ABSTRACT
IoT technology is emerging as a fully developed automation that could be integrated in various web applications, which will be present in upcoming generations of the World Wide Web. Blockchain, like IoT, is a burgeoning field whereby every system associated in the blockchain incorporates a disseminated ledger that improves safety and consistency. Due to the blockchain network abilities to accomplish smart contracts and consensus, unauthorized users are unable to undertake any fault transactions. The IoT and blockchain can be aggregated to improve application performance dynamically at run time. However, controlling and monitoring the machines linked to sensors in an IoT background and mining the blockchain will always be a technical challenge to the researchers. With this context, this paper enables to review the fundamentals of IoT, blockchain field, and its topographies. In this paper, design architecture, namely, IoT Blockchain Assurance-Based Compliance to COVID Quarantine, is proposed and concluded up with novel architectural framework that improves the efficiency of data safety and data transparency. Unlicensed users are not permitted to conduct any erroneous transactions within the blockchain network, which has the capability to engage in smooth contracts and agreement, thus extending the safekeeping between clinicians and chronically ill patients. This methodology was created with immobile elderly chronically ill patients in mind who are suffering from COVID that require on-the-spot treatment and continuous monitoring by a doctor in mind. This paper is designed to analyze the performance of proposed IoT Blockchain Assurance-Based Compliance to COVID Quarantine with Ethereum private blockchain network beneath a genesis block and the results are conferred. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
ABSTRACT
With the advancement of digital technology, a large number of medical images are created in the field of digital medicine. Object detection is a critical field of study in medical image analysis, detection and processing. Object detection is the categorization of the pixels in an image that determines where the objects in the image are located. Image classification is the global classification of an image's pixels. The objects are identified but not located from all of the pixels in the image. The intelligent identification process for the adjuvant diagnosis to assist medical doctors of various disciplines is in high demand. The need for the high accurate object detection method for the medical images remains a major challenge toward disease detection in the health care. With this summary overview, a novel model is proposed for lung CT scan image detection as 18-Convolutional Layered Deep U-Net architecture for diagnosis of COVID-19 detection through object detection. Model fitting is done with 18-Convolutional Layered Deep U-Net architecture, and the model design is formed with four contraction path and four expansive path layers. The customized model design is fine-tuned with the parameter optimization. The object detection is done, and the performance is analyzed. The dataset is also fitted with existing deep convolution neural network models like region-based, threshold, edge-based and clustering-based method, and the performance is analyzed and compared with proposed model with metrics like pixel accuracy, intersection over union and dice coefficient. Implementation results show that the proposed models have pixel accuracy of 98.32%, intersection over union of 48.7% and dice coefficient of 97.56% compared to existing object detection models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.