safe-haven assets for bitcoins. In this quest, the present study aims to investigate the safe haven for bitcoins by examining the dynamic relationship between bitcoins, gold, foreign exchange, and stablecoins. This is achieved by calculating hedge ratios and portfolio weight ratios for various asset classes, by employing adaptive-based techniques such as generalized orthogonal generalized autoregressive conditional heteroscedasticity, corrected dynamic conditional correlation, corrected asymmetric dynamic conditional correlation, and asymmetric dynamic conditional correlation under various market and time-varying conditions. The empirical estimate reveals that all the selected asset classes are effective risk diversifiers for bitcoins. However, among all the asset classes, as per the hedge and portfolio weight ratio, Japanese yen, stablecoin for Japanese yen and Great Britain Pound, and Crypto Holding Frank Token (lowest-cost hedging strategies) are the most effective risk diversifiers when compared with bitcoins. Moreover, while considering external economic shocks, the empirical estimate posits that stablecoins are more stable risk diversifiers compared to the asset class they represent. Furthermore, in terms of the bivariate portfolio analysis formed with bitcoin, this study concludes that the weight of bitcoin is more stable when combined with gold, tether gold, Euro, Great Britain Pound, Swiss franc, and Japanese Yen. Thus, these assets are attractive for long-term investment strategies. This study provides investors and policymakers with significant insight into understanding safe-haven assets for bitcoin’s volatility and constructing a flexible portfolio that is dependent on the investment timeline and the prevailing market conditions.
Bitcoin is a promising investment asset for the future, offering a viable option for long-term investors. This study seeks to evaluate and compare the effectiveness and performance of several models, including Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Gradient Boosting Regression Process (GBRP), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH), in predicting Bitcoin prices. The dataset spans Bitcoin price data from 2012 to 2024. The findings reveal that each model demonstrates a positive trend in forecasting Bitcoin price movements. Therefore, Bitcoin is a valuable asset for those willing to invest; however, it may not be suitable for novice investors due to its high volatility. This study advances the development of predictive models leveraging machine learning and statistical methodologies, offering critical insights into Bitcoin’s price behavior for informed investment decision-making.
The article examines methods and information technologies aimed at ensuring the secure integration of the Ethereum blockchain with Internet of Things (IoT) systems. The relevance of the study is driven by the rapid development of IoT, which is accompanied by increasing cybersecurity threats, including unauthorized data access, man-in-the-middle (MITM) attacks, device identifier spoofing, and the low transparency of centralized systems. The use of Ethereum blockchain technology, particularly smart contracts, opens new opportunities for creating decentralized security management models for IoT devices, enhancing trust levels, automating processes, and minimizing third-party interference risks. The “Problem statement” section outlines the key challenges in securing IoT networks, including the vulnerabilities of centralized solutions, limited computational resources of devices, and the need to develop autonomous access control systems. The “Analysis of Recent Research and Publications” section summarizes modern approaches to integrating Ethereum blockchain into the IoT field, including tokenized identification mechanisms, access control, and transaction processing using smart contracts. It is noted that leading researchers suggest Layer-2 solutions (state channels, zk-rollups, Plasma) aimed at reducing the load on the main blockchain and improving scalability. The aim of the article is to systematize modern methods of integrating the Ethereum blockchain with IoT and develop recommendations for their implementation, considering the limited resources of devices. The “Research results” section presents a secure IoT system architecture concept based on decentralized account management, local storage of cryptographic keys on devices, the use of optimized transaction signing algorithms, and the introduction of a hybrid data storage model based on IPFS. The proposed model minimizes the risk of unauthorized access, increases transparency in the interaction between IoT devices, and reduces computational resource costs. The “Conclusions and prospects for further research” section emphasizes that the implementation of the Ethereum blockchain in IoT promotes the development of secure decentralized platforms. However, it requires addressing issues such as energy consumption, transaction costs, and optimizing client applications for low-performance devices. Future research should focus on developing effective scalability tools, adapting smart contracts to IoT specifics, and improving integration with cloud computing platforms for storing large data sets.
Blockchain is becoming one of the fundamental technologies for the IoT, as it is a distributed ledger technology (DLT), and the environment provides secure and trustworthy mechanisms for distributed data management. In this paper, we propose an intelligent framework integrating blockchain technology, IoMT, and AI to monitor resource utilization, analyze the system's behavior, and optimize the utilization of the resources-an investigational setup. We simulated an IoT environment in which devices generate memory usage data sent on the network, thanks to the MQTT protocol, and then saved securely in a blockchain. Following this, automatic pattern analysis is adopted to identify the market demand and distribute the resources to the individual blocks in a way that will make each perform to the maximum. The structure we designed is such that the proposed system could expand and be compatible with many other IoT deployments, bringing about a new, innovative way of handling distributed data where confidentiality and security are most important. An experimental evaluation we conducted using simulations under controlled situations shows that the system can lower the latency and ensure a higher overall system reliability while at the same time preventing tampering and supporting the verification of data. The study's findings illustrate that Blockchain-based IoT systems are one of the technologies that can revolutionize several urban living areas, including but not limited to smart cities, industrial automation, and healthcare monitoring. Further works will focus on expanding this approach to its application in the real world, where problems like network scalability, consensus mechanism overhead, and the energy efficiency of the IoT devices are to be considered.
The integration of blockchain technology within the domain of digital cultural heritage resources (DCHR) has emerged as a pivotal approach over the past decade, offering a secure and transparent platform for documentation, ownership transfer, and verification. This paper investigates the current challenges in managing DCHR and analyzes the corresponding solutions. We discuss outstanding technical implementations such as the “Digital Dunhuang Open Material Library” and “Salsal.” Our study identifies emerging blockchain trends in cultural heritage preservation and proposes future research directions. Through an analytical examination of distributed consensus mechanisms and blockchain deployment scenarios, this study proposes methodological pathways for implementing the DCHR management framework. We call for continued optimization of consensus protocols, expansion of blockchain application scenarios, and establishment of supportive policies to bridge the gap between decentralized innovation and institutional compliance requirements.