Blockchain is increasingly used in industrial, financial, and IoT settings for secure and auditable transaction processing; however, existing leader election and consensus methods, such as PBFT, Raft, and reputation-based schemes, suffer from static leadership, unfair vote distribution, and limited scalability. To address these gaps, we propose VLSA (Vote-based Leader Selection Algorithm), a decentralized rotation-based mechanism that ensures fairness in leader election, and MPoAh (Modified Proof-of-Authentication), a lightweight consensus protocol tailored for multi-party signatures. Our implementation, built with Python, CouchDB, and Ed25519 cryptography, achieves a 35% reduction in signature and verification latency and a 30% decrease in on-chain storage compared to state-of-the-art approaches. Simulation further shows 95% packet delivery, average authentication latency of 12 ms, and ledger throughput of 250 tx/s. These results demonstrate that the proposed system enables democratic participation in consensus, supports deployment on resource-constrained devices, and strengthens resistance against insider and Sybil attacks, thereby advancing secure and scalable blockchain-based authentication.

Traditional insurance policy models involve cumbersome multiparty verification and processing, leading to a prolonged and time-consuming procedure resulting in claim leakage. The existing insurance and blockchain-based systems have no module specifically for electric vehicles to cover physical damage. This becomes particularly significant in electric vehicles (EVs), where insurance is essential due to the high cost of vehicle parts and the vehicles themselves. Electric vehicles with various sensors and IoT devices are susceptible to physical damage and attacks. To address these challenges and provide robust financial support to policyholders, an enhanced blockchain-based electric vehicle insurance policy (BE-VIP) is proposed to cover vehicle damages. BE-VIP leverages sensory and telemetry data from vehicle sensors, IoT devices, and drivers’ behavior for a more comprehensive analysis. However, the insecure nature of the public network in the internet of electric vehicles (IoEV) exposes it to various security threats and attacks. Recognizing this, BE-VIP emphasizes implementing a lightweight privacy-preserving and efficient authentication protocol to enhance network security. A secure driver-driving score (DDS) is proposed to reward and punish the vehicle based on driving behavioral data and easy insurance policy transfer from the previous owner to the current owner. To prevent fraudulent accidental claims, a YOLOv8 model-based damage detection model is combined with IPFS to create permanent evidence of an accident. The feasibility of the BE-VIP model is rigorously evaluated through a comprehensive analysis, considering factors such as computational complexity and gas consumption required for execution over the Ethereum blockchain network.

Abstract Alzheimer's disorder (AD) causes permanent impairment in the brain's memory of the cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease in the initial stages is challenging for researchers. Deep learning and machine learning‐based techniques can help resolve many issues associated with brain imaging exploration. Brain MR Images (Brain‐MRI) are used to detect Alzheimer's in computable research work. To correctly categorize the stages of Alzheimer's disease, discriminative features need to be extracted from the MR images. Recently, many studies have used deep learning methods for the early detection of this disorder. However, overfitting degrades the deep learning method's performance because the dataset's selection images are smaller and imbalanced. Some studies could not reach more discriminative and effectual attention‐aware features for Alzheimer's stage classification to increase the model performance. In this paper, we develop a novel hierarchical residual attention learning‐inspired multistage conjoined twin network (HRAL‐CTNN) to classify the stages of Alzheimer's. We used augmentation approaches to scale insufficient and imbalanced data. The HRAL‐CTNN is efficiently overcoming the issues of not obtaining efficient attention‐aware and generative features for Alzheimer's stage classification. The proposed model solved the problem of redundant features by extracting attentive discriminant features, and scaling imbalance data by data augmentation, after that training and validation using HRAL‐CTNN. The execution of this proposed work has been performed on the ADNI MRI dataset. This work achieved outstanding accuracy of 99.97 0.01% and F1 score of 99.30 0.02% for Alzheimer's stage classification. This model proposed by our group outperformed the existing related studies in terms of the model's performance score.

Security and privacy are becoming increasingly difficult to preserve in today’s fast-growing data world. We use the Internet of Medical Things (IoMT) to make quick diagnoses and get results. IoMT devices measure the human body’s functioning in various parameters and collect, process, and store data in cloud servers. There is a public channel used to collect data from humans and send it to the cloud, which is highly unreliable and vulnerable to attacks. Another risk is storing data in a single centralized system, which can be vulnerable to a single-point failure. Blockchain is being used for secure data storage in a decentralized manner to avoid single-point failure. We propose a secure IoMT-based data collection method for patients and storing the data on the blockchain in accordance with general data protection regulation (GDPR). In the proposed system, IoMT devices send data to the cloud via the patient’s personal digital assistant (PDA), and the cloud server transacts data on the blockchain. We propose a miner selection algorithm to avoid bias in the blockchain. We simulate various attacks on open channels between IoMT devices and the cloud servers. This scheme is implemented on a custom Python-based blockchain. IoT simulation is performed using the Bevywise IoT simulator and the message queuing telemetry transport (MQTT) simulator. The security protocol is analyzed using Scyther.