Blockchain Papers

Joseph Mani, Hothefa Shaker, Nadheera Al Hosni

No abstract is available for this record.

Kulanshu Sharma, Rahul

No abstract is available for this record.

Gurpreet Kour Sodhi, Mekhla Sharma, Rajan Miglani

No abstract is available for this record.

Anandhavalli Muniasamy, Salma Abdulaziz Saeed Alquhtani, Linda Elzubair Gasim Alsid

No abstract is available for this record.

D. Bhowmick, Debabrata Barik, Debkumar Ghosh

No abstract is available for this record.

Mortaza Ojaghlou

No abstract is available for this record.

Yüksel Akay Ünvan, Cansu Ergenç

This study aims to find the best performing model in predicting cryptocurrencies using different machine learning models. In our study, an analysis was performed on various cryptocurrencies such as Aave, BinanceCoin, Bitcoin, Cardano, Cosmos, Dogecoin, Ethereum, Solana, Tether, Tron, USDCoin and XRP. Decision Trees, Random Forests, KNearest Neighbours (KNN), Gradient Boost Machine (GBM), LightGBM, XGBoost, CatBoost, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Short Term Memory networks in Long Comparisons (LSTM) models were used. The performance of the models is compared with Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The study results show that there is no single model that consistently outperforms others for all cryptocurrencies. Models such as XGBoost and Random Forests show consistent and strong performance across different cryptocurrencies, proving their robustness in this particular use case. Deep learning algorithms, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs), show significant accuracy in predicting some cryptocurrencies.

Farshid Mehrdoust

No abstract is available for this record.

S. Deepak, Preeti Gulia, Nasib Singh Gill, Mohammad Yahya · 7 authors

Internet of Things (IoT) plays an essential contribution in connecting devices and enabling seamless data exchange, leading to increased efficiency and convenience. However, security concerns in IoT systems are significant, as compromised devices can lead to data breaches and privacy violations. Blockchain technology can enhance IoT security by providing decentralized consensus, immutability, and transparent transaction records, ensuring secure and trustworthy communication and data integrity. This review article gives a succinct but thorough understanding of blockchain technology, covering architecture of blockchain, working principles, types, applications, platforms, and its role in the IoT environment. The study highlights potential benefits of blockchain like enhanced security and privacy, and explores its integration with IoT. Additionally, the study discusses various real-world applications, examines blockchain platforms, and addresses the limitations and challenges associated with blockchain technology. This review serves as a valuable resource for researchers and practitioners seeking a deeper understanding of blockchain’s potential and its implications in the IoT landscape.

Nathalie Tan Yhe Huan, Zuriati Ahmad Zukarnain

By 2025, the Internet of Things (IoT) infrastructure is projected to encompass over 75 billion devices, facilitated by the increasing proliferation of intelligent applications. The Internet of Things ecosystem consists of sensors that function as data generators and applications that necessitate financial transactions to compensate the data producers. Security is a highly important concern. Employing blockchain technology makes it feasible to enhance security by maintaining payments in a ledger that is not just secure but also translucent, distributed, and immutable. This article provides an introductory overview of the Internet of Things (IoT) and subsequently delves into the many security threats and vulnerabilities arising within the IoT framework. This study provided an overview of the blockchain, focusing on its categorization and important properties. Moreover, this article examines the necessity of combining blockchain technology with the Internet of Things (IoT), in addition to reviewing relevant literature and the studies conducted by other scholars. This article offers insight into the uses of blockchain on the Internet of Things (IoT).

Qichuan Huang

This study investigates the Fear & Greed Index, an indicator designed to reflect market sentiment regarding Bitcoin price, intending to utilize it as a predictive parameter for future price fluctuations. Due to the substantial volatility in Bitcoin prices and its significant influence on prediction outcomes, the dataset was preprocessed through monthly filtering and normalization. To forecast Bitcoin prices, an array of machine learning algorithms, including linear regression, random forest, and XGBoost, as well as their enhanced counterparts, were employed. The optimal model was identified by comparing the Grid Search XGBoost analysis results. This research holds implications for accurately predicting Bitcoin prices and underscores the impact of market sentiment on its valuation.

P. Preethy Jemima, C. Pretty Diana Cyril

No abstract is available for this record.

Vaibhav Bhajanka, Nitisha Pradhan

No abstract is available for this record.

Lisa JY Tan

No abstract is available for this record.

Areej Alhogail

No abstract is available for this record.

Manasa S. Desai, M. B. Nirmala

No abstract is available for this record.

Juvvala Sailaja, Kovvuri N. Bhargavi, G. L. Narasamba Vanguri, Nagireddi Suryakala · 5 authors

Crypto currencies have emerged as a popular investment option in recent years, with Ethereum being one of the most prominent ones.Accurate price prediction of Ethereum can provide valuable insights to investors and traders for making informed decisions.In this study, we utilized two time series prediction models, ARIMA (Auto Regressive Integrated Moving Average) and Facebook Prophet, to predict the price of Ethereum.This research focuses on collecting legacy price data of Ethereum from a reliable source.The data was preprocessed to handle missing values and outliers.ARIMA and Facebook Prophet models were then implemented on the preprocessed data to generate Ethereum price forecasts.The models were trained using a time period of historical data and validated using a hold-out set of data.The MSE, which measures the squared discrepancies between predicted and real Ethereum prices, was used to assess the models' performance.Lower MSE values indicate better model performance.The results revealed that Facebook Prophet outperformed ARIMA in terms of MSE, indicating superior accuracy in Ethereum price prediction.The higher accuracy of Facebook Prophet may be attributed to it's ability to handle seasonality, trend changes, and outliers, which are common characteristics of crypto currency price data.In conclusion, this study demonstrates the effectiveness of time series forecasting models, specifically ARIMA and Facebook Prophet, in predicting Ethereum prices.The findings suggest that Facebook Prophet may be a more accurate model compared to ARIMA for Ethereum price prediction, as evidenced by lower MSE values.The study provides valuable insights for investors and traders interested in utilizing forecasting models for Ethereum price prediction, and may serve as a basis for further research in this area.

Likhitha Amasala, Mahesh Datta Sai Ponnuru, P. Srideviponmalar

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors.

Azamjon Muminov, Otabek Sattarov, Daeyoung Na

In the Bitcoin trading landscape, predicting price movements is paramount. Our study focuses on identifying the key factors influencing these price fluctuations. Utilizing the Pearson correlation method, we extract essential data points from a comprehensive set of 14 data features. We consider historical Bitcoin prices, representing past market behavior; trading volumes, which highlight the level of trading activity; network metrics that provide insights into Bitcoin’s blockchain operations; and social indicators: analyzed sentiments from Twitter, tracked Bitcoin-related search trends on Google and on Twitter. These social indicators give us a more nuanced understanding of the digital community’s sentiment and interest levels. With this curated data, we forge ahead in developing a predictive model using Deep Q-Network (DQN). A defining aspect of our model is its innovative reward function, tailored for enhancing predicting Bitcoin price direction, distinguished by its multi-faceted reward function. This function is a blend of several critical factors: it rewards prediction accuracy, incorporates confidence scaling, applies an escalating penalty for consecutive incorrect predictions, and includes a time-based discounting to prioritize recent market trends. This composite approach ensures that the model’s performance is not only precise in its immediate predictions but also adaptable and responsive to the evolving patterns of the cryptocurrency market. Notably, in our tests, our model achieved an impressive F1-score of 95%, offering substantial promise for traders and investors.

Serkan NAS, Eyşe Ergin Ünal

Amaç–Bitcoin başta olmak üzere kripto varlık fiyatlarında meydana gelen hızlı değişimler gerek finansal yatırımcı gerekse medya tarafından ilgi görmektedir. Buna bağlı olarak kar elde etmek amacı başta olmak üzere pek çok farklı motivasyonla birçok araştırmacı ve finansal aktör, Bitcoin fiyatını etkileyen çeşitli faktörleri belirlemeye çalışmaktadır. Bitcoin fiyat hareketlerini etkilediği düşünülen Fed faiz oranı, altın ve Bitcoin’in farklı fiyat göstergeleri gibi öznitelikler üzerine detaylandırılan bir inceleme yürütülmektedir. Bu bağlamda fiyatları tahmin etmek için kullanılan çeşitli makine öğrenme algoritmaları üzerinde sistematik bir analiz yapılmaktadır.Yöntem –Farklı dört makine öğrenme modeli kullanılmış olup farklı tahmin hata oranları elde edilmiş ve her birinin çalışmada kullanılabileceği görülmüştür. Bulgular –Bitcoin veri seti için önerilen en iyi tahmin performansının sırasıyla Tesadüfi Ağaç (RF) %96,38, Karar Ağacı (DT) %96,28, Doğrusal Regresyon 95,06 ve Stokastik Gradient Descent(SGD) Doğrusal Regresyon %93,91 şeklinde olduğunu göstermektedir. Bitcoin fiyat değişimlerininFed faiz oranı ve altından ziyade kendi fiyat değişimlerinden daha yüksek oranda etkilendiği diğer sonuçlar arasında yer almaktadır. Tartışma –Tahmin modellemesinde en iyi sonuçları veren iki algoritmaya bakıldığında, gün içi en yüksek fiyatın son derece etkili olduğu söylenebilmektedir. En düşük fiyat ise ikinci derece en etkili özniteliktir. Söz konusu sonuç, Bitcoin’in en çok kendi fiyat dalgalanmalarından etkilendiğini göstermektedir.

Kanwarpartap Singh Gill, Vatsala Anand, Rahul Chauhan, Ashish Garg · 5 authors

Forecasting the value of Ethereum (ETH) or any other cryptocurrency is a formidable undertaking owing to the inherent volatility and speculative characteristics shown by these digital assets. Nevertheless, it is possible to create price forecasts by using machine learning techniques, namely Recurrent Neural Networks (RNNs), which are capable of capturing temporal relationships within the data. The challenge of forecasting the price of Ethereum (ETH) or any cryptocurrency is a multifaceted endeavour that encompasses aspects of finance, economics, and data science. The practise of technical analysis is the examination of past price charts, patterns, and technical indicators in order to make forecasts about future price fluctuations. The underlying assumption is that previous pricing patterns had the capacity to provide valuable insights into future developments. Nevertheless, it is essential to acknowledge that the effectiveness of technical analysis within the realm of cryptocurrency trading is a subject that engenders much scholarly discourse. The primary objective of this research is to examine the utilisation of Ethereum cryptocurrency and forecast its behaviour via the use of machine learning methodologies, namely Recurrent Neural Networks. The suggested approach demonstrates a high level of accuracy, reaching 95 percent. This significant level of precision will be beneficial for future academics working on this technology.

Jae Won Choi, Young Keun Choi

<span>The majority of research on predicting the price of Bitcoin employs technical methods to enhance long short-term memory models' effectiveness. Although some studies employ different machine learning techniques, such as economic or technical indicators, their precision is inadequate. Thus, this research aims to introduce a model that predicts the price of Bitcoin by utilizing the long short-term memory (LSTM) technique and incorporating gold's economic and technical data as features. The research collected gold and Bitcoin price data from FinanceDataReader for around seven years, from January 1, 2016, to January 22, 2023, consisting of six categories: date, open, high, low, close, volume, and change (based on dollars). The normalized closing price data was trained for 50 epochs, resulting in the loss value reaching close to zero. The model's accuracy was measured by mean squared error, resulting in a score of 0.0004. This study's importance is two-fold: firstly, it can provide cryptocurrency-related businesses with more accurate predictions and improved risk management indicators. Secondly, incorporating economic metrics can address the limitations of overfitting and a single model's poor performance.</span>

Peter T. Yamak, Yujian Li, Ting Zhang, Kyefondeme C. Dakurah

This research proposes a novel approach for forecasting cryptocurrency prices, specifically Bitcoin which dominates the market. Accurately predicting cryptocurrency values is challenging due to their highly volatile nature. The proposed hybrid model uses ResNet Convolutional Neural Network to encode Bitcoin price time series data into discriminative representations. These representations capture long-range dependencies using XGBoost regression. Additionally, wavelet denoising is applied to filter noise from the price data. The combined ResNet-XGBoost-Wavelet model achieves satisfactory results for Bitcoin price forecasting and has practical applications for developing quantitative trading strategies. While incorporating sentiment analysis and additional influencing factors could further improve predictions, this work presents a competitive approach for minimizing investment risks and maximizing profits in the complex domain of cryptocurrency markets.

M Dhanushwar, K Gokul Krishnan, M. P. Gopinath, Shiva Vinod · 5 authors

This research study addresses the challenging issue of predicting the highly volatile price of Bitcoin, which exhibits significant fluctuations. Surprisingly, despite their successful applications in various engineering and scientific domains, Convolutional Neural Networks (CNNs) have been largely neglected in the context of financial time series modeling. Therefore, the primary emphasis of this study is on exploring the potential of CNNs for improving Bitcoin price prediction. Our model harnesses the capabilities of CNN to extract crucial features from historical Bitcoin transaction data, revealing underlying patterns and trends. Subsequently, LSTM is employed to analyze and predict Bitcoin price movements based on these learned features. To bolster prediction accuracy, we integrate external factors, including macroeconomic variables and investor sentiments, with the historical Bitcoin data. The model’s performance is rigorously evaluated on a comprehensive real-world dataset, assessing its effectiveness in predicting Bitcoin’s price direction and magnitude. The results demonstrate promising prediction accuracy, surpassing conventional models and showcasing the potential of leveraging artificial intelligence in cryptocurrency price forecasting.