Summary Centralized healthcare Internet of Things (HIoT)‐based ecosystems are challenged by high latency, single‐point failures, and privacy‐based attacks due to data exchange over open channels. To address the challenges, the shift has progressed toward decentralized HIoT setups that infuse computation closer to a patient node via edge services. As HIoT data are critical and sensitive, trust among stakeholders is a prime concern. To address the challenges, researchers integrated blockchain (BCH) into edge‐based HIoT models. However, the integration of lightweight BCH is required with an edge for proper interplay and leverage effective, scalable, and energy‐efficient computational processes for constrained HIoT applications. Owing to the existing gap, this article proposes a scheme MobEdge, that fuses lightweight BCH, and edge computing to secure HIoT. A local BCH client model is set up that forwards data to edge sensor gateways. The shared data are secured through an access tree control lock scheme that preserves the privacy of health records. For security and signing purposes, we have considered signcryption, and the validated records meta‐information are stored in an on‐chain structure. The scheme is compared on two grounds, security and simulation grounds. On the security front, we do cost evaluation and present a formal analysis model using the Automated Validation of Internet Security Protocols and Applications tool. An edge‐based BCH setup use‐case is presented, and parameters like mining cost, storage cost, edge servicing latency, energy consumption, BCH network usage, and transaction signing costs are considered. In the simulation, the mining cost is 0.6675 USD, and improvement of storage costs are improved by 18.34%, edge‐servicing latency is 384 ms, and signcryption improves the signing cost by 36.78% against similar schemes, that indicates the scheme viability in HIoT setups.
This paper presents the acoustic analysis of speaking voice for screening some of the upper respiratory diseases. The six acoustic parameters of speaking voice were analysed, which were recorded while reading the specific text. The text to read was designed considering phonetic categories of words used and their order of occurrence in the text. The appropriate number of words was combined to form the desired text to read for extracting maximum information from the upper respiratory system. Then recorded speaking voice was analysed for disease-specific corresponding variation in Total Harmonic Distortion (THD),
Technical developments are being done in medical field. In order to improve medical results and healthcare facilities, machine learning and deep learning concepts are being used. Various experiments and efforts are done to detect diseases and provide platforms to provide better healthcare. Involvement of technology has made healthcare field more efficient and trustworthy. The ‘Medical Image Analytics’ is a machine learning as well as deep learning tool that would provide platform for processing medical images and extracting features not visible to human eye and provide accurate results and help to healthcare organizations. It strives to help healthcare organization for providing better healthcare facilities. This project is intended for use in various healthcare fields and organizations. Some features of the disease in medical images can be nit invisible or not clear to human eyes. Improper detection of features can lead to improper detection of diseases and may lead to failure or degradation in health and healthcare facilities. Thus, using techniques like deep learning and machine learning increases the detection of features in medical images. Also, it is helpful if diseases can be detected at an early stage and therefore, the project would aim to detect diseases at an early stage in future.
Different CVDs (CVD) are the leading wreak of mortality and disability worldwide. The pathology of CVD is complex; multiple biological pathways have been involved. Biomarkers act as a measure of usual or pathogenic biological processes. They play a significant part in the definition, prognostication, and decision-making with respect to the treatment of cardiovascular events. Inthis article, we had summarized key biomarkers which are essential to predict CVDs. We had studied prevalence, pattern of expression of biomarkers (salivary, inflammatory, oxidative stress, chemokines, antioxidants, genetic, etc.), its measurable impact, benefits of early detection and its scope. A considerable number of deaths due to cardiovascular diseases (CVDs) can be attributed to tobacco smoking and it rises the precarious of deathfrom coronary heart disease and cerebrovascular diseases. Cytokines which is categorized into pro inflammatory and anti-inflammatory take part in as biomarkers in CHD, MI, HF. Troponin, growth differentiation factor-15(GDF-15), C-reactive protein, fibrinogen, uric acid diagnose MI and CAD. Matrix Metalloproteins, Cell Adhesion Molecules, Myeloperoxidase, Oxidative stress biomarkers, Incendiary biomarkers are useful to predict the risk of UA, MI, and HF. Increased Endothelin-1, Natriuretic peptides, copeptin, ST-2, Galectin-3, mid-regional-pro-adrenomedullin, catecholamines are used to prognosticate Heart failure. Modern technologies like Artificial Intelligence (AI), Biosensor and high-speed data communication made it possible to collect the high-resolution data in real time. The high-resolution data can be analyzed with advance Machine Learning (ML) algorithms, it will not only help to discover the disease patterns but also an real-time and objective monitoring of bio-signals can help to discover the unknown patterns linked with CVD.
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