Blockchain Papers

Zhaoyan Liu, Min Shu, Wei Zhu

Due to spectacular gains during periods of rapid price increase and unpredictably large drops, Bitcoin has become a popular emergent asset class over the past few years. In this paper, we are interested in predicting the crashes of Bitcoin market. To tackle this task, we propose a framework for deep learning time series classification based on contrastive learning. The proposed framework is evaluated against six machine learning (ML) and deep learning (DL) baseline models, and outperforms them by 15.8% in balanced accuracy. Thus, we conclude that the contrastive learning strategy significantly enhance the model’s ability of extracting informative representations, and our proposed framework performs well in predicting Bitcoin crashes.

Jaideep Kaur Mudhar, Jyoteesh Malhotra, Shalli Rani

Cervical cancer is a serious health concern that entails high risks for individuals due to delayed detection and treatment worldwide. Formal screening for the condition is challenging in both developed and developing countries due to a number of factors, including medical costs, access to healthcare facilities, social norms, and delayed symptom manifestation. Bypassing conventional, time-consuming medical procedures, machine learning presents a promising path for the efficient and economical early diagnosis of a variety of diseases, including cervical cancer. However, the fact that existing machine classification techniques for identifying diseases rely heavily on the predictive accuracy of a single classifier poses a significant drawback. Single classification methods alone might not provide the best predictions because of bias, over-fitting, improper handling of noisy data, and outliers, among other issues. Moreover, machine learning algorithms deals with sensitive patient data therefore Security measures are necessary to prevent unauthorized access and safeguard individual and organisations’ privacy, guard against model tampering. This paper proposes a novel framework for cervical cancer automated prediction using ensemble model training and blockchain smart contracts. The research records a noteworthy improvement in prediction test accuracy of 99.7% and train accuracy of 93%, surpassing the accuracy of predictions made by individual categorization techniques.

H T Manohara, C.N. Nagabushan, Ananad J Nagri, Rakshith. K. R

Modern organ donation and transplantation systems have special requirements and challenges in relation to regis-tration, donor-recipient pairing, organ removal, organ delivery, and transplantation. It also present technological, ethical, legal, and clinical constraints. Therefore, to provide a just and effective procedure that improves patient experience and trust, an final result of organ-transplantation & donation system is needed. Numerous Ethereum blockchain-based private solutions are available to facilitate organ donation and transplantation administration; nevertheless, their scalability, accuracy, and authenticity are limited, despite their substantial flexibility. The suggested web platform for managing organ donations, powered by Python, is intended to provide an easy-to-use and safe interface that will expedite and simplify the organ donation procedure. The platform prioritizes user verification and offers unique features for donors, recipients, and administrators. Users can create accounts and manage comprehensive profiles that include personal data, medical histories, and donation preferences. To maximize the likelihood of successful organ transplants, a sophisticated matching system that uses algorithms to match possible donors with recipients based on compatibility parameters including organ type, location, and urgency is essential to its operation. In order to demonstrate the efficacy of the proposed organ donation model, the python driven-model performance parameter are overlaid on the Blockchain-driven performance parameter. Experimental results demonstrate that the proposed model offers better Scalability, Accuracy, Authentication with significant Flexibility. Further, the proposed python-Driven model achieves approximately 8% and 10% improvements in Accuracy and Scalability respectively against the existing model.

Jason P. Rothberg, Colin Keogh, Yury Rusinovich

Xavatar is a media, educational, and therapeutic platform specializing in immersive virtual reality (VR) and augmented reality (AR) content, as well as seamless interconnectivity across various devices such as mobiles, tablets, and computers. It is aimed at improving the lives of patients with chronic mobility and communication disorders, including dementia, Alzheimer's, autism, chronic immobility, isolation, and long-term hospitalization. The project represents a fusion of digital technologies including the Metaverse, artificial intelligence (AI), and Web3, all designed to enhance healthcare interactions and patient support. This opinion piece explores the transformative potential of Xavatar, highlighting its role in shaping future healthcare landscapes through innovative, empathetic, and engaging digital solutions.

Rajesh Kumar, Yong Chen, Zhi-Shuang Gong, Zaid Al‐Huda · 5 authors

Healthcare institutions, including hospitals, clinics, and medical imaging centers, often encounter difficulties in sharing medical images across different systems and facilities. Further, most healthcare systems face concerns related to data security and patient privacy. This paper is based on the development of a blockchain-powered medical image storage and sharing platform. The platform’s architecture includes components such as smart contracts, encryption mechanisms, and decentralized storage systems, which collectively enable seamless and trustworthy medical image sharing. The use of smart contracts provides a reliable framework for access control and data sharing permissions. The encryption mechanisms safeguard sensitive patient information during transmission and storage, bolstering data security and privacy. Additionally, the utilization of decentralized storage systems ensures redundant and distributed data storage, mitigating the risk of data loss, manipulation, or security threats. The research underlying this project involves leveraging blockchain’s inherent properties of decentralization, immutability, and transparency to establish a secure and interoperable infrastructure for medical image exchange. By harnessing the potential of distributed ledger technology, the proposed platform addresses the existing challenges of fragmented systems, limited interoperability, and data silos in medical image sharing. Blockchain technology addresses critical challenges in medical image sharing, paving the way for enhanced collaboration, improved patient care, and increased efficiency in healthcare.

Rachna Rana, Pankaj Bhambri

Blockchain is forward-looking knowledge that may be used to dispense imaginative explanations athwart an assortment of activities involving healthcare. The COVID-19 pandemic has meaningfully affected healthcare on a universal gauge and has hastened the implementation of digital technology. The immutability, decentralization, and transparency of one of these new digital technologies, blockchain, make it particularly valuable in a mix of fields, including the control of access to and maintenance of medical information and mobile health. The chapter thoroughly analyzes the blockchain applications in the healthcare industry, both those relevant to COVID-19 and those not. Sickness control and reconnaissance, the scrutinizing of imperviousness or vaccine permits, and communication tracing were the main COVID-19-associated solicitations described. Management of electronic medical records, the Internet, and social media were the top three non-COVID-19 solicitations monitoring the supply chain and the Internet of Things (for instance inaccessible observing or transportable condition). Nine (2%) educations specified practical medical use and agreement, while 277 (66%) of the 415 reports described the technical performance of blockchain prototype platforms. The remaining investigations (129 [31%] of 415) were all strictly technical in nature. Ethereum and Hyperledger were the most frequently utilized platforms. A blockchain network helps realm and conversation persistent information in the healthcare business. Blockchain science has an opportunity to precisely recognize medical errors that range from hilarious to horrible. Bitcoin has implications for solving trial deception and improving the experiences of patients.

Kartheek Chandu

Abstract: Criminal activities in India are on the rise, with many incidents going unreported. Despite the availability of an online portal for storing the First Information Reports (FIRs) handwritten FIRs always will remain common due to some of the traditional methods and practices. And probably in most cases, the complainants should personally visit the police stations in order to file a offense report. Crime and Criminal Tracking Network and Systems (CCTNS) was launched in 2010 for national wide e-governance, it will operates on a centralized system and it is particularly limited to some individual states. Therefore, there is a need for the decentralized solution in order to ensure failure of a single point and secure managing of criminal complaints from the unauthorized access.

Xuetao Pu, Rong Jiang, Zhiming Song, Zhihong Liang · 5 authors

The rapid development of the Hospital Information System has significantly enhanced the convenience of medical research and the management of medical information. However, the internal misuse and privacy leakage of medical big data are critical issues that need to be addressed in the process of medical research and information management. Access control serves as a method to prevent data misuse and privacy leakage. Nevertheless, traditional access control methods, limited by their single usage scenario and susceptibility to single point failures, fail to adapt to the polymorphic, real-time, and sensitive characteristics of medical big data scenarios. This paper proposes a smart contracts and risk-based access control model (SCR-BAC). This model integrates smart contracts with traditional risk-based access control and deploys risk-based access control policies in the form of smart contracts into the blockchain, thereby ensuring the protection of medical data. The model categorizes risk into historical and current risk, quantifies the historical risk based on the time decay factor and the doctor's historical behavior, and updates the doctor's composite risk value in real time. The access control policy, based on the comprehensive risk, is deployed into the blockchain in the form of a smart contract. The distributed nature of the blockchain is utilized to automatically enforce access control, thereby resolving the issue of single point failures. Simulation experiments demonstrate that the access control model proposed in this paper effectively curbs the access behavior of malicious doctors to a certain extent and imposes a limiting effect on the internal abuse and privacy leakage of medical big data.

Bing Ventures

No abstract is available for this record.

Jyotiraditya Gandhi, Krishnakumar Maurya, Viveksingh Panwar, Utpalkumar Patel · 6 authors

Abstract: In recent years, the integration of blockchain technology with healthcare systems has garnered considerable attention due to its potential to enhance security, privacy, and interoperability in managing patient medical records. This paper proposes a novel approach to patient medical record management by leveraging Ethereum blockchain and InterPlanetary File System (IPFS) for storage, within a government-controlled framework. The system ensures secure and immutable storage of patients' medical records, accessible only by verified medical professionals, thus facilitating informed diagnosis and treatment. Additionally, the platform provides mechanisms for patient recourse in case of inaccuracies, as well as potential integration with insurance agencies. Furthermore, the proposed system envisages a future extension to facilitate anonymized data sharing with the scientific community, thereby contributing to advancements in medical research. This paper provides a comprehensive academic description of the proposed approach, discussing its technical architecture, security measures, regulatory framework, and potential impact on healthcare delivery and research.

Guanyi Wang, Chen Chen, Ziyu Jiang, Gang Li · 6 authors

CryptoKitties, a trendy game on Ethereum that is an open-source public blockchain platform with a smart contract function, brought nonfungible tokens (NFTs) into the public eye in 2017. NFTs are popular because of their nonfungible properties and their unique and irreplaceable nature in the real world. The embryonic form of NFTs can be traced back to a P2P network protocol improved based on Bitcoin in 2012 that can realize decentralized digital asset transactions. NFTs have recently gained much attention and have shown an unprecedented explosive growth trend. Herein, the concept of digital asset NFTs is introduced into the medical and health field to conduct a subversive discussion on biobank operations. By converting biomedical data into NFTs, the collection and circulation of samples can be accelerated, and the transformation of resources can be promoted. In conclusion, the biobank can achieve sustainable development through "decentralization."

Junsheng Mu, Michel Kadoch, Tongtong Yuan, Wenzhe Lv · 6 authors

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.

Krishan Kansal

Abstract: In the era of digital innovation, Artificial Intelligence (AI) has emerged as a pivotal catalyst, unlocking new avenues for the evolution of Web 3.0. Web 3.0 signifies the next phase of the internet, characterized by decentralized structures, peer-topeer networks, and cutting-edge technologies like blockchain and smart contracts. This research provides an in-depth exploration of the role played by AI in shaping Web 3.0, delving into both its opportunities and challenges. AI proves instrumental in processing and analysing vast datasets with increased efficiency, fostering intelligent decision-making and insightful outcomes. The paper extensively covers essential Web 3.0 concepts and technologies, encompassing the Semantic Web and ontologies, and underscores AI's transformative potential across diverse industries such as healthcare, finance, and education. An analysis of the challenges posed by AI in the Web 3.0 landscape, including issues of data privacy, bias, trust, and ethics, is presented. Furthermore, the research examines the broader societal implications of AI in Web 3.0. Conclusively, the paper outlines future directions and implications of AI within the Web 3.0 framework, proposing potential areas for subsequent research. By contributing to a comprehensive understanding of AI's impact on web development and its broader societal implications, this research aims to guide future endeavours in this dynamic field.

Tarandeep Kaur Bhatia, Prerana, Sudhanshu Singh, Navya Saluja · 5 authors

INTRODUCTION: An analysis of the convergence of blockchain and artificial intelligence (AI) technology demonstrates how these technologies can work together to revolutionize data management across a wide range of industries with their synergistic potential. OBJECTIVES: This paper discusses the integration of blockchain and artificial intelligence, the authors present an innovative framework that takes advantage of their strengths. As a result of blockchain's immutability and transparency, data can be securely stored and shared within this framework, making it ideal for sectors such as healthcare, finance, and supply chain. METHODS: To begin with, the paper discusses blockchain and artificial intelligence individually, emphasizing their respective advantages in decentralized data storage and intelligent decision-making. Blockchain-AI convergence is inevitable as both deal with data and value. RESULTS: As a result, the research paper highlights how blockchain and AI technologies can be transformed into transformative technologies. CONCLUSION: Using the synergistic framework presented in this paper, data management can be made more secure, transparent, and intelligent, with implications that go beyond traditional industries into emerging fields like the Internet of Things (IoT) and smart cities.

Naresh Kshetri, Rahul Mishra, Mir Mehedi Rahman, Tanja Steigner

In the ever-evolving healthcare sector, the widespread adoption of Internet of Things and wearable technologies facilitates remote patient monitoring. However, the existing client/server infrastructure poses significant security and privacy challenges, necessitating strict adherence to healthcare data regulations. To combat these issues, a decentralized approach is imperative, and blockchain technology emerges as a compelling solution for strengthening Internet of Things and medical systems security. This paper introduces HNMblock, a model that elevates the realms of epidemiological monitoring, medical system security, and wellness enhancement. By harnessing the transparency and immutability inherent in blockchain, HNMblock empowers real-time, tamper-proof tracking of epidemiological data, enabling swift responses to disease outbreaks. Furthermore, it fortifies the security of medical systems through advanced cryptographic techniques and smart contracts, with a paramount focus on safeguarding patient privacy. HNMblock also fosters personalized health care, encouraging patient involvement and data-informed decision-making. The integration of blockchain within the healthcare domain, as exemplified by HNMblock, holds the potential to revolutionize data management, epidemiological surveillance, and wellness, as meticulously explored in this research article.

M Suguna, M. Sangeetha, N. Shanmugapriya, G Pavithra · 5 authors

Over the years, advancements in the work have been incorporated towards professor relationships and data collection techniques to model a wide variety of human activities and behaviors. Most sensor data come from smart devices such as cleaners and cleaners that provide the ability to manipulate and extract data from monitoring data for monitoring and healthcare. Due to the high popularity and use of smart devices as respondents, performance recognition systems are more accurate and easier to use. Identifying smooth designs is a difficult task in contrasting environments and scattering tube data. This work presents a knowledge-based model based on job descriptions that reflects the work of the worker. The knowledge model is based on two new approaches: to consider a functional degree scheme between measuring sensor energy and functional consciousness to model controversial sensor data and establish the relationship between them. Low efficiency (easy work) and high level (weak). In this article we make a case why ontology can contribute to blockchain design. To support this issue, we make an analysis translate the tonnage ontology and some of its representations into the smart contracts that enable it implement traceability restrictions on the original traceability feature and Ethereum blocking platform.

Sumit Oberoi, Sugandh Arora, Balraj Verma, Krishna Kanta Roy

This study aims to identify artificial intelligence and blockchain technology's publication productivity and intellectual structure in the healthcare industry. This study employs a bibliometric-content analysis technique to determine intellectual structure and publication productivity. The Scopus database analyses identified research articles from 2018 to 2023. The findings of the thematic mapping show that AI and blockchain are emerging techniques and topics such as “smart healthcare”, “patient-centric”, “healthcare management”, “virtual & augmented reality”, “decentralization”, etc. are the potential and new dimensions that can be looked upon in future themes. This study advances knowledge by providing a current and future overview of AI and blockchain integration in the healthcare industry that would create new and enhance existing research streams.

Et al. Satish V. Kakade

Blockchain technology has been incorporated into the Healthcare Internet of Things (IoT) landscape as a revolutionary solution to tackle issues related to the sharing of medical records. This paper presents an innovative method that utilizes Temporal Blockchain for the purpose of Provenance Tracking. The introductory section provides context by delineating the significance of trust and transparency in medical data sharing within the healthcare IoT ecosystem. The study examines current blockchain solutions, delving into frameworks such as Hyperledger Fabric, Ethereum, Corda, and specialized approaches like temporal blockchain. The paper examines the difficulties associated with tracking the origin of data, concerns regarding privacy, problems related to scalability, and the need to comply with regulations. These challenges provide the context for the proposed methodology. The main emphasis is on Temporal Blockchain, integrating temporal elements to improve the tracking of origin and history. The evaluation parameters, such as security, provenance tracking, scalability, interoperability, privacy compliance, and performance, undergo a thorough assessment. The attained values demonstrate a strong emphasis on security at a high level, thorough tracking of origin and history, and strict adherence to privacy regulations. Nevertheless, the need for scalability and interoperability necessitates meticulous consideration. The study showcases the capacity of Temporal Blockchain to establish trust and enhance transparency in the sharing of medical records. The future scope focuses on tackling scalability challenges, improving interoperability, and making continuous optimization efforts. The proposed approach highlights notable accomplishments and emphasizes the continuous development and collaborative aspect of Blockchain-Based Medical Record Sharing in Healthcare IoT.

K. Murthy, S N Prasad

Neurological disorders are a significant health challenge globally, affecting millions of individuals and imposing a considerable economic burden on healthcare systems. Early and accurate diagnosis plays a crucial role in improving patient outcomes and managing these disorders effectively. This abstract presents a novel approach that combines blockchain technology with deep learning algorithms to enhance the detection of neurological disorders. The proposed system leverages the decentralized and transparent nature of blockchain to securely store and share medical data, enabling seamless collaboration among healthcare providers, researchers, and patients. This infrastructure ensures data integrity, privacy, and accessibility, addressing critical concerns in medical data management. Furthermore, the deep learning approach employs advanced neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze large-scale neurological data, including medical images, electroencephalograms (EEGs), and clinical records. By leveraging the power of deep learning, the system can automatically extract relevant features and patterns from complex neurological data, enabling accurate diagnosis and early detection of various disorders. The integration of blockchain and deep learning offers several advantages. Firstly, it facilitates secure and decentralized storage of medical data, ensuring patient privacy and data integrity. Secondly, it enables seamless data sharing and collaboration among multiple stakeholders, promoting knowledge exchange and enhancing research capabilities. Lastly, deep learning algorithms improve the accuracy and efficiency of neurological disorder detection, enabling timely interventions and personalized treatment plans. The proposed system holds great potential in revolutionizing the field of neurological disorder diagnosis and management. By leveraging the combined power of blockchain and deep learning, healthcare providers can enhance their diagnostic capabilities, leading to improved patient outcomes, reduced healthcare costs, and accelerated research advancements. However, further research and development are necessary to address technical challenges, scalability issues, and regulatory considerations to realize the full potential of this innovative approach.

Lakshay Kumar, Aayush Sharma, Harshal Sharma, Swati Sharma

No abstract is available for this record.

Cristiano André da Costa, Felipe André Zeiser, Rodrigo da Rosa Righi, Rodolfo Stoffel Antunes · 11 authors

No abstract is available for this record.

Rocco de Filippis, Abdullah Al Foysal

In an era marked by rapid technological advancement, the fusion of Artificial Intelligence (AI), Machine Learning (ML), and Distributed Ledger Technology (DLT), commonly referred to as blockchain, represents a pioneering frontier in healthcare and psychology.This paper explores the transformative potential of integrating these technologies to reimagine traditional practices and unlock novel approaches to patient care, diagnostics, therapy, and mental health management.Specifically, it investigates the unique and complementary roles that AI, ML, and DLT can play within healthcare and psychology, presenting a detailed roadmap for researchers, practitioners, and stakeholders.Through AI and ML's advanced analytics and predictive capabilities, and blockchain's secure, decentralized data management, this paper demonstrates how these technologies can collectively enhance diagnostic precision, personalize treatment plans, optimize resource allocation, and streamline administrative workflows.Central to this study is a proposed technical architecture, illustrating how AI, ML, and DLT can be integrated within healthcare workflows.This includes using blockchain for secure, verifiable patient data storage and off-chain AI/ML processing for real-time, data-driven insights.Additionally, this paper discusses practical methods, such as zero-knowledge proofs and federated learning, to maintain privacy and regulatory compliance in handling sensitive health data, especially in mental health contexts.Addressing the importance of ethical considerations, this paper highlights best practices in responsible innovation, emphasizing transparency, accountability, and fairness in the deployment of these technologies.Compliance with frameworks like GDPR and HIPAA is discussed as crucial for ensuring patient rights and establishing trust in data handling practices.Moreover, the paper underscores the need for interdisciplinary collaboration, identifying structured models for joint efforts between healthcare professionals, data scientists, and blockchain developers.Examples include cross-disciplinary training sessions, shared project management How to cite this paper:

Dominique Monlezun

Chapter 7 unites the different dimensions explored in each of the earlier chapters into a cohesive whole to understand the artificial intelligence (AI)-powered global public health ecosystem as humanity’s common home: its decentralized organic design (financing and integral sustainable development), framework (data architecture and political economics), inhabitants (culture and demographics), and foundation (ethics and human security balancing national security). It summarizes the key findings for these domains from the earlier chapters while highlighting emblematic AI case uses. It considers financing advances, including in universal health coverage, public–private partnerships, digital global health diplomacy, finance tracking, and value-based health. This chapter moves on to integral sustainable development advances, including in AI for the sustainable development goals, precision agriculture, climate change, affordable clean energy, equity, and generative AI (including ChatGPT). It then considers data architecture advances, including in the United Kingdom’s hybrid data architecture, India’s federated data architecture, swarm learning, gossip learning, blockchain, edge computing, application programming interfaces, augmented public health intelligence, quantum computing, zero-trust security, blockchain, and data solidarity. This chapter then considers political economic advances particularly from the perspective of Political Liberalism–bridging democracies and autocracies, including in data governance models (spanning Europe’s general data protection regulation and Japan’s agile governance), managed strategic competition, and World Health Organization coordination. Finally, this chapter considers AI ethics for the health ecosystem. Particular emphasis is given to how population aging, multicultural diversity, and human security requires more inclusive discussion of diverse perspectives, as through Personalist Social Contract ethics to generate and sustain substantive convergence on the unifying values of human dignity, rights, and sovereignty that then give rise to effective and equitable collective action. This chapter concludes by applying the abovesaid dimensions to concrete AI use cases for the global public health ecosystem that illustrates this integral approach, including ethics by design or embedded AI ethics (within existing ecosystem operations and structures), democratizing AI or personalizing AI (as with end-to-end AI platforms and edge computing expanding and interlinking free and affordable AI services for larger audiences), and ecosystem interoperability (uniting political economic interoperability, data interoperability, and moral interoperability to leverage global resources and insights for local communities leading their own projects).

Pranay Shah, Sushruta Mishra, Angelia Melani Adrian

No abstract is available for this record.