Bitcoin, one of the major cryptocurrencies, presents great opportunities and\nchallenges with its tremendous potential returns accompanying high risks. The\nhigh volatility of Bitcoin and the complex factors affecting them make the\nstudy of effective price forecasting methods of great practical importance to\nfinancial investors and researchers worldwide. In this paper, we propose a\nnovel approach called MRC-LSTM, which combines a Multi-scale Residual\nConvolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to\nimplement Bitcoin closing price prediction. Specifically, the Multi-scale\nresidual module is based on one-dimensional convolution, which is not only\ncapable of adaptive detecting features of different time scales in multivariate\ntime series, but also enables the fusion of these features. LSTM has the\nability to learn long-term dependencies in series, which is widely used in\nfinancial time series forecasting. By mixing these two methods, the model is\nable to obtain highly expressive features and efficiently learn trends and\ninteractions of multivariate time series. In the study, the impact of external\nfactors such as macroeconomic variables and investor attention on the Bitcoin\nprice is considered in addition to the trading information of the Bitcoin\nmarket. We performed experiments to predict the daily closing price of Bitcoin\n(USD), and the experimental results show that MRC-LSTM significantly\noutperforms a variety of other network structures. Furthermore, we conduct\nadditional experiments on two other cryptocurrencies, Ethereum and Litecoin, to\nfurther confirm the effectiveness of the MRC-LSTM in short-term forecasting for\nmultivariate time series of cryptocurrencies.\n
Abstract The traditional traceability system for agricultural product transactions is susceptible to information alteration and damage, which may lead to issues regarding product quality and food safety. And blockchain is a technology that boasts tamper-proofing, complete traceability and time-stamped storage. Considering the above, this study proposes a new blockchain-based approach to the quality management of agricultural products and introduces Solidity-based prototype smart contracts for agricultural product transactions. Test results show that the new traceability system can offer good performance in terms of data upload and block response time. The method proposed in this paper can be used as a solution for quality management of agricultural product that boasts whole process transparency, full-link reliability and joint supervision by all nodes in the system.
Blockchain technology and distributed ledger has attracted massive attention and has triggered multiple projects in different industries. Blockchain is one of the most important technical invention in the recent years. It serves as an immutable ledger which allows transactions to take place in a decentralized manner. Blockchain based applications are springing up and are covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability and security problems waiting to be overcome. This paper presents a comprehensive overview on blockchain technology.
The works produced within the music industry arepresented to their listeners on a digital platform,taking advantage of technology. The problems of thepast, such as pirated cassettes and CDs, have left theirplace to the problem of copyright protection on digitalplatforms today. Block chain is one of the mostreliable and preferred technologies in recent timesregarding data integrity and data security. In thisstudy, a blockzincir-based music wallet model isproposed for safe and legal listening of audio files.The user's selected audio files are converted intoblock chain structure using different techniques andalgorithms and are kept securely in the user's musicwallet. In the study, performance comparisons aremade with the proposed model application in terms ofthe length of time an ordinary audio player can addnew audio files to the list and the response times ofthe user. The findings suggest that the proposedmodel implementation has acceptable differences inperformance with an ordinary audio player.
The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process.
Rohan Kumar C L, Ali M. Zain, Ali M. Zain, A V Prajwal · 5 authors
Fraudulent transactions have a huge impact on the economy and trust of a blockchain network. Consensus algorithms like proof of work or proof of stake can verify the validity of the transaction but not the nature of the users involved in the transactions or those who verify the transactions. This makes a blockchain network still vulnerable to fraudulent activities. One of the ways to eliminate fraud is by using machine learning techniques. Machine learning can be of supervised or unsupervised nature. In this paper, we use various supervised machine learning techniques to check for fraudulent and legitimate transactions. We also provide an extensive comparative study of various supervised machine learning techniques like decision trees, Naive Bayes, logistic regression, multilayer perceptron, and so on for the above task.
Abstract From last many years it has been a trend to invest in cryptocurrency especially (Bitcoin) because it is one of the most popular and decentralized digital currency. However, its prices keep on fluctuating very much that makes it difficult to predict. So, our research aim is to find the less time consuming and accurate model for the prediction of Bitcoin price from different machine learning models like (Multivariate Linear Regression, Theil-Sen Regression, Huber Regression) and deep learning algorithms like (LSTM, GRU). The dataset that we will use for our prediction purpose will be stored in MongoDB (Big-Data Tool) because it consists of huge data points. We have also implemented IOT in our system to create an alert system, which alerts user when the value of bitcoin price reaches a threshold value.
Tahmid Hasan Pranto, Abdulla All Noman, Atik Mahmud, AKM Bahalul Haque
The agricultural sector is still lagging behind from all other sectors in terms of using the newest technologies. For production, the latest machines are being introduced and adopted. However, pre-harvest and post-harvest processing are still done by following traditional methodologies while tracing, storing, and publishing agricultural data. As a result, farmers are not getting deserved payment, consumers are not getting enough information before buying their product, and intermediate person/processors are increasing retail prices. Using blockchain, smart contracts, and IoT devices, we can fully automate the process while establishing absolute trust among all these parties. In this research, we explored the different aspects of using blockchain and smart contracts with the integration of IoT devices in pre-harvesting and post-harvesting segments of agriculture. We proposed a system that uses blockchain as the backbone while IoT devices collect data from the field level, and smart contracts regulate the interaction among all these contributing parties. The system implementation has been shown in diagrams and with proper explanations. Gas costs of every operation have also been attached for a better understanding of the costs. We also analyzed the system in terms of challenges and advantages. The overall impact of this research was to show the immutable, available, transparent, and robustly secure characteristics of blockchain in the field of agriculture while also emphasizing the vigorous mechanism that the collaboration of blockchain, smart contract, and IoT presents.
Rahul Pitale, Kapil Tajane, S. S. Khandagale, Vidhya Gadewar · 6 authors
The increase in fake goods is severely affecting the industrial sector and consumers. According to the survey, incidences involving fake products have increased in recent years, which have had a negative impact on sales, profits, and brand recognition. Far from reducing the ability of a company to make money, this counterfeiting also affects the consumer's ability to trust their goods in open market. Without proper tracking and security measures, consumers gradually stop trusting brands to keep their consumers safe from theft. This is happening because the manufacturing and distribution processes are hidden to consumers and this information is easily manipulated or falsified by others. So, users must have a method to determine if a product is genuine or not. This study proposes a blockchain based anti-counterfeiting system for product traceability throughout the supply chain. By using public or permissionless blockchain all the information throughout the supply chain will be recorded in the blockchain network in the form of blocks that are immutable, transparent, secure, tamper-proof, and trusted. This proposed method uses QR code for making the system further secure.
Bir paranın sağlam olup olmadığı iki değere bakılarak anlaşılabilmektedir. İlki arzını gösteren stok durumu, ikincisi ise devam eden süreçte üretilecek olan birimi gösteren akış değeridir. Stok ve akış arasındaki oran, para olarak tanımlanan malın sağlamlığının göstergesi olarak ifade edilebilmektedir. Bitcoin, toplam arzı 21.000.000 adet ile sınırlı olan bir kripto paradır. Arzının sınırlı olması, fiyatını yükseltecek bir etmen olarak düşünülmektedir. Stok Akış Modeli de arzı sınırlı olan varlıklar için kullanılabilir. Bu çalışmada zaman serisi analiz modellerinden Facebook Prophet algoritması kullanılarak Bitcoin fiyat tahmini yapılmıştır. 2013-2020 yılları arasındaki günlük verilerin kullanıldığı çalışmada diğer çalışmalardan farklı olarak Stok Akış Modeli’nden elde edilen Stok Akış Oranı da modele eklenmiştir. Doğruluk ölçüleri ile desteklenen çalışma sonuçlarına göre Stok Akış Oranı’nın modele dâhil edilmesi ile Facebook Prophet algoritması kullanıldığında modelin performansının arttığı sonucuna ulaşılmıştır. Son olarak, Prophet yöntemi, ARIMA yöntemine göre daha etkin sonuçlar verdiği elde edilen bulgular arasındadır.
The most challenging problem before the world is providing enough food for the gigantic leap in population. There are numerous reasons for the shortage of food and technological innovations in agriculture are required to overcome the shortages in food supply. Sustainable Development goals (SDGs) provides the futuristic vision and ICT along with other latest technologies will help achieve these development goals at a brisk speed. Recent technological developments as use of mobile-broadband access devices, Internet of Things (IoT), Specialized Robots, Drones, big data analytics and Artificial Intelligence has given farmers tools and technologies to scale agricultural production and marketing of their agricultural products. In this paper, we will discuss how Smart Agriculture is changing the face of agriculture in Budaun, a small city in Uttar Pradesh. Smart agriculture coupled with block chain technology is being used to achieve sustainability in agriculture growth. The production/yield has risen by over 20% and the profits have increased by over 30% with the new technology.
Since its inception, Bitcoin has been subject to numerous thefts due to its enormous economic value. Hackers steal Bitcoin wallet keys to transfer Bitcoin from compromised users, causing huge economic losses to victims. To address the security threat of Bitcoin theft, supervised learning methods were used in this study to detect and provide warnings about Bitcoin theft events. To overcome the shortcomings of the existing work, more comprehensive features of Bitcoin transaction data were extracted, the unbalanced dataset was equalized, and five supervised methods—the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) techniques—as well as three unsupervised methods—the local outlier factor (LOF), one-class support vector machine (OCSVM), and Mahalanobis distance-based approach (MDB)—were used for detection. The best performer among these algorithms was the RF algorithm, which achieved recall, precision, and F1 values of 95.9%. The experimental results showed that the designed features are more effective than the currently used ones. The results of the supervised methods were significantly better than those of the unsupervised methods, and the results of the supervised methods could be further improved after equalizing the training set.
Could BTCBAM Become a Strong Alternative to BITCOIN?<br> <br> BTCBAM coin is currently one of the 54 most reliable coins in the world, using the same algorithm as Bitcoin (SHA-256). While there are over 8 thousand coins / tokens in the world, it is one of the 340 coins using the blockchain platform. For this reason, it is taking firm steps towards becoming a new Bitcoin with its strong and secure infrastructure compared to ERC-20 based tokens that can be easily produced in the rapidly growing crypto money industry.<br> <br> <br> SPEKTRAL INVESTMENT BANK BTCBAM PARTNERSHIP INVESTMENT OPPORTUNITY <br> Kosovo-based Spektral Investment Bank is the first investment bank with technical and security-based capital. The Bank has a unique capital structure consisting of pre-valued exclusive license rights for pharmaceutical patents and calcite mines and pre-made reserve determination reports.<br> <br> With 800 million EU in-kind capital, the Spectral Investment Bank prioritizes bio-medical and pharmaceutical innovation and tokenization of mining securities to provide solid guarantees for high-risk cryptocurrency-based operational leverages, thus offering a significant risk reduction for dynamic financial options. This is the first real-world example of an operational merging between a cryptocurrency investment bank and a blockchain project.<br> <br> BTCBAM, one of the most successful blockchain projects developed by the Turks, signed a cooperation and collateral usage agreement with Spektral Investment Bank to establish Europe's first crypto investment exchange. Spectral Investment Bank also provides in-kind collateral guarantees for the coin, which has a total of 7 block chains to be produced by the BTCBAM team.<br> <br> Spektral Investment Bank acquired 25% of BTCBAM and Bitturex. In return, the Kosovo Investment Bank will provide full-scale project envelopes for each project to be listed on Bitturex. The bank will also provide guarantees for tokenization of projects that receive crypto funds to maximize their commercial potential.<br> <br> <br> <br> Where Can BLOCK CHAIN Technology Reach With BTCBAM? <br> The rapid development of blockchain technology and their numerous emerging applications has received huge attention in recent years. The distributed consensus mechanism is the backbone of a blockchain network. It plays a key role in ensuring the network’s security, integrity, and performance. Most current blockchain networks have been deploying the proof-of-work consensus mechanisms, in which the consensus is reached through intensive mining processes. However, this mechanism has several limitations, e.g., energy inefficiency, delay, and vulnerable to security threats. To overcome these problems, a new consensus mechanism has been developed recently, namely proof of stake, which enables to achieve the consensus via proving the stake ownership. This mechanism is expected to become a cutting-edge technology for future blockchain networks. On this whitepaper, you will learn about proof of stake mechanism and BTCBAM coin, which has a blockchain algorithm and uses a proof of stake mechanism. <br> BENEFITS AND APPLICATIONS <br> Although blockchain technology attracts a lot of attention due to the successful implementation of cryptocurrencies, its benefits extend far beyond. The key benefits of blockchain technology are as follow: <br> • Decentralization: <br> Blockchain networks are not controlled by a central controller. Thus, they do not have any single point of failure. Instead, all the nodes reach the agreement on the state of the network by participating in the distributed consensus mechanisms. <br> • Transparency: <br> Data stored in a blockchain is visible to all network participants. <br> • Immutability: <br> Once the data are stored in the blockchain, it is extremely difficult to be altered. Moreover, thanks to the distributed consensus mechanisms, the network can achieve consensus on the data even in a trustless environment. <br> • Security and Privacy: <br> Using cryptographically secure mechanisms, the privacy and security of the network participants can be significantly enhanced. Users in the network use a pair of public and private keys for identification and verification. When a user makes a transaction, a digital signature is used.<br> <br> BTCBAM TO LEAD THE DIGITAL TRANSFORMATION<br> <br> During the research and development activities that started 5 years ago, crypto money sector analyzes were made. As a result of these analyzes, two main points were determined as goals. The first of these was that in very few of the stock exchanges that provide trading services, the investment owner had its own crypto currency, and another was that coins could not find enough place in daily life. BTCBAM was built on the basis of these two goals and took its current form with the influence of other elements.<br> In this direction, the first step was to integrate the BTCBAM coin into life with its visa and master card features.<br> The BTCBAM Application is integrated with the BTCBAM coin and the exchanges it is traded on. Therefore, when you need cash, you can instantly sell your BTCBAM coins on the stock exchanges where they are traded and you can order to transfer them to your card with the mobile application when you need / want to use them.<br> BTCBAM Card is a prepaid card that is loaded with money (debit) before using it, can be spent as much as it is loaded, and allows you to shop advantageously with many member merchants. The loaded amount can be spent on the internet and at all POSs in stores. Money upload and withdrawal transactions can also be made from ATMs. You can also use it on crypto exchanges.<br> <br> <br> <br> <br> COINPAYMENTS WILL MOVE BTCBAM TO SHOPPING SITES<br> <br> Coinpayments is The World’s Most Trusted Crypto Payments Partner. Over $ 10 Billion In Crypto Payments Since 2013. Now BTCBAM coin is also included in coinpayments as a payment instrument. Thus, primarily in the crypto industry as a clearing tool<br> <br> Canadian e-commerce giant Shopify has added a series of acceptable cryptocurrencies in partnership with CoinPayments.<br> <br> Canadian e-commerce giant Shopify has partnered with CoinPayments to allow its customers to pay merchants in more than 1,800 digital currencies as opposed to an older basket of only 300, based on its ongoing partnership with BitPay. The fact that the BTCBAM coin is low in Coinpayments will also pave the way for it to be a valid coin in the Shopify infrastructure.<br> <br> BTCBAM MAKES A DIFFERENCE WITH ALTERNATIVE EARNINGS<br> <br> Among the cryptocurrencies that offer staking services and have maintained this for a long time, there are Tezos (XTZ), Cosmos (ATOM), EOS, Algorand (ALGO). In addition to these, Ethereum (ETH) is probably the most popular recently.<br> At the moment, 24 coins can be staked on the crypto money transaction platform Binance. These coins include Algorand (ALGO), TomoChain (TOMO), Harmony (ONE), DASH, Cosmos (ATOM), Polkadot (DOT) and Komodo (KMD).<br> <br> BTCBAM coin is also among the coins with staking feature. Thus, it provides its investors with the opportunity to earn additional coins with staking, as well as making a profit by investing in stock markets.<br> <br> BTCBAM coin, which is the first project of the BTCBAM team, will continue to bring new coins to the cryptocurrency sector with its strong partnership structure, Cryptocurrency Investment Bank partnership and guarantee, as well as its experience.
D. Srivatsa, N. S. Jai Aakash, S. Sahisnu, Priyanka Kumar
Abstract The supply chain management industry is struggling with inadequate resources for efficient authenticity verification. Blockchain technology and smart contracts can overcome such conventional limitations to authenticate products in an easy, economical and secure manner. Decentralized and immutable blockchain systems allow product tracking to its origin.In this paper we have proposed and implemented a system for product authentication using blockchain technology based on ethereum platform by making use of smart contracts. We have analysed the existing centralized system and the need to shift from the existing centralized system to a decentralized blockchain based ledger technology. The use of blockchain technology in supply chain technology has reduced the complexity in product authentication by making the entire history of the product available from its production stage till it reaches the customer.
Open access
Blockchain Technology Applications and Security
Currency Recognition and Detection
Advanced Steganography and Watermarking Techniques
Increasing fluctuations in pricing and having great profit potential, utilization in advanced machine learning technologies to make robust predictions of cryptocurrencies especially bitcoin have attracted great attention in recent years. In this study, various statistical techniques; Moving Average Analysis and Autoregressive Integrated Moving Average and machine learning (ML) techniques; Artificial Neural Network, Recurrent Neural Network (RNN) and Convolutional Neural Network have been conducted and compared to predict the future value of Bitcoin cryptocurrency price. They have been applied for the univariate time series analysis with a window size of 32. To prove the usefulness of ML algorithms, and to show that the results of RNN is a better, mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) indicators have been applied. The study revealed that recurrent neural network yields better results than other methods in predicting daily Bitcoin price in terms of MSE, MAE and MAPE metrics. Besides, Wilcoxon-Mann-Whitney nonparametric statistic test is applied to test the performance between ARIMA and machine learning algorithms.
Blockchain innovation with its property of immutable data isn't just limited to digital cryptographic forms of money yet in addition for various domains like healthcare, logistics, rentals, entertainment, social media and so on. Ventures are investigating this innovation to adopt in main line of business, which will give certainty on security ceasing data breach. This research paper has built up a hybrid blockchain framework which can be utilized on premise or over the cloud or a combination of both and can be utilized by various departments for protecting transactions in small and medium scale businesses with an uprightness of high security, transparency, remote devices, accessibility and cost adequacy. This created framework compares with the traditional approach model on some significant factors for statistical assessment. The analysis discovers how this hybrid blockchain framework is more productive and secured than the client server approach model and aims to be utilized as platform as a service both in local or hybrid cloud infrastructure
The prediction of the financial market has gradually attracted attention from investors. As an emerging market, digital currency has become an indispensable and essential part. The common analysis methods are mainly machine learning and its derivatives. The daily frequency data of Bitcoin prices are obtained in this paper from June 2018 to September 2020. The Hidden Markov Model (HMM) is used to predict Bitcoin prices. In the model, the input observation sequence of the model is selected, including closing price, trading volume, and hidden state. They are in line with the current mainstream market perception: bull market, stable, and bear market. The results show that the relative error of the short-term forecast is relatively low, 0.347%. Compared with traditional models and machine learning methods, the Hidden Markov Model can better identify the state of the digital currency market and predict the direction of price movements, which verifies its application feasibility in the financial market.
Befekadu G. Gebraselase, Bjarne E. Helvik, Yuming Jiang
Bitcoin has become the leading cryptocurrency system, but the limit on its transaction processing capacity has resulted in increased transaction fees and delayed transaction confirmation. As such, it is pertinent to understand and probably predict how transactions are handled by Bitcoin such that a user may adapt the transaction requests and a miner may adjust the block generation strategy and/or the mining pool to join. To this aim, the present paper introduces results from an analysis of transaction handling in Bitcoin. Specifically, the analysis consists of two-part. The first part is an exploratory data analysis revealing key characteristics in Bitcoin transaction handling. The second part is a predictability analysis intended to provide insights on transaction handling such as (i) transaction confirmation time, (ii) block attributes, and (iii) who has created the block. The result shows that some models do reasonably well for (ii), but surprisingly not for (i) or (iii).
The transaction and market of bitcoin is volatile, meaning it’s uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a "well-formulated" statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency’s performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.
Reem K. Alkhodhairi, Shahad R. Aljalhami, Norah K. Rusayni, Jowharah F. Alshobaili · 6 authors
Currently, Bitcoin is the world’s most popular cryptocurrency. The price of Bitcoin is extremely volatile, which can be described as high-benefit and high-risk. To minimize the risk involved, a means of more accurately predicting the Bitcoin price is required. Most of the existing studies of Bitcoin prediction are based on historical (i.e., benchmark) data, without considering the real-time (i.e., live) data. To mitigate the issue of price volatility and achieve more precise outcomes, this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open, high, low, and close (OHLC)—prices. Seeking a better prediction model, the present study proposes time series-based deep learning models. In particular, two deep learning algorithms were applied, namely, long short-term memory (LSTM) and gated recurrent unit (GRU). Using real-time data, the Bitcoin candlesticks were predicted for three intervals: the next 4 h, the next 12 h, and the next 24 h. The results showed that the best-performing model was the LSTM-based model with the 4-h interval. In particular, this model achieved a stellar performance with a mean absolute percentage error (MAPE) of 0.63, a root mean square error (RMSE) of 0.0009, a mean square error (MSE) of 9e-07, a mean absolute error (MAE) of 0.0005, and an R-squared coefficient (R2) of 0.994. With these results, the proposed prediction model has demonstrated its efficiency over the models proposed in previous studies. The findings of this study have considerable implications in the business field, as the proposed model can assist investors and traders in precisely identifying Bitcoin sales and buying opportunities.
Andreas Sendros, George Drosatos, Pavlos S. Efraimidis, Nestor C. Tsirliganis
Blockchain is a distributed, immutable ledger technology initially developed to secure cryptocurrency transactions. Following its revolutionary use in cryptocurrencies, blockchain solutions are now being proposed to address various problems in different domains, and it is currently one of the most “disruptive” technologies. This paper presents a scoping review of the scientific literature for exploring the current research area of blockchain applications in the agricultural sector. The aim is to identify the service areas of agriculture where blockchain is used, the blockchain technology used, the data stored in it, its combination with external databases, the reason it is used, and the variety of agricultural products, as well as the level of maturity of the respective approaches. The study follows the PRISMA-ScR methodology. The purpose of conducting these scoping reviews is to identify the evidence in this field and clarify the key concepts. The literature search was conducted in April 2021 using Scopus and Google Scholar, and a systematic selection process identified 104 research articles for detailed study. Our findings show that in the field, although still in the early stages, with the majority of the studies in the design phase, several experiments have been conducted, so a significant percentage of the work is in the implementation or piloting phase. Finally, our research shows that the use of blockchain in this domain mainly concerns the integrity of agricultural production records, the monitoring of production steps, and the monitoring of products. However, other varied and remarkable blockchain applications include incentive mechanisms, a circular economy, data privacy, product certification, and reputation systems. This study is the first scoping review in this area, following a formal systematic literature review methodology and answering research questions that have not yet been addressed.
R. Sujatha, V Mareeswari, Jyotir Moy Chatterjee, Abd Allah A. Mousa · 5 authors
Bitcoin is a decentralized digital currency without a central bank or single administrator sent from user to user on the peer-to-peer bitcoin blockchain network without intermediaries' need. In this Bitcoin trend analysis work, initial attributes are considered from five sectors based on financial, social, token, network, and that count to thirteen attributes. The thirteen attributes considered are price, volume, market cap, a mean dollar invested age, social volume, social dominance, development activity, transaction volume, token age consumed, token velocity, token circulation, market value to realized value, and realized cap. We apply the attribute selection and trend analysis mapped with potential seven attributes: Price, Volume, Market Cap, Social Dominance, Development Activity, Market Value to Realized Value & Realized Cap. We have conducted Nonlinear Autoregressive with External Input analysis considering seven attributes. The work employed three training algorithms to train a neural network as Levenberg-Marquard, Bayesian Regularization, and Scaled Conjugate Gradient algorithm. The Error histogram and regression plots results indicate that the Bayesian Regularized Neural Network is showing good performance and thus provides a better forecast.
Muhammad Nasir Mumtaz Bhutta, Amir A. Khwaja, Adnan Nadeem, Hafiz Farooq Ahmad · 9 authors
Blockchain is a revolutionary technology that is making a great impact on modern society due to its transparency, decentralization, and security properties. Blockchain gained considerable attention due to its very first application of Cryptocurrencies e.g., Bitcoin. In the near future, Blockchain technology is determined to transform the way we live, interact, and perform businesses. Recently, academics, industrialists, and researchers are aggressively investigating different aspects of Blockchain as an emerging technology. Unlike other Blockchain surveys focusing on either its applications, challenges, characteristics, or security, we present a comprehensive survey of Blockchain technology’s evolution, architecture, development frameworks, and security issues. We also present a comparative analysis of frameworks, classification of consensus algorithms, and analysis of security risks & cryptographic primitives that have been used in the Blockchain so far. Finally, this paper elaborates on key future directions, novel use cases and open research challenges, which could be explored by researchers to make further advances in this field.