In the current landscape of vaccine distribution, the pervasive threat of illicit distributors exploiting transaction data vulnerabilities raises serious concerns about vaccine safety and authenticity. To address this challenge, this research introduces an innovative blockchain-based system aimed at countering vaccine counterfeiting and enhancing the transparency of distribution processes. Leveraging Supply Chain Management principles, the system orchestrates the complexities of vaccine distribution, offering comprehensive visibility from central government initiation to healthcare professional administration via an intuitive mobile application. Blockchain technology strengthens data security, guarding against fraudulent activities, while the integration of QR codes expedites data processing and furnishes patients with detailed vaccine information. All transactions are executed through smart contracts, ensuring trust and transparency, with completed contracts securely recorded on the Ethereum blockchain. This research presents a groundbreaking solution to combat vaccine counterfeiting, establishing a resilient and transparent framework for distribution, providing real-time monitoring, and empowering patients and stakeholders with essential vaccine details. In summary, the amalgamation of Ethereum blockchain technology and QR codes in this mobile-based vaccine tracking system offers a transformative approach to fortify the safety and integrity of vaccine distribution, ultimately benefiting society as a whole.
Jameel Almalki, Saeed M. Alshahrani, Nayyar Ahmed Khan
Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that connect the IoMT systems is gaining popularity. This study focuses on identifying the use of blockchain in innovative healthcare units empowered by federated learning. A collective use of blockchain with intrusion detection management (IDM) is beneficial to detect and prevent malicious activity across the storage nodes. Data accumulated at a centralized storage node is analyzed with the help of machine learning algorithms to diagnose disease and allow appropriate medication to be prescribed by a medical healthcare professional. The model proposed in this study focuses on the effective use of such models for healthcare monitoring. The amalgamation of federated learning and the proposed model makes it possible to reach 93.89 percent accuracy for disease analysis and addiction. Further, intrusion detection ensures a success rate of 97.13 percent in this study.
Abdul Muqsit Haji Jafari, Ravi Kumar Patchmuthu, Sharul Tajuddin
The COVID-19 pandemic has presented numerous challenges around the world, including lockdowns, remote working and studying, and travel restrictions imposed by governments to curb the spread of the virus. As vaccines have become more widely available, restrictions have begun to ease for those vaccinated. Many countries use paper-based COVID-19 vaccination certificates to prove vaccination status. However, traditional certificates are vulnerable to forgery and counterfeiting. We developed a blockchain-based system where vaccination certificates are stored and accessed via smart contracts on an Ethereum blockchain. The certificates are stored in a tamper-proof, decentralized manner, ensuring secure verification of vaccination status. This research project successfully designed and implemented a system that securely stores and verifies vaccination certificates using blockchain, demonstrating the benefits of this approach. According to our knowledge, this is the first kind of research initiative in Brunei to develop a blockchain-based immutable COVID-19 Vaccination certificate. Despite its advantages, blockchain still has its flaws, particularly in scalability and adoption, which should be considered for further optimization of the proposed system.
Mohamed Yousuff, J. Jayashree, J. Vijayashree, R. Anusha
In contemporary times, technology is exerting a transformative influence on every facet of human existence. Presently, it is intricately intertwined with nearly all aspects of modern society. Various technological advancements have surfaced recently, including the emergence of blockchain. In order to effectively monitor organizational assets, the utilization of blockchain technology, a decentralized and unalterable ledger, could be employed. Utilizing blockchain technology facilitates secure and cost-effective monitoring and exchange of a wide range of tangible or intangible assets. The blockchain network can monitor and record various activities, such as requests, purchases, accounting, operations, and other related functions. Furthermore, because all individuals within the network have access to identical and current information, it is possible to oversee each transaction stage, thereby increasing confidence and facilitating the emergence of novel prospects and efficacy. The fundamental components of a blockchain include distributed ledgers, immutable data, and intelligent contracts. The blockchain process involves recording individual transactions as discrete blocks of data, which are subsequently linked to the preceding blocks to enable the system’s operation. The emergence of advanced technologies, such as blockchain, has provided a viable solution for addressing the monitoring of patients affected by the coronavirus. The utilization of blockchain technology has the potential to combat pandemics through its ability to facilitate the timely identification of epidemics, safeguarding of sensitive personal information through smart contracts, and streamlined exchange of information. This chapter will explore various use cases of blockchain technology, highlighting the implementation of novel tracking systems enabled by this technology.
Blockchain technology and the metaverse have the potential to revolutionize dentistry and healthcare by enhancing data security, patient empowerment, disaster victim identification, and the delivery of dental services. In this review, we discuss the current state of the art in the field of dentistry and the future of dentistry, highlighting the advantages and challenges of utilizing blockchain technology for disaster victims’ identification. Blockchain's applications in Disaster Victim Identification (DVI) offer a humanitarian dimension, helping bring solace to families in times of tragedy. Moreover, blockchain's potential to establish virtual health clinics and telemedicine platforms could bridge healthcare gaps in underserved regions.
Open access
Blockchain Technology Applications and Security
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Mohamed Abdel‐Basset, Ibrahim Alrashdi, Hossam Hawash, Karam M. Sallam · 5 authors
In the aftermath of the COVID-19 pandemic, the need for efficient and reliable disease diagnosis in smart cities has become increasingly serious. In this study, we introduce a novel blockchain-based federated learning framework tailored specifically for the diagnosis of pandemic diseases in smart cities, called BFLPD, with a focus on COVID-19 as a case study. The proposed BFLPD takes advantage of the decentralized nature of blockchain technology to design collaborative intelligence for automated diagnosis without violating trustworthiness metrics, such as privacy, security, and data sharing, which are encountered in healthcare systems of smart cities. Cheon–Kim–Kim–Song (CKKS) encryption is intelligently redesigned in BFLPD to ensure the secure sharing of learning updates during the training process. The proposed BFLPD presents a decentralized secure aggregation method that safeguards the integrity of the global model against adversarial attacks, thereby improving the overall efficiency and trustworthiness of our system. Extensive experiments and evaluations using a case study of COVID-19 ultrasound data demonstrate that BFLPD can reliably improve diagnostic accuracy while preserving data privacy, making it a promising tool with which smart cities can enhance their pandemic disease diagnosis capabilities.
Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial Intelligence (AI), and other cutting-edge digital and innovative technologies have all offered promising solutions in such situations. AI provides advanced and innovative techniques for classifying and detecting symptoms caused by the coronavirus. Additionally, Blockchain may be utilized in healthcare in a variety of ways thanks to its highly open, secure standards, which permit a significant drop in healthcare costs and opens up new ways for patients to access medical services. Likewise, these techniques and solutions facilitate medical experts in the early diagnosis of diseases and later in treatments and sustaining pharmaceutical manufacturing. Therefore, in this work, a smart blockchain and AI-enabled system is presented for the healthcare sector that helps to combat the coronavirus pandemic. To further incorporate Blockchain technology, a new deep learning-based architecture is designed to identify the virus in radiological images. As a result, the developed system may offer reliable data-gathering platforms and promising security solutions, guaranteeing the high quality of COVID-19 data analytics. We created a multi-layer sequential deep learning architecture using a benchmark data set. In order to make the suggested deep learning architecture for the analysis of radiological images more understandable and interpretable, we also implemented the Gradient-weighted Class Activation Mapping (Grad-CAM) based colour visualization approach to all of the tests. As a result, the architecture achieves a classification accuracy rate of 0.96, thus producing excellent results.
This paper explores the potential of using ChatGPT, a state-of-the-art conversational AI system, to enhance engagement and understanding of cryptocurrency. We first provide a comprehensive review of the existing literature on both cryptocurrency and ChatGPT. We then describe the background of cryptocurrency and the capabilities of ChatGPT. We present our methodology for collecting and preprocessing a dataset of cryptocurrency-related conversations and fine-tuning ChatGPT using reinforcement learning. Our results demonstrate the effectiveness of ChatGPT in generating contextually appropriate responses to cryptocurrency-related queries, with potential applications in areas such as customer support and education. However, we also identify challenges and limitations associated with the deployment of ChatGPT in this domain, including the need for robust data privacy measures and addressing potential biases. Our findings suggest promising directions for future research in enhancing conversational engagement and understanding of cryptocurrency through ChatGPT
Open access
Artificial Intelligence in Healthcare and Education
Intelligent and sustainable healthcare systems can considerably benefit from applying Computational Intelligence (CI) and Artificial Intelligence (AI). These technological breakthroughs can reduce the ecological footprint and raise the bar for excellence. Yet, the broad adoption of such technologies for cutting-edge Internet of Things (IoT) applications generates enormous amounts of data, which can heavily strain the available computational resources. The major motivation behind this study is to provide evidence that Gated Recurrent Units (GRUs), a sophisticated subclass of Recurrent Neural Networks (RNNs), can outperform traditional RNNs. These technologies can be effective in identifying and treating breast cancer. This study collects data from tagged IoT devices and trains a GRU-RNN classifier. The Wisconsin Diagnostic Breast Cancer (WDBC) data tests the system’s accuracy. The results show the proposed Internet of Medical Things (IoMT) is more effective than the current methods in recall, accuracy, and precision while preserving 95% of the original GRU-RNN.
The spread of infectious diseases in crowded spaces such as shopping malls, markets, and hospitals is a growing concern. In order to mitigate this risk, it is crucial to develop a method that leverages the power of distributed crowd to learn, de- tect, and alert individuals about potential health hazards. Hence, the integration of federated learning (FL), and blockchain (BC) to provide intelligent platforms that facilitate pervasive AI and trust amongst IoT devices and smart phones can play a significant role in achieving this goal. In this study, we propose a new technique named BC-FL Location-Based, which utilizes smart applications installed on IoT devices and smart phones to detect and predict imminent health risks. The technique works by using algorithms such as maximal clique to detect individuals in close proximity and sharing their health data through a blockchain network. A smart contract then triggers a node with sufficient resources to gather users' learning experiences from the blockchain, aggregate it, and run a model to determine if any of the individuals present in the area are infected. To demonstrate the effectiveness of the proposed technique, we conducted simulation experiments using Ethereum-based private blockchain network, where nodes represent individuals in different locations. We used the maximal clique algorithm to simulate the movement of individuals and compared the results of the model run on individual data versus aggregated data. Experiments showed promising results, with accuracy of detection increasing to 99% when using iid data and 90% when using non-iid data.
Purpose ChatGPT from OpenAI is an amazing example of machine learning technology. This technology has now become an important issue for high-tech plagiarism concern. Indeed, there are many concerns about using this tool, perhaps using other technologies to make ChatGPT safer. Non-fungible tokens (NFTs) may be a way out. This paper aims to discuss such an alternative. Design/methodology/approach To preventing with high-tech plagiarism created by the ChatGPT tool two ways can help schools, universities and scientific centers to prevent academic plagiarism: first, by banning ChatGPT and adjusting teaching styles, and second, by using detecting AI-produced content. In this viewpoint, the authors suggest a third way that can be a way out. Findings NFTs technology has the ability to add a non-fungibility feature to any digital object (image, text or video). Therefore, any text produced by artificial intelligence tools can be given a specific NFT code. With this work, the authors add a feature to texts produced by artificial intelligence, that is, the non-fungibility feature. Originality/value In this viewpoint, how and why NFTs may be a usefully added value in preventing acts of high-tech plagiarism on ChatGPT is discussed.
Open access
Artificial Intelligence in Healthcare and Education
Anwar Ali Sathio, Shafiq Ahmed Awan, Ali Orangzeb Panhwar, Ali Aamir · 6 authors
During the COVID-19 pandemic, finding effective methods to prevent the spread of infectious diseases has become critical. One important measure for reducing the transmission of airborne viruses is wearing face masks but enforcing mask-wearing regulations can be difficult in many settings. Real-time and accurate monitoring of mask usage is needed to address this challenge. To do so, we propose a method for mask detection using a convolutional neural network (CNN) and blockchain technology. Our system involves training a CNN model on a dataset of images of people with and without masks and then deploying it on IoT-enabled devices for real-time monitoring. The use of blockchain technology ensures the security and privacy of the data and enables the efficient sharing of resources among network participants. Our proposed system achieved 99% accuracy through CNN training and was transformed into a blockchain-enabled network mechanism with QR validation of every node for authentication. This approach has the potential to be an effective tool for promoting compliance with mask-wearing regulations and reducing the risk of infection. We present a framework for implementing this technique and discuss its potential benefits and challenges
With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.
COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.
Open access
COVID-19 diagnosis using AI
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
With the significant advantages of system complexity and operating costs, train autonomous circumambulate system (TACS) is gradually replacing the traditional communication-based train control system as the next-generation train operation control system development direction. As train operation and control become more decentralized and autonomous, real-time and accurate obstacle detection, apart from route-level protection, is quite desirable in TACS. Most of the existing researches about obstacle detection focus on detection algorithm optimization based on the once-deployed lifelong use principle, whereas model reoptimization based on the actual operating environment under unexpected situations and model sharing among multiusers are largely ignored. In this article, we design a novel obstacle detection system in TACS based on blockchain-empowered edge intelligence (EI). To make full use of the massive raw unannotated data collected online, we first propose an semisupervised learning-based TACS obstacle detection model. Considering the resource-hungry model training, we introduce EI into TACS and propose a multiagent reinforcement learning-based task offloading algorithm for secure and efficient computation offloading coordination. Furthermore, we propose a blockchain-based model sharing scheme to facilitate the multimodel parameter exchange and improve the obstacle detection accuracy. Extensive simulation results show that the designed obstacle detection system can effectively improve the TACS obstacle detection performance.
Hassaan Malik, Tayyaba Anees, Ahmad Naeem, Rizwan Ali Naqvi · 5 authors
Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy must be employed to isolate COVID-19. When it comes to determining the identity of COVID-19, one of the most significant obstacles that researchers must overcome is the rapid propagation of the virus, in addition to the dearth of trustworthy testing models. This problem continues to be the most difficult one for clinicians to deal with. The use of AI in image processing has made the formerly insurmountable challenge of finding COVID-19 situations more manageable. In the real world, there is a problem that has to be handled about the difficulties of sharing data between hospitals while still honoring the privacy concerns of the organizations. When training a global deep learning (DL) model, it is crucial to handle fundamental concerns such as user privacy and collaborative model development. For this study, a novel framework is designed that compiles information from five different databases (several hospitals) and edifies a global model using blockchain-based federated learning (FL). The data is validated through the use of blockchain technology (BCT), and FL trains the model on a global scale while maintaining the secrecy of the organizations. The proposed framework is divided into three parts. First, we provide a method of data normalization that can handle the diversity of data collected from five different sources using several computed tomography (CT) scanners. Second, to categorize COVID-19 patients, we ensemble the capsule network (CapsNet) with incremental extreme learning machines (IELMs). Thirdly, we provide a strategy for interactively training a global model using BCT and FL while maintaining anonymity. Extensive tests employing chest CT scans and a comparison of the classification performance of the proposed model to that of five DL algorithms for predicting COVID-19, while protecting the privacy of the data for a variety of users, were undertaken. Our findings indicate improved effectiveness in identifying COVID-19 patients and achieved an accuracy of 98.99%. Thus, our model provides substantial aid to medical practitioners in their diagnosis of COVID-19.
Open access
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Abdulla All Noman, Mustafizur Rahaman, Tahmid Hasan Pranto, Rashedur M. Rahman
The scarcity and diversity of medical data have made it challenging to build an accurate global classification model in the healthcare sector. The prime reason is privacy concerns and legal obstacles which limit data-sharing scope among institutions in healthcare. On the other hand, data from a single source is hardly sufficient to develop a universal diagnosis model. While federated learning is a potential solution to privacy and data diversity concerns (allows distributed model training), an apt aggregation process for multi-class and heterogenous medical data is still at the outset. This study aims to propose a federated learning mechanism that can effectively learn from multi-class and heterogenous respiratory medical data. The proposed system trains and aggregates the local model by leveraging blockchain technology, ensuring privacy. While aggregating the local models, we introduced the weight manipulation technique that, unlike any other studies, uses the local model test accuracy as the principal parameter. The resulting metric scores show that learning from diverse and heterogenous data, the performance of the proposed federated model is analogous to a single-source model (learning from single source data). Using the novel aggregation technique, the highest testing accuracy of 88.10% has been achieved for five classes, compared to the less complex single source model, which achieved 88.60% testing accuracy. A similar trend has been observed for models with three and four classes. For developing better synergy among organizations, this study introduces an incentive mechanism for the contributing institution while the blockchain stores the records to make the system transparent and trustworthy. The proposed mechanism has been implemented using a web system, which demonstrates how the weight manipulation technique can effectively learn from heterogeneous and multi-sourced data while preserving privacy.
Hossein Hassani, Kimia Norouzi, Alireza Ghodsi, Xu Huang
Multitudinous health data are continually being produced as our activities, including medicine, evolve into the digital age where data plays a decisive role. Challenges come along as well, concerning the collection, secure storage, verification and secure access to the continuously growing data at such a broad scale before valuable information can be extracted to contribute to medical advancement nowadays. With the decentralization feature, huge successes of blockchain technology in overcoming similar challenges in the finance and cryptocurrency sector brought us the confidence to investigate and reveal its immeasurable potential for the health sector, specifically in dentistry. Dentistry is an important area of healthcare, but there is relatively little research focusing on its interactions with blockchain technology. Given the limited amount of existing research on this specific subject, this paper focuses on blockchain in dentistry and aims to provide a conceptual framework for the possible applications of blockchain in dentistry. The framework is organised by different areas of dentistry operations so that dental professionals can easily refer to and identify areas of interest. This contributes to increasing the awareness of blockchain technology among dental professionals and promoting blockchain-empowered revolutions in dentistry. This paper also discusses how blockchain fits alongside other emerging technologies, the challenges that have to be overcome to maximise the functionality and efficiency of this technology, as well as future research directions concerning blockchain implementations in the dental industry.
The Internet of Medical Things (IoMT) heralds a transformative era in healthcare, with the potential to revolutionize patient care, healthcare services, and medical research. As with all technological progressions, IoMT introduces a suite of complex challenges, predominantly centered on security. In particular, ensuring the integrity, confidentiality, and availability of health data in real-time communication stands paramount, given the sensitivity of the information and the ramifications of potential breaches or misuse. In light of these challenges, existing security frameworks, while commendable, exhibit limitations. Specifically, they often grapple with comprehensive anomaly detection, effective resistance to replay attacks, and robust protection against threats like man-in-the-middle attacks, eavesdropping, data tampering, and identity spoofing. The proposed framework integrates state-of-the-art encryption techniques, cutting-edge pattern recognition modules, and adaptive learning mechanisms. These components collaboratively ensure data integrity during transmission, provide robust resistance against conventional and novel attack vectors, and adapt to evolving threats through continuous learning. Moreover, the framework incorporates sophisticated checksum techniques and advanced behavioral analysis, further enhancing its protective capabilities. Our system demonstrated significant improvements in anomaly detection and attack resistance metrics, consistently outperforming benchmark solutions like MRMS and BACKM-EHA.