S. Balasubramani, Soumen Pramanick, Rohit Kumar Singh, Dhananjay Kumar
In recent years, counterfeit products have played an important role in the product making industry. It has also affected the company's brand, sales, and profits. Blockchain technology is now being used to identify authentic products and detect counterfeits. Blockchain is a distributed, expanded, digital book that stores business data as blocks in multiple archives linked by chains. No block can be changed or modified because blockchain technology is secure. Customers or users who use the blockchain technology does not have to be depend on mediator to ensure product security of the system. Counterfeit products can be effectively combated using serial numbers which is assigned to the product uniquely. With emerging Blockchain technology trends in this project. As a result, this system can be used to store product information as well as generate distinct product codes for items such as blocks. It generates a unique code for the user and matches it to the Blockchain network. If the input matches, the customer will be notified; if the input does not match, the customer will be notified that the product is counterfeit.
Amaç: Bu çalışmanın amacı, Bitcoin ve Ons arasındaki volatilite aktarımını incelemektir. Bu nedenle, yatırımcılar riskten korunmak için portföylerinde Bitcoin’e yer vermeli mi ve Bitcoin Ons’a alternatif bir yatırım aracı mı konuları araştırılmıştır. Tasarım/Yöntem: Araştırmada öncelikle değişkenler getiri serisine çevrilmiş ve birim kök testleri sınanmıştır. Daha sonra, Bitcoin ve ONS arasındaki ilişki çok değişkenli stokasitik volatilite metodu ile incelenmiştir. Eviews9 ve WinBUGS14 paket programları yardımı ile analizler yapılmıştır. Bulgular: Analiz sonuçlarına göre, Bitcoin ve Ons değişkenlerinde meydana gelen şokların kalıcı etkiye sahip olduğu saptanmıştır. Bitcoin’den Ons’a doğru tek yönlü volatilite aktarımı olduğu tespit edilmiştir. Ayrıca Bitcoin’den Ons’a doğru gerçekleşen volatilite aktarımının pozitif olduğu belirlenmiştir. Sınırlılıklar: Çalışmada, 03.02.2012–13.01.2022 dönem aralığının alınması ve sadece iki değişkenin kullanılması araştırımın sınırlılıklarıdır. Ayrıca bu tarih aralığının alınmasının nedeni 2012 dönemi öncesi Bitcoin verisine ulaşılamaması ve analizlerin 2022 yılı Ocak ayında yapılmasıdır. Özgünlük/Değer: Çalışmanın diğer çalışmalardan ayrılan özelliği, Çok Değişkenli Stokastik Volatilite Metodu ile analizlerin yapılmasıdır. Ayrıca bu konuda literatürde çok çalışma olmaması ve literatüre katkı sunulması hedeflenmektedir.
Cryptocurrency is a fascinating area of research developed due to the rapid development of financial technologies. One of the well-received cryptocurrencies is Bitcoin. The motivation behind this paper is to predict bitcoin prices with high accuracy using various regression-based models. Bitcoin is the fastest growing cryptocurrency. We have seen drastic changes in bitcoin prices over time. To make predictions for such changes, we use machine learning techniques over real-time data recorded for every 24-hour time interval since the presence of bitcoin beginning in the year 2009. We select eleven different regression models, analyze these models and obtain the best regression-based model for bitcoin price prediction. The results obtained from the study depict that the Bayesian ridge regressor outperforms all the other regression-based models followed by the Linear regressor.
The development of Internet technology provides a lot of convenience for the promotion of smart agriculture. At present, smart agriculture has gradually realized unmanned and automatic management, which can realize monitoring, supervision, and real-time image monitoring. However, the data in smart agriculture system cannot be guaranteed to be complete and vulnerable to attack. Based on this, this paper studies and analyzes the application of edge computing and blockchain in smart agriculture systems. Based on the simple analysis of the development of smart agriculture, the edge computing framework and the advantages of blockchain are used to build the framework system of smart agriculture. The classical architecture of edge computing and the confidentiality of blockchain are used to realize the analysis and storage of data. In view of the shortcomings of crop image overlap detection, it is proposed to detect the overlapping area and determine the feature points to analyze the image based on the edge computing and hash algorithm. In terms of data integrity, based on the advantages of blockchain, an edge data detection method based on short signature is proposed, and experiments are designed to analyze the accuracy and effectiveness of the algorithm. The simulation results show that the image mosaic algorithm can extract the contour information of the image and realize the fast image matching. The edge data integrity calculation based on short signature can meet the requirements and shorten the response time.
B. R. Rajakumar, B. R. Rajakumar, D. Binu, D. Binu · 6 authors
This paper introduces a new bitcoin predictin model that includes three major phases: data collection, Feature Extraction and Prediction. The initial phase is data collection, where Bitcoin raw data are collected, from which the features are extracted in the Features Extraction phase. The feature extraction is a noteworthy mechanism for detecting the bitcoin prices on day-by-day and minute-by –minute. Such that the indexed data collected are computed regarding certain standard indicators like Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Index (RSI) and Rate of Change (ROC). These technical indicators based features are subjected to prediction phase. As the major contribution, the prediction process is made precisely by deploying an improved DBN model, whose weights and activation function are fine-tuned using a new modified Lion Algorithm referred as Lion Algorithm with Adaptive Price Size (LAAPS). Finally, the performance of proposed work is compared and proved its superiority over other conventional models.
Hariket Sukesh Kumar Sheth, A. K. Ilavarasi, Amit Kumar Tyagi
Because of its ability to make educated judgments, deep learning has gained massive attention in recent years. Many of today's deep learning systems rely on centralized servers and lack operational transparency, traceability, dependability, security, and trustworthy data provenance. Furthermore, using centralized data to train deep learning models exposes them to the single point of failure issue. The relevance of combining blockchain technology and deep learning is highlighted in this study. Deep-learning (DL) techniques are used to authenticate and identify abnormalities and provide security for systems (in cryptographic and biometric systems). Confidentiality and effectiveness must be balanced since network sensors are energy-constrained devices, which is the most crucial idea to consider when establishing a security system that relies on deep-learning techniques and blockchain. Centralized systems have several flaws. Mostly, a network with significant demand for smart devices creates a prodigious amount of data. There is always the possibility that one or more of the centralized network's major components would fail, causing a catastrophic (or full) system failure. The data acquired by the centralized cloud storage frequently necessitates third-party modification. This may result in data breaches, jeopardizing the privacy of the end-user. This paper primarily focuses on the security models proposed in terms of authentication and security using Blockchain, Deep Learning, or the integration of both based on certain characteristics to identify and organize the literature, inclouding type, models, consensus protocols, applications, services, and deployment goals.
Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, “convolutional neural network” or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0–7), filters (10–40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10–12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.
Dubbed as distributed ledger technology, the based on blockchain (DLT). The fundamental features of blockchains include a shared ledger, updates that happen in almost real time, are time- and date-stamped, are secured, and support for programming programs. Human brains use nodes that are connected, which are similar to cells in the brain, to accomplish deep learning. As a result, human brains with many Deep learning is accomplished by using hidden layers.The confluence of block chain technology and artificial intelligence has been the focus of extensive recent research in payment information (AI). It is covered to use these artificial neural networks. The usage of blockchain technology can cut humankind’s carbon emissions. AI. If AI robots take the role of human workers, mining companies will dramatically drop in energy and cost use.
This study aims to determine the effect of the S&P 500 Stock Index Price and Gold Price movements on Bitcoin Price movements. This research uses quantitative methods and data collection used is secondary data. Secondary data sources are obtained through the website finance.yahoo.com. The data processing technique will be carried out using SPSS 23 program and the method analysis in this study using descriptive and verification analysis consisting of Multiple Linear Regression method. The results of this study show that the stock index price has a positive effect on the movement of the Bitcoin price, while the gold price has a negative effect on the movement of the Bitcoin price.
P. William, N Yogeesh, S. Vimala, Pratik Gite · 5 authors
With ever-evolving internet models automating and digitizing innumerable industrial and domestic applications, today’s world has achieved tremendous gains in the exchange of healthcare data and the initiation of essential measures. Private information and data integrity are the two most important considerations in today’s healthcare data-sharing environment. Encryption is critical when it comes to health records and other sensitive data in order to prevent unauthorized parties from seeing or accessing the data. There may be no feasible method to deploy typical encryption algorithms through the intermediary node, and the intermediary nodes should not be given excessive weight. Blockchain technology may be used to encrypt data between nodes in the 5G network’s design, and this article addresses the different security difficulties that exist in the current mechanism and the current strategies for IoT-driven healthcare monitoring frameworks’ security solutions. The proposed approach was evaluated using a variety of performance parameters, and the findings indicated that it outperformed the alternatives.
Artificial Intelligence and Decision Support Systems
This paper discusses, trying to accurately assess the price of Bitcoin by looking at different parameters affects the value of Bitcoin. In our work, we focus on understanding and seeing the evolution of Bitcoin daily market, a1 and gaining intuition in the most relevant aspects surrounding the Bitcoin price. In the meantime, market capitalization of publicly traded cryptocurrencies exceeds $ 230 billion. The most important cryptocurrency, Bitcoin, is used primarily as a digital value store, and its pricing opportunities have been extensively considered. These features are described in more detail in the following paragraph: details of the main Bitcoin, as described in the paper. Bitcoin is the most expensive digital currency in the market. However, Bitcoin prices have been highly volatile, making it difficult to forecast. As a result, the goal of this research is to find the most efficient and accurate model for predicting Bitcoin prices using various machine learning algorithms. Several regression models with scikit-learn and Keras libraries were tested using 1-minute interval trading data from the Bitcoin exchange website bit stamp from January 1. 2012 to January 8, 2018. The best results showed a Mean Squared Error (MSE) as low as 0.00002 and an R- Square (R2) as high as 99.2 percent.
Harikumar Pallathadka, Korakod Tongkachok, Prashant Subhash Arbune, Samrat Ray
Rapid expansion of the world's population has been facilitated by technology, which has made knowledge and business readily available to its users at the touch of a button. New technologies like the internet have disrupted the traditional methods of communication and information exchange for ideas, money, or commerce. As a result, the internet is seen as a disruptive technology that has shook the world to its core. Using e-commerce platforms, we can order meals, groceries, and other necessities without leaving our homes or offices. Aside from using our debit or credit cards, we also use alternative wallets like PayTM. Additionally, a huge section of the globe transacts money using virtual currencies, often known as cryptocurrencies. As a safety measure, these tokens and coins have been encrypted for usage on the internet. They're a means of exchange that relies on encryption to verify the legitimacy of new currencies. These virtual currencies are kept in E-Wallets and sent safely and anonymously via the internet on a peer-to-peer basis within seconds. This article performs an analytical study of various cryptocurrencies. It also discusses opportunities and challenges in the vibrant field of cryptocurrencies.
With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors. In this paper, we focus on predicting the pump probability of all coins listed in the target exchange before a scheduled pump time, which we refer to as the target coin prediction task. Firstly, we conduct a comprehensive study of the latest 709 P&D events organized in Telegram from Jan. 2019 to Jan. 2022. Our empirical analysis reveals some interesting patterns of P&Ds, such as that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity. Here channel refers a form of group in Telegram that is frequently used to coordinate P&D events. This observation inspires us to develop a novel sequence-based neural network, dubbed SNN, which encodes a channel's P&D event history into a sequence representation via the positional attention mechanism to enhance the prediction accuracy. Positional attention helps to extract useful information and alleviates noise, especially when the sequence length is long. Extensive experiments verify the effectiveness and generalizability of proposed methods. Additionally, we release the code and P&D dataset on GitHub: https://github.com/Bayi-Hu/Pump-and-Dump-Detection-on-Cryptocurrency, and regularly update the dataset.
P. William, Dushyant Jadhav, Pratik Cholke, M. A. Jawale · 5 authors
In recent years, blockchain technology has gained more attention, and a plethora of applications have developed. It's a digital money. In addition to solving the problem of double-spending, Bitcoin is a well-known Blockchain application since it does it without relying on a central authority. Blockchain technology provides a tamper-proof basis for any application that relies on it. With this article's help, shoppers will no longer be solely reliant on merchants to verify the legitimacy of things. We present a decentralised Blockchain system for anti-counterfeiting items. This system enables producers to deliver authentic products without managing direct-operated storefronts, considerably lowering the cost of product quality assurance.
Blockchain Technology Applications and Security
Currency Recognition and Detection
Artificial Intelligence and Decision Support Systems
Since blockchain technology has proven to be effective in the development of a wide range of industries, its use in other fields is also being expanded. Agriculture is one such sector, where blockchain technology is being used to improve farm business operations. Today, several agribusiness firms are utilizing technology to improve food supply chain tracking. For example, Farmers Edge, the world’s leading company that revolutionized the field of digital agriculture through its work in providing advanced artificial intelligence solutions, as well as new opportunities that give agriculture a globally advanced future for all stakeholders, has taken a significant step forward. The issue of blockchain network technology and its applications in agriculture will be discussed in this study, as well as the key advantages that this technology can provide, when employed to make the lives of both producers and consumers easier. In addition, a total of 79 research papers were evaluated, with a focus on the state of blockchain technology in agriculture, related issues, and its future importance, as well as relevant contributions to this new technology and the distributions of this study by different countries.
coinbase pro tech ☎️1855-765-1928☎️ support phone number
\n\ncoinbase pro tech ☎️1806-318-0965☎️ support number,coinbase pro support number, coinbase pro phone number, coinbase pro helpline number,coinbase pro customer support number At Coinbase, we’re focused on offering more ways for customers to earn crypto rewards. Today, we’re expanding our staking offerings to include Cardano (ADA) with plans to continue to scale our staking portfolio in 2022.Cardano is one of the top ten most valuable cryptocurrencies by market cap. It’s a proof-of-stake blockchain designed to be a next-gen evolution of Ethereum — with a blockchain that seeks to be more flexible, sustainable, and scalable. Cardano aims to enable smart contracts to allow developers to build a wide range of decentralized finance (DeFi) apps, new crypto tokens, games, and more. When users stake their crypto, they make the underlying blockchain of that asset more secure and more efficient. And in exchange, they are rewarded with additional assets from the network, which are paid out as rewards. While it has been possible for individuals to stake Cardano on their own, or via a delegated staking service, the process can be confusing and complicated. With today’s launch, Coinbase is offering an easy, secure way for any retail user to actively participate in the Cardano network and earn rewards.
Cryptocurrency is an excellent investment opportunity. Predicting the prices of cryptocurrencies is a problem that has been around since the origin of cryptocurrencies. Many machine learning approaches are proposed to solve the problem but none of them couldn't solve the problem with expected accuracy. Recent developments in deep learning have proven that using deep learning is one of the best approaches for predicting the prices of cryptocurrencies. In this paper, how LSTM networks are an upgrade over the previous solutions (regression, traditional neural networks, simple recurrent neural networks) and how they can be implemented for predicting the price of any cryptocurrency have been demonstarted.
Counterfeit goods have become a global problem as consumers are being deceived into buying unauthentic goods with no way to validate the authenticity. Recently, blockchain has become more popular as it fosters trust between untrusting participants. This paper uses blockchain technology to combat the sale of counterfeit products. We use blockchain to allow manufacturers to add authentic product serial numbers onto the ledger; consumers can then use the serial number to verify the authenticity of a product before purchasing it. Blockchain plays a pivotal role in ensuring that data was not tampered with - creating a trusted environment.
Analyn N. Yumang, Arianne I. Rojas, Clark Joshua R. Viray
Nowadays, as modern cryptocurrency machines operate faster and hotter, the fans are insufficient to cool constant CPU and GPU usage. The researchers built specialized Mining Rig Cases for these machines to withstand excessive heat. Aside from temperature, another environmental factor that affects the performance of Cryptocurrency machines is relative humidity. The researchers created an IoT-based Monitoring system to regulate and monitor the Cryptocurrency machines as excessive heat and humidity can decrease the lifespan of these machines. The Infrared camera used color mapping and Otsu’s Segmentation Algorithm for image processing. Otsu Segmentation Algorithm separates the background and foreground of the image while Color Mapping Algorithm converts colors of the image into temperature. The researchers also tested the power consumption and data sizes to determine the efficiency of the monitoring system. Using Convolutional Neural Network, the researchers trained 300 images to assess the state of the Cryptocurrency Mining rig. Additionally, a two-tailed T-Test will determine any significant difference between the two algorithms. Upon training the images, the results obtained show that the two-tail P-value of temperature and humidity is 0.35 and 0.2566, respectively, which affirms no significant difference. However, the power consumption and data size had a substantial difference with P-values of 0.0004 and 3E-06, respectively. Moreover, it shows that the application of Otsu reduces the data size and consumes more power due to deep learning.
Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver.
In the research, the causal relationships between Bitcoin, gold and oil prices were examined. The data of the research covers the period from 2015 to July 2020 and consists of daily price values. Augmented Dickey-Fuller Unit Root Test was used to see whether the stochastic process changes with time. Bitcoin and gold series do not contain a unit root since the oil series is stationary at the level while the difference is stationary. The reason why the series containing unit roots are not stationary is due to structural breaks or not, was investigated by Bai-Perron Unit Root Test with Multiple Structural Breaks. According to the test, it was determined that the Bitcoin series has one break and two regimes, while the gold series has two structural breaks and three different regimes. Whether the research series are cointegrated or not was investigated with the Gregory and Hansen test. The causality between the series was examined with the Toda-Yamamoto causality test, which is based on the VAR (Vector Autoregression) model and examines the causality in the series regardless of the unit root. A two-way causality relationship was determined between the eight lag-long Gold series and the Bitcoin series. In other cases, a causal relationship has not been established. As a result, we give an evidence that Bitcoin and gold prices series followed a parallel pattern while with oil not. Therefore, investors can add Bitcoin into their portfolios to make balance of the risk and return.
It has been a decade since the formation of the first cryptocurrency in the world. Bitcoin was The first ever crypto invented by Satoshi Nag moto in 2008. It is a digital currency or virtual currency which is not controlled by the central government and operates without a central bank or single administrator. Block chain technology is used in the cryptocurrency, the cryptocurrency block chain is the chain of the blocks, where each block contains a hash of the preceding block till the top block of the chain. A network gets formed by the blocks where block chain represents a public ledger of the transaction happened in the network. Bitcoin and other cryptocurrencies have seen a rise in the attention of different sectors in the last few years. It has been in the eyes of everyone for the perks. It offers to be comparable to a fiat currency structure of banking sector, stakeholders, government and individual investors. Although research and awareness about cryptocurrencies or digital currency are very less and it is on the initial stage, this paper provides an important look and guide about the different aspects of cryptocurrency. The authenticity of this paper is on the discussion at various intervals of law and regulation with the consumption of high energy, possibility of crash collisions and security threat on the network attacks. The relative observations on future of applications of bitcoin can be seen throughout the paper.
Cryptocurrency is a relatively new financial sector combination of encryption and currency that is gaining popularity throughout the world. The renewable energy business is likely to be transformed by blockchain technologies. However, no study has been conducted on the use of digital currency. As a result, the focus of this study is on a number of mining potency tests, notably for dogecoin mining. Bitcoin and other cryptocurrencies have received a lot of attention in recent years. Mining generates bitcoins as a fee for corroboratory dealings. This methodology requires a large quantity of process power and electricity. Mining used to be done on personal computers, but now specialized equipment has been designed to boost the mining pace while simultaneously increasing power consumption. As a result, the goal of this study is to look at the influence of hardware efficiency and determine if changing the algorithm to Proof-of-Stake (POS) and using renewable energy as a source of power for mining cryptocurrency will reduce the consumption, improve the mining process, and increase mining profit. During this study, mineworker performance and therefore the profitableness of mining cryptocurrency with the assistance of solar renewable energy, because it is the main supply for mining dogecoin, and we even went through the method of mining dogecoin with the assistance of Nvidia RTX 3080, which could be a non-LHR card, have been performed with and without overclocking the GPU. Considering renewable energy because the supply for mining helps the miners and therefore the setting.