Security, data privacy and decentralization are significant challenges in the Internet of Things (IoT) domain. These challenges are inherited attribute of another emerging technology, Blockchain. This enforced convergence of IoT and Blockchain, attracting researchers to study on the effective use of Blockchain’s strength to solve the challenges of IoT. Rapid IoT adoption requires standardization and mature solution on security, data protection for compliance and performance for commercialization. These demands made a surge in variant blockchain flavours and combinations catering to different problems, and one such is Lightweight Scalable Blockchain (LSB). LSB had considerable caveats that require improvement for better adoption in the IoT domain. This paper focuses on encrypting transaction transmission, improving transaction flow, block validation, hash quality, hash rate and storage cost to improvise security and performance. The experimental evaluation is demonstrated on data from the temperature sensor to showcase superior applicability of the proposed work in the IoT domain. Implementation and result comparison with conventional LSB proves, the following achievements 1) An additional layer of transaction encryption using hybrid Elliptic Curve ElGamal (EC-ElGamal) method increases the security of the transmitted transaction for security enhancement. 2) Obtained 20% reduction in transaction processing time, 22% reduction on block validation processing time, 53% improvement on the hash operation and quality with an overall 7% saving on the storage cost thereby increased the overall performance.

Blockchain technology, serving as the backbone for decentralized systems, facilitates secure and transparent transactional data storage across a distributed network of nodes. Blockchain platforms rely on distributed ledgers to enable secure peer-to-peer transactions without central oversight. As these systems grow in complexity, analyzing their topological structure and vulnerabilities requires robust mathematical frameworks. This paper explores applications of graph theory for modeling blockchain networks to evaluate decentralization, security, privacy, scalability and NFT Mapping. We use graph metrics like degree distribution and betweenness centrality to quantify node connectivity, identify network bottlenecks, trace asset flows and detect communities. Attack vectors are assessed by simulating adversarial scenarios within graph models of blockchain systems. Overall, translating blockchain ecosystems into graph representations allows comprehensive analytical insights to guide the development of efficient, resilient decentralized infrastructures.

Blockchain technology has the potential to revolutionize the insurance industry by bringing unprecedented levels of transparency, security and autonomous continuity. By leveraging this technology, insurance companies can streamline their processes, reduce costs and provide better services to their customers. The proposed framework represents a significant step forward in the industry’s evolution, with the potential to create a more efficient and effective insurance ecosystem for all stakeholders involved. This framework offers a new and innovative approach to address many of the challenges associated with traditional processes in the insurance applications. By utilizing Blockchain and Internet of Things (IoT) technologies, the framework aims to provide greater transparency, security, efficiency and real-time data decision ultimately leading to an improved customer experience. The accomplished work serves as a foundation for further research and development in the field of blockchain-based insurance applications, with the goal of designing a lightweight, platform-agnostic, auditable, real-time and provable solution.

Deep learning has shown tremendous potential for transforming healthcare by enabling more accurate diagnoses, improved treatment planning and better patient outcome predictions. In this comprehensive survey, we provide a detailed overview of the state-of-the-art deep learning techniques and their applications across the healthcare ecosystem. We first introduce the fundamentals of deep learning and discuss its key advantages compared to traditional machine learning approaches. We then present an in-depth review of major applications of deep learning in medical imaging, electronic health record analysis, genomics, medical robotics and other domains. For each application, we summarize key advancements, outline the technical details of state-of-the-art methods, discuss challenges and limitations and highlight promising directions for future work. We examine cross-cutting challenges in deploying deep learning in clinical settings, including interpretability, bias and data scarcity. We conclude by proposing a roadmap to accelerate the translation and adoption of high-impact healthcare applications of deep learning. Overall, this survey provides a comprehensive reference for researchers and practitioners working at the intersection of deep learning and healthcare.

This study introduces GenePixKolor (GPK) Fusion, an innovative approach to non-fungible token (NFT) generation and rarity ranking tailored for the gaming industry and tokenomics ecosystems. GPK Fusion leverages genetic algorithms, image processing and machine learning to create a comprehensive, four-stage system that optimizes both the trait generation and visual appeal of NFTs, while providing an advanced rarity ranking mechanism. The GPK Fusion’s rarity ranking method uniquely combines trait-based and pixel-based evaluations. Pixel rarity was assessed using color distribution for overall aesthetic appeal, and trait rarity was assessed using trait combinations. Fusing pixel rarity with trait rarity provides a more holistic assessment of an NFT’s uniqueness, balancing functional value with visual attractiveness. This approach addresses the limitations of existing rarity calculation methods, offering a more refined and comprehensive evaluation of the NFT. Empirical comparisons demonstrate that GPK Fusion consistently produces NFTs with superior trait combinations and enhanced visual appeal compared to traditional methods. Its rarity ranking shows a strong correlation with practical valuation strategies in real-time trading environments and enhances the NFT marketplaces. This research contributes to the evolving field of NFT design and valuation by providing game developers and tokenomics strategists with a powerful tool for creating, evaluating and ranking digital assets. GPK Fusion’s methodology opens new ways for creating more engaging, visually striking and balanced NFTs. GPK potentially revolutionizes asset creation and valuation in the rapidly growing intersection of gaming and blockchain technologies.

Presented herein is a novel algorithm for multi-round, zero-knowledge proof (ZKP), devised specifically for authenticating factorisation proofs within a variety of cryptographic applications. This advanced algorithm, while maintaining computational complexity within acceptable bounds, offers a secure and proficient solution. The functionality of the algorithm is marked by multiple rounds of interaction between the Prover and Verifier. Initially, the Prover generates a random value and calculates a commitment. Subsequently, the Verifier issues a random challenge, eliciting a computed response from the Prover. To validate the proof, the Verifier verifies the equality of the commitment and the computed response. Efficaciousness of the proposed multi-round ZKP algorithm is demonstrated across diverse input sizes and parameters. Results indicate a success rate exceeding 90% on average, showcasing the robustness of the method. The recurring interaction between the Verifier and Prover enhances the Prover's authentication, thereby improving the algorithm’s reliability. Implementation of the algorithm, achievable through standard cryptographic tools and protocols, can fortify the security of multiple cryptographic applications. A significant application can be found in Digital Identity Management Systems (DIMS). Currently, these systems are vulnerable to a myriad of threats, including identity spoofing, data breaches, and internal security risks. The application of the ZKP algorithm can simultaneously augment security and withhold sensitive information, potentially transforming the DIMS security landscape. Future research may focus on improving the efficiency and scalability of the multi-round ZKP algorithm. There also remains a vast potential for exploring additional applications of this technique within various cryptographic domains.