Recently, financial globalization is an extremely improved in distinct manners for enhancing service quality with advanced resources. An effectual application of bitcoin Blockchain (BC) approaches allows the shareholders to be concern regarding the risk and return of financial product. The shareholders mainly concentrate on the predictive of risk and return rates of financial product. Thus, an automated return rate bitcoin predictive method develops vital for BC financial product (FP). A newly planned machine learning (ML) and deep learning (DL) techniques offers a way for the return rate predictor systems. This work designs a Lion Swarm Optimization with Deep Learning Driven Predictive Model on Blockchain Financial Product Return Rates (LSODL-BFPRR) technique. The projected LSODL-BFPRR technique lies in the effectual forecasting of return rates in the BC financial sector. In the presented LSODL-BFPRR technique, stacked bidirectional gated recurrent unit (SBiGRU) approach was exploited for return rate classification. To modify the hyperparameters based on the SBiGRU approach, the LSO algorithm is used. The LSODL- BFPRR technique exploits Ethereum (ETH) return rate as the target. The experimental outcomes of the LSODL-BFPRR technique are tested using a series of simulations and the results demonstrate the effectual predicting results of the LSODL-BFPRR technique over other ones.
In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combina-tion of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The sec-ond key discovery was that a strong combination forecasting model that responds to the multiple fluctua-tions that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction accuracy criterion and matching curve-fitting in this work demonstrated that if the residuals of the revised model are white noise, the forecasts are unbiased. Future work investigating robust hybrid model forecasting using fuzzy neural networks would be very interesting.
Applications of centralised databases have been found in many areas. Almost every organization, private or public, makes use of centralised databases. These databases hold the entire scenario about the organization, like records of their customers, transactions occurring in the organization, past analysis, current performance, future prediction, and a lot more things that can be calculated over them. Basically, these databases are the roots of a particular organization. But the issue regarding these centralised databases is that their access is totally open to some individuals or a particular group of people within the organization. The issue that has been identified is corruption. It is difficult for an outsider to manipulate the data, but it is very simple for anyone within the organisation to do so. Now here the owner of the organization, whether an individual or the government, and the customers of the organisation face trouble because these manipulations are done in such a fine manner that only the person manipulating them knows the changes, while the owner and the customer are unaware of them. These things are more likely to be seen in government sectors. So in order to overcome the existing problem, we are considering using block chain technology in our project. Since the block chain is a distributed network, manipulation of the data is impossible by a single corrupt person. Each minute change made in a ledger can be tracked using a block chain. Many applications have already been deployed to address this issue, but this paper is about generalization, which will be based on a multi-purpose use case rather than a specific use case. This paper gives a generalised solution to solve the above-mentioned problem in any field or use case.
VĂctor Abraham VillagrĂĄ GonzĂĄlez, LuÄąĚs SĂĄnchez, Jorge Lanza, Juan RamĂłn Santana ¡ 6 authors
As data becomes the new fuel of the economy and a key asset to address our societal challenges, we cannot afford to have the data of businesses, public sector and individuals stored and kept unexploited or, what is worse, exploited by others that actually have the resources and capacity to do it. This is affecting not only our economic performance but also our security, safety and sovereignty. Among the plethora of data sources that exist nowadays, the Internet of Things (IoT) is recognised as a game-changer technology that expands its applicability to a huge variety of domains. Its main asset is, precisely, the data that the myriad of sensors embedded in the environment are constantly generating. The diffusion of platforms for IoT data sharing and monetisation is one of the key success factors which may help to drive the data economy and industrial transformation. In this paper, we are presenting a data sharing platform based on Blockchain, so-called Blockchain-based IoT Data Marketplace (BIDM) over which data producers and data consumers are able to share data in a decentralised and trustworthy manner. The BIDM enables a data marketplace where owners of IoT infrastructures can expose the observations that their devices generate while retaining control over who accesses each observation and directly getting revenues according to the price they have set. The evaluation that we have carried out of the BIDMâs behaviour and performance in terms of operational execution times and scalability has been the basis for the discussion that we are presenting on the shortcomings that are typically associated with the use of Blockchain technologies as enablers for data marketplaces. This discussion also includes the evaluation of the challenges that must be considered for the creation of secure and interoperable Data Spaces based on Blockchain.
Alireza Ghadertootoonchi, masoumeh bararzadeh, Maryam Fani
Bitcoinâs (BTC) mining process utilizes the proof of work (PoW) concept as the consensus algorithm., which consumes electricity. As a result., there are concerns regarding its sustainability and carbon emission. If one wants to estimate these, they first should have an estimation of the network's energy consumption which is hard to calculate due to its decentralized nature. To do so., two methods are conceivable. First, considering electricity price and miner's revenue (electricity costs are considered as a part of total revenue., then divided by electricity price to obtain electricity consumption), second, using hash rate along with the efficiency of mining devices. The latter is utilized in this study for short- and long-term predictions. In the short-term, recurrent neural network (RNN) is used, whereas in the long-term logistic functions are applied. It has been concluded that the ultimate energy consumption of the Bitcoin network, which will happen around 2025, is in the range of 40.4 to 73.41 TWh annually and its estimated value is 58.56 TWh.
With the rise of internet accessibility and situations like the pandemic, the world has seen an ever-increasing number of people turning to online shopping. Over the years, these E-commerce websites have been able to win the trust of their users. With this trust in mind, people have started buying high-value goods like electronics, smart gadgets and even furniture. While these goods are delivered with ease, another important concern in regard to these items is product warranties. Present-day warranties exist in two forms a) Physical Warranties and b) Digital Warranties. Physical warranties involve paperwork, can be easily tampered with and manipulated and are almost non-transferable. Digital warranties, although they do not need paperwork, do not ensure complete ownership transfers. To overcome the abovementioned issues, we propose a new blockchain-based solution. Our proposed solution uses Non-Fungible Tokens to issue and verify warranties. This research proposes a new way of issuing, managing and validating product warranties by making use of Non-Fungible Token(NFT). The NFT Warranty stays on the blockchain as long the warranty is valid after which it âburnsâ.
Elisha Gras, Rosmi George, Kington Churchill, M Kiruthika
Structured data analysis has historically achieved remarkable success. However, the analysis of massive amounts of unstructured video data is still a challenging problem. Over one billion people use YouTube, a Google corporation, which generates billions of views. YouTube data is being created in extremely large quantities, and with a massive demand to store, analyze, and carefully study large amounts of data to make it usable for big data analytics. For the analysis of these YouTube data, the absence of YouTube Shorts which has become a current trend is the limitation. To address this limitation, YouTube Shorts along with traditional duration videos have been considered for analysis in this work. For analyzing these data, various Machine Learning (ML) techniques like clustering and classification have been considered to categorize the content creators into three different categories such as highly rated, moderately rated and lowly rated. These ratings for recommendation assist content creators in enhancing the value of their brand and the users are benefited by consuming, exchanging shares and promoting the content. The results observed show that the accuracy of the Random Forrest and Gradient Boosting classifiers have equivalent performance i.e., around 98% which are suitable for the above recommendation system. Hence, the objective to encourage investors and fans to become active stakeholders and owners of the creatorâs micro-economy is addressed in this paper.
Change address identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current-related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multiconditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3% more one-time change addresses than other heuristics. On top of the multi-input heuristic clustering method, the proposed method also improves the address clustering performance by 5.7%, achieves optimal recognition results compared with similar methods, and significantly reduces the false positive rate of recognition results. This work provides the technical basis for antimoney laundering efforts based on entity identification. Code and data could be accessed from https://github.com/ECNU-Cross-Innovation-Lab/BitcoinAddressClustering.
Mohamed Moetez Abdelhamid, Layth Sliman, Raoudha Ben Djemaa, Boussad Ait Salem
Due to its features regarding immutability, transparency and traceability, blockchain has emerged as a promising solution in various application domains. However, despite its advantages, blockchain suffers from major drawbacks in terms of scalability and performance. This is due to blockchainâs ever-growing nature. Several research studies focus on optimizing blockchain by adopting new techniques and solutions such as pruning, sharding, SegWit and off chain storage solutions. However, so far, most of the suggested solutions entail massive technical burden, increase complexity and reduce transparency and security. In this work, after conducting a comparison among existing blockchain pruning solutions, we present a new intelligent pruning technique that only prunes spent transactions or previous versions of smart contracts. In the proposed approach, data are pruned according to data usage, allowing each participant to independently provide a data list that should be pruned. Our solution involves an intelligent selection process based on particle swarm optimization that uses node parameters to select the best list from all agents lists, this list will be adopted by all the blockchain network members. The approach is implemented in our ABISchain blockchain. ABIshchain pruning solution will reduce the storage requirements for nodes, increase the overall performance and security level as joining nodes will need only to fetch a pruned blockchain (which is more secure than joining blockchain network with a blockchain snapshots provided by other nodes).
Blockchain sharding has been increasingly used to improve blockchain systemsâ performance, in which a blockchain is split into multiple smaller, disjoint shards. In practice, however, sharding can only achieve limited throughput and latency improvement, especially for theuser-perceived transaction confirmation delay.The performance degradation is believed to be caused by the cross-shard transactions. However, we show, through comprehensive system deployment and measurement studies, that the main culprit is theimbalanced transaction loadon different blockchain shards. To address this problem, we propose a novel sharding system, called LB-Chain, whichdynamicallybalances the transaction load on different shards by periodicallymigrating active accountsfrom heavily-loaded shards to less-loaded ones. We have implemented a prototype of LB-Chain, and evaluated its performance through large-scale blockchain deployment using real-world transaction traces. Extensive experiments confirm that LB-Chain significantly boosts sharding performance, reducing the transaction confirmation delays by up to 90% while increasing the transaction throughput by more than 10%. The delay difference between different accounts is also reduced dramatically, leading to improved fairness in the system.
Machine learning and deep learning algorithms produce very different results with different examples of their hyperparameters. Algorithm parameters require optimization because they aren't specific for all problems. In this paper Long Short-Term Memory (LSTM), eight different hyperparameters (go-backward, epoch, batch size, dropout, activation function, optimizer, learning rate and, number of layers) were used to examine to daily and hourly Bitcoin datasets. The effects of each parameter on the daily dataset on the results were evaluated and explained These parameters were examined with hparam properties of Tensorboard. As a result, it was seen that examining all combinations of parameters with hparam produced the best test Mean Square Error (MSE) values with hourly dataset 0.000043633 and daily dataset 0.00073843. Both datasets produced better results with the tanh activation function. Finally, when the results are interpreted, the daily dataset produces better results with a small learning rate and small dropout values, whereas the hourly dataset produces better results with a large learning rate and large dropout values.
Piergiuseppe Di Pilla, Remo Pareschi, Francesco Salzano, Federico Zappone
Introduction In recent years, software ecosystems have become more complex with the proliferation of distributed systems such as blockchains and distributed ledgers. Effective management of these systems requires constant monitoring to identify any potential malfunctions, anomalies, vulnerabilities, or attacks. Traditional log auditing methods can effectively monitor the health of conventional systems. Yet, they run short of handling the higher levels of complexity of distributed systems. This study aims to propose an innovative architecture for system auditing that can effectively manage the complexity of distributed systems using advanced data analytics, natural language processing, and artificial intelligence. Methods To develop this architecture, we considered the unique characteristics of distributed systems and the various signals that may arise within them. We also felt the need for flexibility to capture these signals effectively. The resulting architecture utilizes advanced data analytics, natural language processing, and artificial intelligence to analyze and interpret the various signals emitted by the system. Results We have implemented this architecture in the DELTA (Distributed Elastic Log Text Analyzer) auditing tool and applied it to the Hyperledger Fabric platform, a widely used implementation of private blockchains. Discussion The proposed architecture for system auditing can effectively handle the complexity of distributed systems, and the DELTA tool provides a practical implementation of this approach. Further research could explore this approach's potential applications and effectiveness in other distributed systems.
Sandi Gec, Vlado Stankovski, Dejan LavbiÄ, Petar Kochovski
IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts' correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system's algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts' development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability.
Machine learning has a wide range of applications to meet the complexity of data and various expectations for prediction types.In this study, a comprehensive review of various machine learning approaches for Bitcoin price prediction will be proposed.After examining previous research on cryptocurrency prediction using Long-Short Term Memory (LSTM), Multi-layer Perceptions (MLP), and Support Vector Machine (SVM), with the focus on LSTM, it can be found that LSTM is a widely employed method in Bitcoin price prediction because of its advantages in incorporating both long-term and short-term dependencies.This paper reviews a series of research papers by comparing the differences between the methods they implemented, to a limited extent, based on their predictive power, replicability, and model limitations.Furthermore, some potential improvements and explored innovations for future studies also be discussed.
B. Bhavya Likhitha, Chahat Raj, Mir Salim Ul Islam
Cryptocurrency has emerged as a revolutionary innovation that has been replacing traditional finances and enthralling the worldwide technology landscape. This has gained a lot of popularity worldwide for its potential to enable peer-to-peer transactions and offer opportunities for investment and novelty. Nevertheless, it gives rise to issues concerning regulatory adherence, instability, and security apprehensions, turning them into a topic of continuous evaluation and investigation within the fields of finance and technology. This research paper presents a comprehensive exploration of the historical evolution of âEthereumâ as one of the leading blockchain platforms, with a primary focus on price prediction using a long-short-term memory (LSTM) machine learning model. The study includes various critical aspects of Ethereum, starting from its historical evolution to its potential future scope in scaling solutions and payments, and also covering the insights of Ethereumâs tokenomics, utility, and beyond. In addition, the methodology involves using the LSTM model to analyze data from Ethereum. The accuracy of price predictions is assessed by evaluating error metrics and further improved by visualizing the data through graphs that show indicators. This paper gives an in-depth perspective for anyone who is seeking a holistic understanding of cryptocurrencies, mainly concentrated on Ethereum, and also provides valuable guidance to investors, developers, and enthusiasts, encouraging them to make knowledgeable decisions in the ever-changing blockchain ecosystem.
Khaled Almiani, Young Choon Lee, Tawfiq Alrawashdeh, Amirmohammad Pasdar
The usage of blockchain technology has been significantly expanded with smart contracts and blockchainoracles. While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain data. However, the validity and accuracy of such off-chain data can be questionable that compromises the transparency and immutability chacteristics of blockchain. Despite many studies on the trustworthiness of blockchain oracles, more precisely, off-chain data, their solutions are often âshort-sightedâ and dependent on binary decisions. In this paper, we present a novel graph-based profiling method to determine the trustworthiness of blockchain oracles. We construct a graph with oracles as nodes and cumulative average discrepancies of validity and accuracy of data as edge weights. Our profiling method continues to update the graph, edge weights in particular, to distinguish trustworthy oracles. Clearly. this discourages the provision of false and inaccurate data. We have conducted an evaluation study to see the effectiveness of our proposed method, in which we have run the experiments utilizing the Ethereum network. Additionally, we have also calculated the cost of running these experiments. Consequently, our experiment results show that the proposed method achieves around 93% accuracy in identifying the trustworthiness of data sources.
Rohan Maheshwari, Sriram Praveen V A, G Shobha, Jyoti Shetty ¡ 6 authors
A key motivator for the usage of cryptocurrency such as bitcoin in illicit activity is the degree of anonymity provided by the alphanumeric addresses used in transactions. This however does not mean that anonymity is built into the system as the transactions being made are still subject to the human element. Additionally, there is around 400 Gigabytes of raw data available in the bitcoin blockchain, making it a big data problem. HPCC Systems is used in this research, which is a data intensive, open source, big data platform. This paper attempts to use timing data produced by taking the time intervals between consecutive transactions performed by an address and make an identification of the nature of the address (illegal or legal). With the use of three different goodness of fit run tests namely KolmogorovâSmirnov test, Anderson-Darling test and CrameĚrâvon Mises criterion, two addresses are compared to find if they are from the same source. The BABD-13 dataset was used as a source of illegal addresses, which provided both references and test data points. The research shows that time-series data can be used to represent transactional behaviour of a user and the algorithm proposed is able to identify different addresses originating from the same user or users engaging in similar activity.
In recent years, with the rapid development of the digital economy, digital currencies such as Bitcoin and Ethereum have become increasingly popular among the public. Tracking and regulating digital currency transactions have become a challenging technology for the healthy development of the digital economy. That is because blockchain and peer-to-peer networks are the underlying technologies of digital currencies. Blockchain transaction has some new features, such as stronger anonymity and distributed storage. Therefore, it is difficult for the regulatory system to track the transaction relationships among users. Recent studies have shown that the accuracy, time, and space costs of transaction tracking, as well as the trade-offs between them, still need to be improved. In this article, we propose a new blockchain transaction tracking model called BT2(Bitcoin Transaction Tracking Model). BT2first combines an improved sampling aggregation algorithm with a graph neural network. And then, it exploits an inductive aggregation method to effectively generate rich node embeddings for a small number of unobserved nodes. Next, the node embedding vectors are used to generate edge information among nodes through message-passing functions. Finally, we can use the edge information to obtain the relations among Bitcoin accounts. This paper evaluates the model on the real-world dataset and explores the impact of various parameters, such as network depth and iteration time, etc. From the experimental results, the modelâs average AUC (area under the ROC curve) can reach up to 0.93, and the average accuracy is 86%. The numerical results indicate that the performance of BT2is better than the state of art methods.