Aiming to solve the problems of low fault tolerance, low throughput, and high delay in traditional methods, an improved method of the blockchain cross-chain consensus algorithm based on weighted PBFT is proposed. This article constructs a blockchain cross-chain exchange model based on cluster centers and divides the nodes in the blockchain system into consensus service nodes, cross-chain exchange nodes, and application nodes to improve the performance of consensus computing services. On this basis, according to the weighted PBFT consensus mechanism, the blockchain consensus environment is set up, and the distribution of nodes in the consensus domain and the blockchain signature scheme are obtained. Therefore, the blockchain cross-chain consensus optimization algorithm is designed to reduce throughput and delay and optimize the consensus effect. The experimental results show that the proposed method can effectively improve the shortcomings of traditional methods, with high throughput and low latency, and strong security. It shows that it is a low resource consumption and secure consensus method.
Tao Hai, Jincheng Zhou, S. Srividhya, Sanjiv Jain · 6 authors
Abstract Blockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.
This paper is an attempt to find the energy required for the comminution of fault zone rocks and also to determine the energy required to grind ore from infinite size to the desired particle size in non-traditional approach, for various value additions. The results in the present investigations also confirm about the brittleness test and friability tests, whose values depend on the drop weight and its height for different types of fault zone rock. Also the results of its brittleness tests determine the grindability of fault zone rocks. All the outcome results are then secured with the help of decentralized and immutable record-keeping system using Blockchain technology. The Blockchain network in the present investigations not only allows any users to enhance the performance but also it will secure the experimental outcomes in immutable distributed ledgers through smart contracts to increase transparency between users in a trusted manner.
Discover the intersectionality of Decentralized Identifiers (DIDs) and Non-Fungible Token (NFTs) usage within the Ethereum blockchain by analyzing Ethereum and NFT platforms to provide as much qualitative and quantitative context as possible.
Puja S. Prasad, G. N. Beena Bethel, Ninni Singh, Vinit Kumar Gunjan · 6 authors
Medical image analysis technology based on deep learning has played an important role in computer-aided disease diagnosis and treatment. Classification accuracy has always been the primary goal pursued by researchers. However, the image transmission process also faces the problems of limited wireless ad-hoc network (WAN) bandwidth and increased security risks. Moreover, when user data are exposed to unauthorized users, platforms can easily leak personal privacy. Aiming at the abovementioned problems, a system model and an access control scheme for the collaborative analysis of the diagnosis of diabetic retinopathy (DR) are constructed in this paper. The system model includes two stages of data cleaning and lesion classification. In the data cleaning phase, the private cloud writes the model obtained after training into the blockchain, and other private clouds use the best-performing model on the chain to identify the image quality when cleaning data and pass the high-quality image to the lesion classification model for use. In the lesion classification stage, each private cloud trains the classification model separately; uploads its own model parameters to the public cloud for aggregation to obtain a global model; and then sends the global model to each private cloud to achieve collaborative learning, reduce the amount of data transmission, and protect personal privacy. Access control schemes include improved role-based access control (RAC) used within the private cloud and blockchain-based access control used during the interaction between the private cloud and the public cloud program (BAC). RAC grants both functional rights and data access rights to roles and takes into account object attributes for fine-grained level control. Based on certificateless public-key encryption technology and blockchain technology, BAC can realize the identity authentication and authority identification of the private cloud while requesting the transmission of model parameters from the private cloud to the public cloud and protect the security of the identity, authority, and model parameters of the private cloud to achieve the effect of lightweight access control. In the experimental part, two retinal datasets are used for DR classification analysis. The results show that data cleaning can effectively remove low-quality images and improve the accuracy of early lesion classification for doctors, with an accuracy rate of 90.2%.
Abstract:The DApp (Decentralised application) being developed enables easy verification of credentials by storing the certificates on Ethereum blockchain network using IPFS (Inter Planetary File System) which is a distributed file system, thereby making the information stored immutable and secure. The website is being developed in three phases. In the first phase, the college enrolls students and uploads their credentials on the Ethereum blockchain. In the second phase, students can view their credentials and access requests sent by companies. In the third phase, companies can send access requests to students whose credentials they want to verify. Once the students accept the access requests, companies can view and verify the certificates.
In today's healthcare environment, incorporating cutting-edge technology is crucial to tackle the increasing difficulties and guarantee effective patient care. Comprehensive healthcare information relies heavily on multimedia data, including various sources such as photos, videos, and sensor data. This paper explores the importance of Multimedia Data Processing and Analysis in healthcare and emphasizes the need for creative frameworks to manage this data efficiently. The current solutions need help with security, transparency, and interoperability, therefore requiring a fundamental change in approach. This study introduces the Hybrid Blockchain Framework for IoT-Healthcare Application (HDF-IoT-HA), which combines web-based communication, dual networks consisting of miners and execution nodes, and a hybrid blockchain system. The structure places a high emphasis on ensuring that data interactions between patients and medical professionals are both safe and transparent. The simulation results demonstrate the impressive capabilities of HDF-IoT-HA, including an average Transaction Efficiency of 97.63%, a reduction in latency of 9.82 ms, an improvement in system reliability of 27.46%, a security rating of 95.66%, and an extended network lifetime of 135.11 hours. These results highlight the framework's effectiveness in improving communication in healthcare, maintaining data security, and strengthening the dependability of systems in IoT-enabled medical applications.
The invention of the Internet provided fast communication; however, the lack of trust is the main problem in Internet-based technologies. Blockchain technology is a distributed ledger technology that has established trust in various trustless environment. This article presents a general architecture of blockchain technology along with a brief history. The paper provides the current status and trend of the research in this area. Finally, various existing applications and future possibilities of blockchain technology are also presented
Yang Zhang, Yongquan Liang, Bin Jia, Pinxiang Wang · 5 authors
Aiming to address the unsatisfactory performance of existing distributed deep learning architectures, such as poor accuracy, slow network communication, low arithmetic speed, and insufficient security, we propose and design a learning model based on a distributed deep learning and blockchain architecture. We use a hybrid parallel algorithm based on blockchain (HP-B) to build a distributed deep consensus learning model. The HP-B algorithm is grouped according to the performance of computing nodes participating in training, network links and training samples, and the grouped computing equipment performs optimal distributed computing. The purpose of this approach is to solve the security and scalability concerns and improve the convergence speed and accuracy of deep learning. The proposed method achieves good results on the CIFAR-100, CIFAR-10, and IMAGENET data sets. Finally, the distributed deep learning model based on blockchain is combined with the generative adversarial network to solve the segmentation problem of medical imaging data, and the experimental results are superior to those of other networks.
Brain Tumor Detection and Classification
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
In teleradiology, medical images are transmitted to offsite radiologists for interpretation and the dictation report is sent back to the original site to aid timely diagnosis and proper patient care. Although teleradiology offers great benefits including time and cost efficiency, after-hour coverages, and staffing shortage management, there are some technical and operational limitations to overcome in reaching its full potential. We analyzed the current teleradiology workflow to identify inefficiencies. Image unavailability and delayed critical result communication stemmed from lack of system integration between teleradiology practice and healthcare institutions are among the most substantial factors causing prolonged turnaround time. In this paper, we propose a blockchain-based medical image sharing and automated critical-results notification platform to address the current limitation. We believe the proposed platform will enhance efficiency in workflow by eliminating the need for intermediaries and will benefit patients by eliminating the need for storing medical images in hard copies. While considerable progress was achieved, further research on governance and HIPAA compliance is required to optimize the adoption of the new application. Towards an idea to a working paradigm, we will implement the prototype during the next phase of our study.
The current development of blockchain, technically speaking, still faces many key problems such as efficiency and scalability issues, and any distributed system faces the problem of how to balance consistency, availability, and fault tolerance need to be solved urgently. The advantage of blockchain is decentralization, and the most important thing in a decentralized system is how to make nodes reach a consensus quickly. This research mainly discusses the blockchain and K-means algorithm for edge AI computing. The natural pan-central distributed trustworthiness of blockchain provides new ideas for designing the framework and paradigm of edge AI computing. In edge AI computing, multiple devices running AI algorithms are scattered across the edge network. When it comes to decentralized management, blockchain is the underlying technology of the Bitcoin system. Due to its characteristics of immutability, traceability, and consensus mechanism of transaction data storage, it has recently received extensive attention. Blockchain technology is essentially a public ledger. This is done by recording data related to trust management to this ledger. To collaboratively complete artificial intelligence computing tasks or jointly make intelligent group decisions, frequent communication is required between these devices. By integrating idle computing resources in an area, a distributed edge computing platform is formed. Users obtain benefits by sharing their computing resources, and nodes in need complete computing tasks through the shared platform. In view of the identity security problems faced in the sharing process, this article introduces blockchain technology to realize the trust between users. All participants must register a secure identity in the blockchain network and conduct transactions in this security system. A K-means algorithm suitable for edge environments is proposed to identify different degradation stages of equipment operation reflected by multiple types of data. Based on the prediction of the fault state for a single type of data, the algorithm uses the historical data of multiple types of data together with the prediction data to predict the fault stage. During the research process, the average optimization energy consumption of K-means algorithm is 14.6% lower than that of GA. On the basis of designing a resource allocation scheme based on blockchain, the problem of how the participants can realize reliable resource use according to the recorded data on the chain is studied. The article implements the verification of the legality of the use of blockchain resources. In addition, a control node is introduced to master the global real-time information of the network to provide data support for the user's choice.
As Blockchain is a distributed digital ledger system, it focuses on various sectors such as bitcoin, the banking sector, the corporation sector, the real estate and the healthcare sector. Each block in the blockchain contains the hash value, timestamp and transaction data of their previous block. The consensus algorithms plays a major role in the blockchain framework. This consensus algorithm maintaining the safety and efficacy of blockchain. The consensus protocols determines how the agreement to add the updated block to all nodes in the network works. Every consensus protocols has its own set of performance and scalability features. It is essential to technically compare each consensus mechanism by highlighting their strengths and weaknesses. Consensus algorithms in blockchain can be divided into two types. They are Proof based consensus and voting based consensus. The Proof based consensus shows that they are more qualified than others to do mining work. Voting-based consensus explains that nodes are needed in a blockchain network to exchange decisions for mining a new block or transaction before reaching a final conclusion. Their effectiveness can be enhanced by manipulating the suitable consensus algorithm in the blockchain. Blockchain technology is recently implement in many domains, especially for healthcare Industry.
Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed.
In the modern processed world, it becomes more necessary to certify humans in a very secure way. There are modern square measure applications such as online banking or online search usage techniques that are depended on passwords, keys or individual identification card. These technologies and processes carry the danger that information may be forgotten, lost, or perhaps stolen. Therefore, the forms of identification promise a singular thanks to being ready to certify humans. A secure and confidential identification technique is the use of fingerprints. We proposed here IoT and Blockchain Based Intelligence Security System for Human Detection using an Improved ACO and Heap Algorithm. We proposed IoT and Blockchain Based Technology for ensuring the security of our system.
Md. Rafiqul Islam, Muhammad Mahbubur Rahman, Mohammed Ataur Rahman, Muslim Har Sani Mohamad · 5 authors
The alternative energy generation sources have increased drastically from centralized systems to distributed systems which increases the stability of energy distribution management systems and reduces the distribution cost as well. On the other hand, it reduces the probability of major area electricity blackout chances and decreases the energy distribution loss. For proper distribution and management of energy, there are different types of advanced technologies like artificial intelligence, and the Internet of Things (IoT) available, but a blockchain automated system is one of the best choices and is highly recommended. Various aspects of blockchain technology and energy management system have been discussed in this review paper where a total number of 423 journal papers, articles, and online information sources have been reviewed in the initial stage, and finally, 63 published research articles have been selected for review. There are several topics, including technology overview in energy management systems, blockchain application of energy trading, blockchain technology implementation challenges, distributed energy management system with Ethereum, and a conclusion with some recommendations have been discussed. Blockchain and Distributed Ledger Technology (DLT) are highly transparent, authenticate, and secure systems that can be used for distributing the energy between distributor and consumer without an intermediator which increases the overall efficiency of the system. This paper aims to highlight the blockchain and distributed ledger technology and how it works as well as optimize the transaction processing cost among the participants of the consortium network. This paper will make a significant contribution to the new research work and in the field of energy management systems.
Tao Xiang, Honghong Zeng, Biwen Chen, Shangwei Guo
Medical image fusion generates a fused image containing multiple features extracted from different source images, and it is of great help in clinical analysis and diagnosis. However, training a deep learning model for image fusion usually requires enormous computing power, especially for large volumes of medical data. Meanwhile, the privacy of images is also a critical issue. In this article, we propose a privacy-preserving blockchain-based medical image fusion (BMIF) framework. First, to ensure fusion performance, we design a new medical image fusion model based on convolutional neural network and Inception network and integrate the proposed model into the consensus process of blockchain. Next, to save computing power of blockchain, we design a consensus mechanism by requesting consensus nodes to train the fusion model instead of calculating useless hash values in traditional blockchain. Then, to protect data privacy, we further present an efficient homomorphic encryption to realize the training of fusion model on encrypted medical data. Finally, we conduct theoretical analysis and extensive experiments on public datasets to evaluate the feasibility and the performance of our proposed BMIF. The results exhibit that BMIF is efficient and secure, and our medical image fusion network performs better than state-of-the-art approaches.
Blockchain mining is a power &resource consuming task, which requires multiple-levels of optimization, both at resource &task level. Over the years, a wide variety of mining optimization models are proposed by researchers, but most of them are applicable only to a subset of mining types. For instance, mining models used for Proof-of-Work (PoW) consensus-based mining, are not applicable for Delegated Proof-of-Stake (DPoS), and other consensus types. This limits the scalability of these models, which reduces their adoptability for dynamic blockchain systems (DBSes). These DBSes utilize different consensus models as per context of data storage, and are widely used by blockchain designers to deploy high-efficiency, and low delay storage solutions. A standard mining optimization solution is not available for such scenarios, due to which researchers & system designers opt for deployment-specific optimizations, which need to be redesigned for each blockchain system. To remove this drawback, a standard blockchain mining optimization model is proposed in this text. This model uses a combination of Genetic Algorithm (GA) & Particle Swarm Optimization (PSO) for solving two different issues. The GA model is used to optimize miner set selection, which will be used for consensus, while the PSO model optimizes the responses from these miner sets depending upon their temporal mining performance. Due to optimum miner set selection, only higher efficiency miner nodes are used for mining the blockchain. While due to performance optimization of these miner nodes, their internal mining efficiency is improved.This efficiency is evaluated in terms of delay & power needed for single block mining w.r.t. blockchain length. It was observed that a combination of these models is capable of enhancing mining speed, with reduced power consumption, and higher mining throughput. Due to this improvement the proposed HBSBA model outperforms most of the recently proposed blockchain mining models. The model was evaluated on DPoS, Proof-of-Authority (PoA), Proof-of-Stake (PoS), and PoW based consensus models, and a delay reduction of 14.5%, throughput improvement of 8.3%, and reduction in energy consumption by 4.6% when compared with various state-of-the-art models. Due to this improvement, the proposed model is applicable for a wide variety of medium to large scaled blockchain mining applications.