Crypto markets present significant challenges in financial time series forecasting with their high volatility and unpredictable nature. In this study, Optuna Based Optimized Transformer (OBOT) was proposed for time series forecasting for Bitcoin, the pioneer of cryptocurrency markets. To compare the proposed OBOT, Autoregressive Integrated Moving Average (ARIMA), Gradient Boosting Trees, Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Temporal Convolutional Network (TCN) with optimized hyperparameters were used. In particular, after the success of Transformer models in natural language processing, studies have been conducted on their potential for time series problems. The models were evaluated using Optuna for hyperparameter optimization and their performance was compared with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The generalized performance of the models was tested by dividing the data set into different time steps (6–12) and training and test sets at different rates (0.5-0.5, 0.7-0.3, 0.8-0.2). The results show that the proposed OBOT approach stands out for Bitcoin with an RMSE value of 0.0079 and a MAE value of 0.0122. These findings reveal that the proposed OBOT approach have significant potential in crypto market forecasting and should be examined in more detail in future studies.
Blockchain technology is extensively employed across various industries, transforming conventional processes, and enhancing efficiency, security, and transparency. The decentralized nature of blockchain prevents any single entity from having control, fostering trust, and minimizing the risk of manipulation. Proposing a blockchain solution is not enough as the success and adoption of such solutions heavily depends on the overall performance. The performance of the blockchain based solutions can be measured using various metrics such as latency, throughput, and CPU utilization. This study is using throughput as a performance metric and demonstrating how the Hyperledger Caliper tool can be used to measure it. To illustrate the use of Hyperledger Caliper, a scenario has been devised involving the presence of multiple organizations and peers within the blockchain network. This test-network comprises three organizations, three certificate authorities, three orderer-nodes and three peers per organization. The proposed network is set up using Hyperledger Fabric blockchain framework. This scenario depicts how remote patient monitoring systems work in a multi-organization environment. The experiment results observe a very minute difference in send rate and throughput when data is injected at 80 and 100 TPS during the write operations but this difference is missing during the read operations as send rate and throughput is observed almost identical.
Talat Kemal Satilmisoglu, Yusuf Sermet, Musa Kurt, İbrahim Demir
Blockchain technology has been used for the digitalization of physical asset management and data management processes in many areas in the industry and academia, including the water domain. Its potential as an immutable data storage system and smart contract integration has provided a plethora of use cases and utility in the domain of hydrology and water resources. This systematic review critically examines the application of blockchain technology in the field of water resources and hydrology. By analyzing 104 academic publications and 37 non-academic studies from 2017 to 15 October 2022, this paper identifies the current state of blockchain applications in water management, delineates their potential use cases, and assesses their practical utility and scalability. Despite the theoretical promise of blockchain for enhancing water governance, data security, and stakeholder trust, the review reveals a noticeable gap between theoretical potential and the existence of workable, real-time applications specifically in water resources management. The findings indicate that while blockchain technology has been effectively implemented in various sectors, its adoption in hydrological domain is still emerging, with limited empirical evidence to support full-scale deployment. The paper concludes with a call for more empirical research to validate theoretical benefits, address scalability and interoperability challenges, and integrate blockchain technology with real-time data networks for sustainable water management practices.
<title>Abstract</title> The emergence of Bitcoin as a decentralized digital currency has underscored the importance of developing advanced techniques for predicting its price fluctuations. This study evaluates the predictive power of Bitcoin-related Google search volumes and Twitter sentiment analysis within short time frames. By leveraging machine learning algorithms and opinion mining, we identify correlations between online behaviors and Bitcoin price movements. Our methodology encompasses data sourcing, preprocessing, exploratory analysis, feature selection using Correlation Analysis, F-regression, Shapley values, and price prediction with a Long Short-Term Memory (LSTM) model. Findings reveal that Google search data, compared to Twitter sentiment, significantly enhances model accuracy and reduces prediction errors. The study suggests future research to investigate other search engines and online news sentiment, acknowledging limitations in data quality and accessibility of historical Twitter data.
Shefali Arora, Ruchi Mittal, Avinash K. Shrivastava, Shivani Bali
Purpose Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages. Design/methodology/approach The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks. Findings By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency. Originality/value There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.
Abstract Predictive demand forecasting plays a pivotal role in optimizing supply chain management, enabling businesses to effectively allocate resources and minimize operational inefficiencies. This paper introduces a novel approach to enhancing demand forecasting processes by leveraging the Ethereum virtual machine within a blockchain framework. The proposed system capitalizes on the inherent security, transparency, and decentralized nature of blockchain technology to create a secure and efficient platform for predictive demand forecasting. The system leverages the Ethereum virtual machine to establish a secure, decentralized, and tamper‐resistant platform for demand prediction while ensuring data integrity and privacy. By utilizing the capabilities of smart contracts and decentralized applications within the Ethereum ecosystem, the proposed system offers an efficient and transparent solution for demand forecasting challenges. The current research focused on Ethereum virtual machine characteristics, features, components, and implementation details. A secured framework for the prediction of demand forecasting systems is proposed. Finally, the authors tried to validate and optimize the gas cost by using distinguished statistics and analysis.
Numerous consensus mechanisms have been suggested to cater to the specific characteristics of fog computing. To comprehensively understand their unique features, performance, and applications in fog computing, it is crucial to conduct a systematic analysis of these mechanisms. For this study, 79 relevant articles were carefully selected based on predefined criteria. Among these articles, 35 employed work-proof-based consensus mechanisms, 24 utilized voting-based mechanisms, and 22 adopted capability-based mechanisms. Among the 26 identified consensus mechanisms, proof of work remains the most prevalent one. It’s important to note that the scope of this paper is limited to the research available in the predominant databases at the time of writing. Future research may expand to include additional databases and more recent literature in this domain.
In a Blockchain network the Certificate Authority (CA) is a vital component. The decentralization feature of the blockchain enables the network to have different CA for different transaction. The CA selection process is executed every time with each new transaction. This process is fine with small size network. However, if the network size increases this selection process can take numerous amounts of time, as the algorithm chosen to select the CA needs to search in entire network which increases the computational time and overhead of the network. If this search space can be decreases then, the resulting computational time and overhead can be decreased. The search space can be reduced by implementing the Agglomerative clustering technique. Each cluster is identified by a cluster index (CV) the average of the response time (R) and Validation Time (V). The nodes want to initiate transaction needs to select a appropriate cluster by entering a target budget (TB). The cluster having the CV less than TB is selected and then, Proof of Stake (PoS) consensus mechanism is applied to select the CA. Finally, the proposed model is evaluated over gas utilization, response time and validation time comparison.
M. Shyamala Devi, J. Arun Pandian, D. Umanandhini, B. Surekha · 5 authors
Bitcoin is the very first decentralized digital money in the entire world. Bitcoin is exceptionally reliable since it uses a block chain instead of a service provider like a banking institution. As a type of digital precious metal like gold, the price of a single bitcoin seems to have been significantly increasing since the year 2010. As a result, bitcoin is extremely risky for speculators as its value varies often. Since prices may now be predicted in real time, traditional techniques to price forecasting have failed to provide sufficient information and responses. This paper recommends Deep Q-Network based Reinforcement Learning technique (DQNRL) to predict the bitcoin price more effectively. The Binance Bitcoin Futures Price Dataset from Kaggle that contains 3082284 samples and is used towards the bitcoin future cost prediction for DQNRL. The DQNRL model starts with the data exploration that portray the bitcoin price with the standard deviation and mean, log transformation of data. After data preparation, the Binance Bitcoin Futures Price dataset is fitted with the existing regression technique and proposed DQNRL model and the performance is analyzed with MAE, MSE and RMSE. The proposed DQNRL exhibits low MAE with 0.2471, low MSE with 0.061 and low RMSE of 0.2469 when compared to other existing regression models after implementation.
Amrita Jyoti, P. K. Gupta, Sonam Gupta, Harsh Khatter · 5 authors
Ethereum is an open-source, public, blockchain-based distributed computing platform and operating system that allows the development and execution of distributed applications without the risk of downtime, fraud, control, or intervention from a third party. Along with serving as a platform, Ethereum also offers a Turing-complete Blockchain programming language that aids in the publication of distributed applications. One of the major Ethereum projects is Microsoft's collaboration with Consensus, which provides Ethereum Blockchain as a Service (EBaaS) on Microsoft Azure to give enterprise clients and developers access to a cloud-based blockchain development environment with a single click. Only the implementation determines the size of the Ethereum blockchain. Geth's Ethereum blockchain is around 11 GB in size, compared to Parity's 6 GB. Although the total size of the Ethereum blockchain, in its entirety, may reach 60GB+. Even though the toolset you require may vary depending on the specific blockchain, the majority of tools are compatible with Ethereum, therefore here we highlighted the various enhancement tools that we use for implementing blockchain applications on the Ethereum platform.
Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis.
Liquidity is the ease of converting an asset (physical/digital) into cash or another asset without loss and is shown by the relationship between the time scale and the price scale of an investment. This article examines the illiquidity of Bitcoin (BTC). Bitcoin hash rate information was collected at three different time intervals; parallel to these data, textual information related to these intervals was collected from Twitter for each day. Due to the regression nature of illiquidity prediction, approaches based on recurrent networks were suggested. Seven approaches: ANN, SVM, SANN, LSTM, Simple RNN, GRU, and IndRNN, were tested on these data. To evaluate these approaches, three evaluation methods were used: random split (paper), random split (run) and linear split (run). The research results indicate that the IndRNN approach provided better results.
Researchers and experts have developed various techniques, models, and methods to predict the price movements of cryptocurrencies, especially Bitcoin.However, among the many techniques studied in the literature, there is still a lack of focus on mining, creating, and developing datasets with specific patterns for predicting the next cryptocurrency price movement.This is an exciting reason to conduct further research.A web-based Patterned Dataset Application and a Telegram bot were constructed to address this issue.These tools read the price position of each cryptocurrency and predict the next price direction based on the last position indicated by the Patterned Dataset Application.The experiment's results show that when the Patterned Dataset Application shows a diamond crash position, it is time to make a purchase; conversely, when it shows a diamond moon position, it is time to make a sale.It is hoped that by utilizing the Patterned Dataset Application, potential losses can be minimized, and there is more potential for profit in cryptocurrency trading.Even though the initial data source comes from Indonesia's most prominent digital cryptocurrency trading market, according to coinmarketcap, namely Indodax, the results of this patterned dataset application can often describe the same cryptocurrency conditions globally.The novelty of this research is to produce a new way of predicting the next cryptocurrency price movement using patterned datasets.At the end of this paper, it will be proven that hypothesis 1 and hypothesis 2 on the results of the patterned dataset are true.
Amid growing concerns over the impacts of climate change on worldwide water security, Bitcoin’s water footprint has rapidly escalated in recent years. The water footprint of Bitcoin in 2021 significantly increased by 166% compared with 2020, from 591.2 to 1,573.7 GL. The water footprint per transaction processed on the Bitcoin blockchain for those years amounted to 5,231 and 16,279 L, respectively. As of 2023, Bitcoin’s annual water footprint may equal 2,237 GL. To address this increasing water footprint, miners could apply immersion cooling and consider using power sources that do not require freshwater. A change in the Bitcoin software could also significantly reduce the network’s water footprint.
This paper provides an in-depth examination of the latest trends in financial technology (FinTech) and their profound impact on the global financial sector.By delving into groundbreaking innovations such as blockchain technology, the integration of artificial intelligence (AI) in banking, and the burgeoning prominence of digital currencies, this study seeks to offer a comprehensive understanding of the current state of FinTech.We explore how blockchain is revolutionizing financial transactions with its decentralization and increased security, while AI in banking is enhancing customer experiences, automating processes, and bolstering risk management.Additionally, the paper highlights the rise of digital currencies, discussing their potential to redefine monetary systems and their influence on global finance.Our investigation extends to the challenges and opportunities presented by these technological advancements, including regulatory hurdles, ethical considerations, and the need for new skill sets in the finance sector.Furthermore, the study contemplates the future trajectory of FinTech, speculating on how emerging technologies like quantum computing and the Internet of Things (IoT) could further transform financial services.This paper aims not only to provide insights into how FinTech is currently reshaping the financial landscape but also to anticipate the future direction of these developments.Through this analysis, we contribute to the broader understanding of FinTech's role in driving innovation, efficiency, and change in the financial world.
In recent years, blockchain technology has received attention because of its decentralized, immutable and other characteristics, but it faces storage and retrieval challenges. To address these challenges, this paper introduces IOTA distributed ledger technology, which solves the scalability and cost problems of traditional blockchains. By analyzing and experimenting the Tangle, the underlying consensus structure of IOTA, this paper reveals the main factors affecting its development, and proposes a segmented adaptive cutting-edge transaction selection algorithm to optimize the system performance. At the same time, based on IOTA distributed ledger, this paper proposes a data encryption storage and retrieval scheme, which speeds up the data link and retrieval speed, and ensures the integrity and security of data. Finally, this paper discusses the application of blockchain in accounting informatization, and puts forward the scheme of building a new generation of accounting informatization platform, which is of great value to the construction of accounting informatization.
Amit Kumar, Neha Sharma, Rahul Chauhan, Manish Sharma
The present research utilizes Topic Modelling as a methodology to acquire a deeper understanding of the goals and operations of Decentralised Autonomous Organisations (DAOs). This is achieved by examining textual data derived from the proposals put forth by these organizations. The issue at hand pertains to comprehending the multitude of ideas inside Decentralised Autonomous Organisations (DAOs) and their alignment with the respective objectives of these entities. Through the application of Topic Modelling, we aim to investigate textual patterns, identify topics, and discern significant themes within the decentralized autonomous organization (DAO) ecosystem. This research endeavor seeks to address the existing research gaps pertaining to the alignment of proposals with organizational objectives. This research aims to fill these knowledge gaps by examining the unique thematic priorities of various decentralized autonomous organizations (DAOs), providing insights into their functions, and elucidating their involvement in investment, community, technological, and monetary issues. Through the utilization of experimental research, this research provides DAO stakeholders with the ability to make wellinformed judgements, prioritize ideas, and customize methods in order to more effectively match with their distinct missions and objectives. Consequently, this research contributes to the enhancement of operational efficiency and governance within DAOs.
This paper explores the integration of social media data and natural language processing methods, specifically utilizing the Transformer model, to predict Bitcoin price movements. We aim to evaluate the effectiveness of using social media data and the Transformer model in forecasting market trends for Bitcoin. By analyzing social media posts and incorporating them into predictive models, we demonstrate the potential of the Transformer architecture in capturing complex dependencies and patterns within sequential Bitcoin prices. Additionally, different clustering methods are applied to process the original social media data in a rolling manner. The evaluation of Transformer-based models on historical data showcases their predictive performance compared to various social media data clustering approaches. Furthermore, the impact of incorporating outliers of social media data into the Transformer model is explored to improve prediction accuracy. The results of this study demonstrate the potential of clustering on social media data and the Transformer model for forecasting market trends.1
Blockchains have been widely adopted to track critical data in complicated applications recently, thus it is necessary to provide efficient infrastructure to analyze large-scale chain data. However, the current blockchain clients are mostly designed for a single node with limited storage space and the separated data analysis systems are not suitable for huge chains due to redundant data transformation and scalability. To address these challenges, we propose BigChain, a distributed blockchain client with built-in bigdata processing capability. BigChain is designed on top of HBase by mapping blockchain entities to HBase’s KV storage to guarantee the rapid growth of on-chain data. By integrating the MapReduce framework, BigChain could leverage the Hadoop big data ecosystem directly to analyze blockchain data in a scalable manner. Additionally, an indexing service is devised accordingly to speed up BigChain. Finally, comprehensive experiments were conducted against Ethereum, and the results indicate that BigChain can outperform typical clients by 10 times in terms of comprehensive scenarios.
Mohsin Kamal, Muhammad Tariq, Mian Ahmad Jan, Houbing Song
Blockchain technology has found applications across diverse domains owing to its ability to establish trust in a decentralized manner. Nevertheless, the integration of blockchain into critical infrastructure domains encounters significant challenges posed by the computational demands and storage requirements associated with the proof-of-work puzzle during the mining process. This scenario becomes particularly complex in the context of applications within the Industrial Internet of Things (IIoT), where stringent timeliness constraints are inherent, notably in functions such as intrusion detection and control. This paper presents a novel solution that takes into account the time-sensitive nature of application constraints within the IIoT. Specifically, we focus on online functions involving intrusion detection and control. By doing so, we address the imperative need for timely and secure data delivery, crucial in maintaining the integrity of hard-to-tamper ledger blocks. These blocks encapsulate measurements that are seamlessly utilized by various system functions and components. The proposed approach optimizes the utilization of heterogeneous resources governing blockchain computations. This optimization ensures that the desired properties for logging within the blockchain are met, enabling the prompt delivery of measurements. The novel collaborative mining technique entails the sharing of nonce ranges among miners, which effectively reduces the overall mining time and enhances the efficiency of the process.