Niaz Chowdhury, Ganesh Chandra Deka, Md Abu Sufian
This research aims to explore the potential application of blockchain, artificial intelligence (AI), and machine learning (ML) technologies in the finance industry, specifically for developing trading strategies in decentralized markets. The emergence of these markets, enabled by blockchain technology, has revolutionized the trading landscape, offering new opportunities for investors and traders. However, challenges such as complexity, trading volume, liquidity, GDP, volatility, and lack of transparency make it difficult to create effective trading strategies. This study employs quantitative data analysis and ML algorithms to analyse trading indicators and social media sentiment data, improving the accuracy of trading signals and generating more profits in decentralized markets. The hypothesis is that the combined implementation of blockchain, AI, and ML in decentralized trading markets can result in more accurate market predictions and generate profitable trading signals. Linear regression, decision tree, random forest, and XGBoost ML models have been built and compared for a recommendation, with the XGBoost model performing the best at 95% by R square and RMSE values comparison. Additionally, AI models, such as logistic regression for sentiment analysis on tweet stock datasets, were utilized to analyse sentiment scores for better trading decisions, with model performance evaluated using a confusion matrix and the overall accuracy result was 95% and model evaluation performance metrics like precision 95%, recall 95%, and f-1 score 95% as weighted avg. This research further explores the integration of ML and AI models into a blockchain framework, enhancing transparency, security, and efficiency in decentralized trading markets. The potential of different AI and ML techniques for predicting market trends and generating profitable trading signals is investigated, along with addressing the likelihood of prejudice, discrimination, and mining strategies in Bitcoin to mitigate these risks in the future.
The spread of the Metaverse has created new moral and legal challenges, especially when it comes to protecting against fraud. This study explores the legal complexities surrounding Ethereum-specific metaverse transactions and provides guidance on how to identify and address fraudulent activity. The focus of this investigation is the field of fraud detection in the Ethereum environment. Current methods of detecting fraud, such as Blockchain analysis and machine learning-based algorithms, are carefully examined. Next, a new approach to fraud detection is put out, which is predicated on a collection of seven machine learning algorithms: K-Nearest Neighbours (KNN), Decision Tree, Support Vector Machine (SVM), Random Forest, KNN, XGBoost (XGB), and Artificial Neural Network (ANN). The results of this study are carefully outlined, demonstrating the accuracy, recall, F1 score, and precision that each of the previously listed algorithms demonstrated. Moreover, this article lays out possible directions for further research, including the incorporation of group approaches and the investigation of creating characteristics to strengthen fraud detection abilities. This academic paper provides a significant and novel insight into the identification of fraudulent activity in Ethereum transactions, highlighting the potential benefits of using machine learning algorithms for this kind of discernment.
Emilyani, Marviola Hardini, Natasya Aprila Yusuf, Achani Rahmania Az Zahra
This research aims to explore the convergence between smart networks, artificial intelligence (AI), and blockchain technology as a foundation for future innovation. The background includes the rapid development of information and communications technology, which is driving increasing integration between AI and blockchain in infrastructure networks. The methods used include a comprehensive literature survey and in-depth analysis of the latest trends in the development of this technology. The issues examined include interoperability, security, and privacy challenges faced in integrating AI and blockchain. The research results show that this convergence promises to improve efficiency, transparency and transparency in a variety of applications, from supply chain management to financial services. However, significant challenges such as scalability and regulation must also be overcome to realize the full potential of this convergence. In conclusion, the merger of AI and blockchain expands the scope of technological innovation by leveraging the strengths of each, but more efforts are needed to address further issues so that this convergence can be implemented widely and sustainably in various industrial fields.
Mohammad Vahidpour, Amir Daneshvar, Mohsen Amini Khouzani, Mahdi HOMAYOUNFAR
Financial markets are characterised by their dynamic, non-linear, and fluctuating nature. Analysing financial time series in these contexts is a complex and challenging task. Candlestick patterns are recognised as among the most widely used financial tools and offer invaluable insights into market sentiment and psychology. However, manual analysis of these patterns presents significant challenges. Therefore, leveraging machine learning methods becomes a necessity for overcoming these challenges. In this study, a four-step framework was introduced in which the data preparation process is executed on the price data of the 20 cryptocurrencies. Forty-eight candlestick patterns were extracted alongside returns. Employing the long shortterm memory (LSTM) neural network, structured with multiple layers, each specialising in a specific cryptocurrency, enables individualised prediction of market returns. Evaluation of model accuracy and sensitivity is conducted via the confusion matrix, and two distinct trading strategies assess the capital portfolio. The research findings underscore the profitability of the proposed model across all scenarios. Candlestick patterns serve as powerful tools for understanding market sentiments and identifying shifts in market trends. However, their standalone efficacy is limited. Integrating them with other technical analysis tools facilitates more informed decision-making and fosters a deeper understanding of market dynamics.
Blockchain is a decentralized network in which data blocks are linked.Through a decentralized peer-to-peer network, users can create shared databases, resulting in a trustworthy and aggregated database known as a blockchain that enhances reliability and security.The distributed nature of the blockchain enables data to be stored on multiple nodes, eliminating the need for a central server or platform.This disintermediation significantly reduces the transaction and administrative costs.The blockchain is particularly valuable in applications where reliability and stability are critical because it establishes an open database that ensures data integrity, making it virtually impossible to tamper with or falsify data.This study explores the diverse applications of the blockchain technology in virtual assets, such as cryptocurrency, decentralized finance, central bank digital currency, nonfungible tokens, and metaverses.In addition, it analyzes the potential prospects and developments driven by these innovative technologies.
The real estate industry is crucial sector of the world economy Buying and selling properties usually takes a long time, is complicated, and can be expensive.There's also a risk of mistakes and fraud, which might cause big financial losses and legal problems.This research paper delves into the innovative use of blockchain technology to address the pervasive issue of fraud within the real estate industry.By introducing a comprehensive system where user authentication is conducted through Aadhaar numbers, properties are represented as Non-Fungible Tokens (NFTs), and transactions involve the crucial oversight of designated inspectors, this paper aims to provide a robust framework for fraud prevention.Additionally, it explores how blockchain's inherent properties prevent instances of double selling, thus bolstering the integrity of real estate transactions.The research demonstrates the effective utilization of blockchain technology and Ethereum's smart contracts.The immutability nature of the blockchain ledger and transactions can provide a safe environment for the real estate sector.The project also identified some challenges and opportunities for blockchain adoption in the real estate industry.One of the challenges is the lack of awareness and understanding of blockchain technology.Another challenge is the regulatory environment, which is currently unclear.However, there are many opportunities for blockchain adoption in the real estate industry, such as streamlining the real estate transaction process, reducing fraud, and improving transparency.
Can Zhao, Yibing Wang, Dejun Wang, Guangyan Sun · 5 authors
The analysis on conformance between legal contracts and smart contracts is necessary and precondition for constructing the secure blockchain. Currently, the analysis method is the language-dependent single qualitative analysis. There does not exist quantitative metrics and quantitative analysis methods. Hence, the quantitative metric and language-independent quantitative analysis method are proposed to analyze the conformance between legal contract and smart contract. First, the definitions and metrics of partial conformance and complete conformance are proposed, and then rewrite logic and language-independent symbol execution are used to construct a language-independent quantitative method; then, the executable formal semantic for legal contract description language Business Process Model and Notation2.0 (BPMN2.0) is presented. Finally, the conformance of the five main methods for mapping BPMN2.0 legal contracts to Solidity smart contracts is analyzed. The results show that three methods have partial conformance, one method has complete conformance, and method has neither complete conformance nor partial conformance.
Jayashree Bhattacharjee, Lata Kumari Pandey, Ranjit Singh, H. Kent Baker
This study explores risk perceptions in cryptocurrency investments among Indian investors. It employs a multistage random sampling survey of 228 investors. Four key factors influence this perception: conceptual clarity, investment education, awareness of investment options, and fear-induced psychological factors. The overall risk perception of crypto investors is high. Based on our findings, we suggest that the Indian government should organize an awareness campaign to create awareness and educate investors about cryptocurrency. Policymakers and investment managers should focus on transforming high-risk investors into lower-risk investors through education and support, fostering a more favorable investment environment.
Virtual currencies, including cryptocurrencies and non-fungible tokens (NFT’s), are increasingly used as rewards in virtual environments. Traditional reward systems have been effective in improving employee satisfaction and retention but with the shift to hybrid or remote work post-COVID-19, organisations need adapted reward systems. However, the problem of this research is that it’s unclear how virtual currencies can be effectively utilised as a reward system, in the Metaverse, and their impact on employee motivation and engagement. This study explores this by reviewing literature, analysing reward mechanisms, and proposing a conceptual model to evaluate the feasibility of such a reward system. The study considers factors like social comparison and loss aversion as well as rewards known to boost motivation and engagement. The flexibility of virtual currencies for conversion or exchange into rewards offers numerous possibilities, with specific reward choices left to organisations’ discretion. This study offers promise to organisations seeking to retain and motivate their employees, ultimately contributing to increased productivity. In turn, employees can benefit from improved job satisfaction and reduced work-related pressures. The study’s conclusion assesses the usefulness of this research and outlines potential areas for future research.
Next Earth is a pioneering metaverse project that has rapidly evolved since its inception. Initially conceived as a virtual world platform, Next Earth has expanded its vision to become a multifaceted ecosystem that blends blockchain technology, virtual reality, and decentralized finance. The project aims to create a digital universe where users can buy, sell, and develop virtual real estate, participate in immersive experiences, and engage in a vibrant community. Next Earth's evolution has been marked by strategic partnerships, innovative features, and a commitment to user empowerment. As the project continues to grow, it is poised to redefine the concept of the metaverse and pave the way for a new era of digital interaction. Moreover, it has been observed that different domain parking players are considering NextEarth domains for future revenue generation.
Standard encryption cannot be utilized in practical communications due to time and storage limits. Dedicated lane keeping (DRL) is a technique that helps self-driving cars navigate congested roads by keeping them in a specific lane (CAVs). Researchers have developed separate networks for distinct types of mixed traffic to cut down on the time and effort typically spent on instruction and coordination. A deep reinforcement learning technique boosts the efficiency of each part and the entire fleet. There is a common misconception that the blockchain is a secure database for private information. A distributed database system in which nodes are directly connected to consensus mechanisms. To ensure data integrity, block blocks on a blockchain network use cryptography and other computer safeguards (such as smart contracts and time stamps). Because of its decentralized design, data storage facilitates collaboration. Digitally signed data records can also be checked to ensure they are accurate. Using hashes to connect individual blocks protects data against tampering by hackers. There is no need for a centralized authority or third party to verify the ledger’s accuracy because everyone can access it anytime. The blockchain allows for a transparent, trustworthy, and auditable system sharing information between entities. Like many other industries, transportation may benefit from the broader implementation of blockchain technology. Based on our findings, a state-run blockchain tailored to the transportation industry was developed and made available to the public. Because of blockchain technology, the car-sharing business model may need to be revised. Blockchain technology utilizes a distributed ledger to record transactions in a way that makes it impossible to alter the underlying data while still allowing for fast access for verification and auditing.
Flavio Corradini, Alessandro Marcelletti, Andrea Morichetta, Barbara Re · 5 authors
Digitalization triggered a transformation in our society, leveraging recent innovations introduced by digital tech-nologies to empower real systems with advanced capabilities. Blockchain emerged as a disruptive technology to advance trustless scenarios by enforcing data immutability and change traceability. Deployed in the Blockchain, smart contracts support such transformation, enhancing the application logic toward a trust scenario. Bug in the smart contract can lead to irreversible consequences, producing faulty transactions or unexpected behaviors. Furthermore, the immutable nature of the Blockchain restricts the ability to make updates if unforeseen circumstances or necessary modifications arise. For these reasons, smart con-tracts' continuous inspection and improvement are fundamental to prevent and react to such issues. In this context, Digital Twin is an emerging approach for creating virtual replicas of assets, processes, or systems. By establishing a bi-directional connection, Digital Twin reflects the twinned entity state while simulating its behavior to enable an iterative evolution. In this paper, we propose an approach that exploits Digital Twin capabilities to support dynamic smart contract improvements. By monitoring and analyzing transactions, Digital Twin can suggest possible refinements to the developers by discovering smart contract inconsistencies or deviations from the expected behavior. Those improvements can refer to a change in logic or parameter optimization. These change proposals are then evaluated in the Digital Twin approach, which eventually updates the smart contract on the Blockchain. We assess the feasibility of the proposed approach using a train management application related to the smart transportation domain.
Résumé : La technologie blockchain promet un véritable développement pour le marché financier et en apportant de nouveaux apports sur le marché. Elle a été adoptée dans plusieurs industries et principalement les services financiers. Elle peut créer des registres décentralisés et distribués spécialement dédiés aux services financiers et peut améliorer l’intermédiation financière en procédant à une rapidité de règlement, une baisse des coûts de transactions ainsi qu’un fonctionnement au continu du marché. L’objectif principal de ce papier est alors d’analyser s’il existe empiriquement une relation de long terme et un impact positif entre l’intégration de la blockchain sur la performance du marché boursier, en prenant le cas de la BVMC. La méthode utilisée est une analyse de cointégration via une modélisation du degré d’intégration de la blockchain au Maroc et la performance du marché boursier, représentée par l’évolution de l’indice MASI, tout en intégrant d’autres variables pour étayer davantage le modèle. La BVMC est en pleine transformation et le nouveau modèle de développement de 2021 lui place un rôle important dans le financement de l’économie. Un des leviers d’accélération le plus importants pour la réalisation de ses objectifs reste la digitalisation. Et la blockchain offre plein d’avantages en termes de transformation digitale pour le marché boursier marocain. Plusieurs projets de création de marché financier opérant sur une blockchain sont en cours dans les pays développés, et plusieurs études théoriques démontrent les avantages de l’intégration de la blockchain sur le marché financier. La BVMC sera tentée de suivre les tendances internationales et la recherche de performance conduira la BVMC à intégrer la technologie blockchain. Les résultats de l’étude démontrent alors clairement que la technologie blockchain va apporter de nouvelles règles au mode de fonctionnement et d’organisation des transactions, et qu’elle aura un impact positif sur la performance du marché boursier marocain. Mots clés : Blockchain, registre décentralisé, performance, coûts de transaction, Classification JEL : G15, G17, O16, O31, O55 Type de l’article : Recherche appliquée Abstract: Blockchain technology promises real development for the financial market and bringing new contribution to the market. It has been addressed in several industries and mainly financial services. It can create decentralized and distributed registers specially dedicated to financial services and can improve financial intermediation by ensuring rapid settlement, lower transaction costs and continuous operation of the market. The main objective of this paper is then to analyze whether there exists empirically a long-term relationship and a positive impact between the integration of blockchain on the performance of the stock market, taking the case of BVMC. The method used is a cointegration analysis via modeling the degree of blockchain integration in Morocco and the performance of the stock market, represented by the evolution of the MASI index, while integrating other variables to further support the model. The BVMC is in the midst of a transformation and the new development model (2021) gives it an important role in financing the economy. One of the most important acceleration levers for achieving its objectives remains digitalization. And blockchain offers plenty of advantages in terms of digital transformation for the Moroccan stock market. Several projects to create financial markets operating on a blockchain are underway in developed countries, and several theoretical studies demonstrate the advantages of blockchain integration in the financial market. The BVMC will be tempted to follow international trends and the search for performance will lead the BVMC to integrate blockchain technology. The results of the study clearly demonstrate that blockchain technology will bring new rules to the way transactions operate and are organized, and that it will have a positive impact on the performance of the Moroccan stock market. Keywords: Blockchain, decentralized ledger, performance, transaction costs, JEL Classification: G15, G17, O16, O31, O55 Paper type: Empirical research
The convergence of blockchain and Internet of Things (IoT) technologies is expected to bring significant benefits to the healthcare industry. The combination of blockchain and IoT can enable secure, transparent, and decentralized data sharing among healthcare providers, patients, and other stakeholders. This chapter provides an overview of the potential applications of blockchain and IoT in healthcare, including remote patient monitoring, supply chain management, electronic health records, and clinical trials. It also discusses the benefits and challenges of this convergence, including data privacy and security, interoperability, and regulatory compliance.
In response to the problems of low accuracy and slow decision-making efficiency in predicting agricultural financing risks, this paper combined smart contract technology and used the LSTM-GRU (Long Short Term Memory-Gated Recurrent Unit) model to analyze agricultural financing risk data. Firstly, data related to agricultural financing risks in 2022 and 2023 were collected on site, and principal component analysis was adopted for dimensionality reduction to accelerate decision-making efficiency. Then, the LSTM (Long Short-Term Memory) model and GRU (Gated Recurrent Unit) model were fused, and agricultural financing risks were predicted. Finally, smart contracts were designed to apply the predicted results of the model to actual financing decisions. By monitoring the execution process of the contract, corresponding operations were executed based on the predicted results of the model. The experimental results showed that the average accuracy of the LSTM-GRU model in predicting agricultural financing risks reached 98.64%, which was 4.29% higher than the GRU model. The decision-making speed was only 0.52 seconds, which improved the prediction accuracy and decision-making efficiency of the model.
Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model’s efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results.
Health record is a collection of medical data pertaining to the patient, managing these data becomes a real concern. Blockchain provide an effective solution to manage the health records and maintain the confidentiality and privacy of the patient. Integrating smart contract with blockchain makes the sharing of health records between medical institutions easier and provide a greater degree of security. In this paper, we introduce a blockchain based smart contract system for securely sharing health records, its used to prevent unauthorized access and maintain data integrity within the blockchain network.
Arshad Ahmad Dar, Faheem Ahmad Reegu, Sarfaraz Ahmed, Gousiya Hussain
The supply chain has become increasingly complex, making it challenging to verify sustainable practices. Blockchain has been viewed as a viable option to improve numerous aspects of sustainable supply chain management (SSCM) in response to increased customer demand for product traceability and proof of origin. This technology was initially developed as a secure distributed ledger for the bitcoin market, but it has since been adopted by several businesses, including the food, healthcare, and logistics sectors. This research seeks to propose a framework for more open, transparent supply chain operations using blockchain and artificial intelligence (AI) technologies. Two research topics will be addressed in relation to the main advantages and challenges in the market for consumer goods: This proposal will employ a qualitative methodology to investigate how blockchain and AI might result in more transparent supply chains from a social and environmental perspective. By considering the integration of blockchain and AI from many perspectives, triangulation will be achieved in order to promote transparency and traceability. We will suggest a single framework that assembles the necessary data pieces that must be maintained digitally in order to enhance sustainability in supply chains management systems utilizing Artificial Intelligence and Blockchain Technology (BCT).
Several construction projects globally suffer due to time and cost overruns. These could be due to reasons attributable to the contracting parties or unforeseen external circumstances. Disputes often arise between the parties regarding which side caused the delay and the compensation terms for the delay. Thus, this study is motivated to explore a digital contractual solution for contract administrators and project managers using blockchain technology-enabled smart contracts. In this regard, the research attempts to develop smart contract clauses that (1) carefully allocate liability and delay accountability at various construction stages; (2) automatically compute the delay compensations and notify the responsible party; and (3) determine the resultant extensions of time and cost, with cost variations. The goal is to develop computable legal contracts for industry and academia focusing on the extension of time, delay compensation, and variation, which can be applied to all projects worldwide, regardless of the complexity or scale.
가상세계에서의 사회경제적 활동을 지원하는 메타버스는 미래 웹 3.0 비즈니스의 핵심이 될 공간으로서 언급된다. 그러나 현재 대부분의 주요 메타버스 플랫폼은 웹 2.0 체제를 고수하며 중앙집권형으로 운영되고 있다. 이에 본 연구는 웹 3.0 형태에서 탈중앙화 자율조직인 DAO로 운영되는 메타버스 플랫폼인 디센트럴랜드의 사례를 살펴보며 메타버스 운영형태에 대해 고찰하였다. 사례분석 결과, 디센트럴랜드는 수평적 운영구조, 공정한 수익배분, 투명성 등의 긍정적인 특징을 가졌지만, 운영 과정에서 중앙화로의 회귀 가능성, 자율적 체계의 악용가능성, 의사 결정의 비효율성 등의 한계점도 포착되었다. 이에 본 연구에서는 DAO 거버넌스로의 전환 시점에 대해 논의하고, 비즈니스 지속성을 위한 노력, 자율적 운영을 위한 조정의 필요성을 시사점으로 제시하였다. 디센트럴랜드 DAO의 긍정적인 특징과 한계성을 바탕으로 본 연구에서 제시하는 시사점은 메타버스 뿐 아니라 웹 3.0을 지향하는 플랫폼 운영에 대해 실무적 측면과 아울러 이론적인 체계를 마련해가는 측면에서도 기여점이 있다.
Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.
This comprehensive technical paper presents a novel multi-modal trust architecture for AI-driven HR systems, focusing on the critical aspects of user acceptance in enterprise-scale people analytics platforms. Through the implementation of advanced zero-knowledge proof protocols, explainable AI frameworks, blockchain-based audit trails, and federated learning approaches, the architecture achieved an 85% improvement in user confidence metrics. The system demonstrates remarkable performance across resistance prediction, technical integration, and trust analytics, processing over 9.5 million daily interactions with 99.999% reliability. Our implementation across 1,850 organizations showed an 82% enhancement in system trustworthiness and a 2.8x improvement in operational efficiency, while reducing algorithmic bias by 89%. The architecture's event-driven design and microservices implementation resulted in a 76% improvement in system responsiveness and a 92% reduction in data processing latency, establishing a new benchmark for trust-centric AI-HR systems.
Exploring the potential pros and cons of Blockchain technology integration in digital marketing is undoubtedly a significant issue. The primary goal of this research is to analyze the role of blockchain technology in digital marketing, considering its advantages, disadvantages, and current acceptance rate. The main goal of the study would be to provide industry professionals and decision-makers with tactical data on blockchain applications. Research highlights the involvement of 402 digital marketing experts across industries in a quantitative survey to achieve the purpose. As per the research's results, the professionals in digital marketing have a common knowledge of blockchain technology and its application in the business field. Aside from such challenges, the survey revealed that many of these challenges include the high implementation costs, the lack of technical competence, and regulatory compliance concerns. Despite these challenges, the survey results still demonstrate that digital marketers are mostly open to implementing blockchain-related solutions in their firms, especially if they appear to increase efficiency, cost savings, and better customer experience. The survey finds that using blockchain technology can transform the digital marketing industry. More studies and training must be done such that digital marketers comprehend the technology and its options as well.
Ali Mansourabady, Fatemeh Tabe, Amir Hossein Rasekh, Ali Ghermezian
Traders and investors are always looking for a way to predict the price of cryptocurrencies to increase their returns and reduce their risks. However, due to unpredictability, instability, and movement, cryptocurrency price prediction is a challenging task. Researchers have proposed different architectures for prediction based on statistical approaches, machine learning (ML), and deep learning (DL) techniques. In this article, we aim to evaluate some of these approaches by implementing their proposed architectures on historical data of Monero (XMR) cryptocurrency from the beginning of 2016 to the end of November 2023 and compare the results. According to the obtained results, the CNN-LSTM-Dense architecture performs better based on the Mean Squared Error (MSE) evaluation metric by achieving a value of 0.00472.