Blockchain Papers

Nuras Naser Saeed Hizam, Madhukar Shelar

No abstract is available for this record.

Goutam Ghosh, Megha Ghosh, Pinakpani Mukherjee

This paper delves into analyzing and predicting cryptocurrency prices, focusing on Bitcoin. It employs displaced moving average (DMA) and long short-term memory (LSTM) techniques. By leveraging these methods, we aim to provide valuable insights for those navigating the volatile world of cryptocurrency investments. After extracting it using Python within a Jupyter Notebook environment, a dataset sourced from Yahoo Finance is utilized. The analysis incorporates 50 DMA and 200 DMA to enhance understanding of bitcoin market dynamics and offers insights into the predictive capabilities of moving averages. Subsequently, the study applies LSTM, a recurrent neural network combining forget and output gates, to enhance memory and assess attributes critical for accurate price forecasting. The study systematically assesses model performance using important metrics like MSE (Mean Squared Error), R-squared (R2) factor, and the RMSE (Root Mean Squared Error) for understanding accuracy and reliability. For comparison, the traditional regression method is also analyzed for Bitcoin price forecasting, revealing the outperformance of LSTM over regression.

Rahul Saha, Sanjana Wankhade, Maaz Mujawar, Ravi Jeswani · 6 authors

Cryptocurrency price prediction is all about the digital currency called as cryptocurrency this software helps the new user to raise the understanding level regarding the cryptocurrency. Cryptocurrency like Bitcoin, Ethereum and etc. Which helps the new user to invest in the cryptocurrency without any fear the user can get his prediction about the desire cryptocurrency the stats of the specific cryptocurrency can be explained with the help of the different graphs. In this software four types of the algorithm are being used Python, CNN, TensorFlow, LSTM which helps to find out the best prediction level which decreases the level of loses to the users. As per the new cryptocurrency bill 2021 the government officially taking steps into the cryptocurrency and the government can issue their own cryptocurrency issued by Reserve Bank of India. Which leads the opportunity for the new investor are software will help that new investor to get a detailed explanation regarding the cryptocurrency.

Saeed Mohammadi Dashtaki, Dashtaki, Reza Mohammadi, Mehdi Hosseini Chagahi, Behzad Moshiri · 5 authors

Predicting cryptocurrency price trends remains a major challenge due to the volatility and complexity of digital asset markets. Artificial intelligence (AI) has emerged as a powerful tool to address this problem. This study proposes a multisource fusion framework that integrates quantitative financial indicators, such as historical prices and technical indicators, with qualitative sentiment signals derived from X (formerly Twitter). Sentiment analysis is performed using Financial Bidirectional Encoder Representations from Transformers (FinBERT), a domain-specific BERT-based model optimized for financial text, while sequential dependencies are captured through a Bidirectional Long Short-Term Memory (BiLSTM) network. Experimental results on a large-scale Bitcoin dataset demonstrate that the proposed approach substantially outperforms single-source models, achieving an accuracy of approximately 96.8\%. The findings underscore the importance of incorporating real-time social sentiment alongside traditional indicators, thereby enhancing predictive accuracy and supporting more informed investment decisions.

Karan Singh Thakur, Rohit Ahuja, Raman Singh

The grain supply chain is crucial for any nation’s self-sustainability due to its huge impact on food security, economic stability, and the livelihoods of several people. The path grain takes from farmers to consumers is opaque and complicated, due to which consumers cannot trust grain quality and its origin. Although blockchain is widely used for fair and secure transactions between farmers and buyers, issues related to transparency and traceability in the grain supply chain, such as counterfeiting and middlemen involvement, have not been adequately addressed. To tackle these issues, a blockchain-based solution is proposed that unites farmers, warehouses, government central and state agencies, transporters, and food corporations on a single platform to enhance transparency, traceability, and trust among all parties. This system involves minting a non-fungible token (NFT) corresponding to each lot of grain approved by government officials. The NFT comprises grain quality, type, temperature data from sensors, weight, and ownership information, which updates as the grain lot moves across the supply chain from central agencies to state agencies and so on. NFTs enable stakeholders to track the grain lot from cultivation to end-users, providing insights into grain conditions and quality. An Internet of Things-based circuit is designed using a Digital-output relative humidity & temperature (DHT22) sensor, which offers real-time temperature and humidity readings, and geolocation coordinates are gathered from the GPS module across the supply chain. Farmers can directly interact with warehouses to sell grains, eliminating the need for middlemen and fostering trust among all parties. The proposed four-tier framework is implemented and deployed on the Ethereum network, with smart contracts interacting with React-based web pages. Analysis and results of the proposed model illustrate that it is viable, secure, and superior to the existing grain supply chain system.

Mehmet Akif Bülbül

The prime aim of the research is to forecast the future value of bitcoin that is commonly known as pioneer of the Cryptocurrency market by constructing hybrid structure over the time series. In this perspective, two separate hybrid structures were created by using Artificial Neural Network (ANN) together with Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO). By using the hybrid structures created, both the network model and the hyper parameters in the network structure, together with the time intervals of the daily closing prices and how many data should be taken retrospectively, were optimized. Employing the created GA-ANN (DCP1) and PSO-ANN (DCP2) hybrid structures and the 721-day Bitcoin series, the goal of accurately predicting the values that Bitcoin will receive has been achieved. According to the comparative results obtained in line with the stated objectives and targets, it has been determined that the structure obtained with the DCP1 hybrid model has a success rate of 99% and 97.54% in training and validation, respectively. It should also, be underlined that the DCP1 model showed 47% better results than the DCP2 hybrid model. With the proposed hybrid structure, the network parameters and network model that should be used in the ANN network structure are optimized in order to obtain more efficient results in cryptocurrency price forecasting, while optimizing which input data should be used in terms of frequency and closing price to be chosen.

Eduardo Augusto de Medeiros Silva, Ivan da Silva Sendin

Selfish Mining is an attack on the proof-of-work-based cryptocurrency consensus mechanism, enabling attackers to gain more than their fair share of rewards. Its existence indicates that the Nakamoto consensus is not incentive compatible and could jeopardize blockchain security. Recently, a method employing the Z-Score to detect selfish mining was proposed. This paper introduces a non-parametric statistical technique to identify traces of selfish miners on the blockchain without assuming any specific statistical distribution for the analyzed data. Additionally, the applicability of this type of analysis is discussed.

Ebuka Orioha

Consumers and brands are at serious risk due to the growth of counterfeit goods, especially in regions like Nigeria. Conventional techniques, such border inspections and market raids by the Standards Organization of Nigeria (SON), are inadequate for detecting counterfeit goods. To ensure traceability, transparency, and immutability in the supply chain, this article suggests utilizing blockchain technology. The decentralized and encrypted characteristics of blockchain, when bolstered by smart contracts, enable efficient product tracking from producers to end users, hence impeding the infiltration of fake goods. Using a permissioned blockchain network, this system attempts to confirm the legitimacy of products at every point along the supply chain—manufacturers, distributors, retailers, and end users. The Remix IDE is used to deploy and test Ethereum-based smart contracts that were created in Solidity for the proposed system. This blockchain-based strategy aims to decrease the spread of counterfeit goods, protect consumer confidence, and preserve brand reputation. To offer a user-friendly interface for wider accessibility, future advancements will link this system with decentralized apps (DApps).

Udit Agarwal, Vinay Rishiwal, Mohd. Shiblee, Mano Yadav · 5 authors

No abstract is available for this record.

Tarundeep Singh, Sarthak Mishra, Deepak Pandey, Chandani Sharma · 6 authors

As a vital resource, Bitcoin has a substantial influence on economic marketplaces. This work accentuates the status of precisely estimating Bitcoin prices by seeing numerous aspects that stimulate its worth. Our goal is to recognize the most pertinent reasons and to forecast Bitcoin prices daily, showcasing outlines in Bitcoin pricing. The dataset embraces regular remarks over a year, covering the preliminary price, peak price, last price, concluding price, Bitcoin trading volume, volumes of other financial gauges, and biased values. These aspects are measured in forecasting the subsequent day's concluding price. The research employments machine learning techniques, precisely linear regression and random forest algorithms, to estimate Bitcoin prices. Exploiting an inclusive dataset from Y-Finance and Wiki, spanning five years of market data, the work accentuates the status of data preprocessing to sustain data eminence and significance. The random forest model is optimized using a grid search for hyperparameters, and performance is evaluated using Root Mean Squared Error and Pearson’s correlation coefficient. This paper highlights how these machine learning methods can improve Bitcoin price predictions, providing practical insights into model development and evaluation within volatile financial markets.

R. Mahesh, Kartik Anilkumar, S Shwetha, Dr.K.Pavan Kumar · 6 authors

Efficiency, security, and transparency have been improved by the smart grid energy management system's combination of IoT and blockchain technologies. Real-time data is collected by IoT devices, and safe transactions are recorded in a decentralized ledger using blockchain. In this chapter, the important elements have been discussed with distributed ledgers, smart meters, and sensors. Demand response, integration of renewable energy sources, and grid resilience have been enhanced by successful implementations. Issues related to interoperability, privacy, and scalability are tackled. The use of AI and ML in energy management, demand forecasting, and anomaly detection is also described in this chapter.

Jingxiang Cui

Currently, there are relatively few studies on Bitcoin price prediction, and accurate prediction of Bitcoin price is the focus of economic policy makers as well as investors. In this paper, we combine the methods of traditional time series model (ARIMA) and deep learning model (LSTM) to analyze the price prediction of bitcoin historical data. The empirical results show that the single LSTM model has the best prediction effect in the three prediction intervals of short-term, medium-term, and long-term. The ARIMA-LSTM hybrid model has a little improvement compared to the ARIMA model, which is chosen in this paper under the assumption that the relationship between the linear and nonlinear parts is additive, which will make the prediction effect worse. This problem will be avoided if the nonlinear combination is used for modeling, making the prediction better.

H.R. Ranganatha, A Syed Mustafa

No abstract is available for this record.

Zalina Fatima Azzahra, Nur Ichsan Utama, Sinung Suakanto, Toni Dwi Setiawan

In distributing crude oil from production wells to oil processing units, the pipeline network is considered the primary means of transporting petroleum products. However, in the process of distributing oil using pipes, several problems are currently still found, namely the discrepancy in the amount of oil sent from gas stations with the amount of oil arriving at oil processing refineries, the falsification of data and the occurrence of oil theft through pipes due to the absence of monitoring along the pipeline route. Pipeline monitoring is complex because pipelines are often located in remote areas and are difficult to reach. Therefore, to overcome this problem, this research proposes a solution by creating a prototype monitoring system to monitor oil distribution through pipes using blockchain technology and supported by sensor devices installed on pipes. This research was developed using Ethereum as a blockchain and the solidity programming language and a private blockchain network to maintain the privacy and security of oil distribution data through pipelines by data records from sensors along the pipe. Based on the results of designing a private blockchain-based oil distribution monitoring system through pipelines that have been proposed in this research, it is hoped that this system can help overcome problems that occur in the oil distribution process through pipelines and can help make it easier for parties to record data and monitor oil distribution in Indonesia.

Adam Fitriawijaya, Taysheng Jeng

Multimodal generative AI and generative design empower architects to create better-performing, sustainable, and efficient design solutions and explore diverse design possibilities. Blockchain technology ensures secure data management and traceability. This study aims to design and evaluate a framework that integrates blockchain into generative AI-driven design drawing processes in architectural design to enhance authenticity and traceability. We employed a scenario as an example to integrate generative AI and blockchain into architectural designs by using a generative AI tool and leveraging multimodal generative AI to enhance design creativity by combining textual and visual inputs. These images were stored on blockchain systems, where metadata were attached to each image before being converted into NFT format, which ensured secure data ownership and management. This research exemplifies the pragmatic fusion of generative AI and blockchain technology applied in architectural design for more transparent, secure, and effective results in the early stages of the architectural design process.

Poorva Nayyar, K. K. Bhardwaj, Saiyam Gupta, Ravi Prakash Chaturvedi · 6 authors

The development of financial technology has given rise to a new kind of asset called cryptocurrency, which has presented a significant potential for study. Forecasting cryptocurrency prices is challenging because of their dynamism and unpredictability. Each of the three recurrent neural network, or RNN, algorithms proposed in this paper may be used to predict the prices of three distinct cryptocurrency types: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The algorithms generate accurate forecasts based on the average absolute percentage error (MAPE). For both cryptocurrency variations, the gated recurrent unit (GRU) fared better in terms of prediction than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models, according to the models' results. It is therefore regarded as the best algorithm.

Geeta Sandeep Nadella, Karthik Meduri, Hari Gonaygunta, Snehal Satish · 5 authors

In the dynamic and rapidly evolving landscape of blockchain technology, traditional fraud detection methods, which often rely on labeled data, face limitations due to the diverse and adaptive nature of fraud. This study introduces a novel framework that employs the K-Means clustering algorithm, a technique celebrated for its unsupervised learning capabilities, to detect anomalous transaction patterns indicative of potential fraud, such as unusually high transaction volumes or rapid transfers between wallets. By circumventing the need for pre-labeled examples of fraudulent activity, our approach significantly enhances adaptability and applicability across various blockchain contexts. We apply this framework to a comprehensive dataset encompassing multiple cryptocurrencies, including Bitcoin, Ethereum, Doge Coins, and Tether, analyzing attributes such as closing prices, volatility, and market volume. The results demonstrate the framework’s effectiveness in isolating outliers and identifying transactions that bear hallmarks of suspicious activity, thereby contributing a powerful tool for proactive fraud detection. This research not only paves the way for future advancements in blockchain security but also reinforces the trustworthiness and integrity of blockchain systems by providing a robust mechanism for identifying and mitigating fraudulent activities without the constraints of traditional, supervised methods.

G. Vijayakumar, Kunwar Singh, Karthika SK

No abstract is available for this record.

N Nagajothi, T. Meyyappan

Bitcoin's growing popularity has spurred interest in understanding its price dynamics. This study investigates the relationship between public sentiment towards Bitcoin and its price fluctuations. By analyzing Facebook and Twitter data, we employed a novel approach combining deep learning and fuzzy logic. Sentiment analysis was conducted using multiple lexicons, followed by clustering and classification of reviews into positive, neutral, and negative categories. Subsequently, a two-level fuzzy logic model integrated sentiment data and Bitcoin prices to predict future prices. The proposed methodology outperformed existing models, demonstrating the effectiveness of our approach in capturing the complex interplay between public opinion and Bitcoin price trends.

Ashutosh Kumar Singh, K. K. Ramachandran, Somanchi Hari Krishna, Chhaya Nayak · 7 authors

No abstract is available for this record.

Bal Ram, Pratima Verma

Blockchain technology has revolutionised how data is stored, managed, and secured. Its decentralised, transparent, and immutable nature presents unique advantages for data security. This paper delves into the application of blockchain technology in enhancing data security, exploring its fundamental principles, mechanisms, real-world applications, benefits, and challenges. By examining case studies across various industries, this paper aims to demonstrate the transformative potential of blockchain technology in securing data and protecting against cyber threats.

Hirotsugu Seike, Yasukazu Aoki, Noboru Koshizuka

Bitcoin, which was launched in 2009, is one of the most popular blockchains. To sustain and secure the system, enough mining power must be needed. However, various factors can encourage miners to leave the Bitcoin network. This risk should be assessed in advance by introducing metrics to detect hash rate changes. For this purpose, this paper proposes a statistical method to determine whether there is a significant difference in computational power for the two given periods. Our proposal consists of three hypothesis tests that consider Bitcoin difficulty adjustments. The first and second tests detect hash rate changes in the mining resources across the Bitcoin network. The third focuses on shifts in the distribution of mining power among different pools. We conducted simulations to elucidate the statistical properties of the detection power of the first and second methods. In addition, we apply our three statistical tests to the Bitcoin block data at height 756,000 through 836,640 (from September 28, 2022 to March 28, 2024). Based on the inference results, we discuss how the mining power had fluctuated by some intervention effects, such as the drop in the Bitcoin price. This provides insights to understand and evaluate the stability of Bitcoin.

Vassilis Papaspirou, Ioanna Kantzavelou, Yagmur Yigit, Λέανδρος Μαγλαράς · 5 authors

The evolution of authentication mechanisms in ensuring secure access to systems has been crucial for mitigating vulnerabilities and enhancing system security. However, despite advancements in two-factor authentication (2FA) and multi-factor authentication (MFA), authentication mechanisms remain weak in system security, particularly when individuals accessing critical systems are involved. In response to this challenge, we propose a novel blockchain-based multi-factor dynamic authentication mechanism (BMFA) that integrates honeytoken technology to enhance security. Our proposed mechanism leverages Ethereum blockchain technology and smart contracts to provide a decentralized and robust authentication framework. By incorporating honeytokens into smart contracts, we introduce a dynamic layer of security that continuously adapts to prevent potential attacks. Our evaluation demonstrates that our BMFA mechanism effectively addresses various security challenges, including brute force attacks, man-in-the-middle attacks, and smart contract vulnerabilities, while providing robust protection against unauthorized access. Our findings emphasise the efficacy of the BMFA mechanism in enhancing system security and mitigating evolving threats in authentication processes for next-generation critical industrial control systems.

Wai Yie Leong, Yuan Zhi Leong, Wai San Leong

Improving blockchain security is essential for guaranteeing the durability, integrity, and secrecy of decentralised transaction networks. This study highlights key strategies and techniques for bolstering blockchain security. These include leveraging cryptographic primitives for data encryption and authentication, implementing robust consensus mechanisms to prevent tampering, and employing network security measures to mitigate external threats. Additionally, smart contract security practices, access control mechanisms, and compliance with regulatory standards play pivotal roles in fortifying blockchain ecosystems. By adopting a multi-layered approach that addresses technical, operational, and regulatory aspects, blockchain systems can enhance their security posture and foster trust among users and stakeholders. Enhancing Healthcare Data Security with blockchain was discussed in the case study. Ongoing research, collaboration, and adherence to best practices are essential for continuously evolving blockchain security in response to emerging threats and vulnerabilities.