Today, internet and device ubiquity are paramount in individual, formal and societal considerations. Next generation communication technologies, such as Blockchains (BC), Internet of Things (IoT), cloud computing, etc. offer limitless capabilities for different applications and scenarios including industries, cities, healthcare systems, etc. Sustainable integration of healthcare nodes (i.e. devices, users, providers, etc.) resulting in healthcare IoT (or simply IoHT) provides a platform for efficient service delivery for the benefit of care givers (doctors, nurses, etc.) and patients. Whereas confidentiality, accessibility and reliability of medical data are accorded high premium in IoHT, semantic gaps and lack of appropriate assets or properties remain impediments to reliable information exchange in federated trust management frameworks. Consequently, We propose a Blockchain Decentralised Interoperable Trust framework (DIT) for IoT zones where a smart contract guarantees authentication of budgets and Indirect Trust Inference System (ITIS) reduces semantic gaps and enhances trustworthy factor (TF) estimation via the network nodes and edges. Our DIT IoHT makes use of a private Blockchain ripple chain to establish trustworthy communication by validating nodes based on their inter-operable structure so that controlled communication required to solve fusion and integration issues are facilitated via different zones of the IoHT infrastructure. Further, C] implementation using Ethereum and ripple Blockchain are introduced as frameworks to associate and aggregate requests over trusted zones.

An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected.

RGB multi channel representation is proposed for images on quantum computers (MCQI) that captures information about colors (RGB channels) and their corresponding positions in an image in a normalized quantum state. The proposed representation makes it possible to store the RGB information about an image simultaneously by using 2n+3 qubits for encoding 2ⁿ× 2ⁿpixel images, whereas pixel-wise processing is necessary in many other quantum image representations, e.g., qubit lattice, grid qubit, and quantum lattice. Simulation of storage and retrieval of MCQI images using human facial images demonstrated that 15 qubits are required for encoding 64 × 64 colored images, and encoded information is retrieved by measurement. Perspectives of designing quantum image operators are also discussed based onMCQI representation, e.g., channel of interest, channel swapping, and restrict version of color transformation.