Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods.

Loneliness among people with severe mental illness during the COVID-19 pandemic: Results from a linked UK population cohort study

Aim/Goal/Purpose Population surveys underrepresent people with severe mental ill health. This paper aims to use multiple regression analyses to explore perceived social support, loneliness and factor associations from self-report survey data collected during the Covid-19 pandemic in a sample of individuals with severe mental ill health. Design/Methodology/Approach We sampled an already existing cohort of people with severe mental ill health. Researchers contacted participants by phone or by post to invite them to take part in a survey about how the pandemic restrictions had impacted health, Covid-19 experiences, perceived social support, employment and loneliness. Loneliness was measured by the three item UCLA loneliness scale. Findings In the pandemic sub-cohort, 367 adults with a severe mental ill health diagnosis completed a remote survey. 29–34% of participants reported being lonely. Loneliness was associated with being younger in age (adjusted OR = -.98, p = .02), living alone (adjusted OR = 2.04, p = .01), high levels of social and economic deprivation (adjusted OR = 2.49, p = .04), and lower perceived social support (B = -5.86, p < .001). Living alone was associated with lower perceived social support. Being lonely was associated with a self-reported deterioration in mental health during the pandemic (adjusted OR = 3.46, 95%CI 2.03–5.91). Practical implications Intervention strategies to tackle loneliness in the severe mental ill health population are needed. Further research is needed to follow-up the severe mental ill health population after pandemic restrictions are lifted to understand perceived social support and loneliness trends. Originality Loneliness was a substantial problem for the severe mental ill health population before the Covid-19 pandemic but there is limited evidence to understand perceived social support and loneliness trends during the pandemic.

HEALTH OF LOCAL COMMUNITIES: DIAGNOSING, IDENTIFYING GAPS AND NEEDS

Purpose: to identify features of diagnosing the health of local communities, to examine the current state and changes in the approaches to identifying gaps and needs for action planning. Materials and Methods. The study makes use of the current data on approaches to identifying gaps and needs health diagnostics of local communities in Ukraine and the world. Results. There are five types of models for health diagnosis and needs assessment: epidemiological diagnosis, public health diagnosis, social diagnosis, asset diagnosis, and rapid diagnosis. Each model has its own vision, as well as advantages and disadvantages. In practice, the selected model can be supplemented with elements of other models in accordance with the resources and purpose of the assessment. Determining the population to be assessed is an important early stage in assessing community health. It can be determined geographically, by a specific area, place of work, residence, or study. The state health department can target the entire population, while a small local non-profit agency is likely to focus only on potential customers. The use of very specific parameters to determine the population makes the assessment more focused and detailed, allows very specific adaptation of health measures. Conclusions. When assessing community health, the boundaries of the target audience may change during data collection and analysis. Analysis and interpretation of epidemiological data may reveal that only working mothers are at high risk for health problems that the organization can address. This refinement of the target audience can occur because of a community health assessment.

Non-Invasive Inspections: A Review on Methods and Tools

Non-Invasive Inspection (NII) has become a fundamental tool in modern industrial maintenance strategies. Remote and online inspection features keep operators fully aware of the health of industrial assets whilst saving money, lives, production and the environment. This paper conducted crucial research to identify suitable sensing techniques for machine health diagnosis in an NII manner, mainly to detect machine shaft misalignment and gearbox tooth damage for different types of machines, even those installed in a hostile environment, using literature on several sensing tools and techniques. The researched tools are critically reviewed based on the published literature. However, in the absence of a formal definition of NII in the existing literature, we have categorised NII tools and methods into two distinct categories. Later, we describe the use of these tools as contact-based, such as vibration, alternative current (AC), voltage and flux analysis, and non-contact-based, such as laser, imaging, acoustic, thermographic and radar, under each category in detail. The unaddressed issues and challenges are discussed at the end of the paper. The conclusions suggest that one cannot single out an NII technique or method to perform health diagnostics for every machine efficiently. There are limitations with all of the reviewed tools and methods, but good results possible if the machine operational requirements and maintenance needs are considered. It has been noted that the sensors based on radar principles are particularly effective when monitoring assets, but further comprehensive research is required to explore the full potential of these sensors in the context of the NII of machine health. Hence it was identified that the radar sensing technique has excellent features, although it has not been comprehensively employed in machine health diagnosis.

ANALISIS LAPORAN PELAKSANAAN KEGIATAN PBL DI KABUPATEN GOWA

Kesehatan masyarakat merupakan suatu usaha untuk mencegah penyakit, memperpanjanghidup, dan meningkatkan kesehatan melalui usaha-usaha pengorganisasisan masyarakat(Winslow, 1920).Pengalaman belajar lapangan (PBL) adalah salah satu bentuk pengalaman belajar mengajar dimasyarakat yang merupakan siklus pemecahan masalah dengan materi substansinya adalah“Community Health Diagnosis” dan Pengembangan Proyek Intervensi. Dengan adanyakegiatan PBL diharapkan dapat meningkatkan pemahaman dan keterampilan mahasiswatentang Ilmu Kesehatan Masyarakat dan aplikasinya di tengah-tengah masyarakat.

Early Stage Breast Cancer Detection Using Wearable Health Diagnosis System

The most prevalent cancer that threatens women’s life is Breast cancer. According to WHO Statistics in 2020, 2.3 Million Women were diagnosed with Breast cancer and 685000 death rate were disclosed globally. In this paper, Wearable Health Diagnosis System (WHDS) based antenna for the identification of the early breast cancer is discussed. Conventional methods are limited by their uncomfortable testing setups, panic environment and failure in results. Recently, textile based antenna for microwave imaging stared to work on the detection of the cancer cells at the earlier stage in breast. WHDS antenna has the requirements of wider bandwidth, high resolution, low Specific Absorption Rate (SAR), bio compatibility, and flexibility. The proposed work is based on the textile antenna using Denim substrate (permittivity = 1.67, thickness = 2 mm) to diagnosis the Early Breast Cancer Tissues (EBCT). Using the following antenna parameters (return loss, E-filed, H-field and SAR values), the position and malignancy of the EBCT is identified. Since the dielectric properties of the cancer cells are high, the influence of the effective permittivity is higher on the E-field and SAR. Along with the above parameters, comparison of various substrate materials (Denim, FR4, and RT duroid) were also tested and Denim is selected for our application as it introduces greater reflection co-efficient and wider bandwidth. The proposed antenna is designed to operate at a frequency of 2–4 GHz. This miniaturised antenna has a volume of 30 × 28 × 2 mm3.

Health Diagnosis of Roadheader Based on Reference Manifold Learning and Improved K-Means

The safe and stable operation of roadheader is of great significance to the efficient and rapid production of a coal mine. Health diagnosis based on vibration signals has been studied in bearings and motors. Complex geological conditions and bad working environment lead to the characteristics of nonlinear and time-varying vibration signals of a roadheader. In this paper, a health state analysis method based on reference manifold (RM) learning and improved K-means clustering analysis was proposed; the method was verified by using the real-time collected roadheader cutting reducer fault signal. Firstly, the comparison signal and analysis signal were extracted from the actual collected vibration data of the roadheader, and the referential analysis samples were constructed through time domain and wavelet packet energy analysis. Then, the characteristic structure of the low-dimensional space of the referential analysis samples is obtained by Locally Linear Embedding (LLE), which is a method of manifold learning. Through the improved K-means clustering analysis method, the low-dimensional structure parameters were analyzed and the clustering effect index was obtained, which was used as the health evaluation index (HEI). Finally, the normal distribution model of the health evaluation index is established, and the confidence interval of the health evaluation index is determined, so as to realize the health state analysis of the roadheader and realize the fault warning function. Through the analysis of data of three sensors, the results show that the roadheader failed on the 15th day, which is consistent with the actual working condition. Through practical analysis, the effectiveness of the method was verified and provided a kind of fault analysis idea and method for equipment working under complex working conditions and the theoretical basis for fault type analysis.