Graphical-design-based symptomatic techniques in pandemics perform a quintessential purpose in screening hit causes that comparatively render better outcomes amongst the principal radioscopy mechanisms in recognizing and diagnosing COVID-19 cases. The deep learning paradigm has been applied vastly to investigate radiographic images such as Chest X-Rays (CXR) and CT scan images. These radiographic images are rich in information such as patterns and clusters like structures, which are evident in conformance and detection of COVID-19 like pandemics. This paper aims to comprehensively study and analyze detection methodology based on Deep learning techniques for COVID-19 diagnosis. Deep learning technology is a good, practical, and affordable modality that can be deemed a reliable technique for adequately diagnosing the COVID-19 virus. Furthermore, the research determines the potential to enhance image character through artificial intelligence and distinguishes the most inexpensive and most trustworthy imaging method to anticipate dreadful viruses. This paper further discusses the cost-effectiveness of the surveyed methods for detecting COVID-19, in contrast with the other methods. Several finance-related aspects of COVID-19 detection effectiveness of different methods used for COVID-19 detection have been discussed. Overall, this study presents an overview of COVID-19 detection using deep learning methods and their cost-effectiveness and financial implications from the perspective of insurance claim settlement.

Automation seems widespread today, yet it is not implemented in daily life. However, most home automation systems are expensive, object-dependent, and lacking in crucial features. The Internet of Things was enabled by this paper’s low-cost home automation system. For development of the IoT, the system used Node-RED, an open-source platform that uses nodes to visualize tasks. This innovation could operate home devices, including plugs, from anywhere. Wireless sensor network (WSN) technology would record and upload data to the web server from each room. Using the publish-and-subscribe Message Queuing Telemetry Transport (MQTT) protocol, these WSN technologies would communicate. The third feature can modify notifications. In situations of doubt, the house member would be notified by email. This proposal promotes home automation through the IoT.

In a world where people are talking about increase in the usage of non-polluting and renewable sources of energy, monitoring and controlling such biogas plants remotely becomes very important. Monitoring the properties like pH, temperature, pressure and total amount of gas produced and consumed will help in understanding the health of the biogas plant. The whole process of offline monitoring has to be replaced with an online automation system. To overcome the barrier of data connectivity issues in the rural area, GSM based data transfer has to be used. In this paper, an IoT based efficiency monitoring system for biogas plants is proposed. An Arduino micro-controller system is deployed and tested to measure the gas production and consumption. Android based application is designed to act as an SMS gateway which is a free replacement for commercial solution. A dashboard plotting the usage statistics is designed to help the plant administrator to monitor the parameters.

Identifying the number of neurons in each hidden layers and number of hidden layers in a multi layered Artificial Neural Network (ANN) is a challenge based on the input data. A new hypothesis is proposed for organizing the synapse from x to y neuron. The synapse of number of neurons to fire between the hidden layer is identified. By the introduction of this hypothesis, an effective number of neurons in multilayered Artificial Neural Network can be identified and self organizing neural network model is developed which is referred as cognitron. The normal brain model has 3 layered perceptron; but the proposed model organizes the number of layers optimal for identifying an effective model. Our result proved that the proposed model constructs a neural model directly by identifying the optimal weights of each neurons and number of neurons in each dynamically identified hidden layers. This optimized model is self organized with different range of neurons on different layer of hidden layer, and by comparing the performance based on computational time and error at each iteration. An efficient number of neurons are organized using gradient decent. The proposed model thus train large model to perform the classification task by inserting optimal layers and neurons.

The majority of the world's population is still facing difficulties in getting access to primary healthcare facilities. Universal health coverage (UHC) proposes access to high-quality, affordable primary healthcare for all. The 17 UN sustainable development goals (SDGs) are expected to be executed and achieved by all the 193 countries through national sustainable development strategies and multi-stakeholder partnerships. This article addresses SDG 3.8—access to good quality and affordable healthcare and two subindicators related to societal impact (SDG 3.8.1 and 3.8.2) through two objectives. The first objective is to determine whether health expenditure indicators (HEIs) drive UHC, and the second objective is to analyze the importance of key determinants and their interactions with UHC in three economic blocks: emerging Gulf Cooperation Council (GCC); developing Brazil, Russia, India, China, and South Africa (BRICS) vis-à-vis the developed Australia, UK, and USA (AUKUS). We use the WHO Global Health Indicator database and UHC periodical surveys to evaluate the hypotheses. We apply state-of-the-art machine learning (ML) models and ordinary least square (traditional—OLS regression) methods to see the superiority of artificial intelligence (AI) over traditional ones. The ML Random Forest Tree method is found to be superior to the OLS model in terms of lower root mean square error (RMSE). The ML results indicate that domestic private health expenditure (PVT-D), out-of-pocket expenditure (OOPS) per Capita in US dollars, and voluntary health insurance (VHI) as a percentage of current health expenditure (CHE) are the key factors influencing UHC across the three economic blocks. Our findings have implications for drafting health and finance sector public policies, such as providing affordable social health insurance to the weaker sections of the population, making insurance premiums less expensive and affordable for the masses, and designing healthcare financing policies that are beneficial to the masses. UHC is an important determinant of health for all and requires an in-depth analysis of related factors. Policymakers are often faced with the challenge of prioritizing the economic needs of sectors such as education and food safety, making it difficult for healthcare to receive its due share. In this context, this article attempts to identify the key components that may influence the attainment of UHC and enable policy changes to address them more effectively and efficiently.