Blockchain, a decentralized database safeguarded by cryptographic security, has gained prominence for its resistance to manipulation. Its application extends notably to ensuring the security of financial transactions, including the acquisition of digital currencies. This study endeavors to develop a system aimed at validating diplomas and academic transcripts, enhancing their authenticity and bolstering the security of document storage. Leveraging the Ethereum Blockchain and Smart Contracts, the methodology focuses on the utilization of specialized codes executed within the Ethereum network. The outcome of this research manifests as a system blueprint designed for the verification of diplomas and transcripts, integrated within a web-based Ethereum Network platform. By harnessing the Ethereum Blockchain's inherent security features and employing Smart Contracts, the proposed system endeavors to streamline the verification process, ensuring the integrity and reliability of academic credentials while fortifying document storage against potential breaches. Through this innovative approach, the study contributes to advancing the authentication and security standards within the realm of academic documentation management.
Blockchain has attracted widespread attention due to its unique features such as decentralization, traceability, and tamper resistance. With the rapid development of blockchain technology, an increasing number of industries are gradually applying blockchain technology to various fields such as the Internet of Things, healthcare, finance, agriculture, and government affairs. However, there are certain differences in the underlying architecture, data structures, consensus algorithms, and other aspects of blockchain technology across different sectors, which restrict transactions to occur within a single blockchain. Achieving interoperability between different blockchains is challenging, hindering data exchange and collaborative business to some extent, inevitably leading to the problem of "data silo". Against this backdrop, this study aims to explore a cross-chain solution based on relay technology to address the current challenges of interoperability between blockchain systems. By employing relay-based cross-chain technology, a blockchain cross-chain collaboration platform is established to simulate the construction of a real cross-chain network. By deploying business contracts, data and resources between heterogeneous blockchains can seamlessly communicate, resolving the challenge of cross-chain interoperability. The research findings demonstrate that the blockchain cross-chain solution based on relay technology can effectively enhance interoperability between different blockchain systems, enabling cross-chain asset circulation and information transmission, highlighting the practical applicability and scalability of this study.
The healthcare industry has advanced its digitalization and use of electronic medical records during the past ten years (EMRs). The EHR system gives the information's proprietor authority over their data and allows them to communicate it to certain individuals. It is challenging for data to maintain security and diagnostic processes because of the enormous volume of data in the medical field. This research presents a novel blockchain-based encryption system using deep learning (BcEs-DLM) for secure medical data management. The concept that is being described encompasses many phases of activities, including safe data management via blockchain, encryption, and optimal key generation. It provides individuals with the ability to manage data accessibility, granting read/write access to hospital authorities, and triggering precautionary agreements. Our recommended approach offers a reliable methodology for generating secure encryption keys and effectively safeguarding sensitive medical data using the block cipher technique. By following this method, you can ensure that patient information remains confidential and protected from unauthorized access. The detection process is performed using medical record sharing. In this paper, we achieved a 97 percent accuracy after training our deep learning model. Additionally, every node of this system is registered and updated on the blockchain
Ateeq Ur Rehman, Nargis Tariq, Mian Ahmad Jan, Fazlullah Khan · 6 authors
In recent years, the healthcare industry has undergone a digital transformation, making patient data publicly available and accessible. Healthcare units make a portion of the data public while keeping the rest private, necessitating various mechanisms for security and privacy. Blockchain technology has been widely adopted in the healthcare sector to secure data transactions. However, public blockchains face challenges in scalability and privacy, whereas private blockchains struggle with centralization, interoperability, and complexity. To address these challenges, we propose an Internet of Medical Things (IoMT)-based hybrid blockchain architecture. The proposed architecture combines the decentralized Ethereum and the centralized Hyperledger Fabric blockchain (Eth-Fab) using SQLite to leverage Ethereum smart contracts with the Hyperledger permission model. Moreover, we introduce access control strategies to enhance patient data authentication and authorization. We have employed machine learning algorithms to assist healthcare practitioners in accurately detecting diseases and making time-efficient decisions. Additionally, we modeled the proposed architecture using the M/M/1 queuing model and derived closed-form expressions for latency, throughput, and server utilization. The validity of these expressions was verified through Monte Carlo simulations. The results demonstrate that higher service times (block generation) yield better outcomes in terms of latency, throughput, and utilization, regardless of the arrival time, i.e., transactions in the mining pool.
This survey paper provides a comprehensive and in-depth overview of blockchain technology and its wide-ranging applications. It begins by introducing the fundamental characteristics and structure of blockchain, with a particular focus on the five major consensus mechanisms and their unique features. The article emphasizes the crucial role of smart contracts and cryptography in the construction and operation of blockchain networks. Furthermore, the paper explores the specific applications of blockchain in three key areas: cryptocurrencies, supply chains, and healthcare security. It highlights the numerous advantages that blockchain brings to these domains, including enhanced security, transparency, and efficiency. The paper also offers valuable insights into the future potential of blockchain technology in these areas, providing a glimpse into the possibilities that lie ahead. Additionally, the article addresses the challenges posed by the "impossible triangle" of decentralization, security, and high performance in blockchain. It discusses the emerging research trends aimed at tackling these challenges, such as cross-chain protocols, privacy protection mechanisms, blockchain expansion strategies, and advanced data storage solutions. The paper presents recent advancements and breakthroughs in each of these research directions, showcasing the ongoing efforts to overcome the limitations of blockchain technology.
The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters, and server computing frequency division. In the resource allocation phase, we optimize communication-computation resource distributions, including bandwidth, transmit power, and computing frequency. We introduce the trust-cost ratio (TCR), a pivotal measure combining trust scores from users’ resources and server history with delay and energy costs. This balance ensures sustained user engagement and trust. The DASHF algorithm, central to our approach, encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a novel fractional programming technique from a recent IEEE JSAC paper [2]. The most challenging part of DASHF is to rewrite an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via carefully designed transformations, in order to be solved by SDR and the Hungarian algorithm. Extensive simulations validate the DASHF algorithm’s efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.
Saba Inam, Shamsa Kanwal, Rabia Firdous, Fahima Hajjej
Improved software for processing medical images has inspired tremendous interest in modern medicine in recent years. Modern healthcare equipment generates huge amounts of data, such as scanned medical images and computerized patient information, which must be secured for future use. Diversity in the healthcare industry, namely in the form of medical data, is one of the largest challenges for researchers. Cloud environment and the Block chain technology have both demonstrated their own use. The purpose of this study is to combine both technologies for safe and secure transaction. Storing or sending medical data through public clouds exposes information into potential eavesdropping, data breaches and unauthorized access. Encrypting data before transmission is crucial to mitigate these security risks. As a result, a Blockchain based Chaotic Arnold's cat map Encryption Scheme (BCAES) is proposed in this paper. The BCAES first encrypts the image using Arnold's cat map encryption scheme and then sends the encrypted image into Cloud Server and stores the signed document of plain image into blockchain. As blockchain is often considered more secure due to its distributed nature and consensus mechanism, data receiver will ensure data integrity and authenticity of image after decryption using signed document stored into the blockchain. Various analysis techniques have been used to examine the proposed scheme. The results of analysis like key sensitivity analysis, key space analysis, Information Entropy, histogram correlation of adjacent pixels, Number of Pixel Change Rate, Peak Signal Noise Ratio, Unified Average Changing Intensity, and similarity analysis like Mean Square Error, and Structural Similarity Index Measure illustrated that our proposed scheme is an efficient encryption scheme as compared to some recent literature. Our current achievements surpass all previous endeavors, setting a new standard of excellence.
Open access
Brain Tumor Detection and Classification
Chaos-based Image/Signal Encryption
Advanced Steganography and Watermarking Techniques
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.
In electronic healthcare, patient medical imaging data is critical for remote diagnostic procedures. The increasing demand to harness the potential of these medical images necessitates their secure sharing among various entities, including hospitals, medical institutions, and insurance companies. However, third-party access and possible manipulation make it challenging to maintain the ownership and integrity of this data. This study introduces a novel approach that combines compression, digital watermarking, symmetric encryption, and blockchain technology to protect medical images from unauthorized third-party interventions. Using the Discrete Wavelet Transform, our proposed technique embeds a compressed watermark into the host image. Specifically, the watermark is encoded into vectors and inserted into the second-level approximation, i.e., the Low-Low of the image using the Least Significant Bit, producing a watermarked image. The watermarked data is encrypted and stored on a blockchain to further safeguard these images’ integrity. This multi-layer security framework not only preserves the integrity and confidentiality of the data but also facilitates transparent and secure sharing among stakeholders. The proposed method achieves a peak signal-to-noise ratio of 63.24dB and a structural similarity index of 1. These results demonstrate the robustness of our solution in protecting and exchanging medical images within the digital healthcare ecosystem, positioning it as an advanced and reliable option for secure data management.
Open access
Advanced Steganography and Watermarking Techniques
Online social networks (OSN) that gather diverse interests have attracted a vast user base. However, centralized online social networks, which house vast amounts of personal data, are plagued by issues such as user privacy and data breaches, tampering, and single points of failure. The centralization of social networks results in sensitive user information being stored in a single location, making data breaches and leaks capable of simultaneously affecting millions of users who rely on these platforms. Therefore, research into decentralized social networks is crucial. However, blockchain-based social networks present challenges related to resource limitations. This paper proposes a reliable and scalable online social network platform based on blockchain technology. This system ensures the integrity of all content within the social network through the use of blockchain, thereby preventing the risk of breaches and tampering. Through the design of smart contracts and a distributed notification service, it also addresses single points of failure and ensures user privacy by maintaining anonymity. Furthermore, it tackles the scalability concerns associated with blockchain-based systems due to excessive computing resource utilization by improving the off-chain storage structure. By adopting Bloom filters and off-chain storage, it effectively alleviates the burden on on-chain storage. Comparative analysis with related studies demonstrates a minimum of 74% cost savings during post uploads. While the proposed system exhibits slightly slower write performance by 10% compared to existing systems, it showcases 13% faster read performance and achieves an average notification latency of 3 seconds. Thus, this system addresses scalability issues present in blockchain-based systems. It offers a solution that enhances data management not only for online social networks but also for resource-constrained system of blockchain-based IoT environments. By applying this system, data can be managed securely and efficiently.
Muhammad Usman, Muhammad Shahzad Sarfraz, Muhammad Umar Aftab, Usman Habib · 5 authors
Industrial Internet of Things (IIoT) applications consist of resource constrained interconnected devices that make them vulnerable to data leak and integrity violation challenges. The mobility, dynamism, and complex structure of the network further make this issue more challenging. To control the information flow in such environments, access control is critical to make collaboration and communication safe. To deal with these challenges, recent studies employ attribute-based access control on top of blockchain technology. However, the attribute-based access control frameworks suffer due to high computational overhead. In this paper, we propose an improved role-based access control framework using hyperledger blockchain to deal with IIoT requirements with less computational overhead making the information control process more efficient and real-time. The proposed framework leverages a layered architecture of chaincodes to implement the improved access control framework that handles the permission delegation and conflict management to deal with the dynamism of the IIoT network. The system uses a Policy Contract, Device Contract, and Access Contract to manage the workflow of the whole access control process. Each chaincode in the proposed framework is isolated in terms of its responsibilities to make the design low coupled. The integration of improved access control with blockchain enables the proposed framework to provide a highly scalable solution, tamper-proof, and flexible to manage conflicting scenarios. The proposed system outperforms the recent studies significantly in computational overhead in extensive simulation results. To verify the scalability and efficiency, the proposed is evaluated against a large number of concurrent virtual clients in simulation and statistical analysis proves that the proposed system is promising for further research in this domain.
Due to exponential demand in IoT based healthcare, the demand for robust mechanisms to ensure data privacy, security, and scalability with the increasing dependence on cloud-based healthcare systems is immensely felt. Current approaches to dealing with health-care data in cloud settings lack the potency to tackle challenges emanating from the distribution of non-IID data, dynamic access control requirements, and secure cross-chain data analysis. These methods could not provide a holistic solution to adapt with the heterogeneous nature of healthcare data while maintaining advanced privacy and security levels over the distributed networks. In this way, the present work proposes to offer a secure and scalable protocol that is based on the blockchain for healthcare cloud data samples. It integrates the following four new methodologies: Adaptive Federated Learning for Healthcare Data, Secure Homomorphic Blockchain Encryption, Dynamic Attribute-Based Encryption for Healthcare, and Proof of Healthcare Privacy (PoHP) consensus based cross-chain federated Analytics with Zero Knowledge Protocol (ZKP) for healthcare. AFL-HD would work with optimal model training over the distributed healthcare data and thereby handle the challenges that are non-IID in nature, while reducing the communication overhead by 30-40%. SHBE would ensure a 1.5x improvement in encryption and decryption times and also enable secure computations on encrypted data samples. Thus, DABE-HC enables dynamic access control policy management in blockchains, while ensuring access control precision in excess of 99%, with near-instant policy updating. CCFA-HC supports X-blockchain privacy-preserving analytics, thereby reducing the cross-chain communication overhead by 20-30%. In this protocol, therefore, cloud healthcare data management is made more scalable, secure, and private. It allows tackling challenges in the healthcare domain and gives a holistic solution supporting meaningful and secure, efficient, and collaborative healthcare data processing and analytics across distributed environments. The impact of this work is immense in providing a foundation for the next generation of secure healthcare data systems.
The rapid expansion of artificial intelligence (AI) in healthcare has revolutionized diagnostic practices, enabling applications such as tumor detection in medical imaging, genomic analysis, and predictive risk modeling for early disease prevention. Despite these advancements, concerns about the opacity, trustworthiness, and auditability of AI systems remain significant barriers to clinical adoption. Medical practitioners, regulators, and patients increasingly demand systems that not only produce accurate results but also provide verifiable guarantees regarding the integrity and accountability of diagnostic processes. Blockchain technology, with its intrinsic features of decentralization, immutability, and consensus-driven validation, offers a promising solution to these concerns. This manuscript investigates the integration of blockchain-powered verifiable AI models for medical diagnosis. We present a comprehensive framework that leverages federated learning for decentralized training, blockchain for immutable storage and consensus validation, and zero-knowledge proofs for cryptographic verification of model outputs. The proposed system ensures transparent audit trails, enhances data integrity, protects patient privacy, and simplifies compliance with regulatory frameworks such as HIPAA and GDPR. Through simulated case studies in medical imaging and predictive diagnostics, we demonstrate that blockchain integration improves diagnostic verifiability, reduces susceptibility to adversarial manipulation, and fosters patient-centric trust. While slight computational latency is introduced, the trade-off is justified by significantly stronger guarantees of transparency, reproducibility, and ethical accountability. This research underscores the transformative role of blockchain in shaping the future of verifiable AI-driven healthcare, providing pathways toward more reliable, transparent, and equitable medical diagnostic ecosystems.
Jan 1, 2024·Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India
Blockchains like Hyperledger Fabric are comparably faster than public blockchains like Ethereum. These Permissioned Blockchains do not need to consider for in-built security like Bitcoin. The core part of Hyperledger Fabric, the orderer, replicates blocks across the blockchain network. The orderer f
Trustless tracking of Resident Space Objects (RSOs) is crucial for Space Situational Awareness (SSA), especially during adverse situations. The importance of transparent SSA cannot be overstated, as it is vital for ensuring space safety and security. In an era where RSO location information can be easily manipulated, the risk of RSOs being used as weapons is a growing concern. The Tracking Data Message (TDM) is a standardized format for broadcasting RSO observations. However, the varying quality of observations from diverse sensors poses challenges to SSA reliability. While many countries operate space assets, relatively few have SSA capabilities, making it crucial to ensure the accuracy and reliability of the data. Current practices assume complete trust in the transmitting party, leaving SSA capabilities vulnerable to adversarial actions such as spoofing TDMs. This work introduces a trustless mechanism for TDM validation and verification using deep learning over blockchain. By leveraging the trustless nature of blockchain, our approach eliminates the need for a central authority, establishing consensus-based truth. We propose a state-of-the-art, transformer-based orbit propagator that outperforms traditional methods like SGP4, enabling cross-validation of multiple observations for a single RSO. This deep learning-based transformer model can be distributed over a blockchain, allowing interested parties to host a node that contains a part of the distributed deep learning model. Our system comprises decentralised observers and validators within a Proof of Stake (PoS) blockchain. Observers contribute TDM data along with a stake to ensure honesty, while validators run the propagation and validation algorithms. The system rewards observers for contributing verified TDMs and penalizes those submitting unverifiable data.
The exponential growth of heterogeneous Internet of Things (IoT) networks has amplified demands for secure, scalable, and sustainable transaction verification mechanisms. Traditional blockchain consensus protocols, such as Proof-of-Work (PoW), offer robust security but impose prohibitive energy costs, limiting their viability for resource-constrained IoT environments. Proof-of-Stake (Po’s) and lightweight consensus schemes improve efficiency but often compromise scalability or fairness. To address this trade-off, this study introduces an energy-aware blockchain consensus framework enhanced by graph neural networks (GNNs) for sustainable, scalable verification across heterogeneous IoT ecosystems. In this approach, GNNs are applied to dynamically model IoT device interconnections, enabling efficient clustering, adaptive leader election, and optimized consensus pathways. By learning the structural and temporal patterns of IoT networks, GNNs reduce redundant computations and allocate verification tasks intelligently, minimizing energy consumption while maintaining security. The consensus framework integrates energy profiling of devices with predictive workload balancing, ensuring equitable participation across diverse hardware capacities. Blockchain provides the foundation for immutable, decentralized trust, while the GNN-enhanced consensus mechanism improves throughput, latency, and energy efficiency in large-scale deployments. Simulation studies of smart grids, industrial IoT, and urban sensor networks demonstrate measurable improvements in energy savings, scalability, and fault tolerance. The proposed architecture contributes to the vision of sustainable blockchain systems that can operate effectively in energy-sensitive, heterogeneous IoT contexts. By fusing blockchain’s decentralized trust with GNN-based intelligence, the framework offers a pathway toward greener, more scalable transaction verification tailored for next-generation IoT infrastructures.
The rapid integration of Internet of Things (IoT) services and applications across various sectors is primarily driven by their ability to process real-time data and create intelligent environments through artificial intelligence for service consumers. However, the security and privacy of data have emerged as significant threats to consumers within IoT networks. Issues such as node tampering, phishing attacks, malicious code injection, malware threats, and the potential for Denial of Service (DoS) attacks pose serious risks to the safety and confidentiality of information. To solve this problem, we propose an integrated autonomous IoT network within a cloud architecture, employing Blockchain technology to heighten network security. The primary goal of this approach is to establish a Heterogeneous Autonomous Network (HAN), wherein data is processed and transmitted through cloud architecture. This network is integrated with a Reinforced Neural Network (RNN) called ClouD_RNN, specifically designed to classify the data perceived and collected by sensors. Further, the collected data is continuously monitored by an autonomous network and classified for fault detection and malicious activity. In addition, network security is enhanced by the Blockchain Adaptive Windowing Meta Optimization Protocol (BAW_MOP). Extensive experimental results validate that our proposed approach significantly outperforms state-of-the-art approaches in terms of throughput, accuracy, end-to-end delay, data delivery ratio, network security, and energy efficiency.
Blockchain based federated learning is a distributed learning scheme that allows model training without participants sharing their local data sets, where the blockchain components eliminate the need for a trusted central server compared to traditional Federated Learning algorithms. In this paper we propose a softmax aggregation blockchain based federated learning framework. First, we propose a new blockchain based federated learning architecture that utilizes the well-tested proof-of-stake consensus mechanism on an existing blockchain network to select validators and miners to aggregate the participants' updates and compute the blocks. Second, to ensure the robustness of the aggregation process, we design a novel softmax aggregation method based on approximated population loss values that relies on our specific blockchain architecture. Additionally, we show our softmax aggregation technique converges to the global minimum in the convex setting with non-restricting assumptions. Our comprehensive experiments show that our framework outperforms existing robust aggregation algorithms in various settings by large margins.
Existing zero-watermarking algorithms for remote sensing images heavily rely on traditional feature extraction techniques, which are vulnerable to targeted attacks and lack discriminability for images captured by different sensors or at different time periods in the same geographical area. To address these limitations, this paper proposes a novel watermarking algorithm based on blockchain and Stacked Denoising Autoencoder (SDAE) to achieve lossless copyright protection for high-resolution remote sensing images (HRRS). The algorithm utilizes SDAE to extract deep and robust features from local square feature regions (LSFR) for watermark construction. Moreover, the algorithm incorporates a watermark registration scheme designed with Hyperledger Fabric and IPFS to ensure secure and trustworthy registration of watermarks and associated parameter information, enhancing the algorithm's uniqueness. Experimental results demonstrate the effectiveness of the proposed algorithm against various watermark attacks and its high discriminability for similar images. This algorithm holds significant potential for wide-ranging applications in the field of lossless copyright protection for HRRS, effectively safeguarding the commercial interests of data providers.
Open access
Advanced Steganography and Watermarking Techniques
This study aims to explore the construction of a personalized recommendation system (PRS) based on deep learning under the hybrid blockchain model to further improve the performance of the PRS. Blockchain technology is introduced and further improved to address security problems such as information leakage in PRS. A Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph consensus algorithm, namely PBDAG consensus algorithm, is designed for public chains. Finally, a personalized recommendation model based on the hybrid blockchain PBDAG consensus algorithm combined with an optimized back propagation algorithm is constructed. Through simulation, the performance of this model is compared with practical Byzantine Fault Tolerance, Byzantine Fault Tolerance, Hybrid Parallel Byzantine Fault Tolerance, Redundant Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. The results show that the model algorithm adopted here has a lower average delay time, a data message delivery rate that is stable at 80%, a data message leakage rate that is stable at about 10%, and a system classification prediction error that does not exceed 10%. Therefore, the constructed model not only ensures low delay performance but also has high network security performance, enabling more efficient and accurate interaction of information. This solution provides an experimental basis for the information security and development trend of different types of data PRSs in various fields.
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.
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.
Ballots security and reliability is two of the crucial things that makes digital based ballots system not widely implemented. Paper-based ballots have the downsides of environmental impact on making paper. Blockchain has one of the advantages on the security aspect where the blocks are secured using cryptographic protocol that makes it hard to be hacked. This paper will be examining the possibility of ballots implementation using a microcontroller-based ballots system. The microcontroller is used because it is cheap to implement and it has the capability to sign the blockchain transaction. This paper is a proof of concept for a smaller scale ballots system for a small organization with two candidate option. The blockchain network used in this paper is a Ethereum test network provided by Ropsten. The number of testing done is 60 times total for the two candidate. The smaller scale testing proven to be successful with the transaction confirmed successful in etherscan.io.