Blockchain Papers

Iqra Sadia Rao, Miss Laiha Mat Kiah, M. Muzaffar Hameed, Zain Anwer Memon

No abstract is available for this record.

Adela Bârã, Simona‐Vasilica Oprea

No abstract is available for this record.

Kamal Kumar, Vinod Kumar, Seema, M. K. Sharma · 6 authors

Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.

Anamika Gupta, Gaurav Pandey, Rajan Gupta, Smaran Das · 7 authors

No abstract is available for this record.

Novan Fauzi Al Giffary, Feri Sulianta

The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02. Keywords: Cryptocurrency, Forecasting, Long Short Term Memory, Mean Square Error, Polynomial Regression, Support Vector Machine

Sanjay Kumar Jena, B. Praveen Kumar, Barunaditya Mohanty, Ayush Singhal · 5 authors

Blockchain and Machine Learning (ML) are state-of-the-art technologies in the digital era. Developing countries have witnessed great digital transformation, and one of the key challenges during this development phase has been to create a platform that integrates the health and vitals of citizens confidentially. The healthcare insurance industry is one sector that has experienced a significant number of instances of fraudulent claims and mismanagement of patient data. These issues often arise due to inadequate technological integration and an over-reliance on manual processes and human intervention. The insurance industry relies on multiple processes between end users to initiate, maintain, and close diverse policies. The proposed model initially recommends a suitable insurance policy for newly admitted patients, but in the case of existing patients, the objectives are to speed up transaction processing and payment settlement securely using a private blockchain. Collectively, these two technologies possess the potential to revolutionize the future. This study aims to incorporate blockchain and ML techniques like Support Vector Machine (SVM) and Random Forest Regression, which can differentiate between fraudulent and legal medical records to recommend personalized policies, streamline claim processing, and ensure the security of sensitive patient information and vital insurance records. The key aim is to create a more patient-centric environment with data transparency. This integrated framework results in a secure, adaptable, and efficient ecosystem that outperforms traditional methods, paving the way for the future of healthcare and insurance services.

Maged Farouk, Nashwa Shaker, Diaa Salama AbdElminaam, Omnia Elrashidy · 11 authors

Econometrics can be used to understand and forecast price movements, assess market efficiency, and explore the factors influencing Bitcoin's value and adaptation. Econometrics is related to bitcoin in seven categories: price analysis and prediction, market efficiency, determination of Bitcoin prices, risk analysis, adaptation and network effects, causality tests, and simulation and stress. Testing these analyses can be invaluable for policymakers, investors, and financial institutions interested in the economics of digital currencies. Bitcoin price prediction in machine learning has many challenges that have deep roots in 2 main properties: cryptocurrencies and complexities in the Machine Learning models. Many problems are associated with machine learning for bitcoin price prediction, such as overfitting, data quality and availability, latent variables, model interpretability, computational complexity, dynamic adaptation, market manipulation, anomalies, data snooping bias risk, and time horizon mismatch. In the paper, we proposed an efficient framework for the prediction of bitcoin using nine different machine learning algorithms (linear Regression, random forest, adaboost, tree, KNN, gradient boosting, constant, neural network, SVM) on five different datasets. The results revealed that linear Regression emerged as the optimal model for the first data set. In the second data set, the random forest model demonstrated superior performance. The third data set exhibited the highest efficacy when the Adaboost model was employed. The fourth data set yielded the best outcomes with the random forest model, while linear Regression was the most effective choice for the final data set.

Munirul Ula, Veri Ilhadi, Zailani Mohamed Sidek

Bitcoin’s daily value fluctuations are very dynamic. Understanding its rapid and intricate price movements demands advanced techniques for processing complex data. This research aims to compare the accuracy of two machine learning methods, Random Forest (RF) and Long Short-Term Memory (LSTM), in predicting Bitcoin price. This research employs RF and LSTM algorithms to forecast Bitcoin prices using a two-year Yahoo Finance dataset. The evaluation metrics used were accuracy based on Mean Absolute Percentage Error (MAPE) and computational power (CPU-Z). As a result of this research, the LSTM model demonstrates higher accuracy compared to the RF model. MAPE reveals LSTM’s precision of 99.8% and RF’s accuracy of 90.1%. Regarding computational time and resources, RF shows slightly better performance than LSTM. The visual comparison further emphasizes LSTM’s better performance in predicting Bitcoin prices, highlighting its potential for informed decision-making in cryptocurrency trading. This research contributes valuable insights into the effectiveness, strengths, and weaknesses of LSTM and RF models in predicting cryptocurrency trends.

Senior Software Engineering, Microsoft, Northlake, Texas, USA., Vijaya Kanaparthi

As Deep Learning (DL) continues to be widely adopted, the growing field of study on the robustness of DL approaches in finance is gaining steam. This paper investigates the robustness of a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) intended for daily closing price predictions of Bitcoin (BTC). The research entails reproducing and adjusting an LSTM design from previous research, with an emphasis on evaluating the robustness of the network. The network is trained using data that has been disturbed by Gaussian noise to assess robustness, and the effect on predictions made outside of the sample is examined. To examine the impact of adding Gaussian noise layers and noisy dense layers on training accuracy and out-of-sample predictions, further robustness tests are conducted. The results show that the LSTM network has remarkable robustness to random disturbances in the data. Nevertheless, the Root Mean Square Error (RMSE) of the prediction increases with the addition of Gaussian noise and noisy dense layers. When random noise is present in the training data, the Autoregressive Integrated Moving Average (ARIMA) model is more vulnerable to it than the LSTM, according to the robustness of the two models. These findings highlight how robustness DL techniques are overall when compared to more conventional linear methods. However, because these models are black-box, the study highlights the significance of comprehensive testing. Although the robustness of the LSTM is impressive, it is important to understand that each network may behave differently depending on the circumstances.

Vivek Mule

Abstract: With the help of specific factors, this effort seeks to improve the present analysis of bitcoin and forecast its price. After conducting a thorough investigation, it was determined which factors all contribute to daily fluctuations in the value of bitcoin. All of the data in this work is made up of various aspects from daily records from the previous few years. The first step in this endeavour is gathering all the data necessary to forecast the price of bitcoin. All of the data was compiled during the previous few years, and it was incorporated into this work. The Recurrent neural network (RNN) algorithm is employed in this work because it provides significantly improved accuracy than earlier techniques. In order for investors to invest in bitcoin easily and for beginners to this market or business, this study forecasts signs of change in the price of the cryptocurrency.

Rui Tanaka, Hideaki Miyaji, Hiroshi Yamamoto

In Japan, the number of companies allowing side jobs is increasing because model employment regulations are revised. However, there are various problems in the situation where the people can engage in both main and side jobs. For example, some small companies may not present details regarding the contract about the side job and may delay or refuse payment for work. In addition, it is difficult to check the progress of workers’ tasks correctly and to prevent long working times. To solve these problems, a management system is necessary for recording the status and the progress of the jobs. On the other hand, blockchain-based data management infrastructure that can share data among various participants while guaranteeing data integrity is getting attention. On the blockchain-based infrastructure, it is possible to issue and distribute various types of tokens (i.e., fungible tokens, non-fungible tokens (NFTs)) for currency and assets. In this study, we propose to utilize the blockchain technology to develop a new system that manage the status of job duties for workers and their respective working times. In this system, an NFT is issued to each worker for identifying which the main or the side job the worker is engaging, and the information on the token is updated based on the attendance status of the token holder. In addition, the system has a function of managing the time spent by the worker for each job by issuing a fungible token. The amount of the issued fungible token corresponds with the upper limit of working times allowed for the worker, and the token is consumed according to working times of the worker.

Huy Tran Tien, Kiet Tran-Trung, Vinh Truong Hoang

Financial anomalies must be detected in order for financial institutions and regulatory bodies to manage risks and avoid fraudulent behavior. Financial anomaly detection is the practice of identifying unexpected or irregular financial transactions or patterns that may indicate fraudulent behavior or errors. It is crucial in today's digital era to prevent fraud, limit financial losses, and maintain secure financial systems. Various types of financial anomalies, such as credit card fraud, money laundering, financial statement fraud, and cryptocurrency fraud, pose significant risks to individuals and organizations. This review critically evaluates a selected research article on the use of blockchain technology in conjunction with data mining techniques to detect financial anomalies. The paper employs the case study method to demonstrate how well the suggested integrated system works in spotting financial anomalies. This review evaluates the article's methodology and conclusions and discusses its implications for practice. The report claims that merging data mining methods with blockchain technology can increase the precision and effectiveness of financial anomaly identification. This research advances knowledge about how block-chain technology and data mining techniques can be used to find financial abnormalities while also offering suggestions for further study and use.

Abhishek Agnihotri, Abhishek Prabhakar, Kapil Kumar Pandey

No abstract is available for this record.

Shuo Chen, Zhiwei Liu, Xiang Xu, Haoyu Gao · 6 authors

No abstract is available for this record.

Udit Agarwal, Vinay Rishiwal, Mano Yadav, Teddy Mantoro · 6 authors

No abstract is available for this record.

Tapasya Choudhary, Vanshika Aggarwal, Shivansh Srivastava, Shivani Trivedi

No abstract is available for this record.

Muhammad Muneeb, Noman Islam, Mana Saleh Al Reshan, Mohammed Hamdi · 6 authors

No abstract is available for this record.

Rishabh Kumar, Gagandeep Marken, Aman Singh

No abstract is available for this record.

C. Ganeshkumar, Arokiaraj David, Jeganthan Gomathi Sankar

No abstract is available for this record.

Elena Schmid, Michael Truebestein, Matthias Daniel Aepli

No abstract is available for this record.

Ye Qiao, Guang Li, Jieying Zhou, Weigang Wu

No abstract is available for this record.

Hema Sundhar Marasani, S. John Justin Thangaraj

No abstract is available for this record.

Malik Abdelrahim, Fadi Al‐Turjman

No abstract is available for this record.

J A Madhurya, K. Meena

No abstract is available for this record.