Mohammad Alja’afreh, Wassim El Ahmar, Mohamad Hoda, Ali Karime
Blockchain technology has lately surged to the peak of scientific and industrial priorities due to its possible benefits over a broad array of industries. This is owing to their demonstrated abilities to address many of the difficulties that are now inhibiting advancements in a variety of industries. Securely gathering and transmitting transaction information, allowing effective and systematic logistics operations, as well as boosting clarity in the entire system are just a few of those concerns. The significant economic and operational benefits of blockchain can benefit a wide range of applications in numerous industries. People, enterprises, organizations, and properties can all benefit from digital identification, which can lead to a flood of new business prospects. Applications in finance, energy, transportation, healthcare, and industrial supply chain are growing rapidly and proving to be tremendously advantageous. In order to build more functional and productive industrial applications, several outstanding concerns must be studied and analyzed further. This article analyses and investigates the possibilities, advantages, as well as drawbacks of using blockchain in a variety of commercial operations. In addition, the research aims to define the prerequisites for implementing blockchain for various industrial applications. The study uncovers several opportunities for blockchain adoption in several industries; yet, there are still some hurdles to overcome to enhance the benefits of this technology and ensure better utilization.
Thestudent academic records maintained by the university system manually arevulnerable to easily being modified or tampered with. These academic records ofstudents are proof of the student's performance for all semesters till thedegree is completed. They are supposed to be present at the time of theinterview or at the university if he wants to continue their studies further.The issue with the conventional approach is that it is simple for a maliciousperson to alter it so that it affects the student's grade in any way. This studyaims to use blockchain to create safe Smart Certificates. It offers a viablealternative for issuing, confirming, and exchanging certificates without fearof their integrity being compromised.
In India, charities lack transparency and are difficult to monitor. This negatively affects people’s willingness to contribute. Blockchain technically offers new currency system solutions as the core technology of the Bitcoin system. This white paper proposes a security system based on blockchain technology and discloses design patterns, architectures, and workflows. Internal Blockchain is becoming more and more important in philanthropy. The applicant provides a blockchain-based decentralized donation tracking system. Built on Ethereum and provides full transparency, accountability, and direct access to target recipients [9].
N Madhusudan Rao, M Sai Veer, Karan Jayachandra, Ch Vinay Santosh · 6 authors
Due to the increase in the use of digital assets, Blockchain and Crypto-currency have become the most trending technology these days. Block-chain uses a purely mathematical concept of hashing. Hashing is a Cryptographic algorithm that converts an input string to an unbreakable output string that is distinct from other strings. The Hash of a string is always distinct and varies a lot just by changing a single character of the input string. This mathematical algorithm is the core concept behind a Blockchain. Major Crypto-currencies such as Bitcoin, Ethereum, etc, use a type of hashing algorithm that generates a hash of each block in the chain. Each hash will have a signature such as 4 zeros at the beginning or at the ending. Or some sort of matching string in the generated hash. These major Cryptocurrencies use a lot of compute power in calculating these hashes and this, in turn, uses a lot of energy. Due to the mining of bitcoin and other major currencies, a lot of energy is being used. This can be reduced by using low-power Microcontrollers such as Arduinos, ESP32s, Raspberry Pis, STM32-based Microcontrollers, etc. These Microcontrollers are cheap and can find a hash of the given input with the available power. And the most important thing here is that they consume way less power compared to a modern computer or mining rig. With this project, we are trying to implement a Block-chain where blocks can be mined over this low-power Microcontrollers and thereby saving a lot of energy.
A food shortage, which has increased with the climate crisis, will be one of the biggest problems of the world, together with water scarcity, in the future and will damage the sustainability of the food supply system. With the effect of the COVID-19 pandemic, food resources are decreasing, and food prices are rising all over the world. The decrease in food sources increases the importance of food tracking even more. The exorbitant price increases after the COVID-19 pandemic are the most concrete indicators of this. Blockchain-based food tracking systems will be of critical importance because they will prevent exorbitant price increases with their contribution to food tracking processes, such as reliability and transparency. In this study, the establishment of a blockchain-based food tracking system in Turkey, its operation, and its results will be discussed. It was concluded that 97.54% of the participants using the established system found the application useful and wanted such an application to become widespread. In addition, comparing the performance data of the established blockchain-based system with other blockchain infrastructures, a value of 0.038 s for latency is 435 times better than Ethereum, one of the most popular blockchain infrastructures. A transmission per second value of 285, reception per second value of 335, and CPU load rate value of 19.22 are obtained with the proposed system.
Cryptocurrencies are now the most popular investment instruments among millenials. Crypto offers great returns in a short period of time. Prior to COVID-19, Crypto experienced significant price fluctuations accompanied by an increase in the number of high transaction volumes. This situation was disrupted by the presence of the COVID-19 which made the world economy devastated, marked by the decline of stock prices in the world, especially in Indonesia. A paired test was conducted in this study to compare the state of Crypto before and during COVID-19 with the variables of Risk, Transaction Volume, Return, and Sharpe Performance. The results showed that there was a significant difference in the variables of Transaction Volume and Return. However, there was no significant difference in the Risk and Sharpe performance before and during COVID-19. This study shows that despite the COVID-19 pandemic, the enthusiasm of investors who transact crypto assets is not affected and they still get returns in accordance with the investments made. The high risk will be followed by a high standard deviation, so that the Sharpe Performance is small. Cryptocurrencies still have many gaps to research, such as regulation, so that many countries have not legalized Crypto transactions. If there is no regulation for Crypto, it is certain that an increase in cybercrime harms crypto investors and threatens global financial stability. Nevertheles, with or without COVID-19, investment transactions gain and lose based on confidence in the limited market. Therefore, the success of confidence fluctuations in crypto encourages the emergence of alternative coins created by investors to conduct an Initial Coin Offering (ICO).
This review focuses on blockchain technology, and its application and common problem with reference solution. The blockchain technology is nascent and complex and involves many different fields, which leads to the development of cryptocurrency. However, the crptocurrency has high volatility that demands prompt solution. Deep learning technology is considered as a promising approach to address this issue. After research, this paper develops four models with high efficiency and accuracy, including NLANN, JNN. LSTM and GRN to realize prediction in crptocurrency.
To be or not to be is the question that Hamlet thinks about day and night. Gold or Bitcoins is an inescapable choice for investors. With the ever rising and falling price of gold and bitcoin, making good trading decisions is of paramount importance. In this paper, we systematically investigate how data can be used to quantify the factors that influence trading and make the final decision. We build time series with the prices of gold and bitcoin for the past five years. We obtained forecast curves with excellent fit by seasonality analysis and ARIMA time series model forecasts.
Amrutanshu Panigrahi, Ajit Kumar Nayak, Rourab Paul
Blockchain technology facilitates transparency, de-centralization, immutability, and security for each transaction. The decentralization characteristics enable the block chain network to have different nodes as the Certificate Authority (CA) for a different transactions. Choosing a different validator for every individual transaction may increase the security perspective of the transaction, but it can lead to one major concern such as the network overhead increase. There can be several numbers of transactions for a single network and selecting a validator every time can cause a block propagation delay which also decreases the network efficiency. To avoid such kind of issue clustering of network nodes can be an emerging solution. In the current research work, the entire network participating nodes are considered for making different clusters based on the response time factor. Every cluster has the average response time as the threshold value. The node that wants to initiate a transaction needs to raise a minimum response time requirement and the cluster selection procedure will be executed based on the received response time value. In the current research work, the K-Means clustering with the Elbow method as an internal validation method is considered to decide the number of clusters. The computational time for both cases is compared to measure the effectiveness of the clustering process on the blockchain network.
Harish Kundra, Sudhir Sharma, P. Nancy, D. Kalyani
Purpose Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model. Design/methodology/approach In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm. Findings The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively. Originality/value In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.
A key aspect of sustainable business development involves protecting a company’s products from counterfeiting. Since this can impact brand’s image and often, product counterfeits are of a minor quality which can be dangerous and even pose health hazards. Counterfeiters gain profits by manufacturing products using lesser quality materials and production methods. Many counterfeit products in the market are difficult to identify for a normal customer and require to be examined by a trained professional which is time-consuming and costly. In this paper, we propose a method for identifying counterfeit by a simple scan of the quick response code on the product. Since these codes are unique and are implemented by unique algorithms, it is almost impossible to forge them. Also, implementing an encrypted peer-to-peer system for the database makes it impossible for attackers to tamper with the database. The proposed method collectively not only helps laypersons to identify the authenticity of products but also an ownership tracking system where products are linked to the owners’ account which will allow them to produce proof of purchase and authenticity when reselling products and when claiming the warranty of the product. This will help in reducing manufacturing and materials costs used from traditional methods against counterfeiting such as Radio-Frequency Identification and the Hologram technique.
Currency Recognition and Detection
User Authentication and Security Systems
Advanced Steganography and Watermarking Techniques
Cryptocurrencies are nowadays getting popular for investment due to its various benefits such as low transaction cost, blockchain secured platform, profit, etc. Bitcoin being top of the market capitalization currency, gained more popularity during covid-19 pandemic. This study focuses on bitcoin price prediction with covid-19 sentiment. Here Long Short Term Memory Deep learning model based on machine learning is used for price prediction. At the end both results i.e., with covid-19 sentiment and without it are compared which shows model performs better by adding sentiments.
Mahdi Madanchi Zaj, Mohammad Ebrahim Samavi, Emad Koosha
One of the recent financial technologies is Block chain-based currency known as Cryptocurrency that these days because of their unique features has become quite popular. The first known Cryptocurrency in the world is Bitcoin, and since the cryptocurrencies market is a contemporary one, Bitcoin is currently considered as the pioneer of this market. Since the value of the previous Bitcoin prices data have a non-linear behaviour, this study aims at predicting Bitcoin price using Grey model, Back Propagation Artificial Neural Network and Integrated Model of Grey Neural Network. Then, the prediction’s accuracy of these methods will be measured using MAPE and RMSE indices and also Bitcoin price data for a five-year period (2014-2018). The results had indicated that wen estimating Bitcoin daily prices, Back Propagation Artificial Neural Network model has the lowest absolute error rate (5.6%) compared to the Grey model and the integrated model. Additionally, for the monthly prediction of Bitcoin price, the integrated model, with the lowest absolute error rate (9%), has a better performance than the two other models.
Cryptocurrency is a new buzz in the financial sector. This study helps user to evaluate the price of cryptocurrency and user can decide and invest on the current crypto coin based on predicted price graphs. We are proposing a system to evaluate daily price of five coins i.e. Bitcoin, XRP, Ethereum, Dogecoin and SHIBA INU with the help of two Machine Learning algorithm models ARIMA (auto-regressive integrated moving average model) and LSTM (long-short-term memory networks) where the database is protected by Blockchain technology. User will be able to trade in the current financial cryptocurrency market by selecting the coin and prediction date, system will display data using Smart Contract coding and the visualized graph with Time Series Forecasting. The system will improve the crypto coin evaluation as it will get trained and dataset will get updated using Machine learning algorithm.
James Mahlaba, Amit Kumar Mishra, Deepak Puthal, Pradip Kumar Sharma
Currently, all document processes in developing countries require third-party (human) verification; this makes said documents susceptible to human error. Blockchain technology makes the need for a central authority redundant as the devices needing to download digital assets are connected to one another via a peer-to-peer network. It allows for the exchange and storage of digital assets without the need for third-party oversight. The main objective of this article is to develop a blockchain solution in an attempt to mitigate corruption. The contribution details a document verification process as a solution to lessen third-party access to sensitive information and ensure that it is safe and untampered with. The solution proposed to this issue was the introduction of blockchain technology to aid in document verification. This makes the entire system decentralized and reduces the amount of third-party access to sensitive information. Using blockchain makes documents immutable thus reducing corrupt activity such as identity theft and ghost accounts. A blockchain implementation in the field of document verification also dissolves the need for certified copies, which then allows for better use of state funds and public service workers. This work can prove to be a major enabler to achieve sustainable development goal-16 (Peace, Justice, and Strong Institutions).
In the context of COVID-19, the circulation of agricultural products is increasingly important for the nutrition and health of people. With the changing needs of society and the advancement of technology, the agricultural product circulation system needs to undergo corresponding changes to adapt to the modern fast-paced social system. Blockchain technology couples with the circulation of agricultural products, as its technical features, such as immutability and a distributed ledger database, ensures the speed and stability of the key information circulation process of agricultural products. The research goal of this paper was to clarify the influence of blockchain technology on the qualification rate and circulation efficiency for agricultural products. Based on the main characteristics of blockchain technology and a summary of domestic and foreign theoretical research, this paper simulated the impacts of blockchain technology on the agricultural product circulation system. The results revealed that blockchain technology can improve the qualification rate of agricultural products and thereby ensure their quality and safety. The introduction of blockchain increased the qualification rate by nearly 30%. Moreover, blockchain technology significantly enhanced the efficiency of the agricultural product circulation system, thereby greatly promoting economic benefits. The introduction of blockchain increased circulation efficiency by nearly 15%. Finally, the introduction of blockchain technology can effectively promote the governance level and reduce the supervision costs of the agricultural product circulation system. Through simulation analysis, we found that blockchain technology has a positive impact on both the qualification rate and circulation efficiency for agricultural products. These findings enrich research into the application of blockchain technology in the management and circulation of modern agricultural products.
Over the years, with the development of e-commerce, people are mostly making online transactions, and the risk of getting scammed has also increased. This in turn forces the financial institutions to improve continuously and upgrade their model. Machine Learning techniques were used to detect fraud in credit card transactions, but working with real-time data can be tough for machine learning to handle. Thus, implementation of blockchain techniques with machine learning to improve the efficiency and accuracy of the model. In the proposed model, Ethereum dataset has been used to check the fraudulent transaction and secure it with the help of machine learning algorithms. Out of all the classifiers XGBoost has attained the highest accuracy of 99.21% for the stated dataset.
Bitcoin is a type of Internet currency that is both a digital asset and a payment method. It enables for anonymous payment from one person to another, making it a popular payment mechanism for online illegal activity. Due to its recent price increase, Bitcoin has gotten a lot of attention from the media and the general public. The goal of this research is to discover the Bitcoin price's predictable price direction. Machine learning models are likely to provide us with the information we require to understand the future of cryptocurrency. It won't tell us what will happen in the future, but it might show us the overall trend and direction in which prices are likely to move. The proposed methodology aims to create a machine learning model that uses data to learn about the patterns in the dataset and then uses a machine learning algorithm to forecast the bitcoin price.
<p>Cybersecurity is an inherent characteristic that should be addressed before the large deployment of smart city applications. Recently, Blockchain appears as a promising technology to provide several cybersecurity aspects of smart city applications. This paper provides a comprehensive review of the existing blockchain-based solutions for the cybersecurity of the main smart city applications, namely smart healthcare, smart transportation, smart agriculture, supply chain management, smart grid, and smart homes. We describe the existing solutions and we discuss their merits and limits. Moreover, we define the security requirements of each smart city application and we give a mapping of the studied solutions to these defined requirements. Additionally, future directions are given. We believe that the present survey is a good starting point for every researcher in the fields of cybersecurity, blockchain, and smart cities.</p>
In recent years, popularity and use of cryptocurrencies has been rising along with their prices and Ethereum is the second most famous cryptocurrency after Bitcoin. Cryptocurrencies are based on blockchain, which is a distributed and empowered technology that has the power to transform any banking systems. It has become an attractive investment for traders as well as individuals looking to invest. The price of Ethereum varies and is controlled by different factors, such as the crypto market in which it is sold, supply and demand. Ethereum is so valuable because it could be used as cash, we could also pay a portion or part of Ethereum to someone in exchange and it is easily guaranteed by the blockchain. Unlike stocks, Ethereum price is much more variable, as it has a trading time of 24-hours a day without any close time. The paper compares the results of three different models, namely Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs) and Bi-directional Long Short-Term Memory (Bi-LSTMs). The dataset consists of the closing price for the last 2000 days that is used to predict both short-term (30 days) and long-term (90 days) Ethereum prices. These prices are being fetched from an API which is in JSON format and are updated every day.
Satoshi Nakamoto invented blockchain technology in 2008. This cutting-edge characteristic of technology has given rise to intriguing notions and themes that developers and researchers can use in their work. Transactions in blocks were decentralized, immutable and digital. The blockchain is a chain of connected blocks created using cryptography and hashing techniques. The purpose of blockchain technology is to store and improve the value of data. Blockchain can store important information in blocks. It improves productivity in industries by utilizing Blockchain technique in a wide range of applications. Peer-to-peer networks are a type of network that helps with data storage. The blockchain technology has a real-time updating feature to keep the records of data up to date and consistent. This analysis started with a look at some of the most popular blockchain applications. Existing blockchain applications were examined and a list of its merits in various field was compiled as proof of concept.
Blockchain is a trending technology that is gaining popularity for its widespread application in various sectors, including education. Academic certificates are an important measure of an individual’s abilities. They are especially important for higher education and employment. However, advanced technologies have enabled the creation of fake documents. The paper counters this problem by proposing a blockchain-IPFS based solution. The proposed system is an Ethereum blockchain and IPFS based system for storing, retrieving, and authenticating educational documents as well as streamlining the entire process of examination and result generation. The proposed system comprises three main modules: Admission; Examination and Results; Third-Party Module. The authenticity of any document is guaranteed since it is shared within a secured environment. Existing research on similar solutions suffers from poor testing, lack of evaluation, and inefficient storage models. The proposed system overcomes these drawbacks. The proposed system was also evaluated for uploading and fetching latency. The average results show that the system has a mean execution time of 10176 milliseconds for uploading a 1.45MB document and a mean execution time of 110.7 milliseconds for fetching 6 documents each of size 214 kB. Thus, the system is found to be efficient in addition to being safe and secure.