Blockchain Assisted Disease Identification of COVID-19 Patients with the Help of IDA-DNN Classifier.

Globally, millions of people were affected by the Corona-virus disease-2019 (COVID-19) causing loads of deaths. Most COVID-19 affected people recover in a few spans of weeks. However, certain people even those with a milder variant of the disease persist in experiencing symptoms subsequent to their initial recuperation. Here, a novel Block-Chain (BC)-assisted optimized deep learning algorithm, explicitly improved dragonfly algorithm based Deep Neural Network (IDA-DNN), is proposed for detecting the different diseases of the COVID-19 patients. Initially, the input data of the COVID-19 recovered patients are gathered centered on their post symptoms and their data is amassed as a BC for rendering security to the patient's data. After that, the disease identification of the patient's data is performed with the aid of system training. The training includes '4' disparate datasets for data collection, and then, performs preprocessing, Feature Extraction (FE), Feature Reduction (FR), along with classification utilizing ID-DNN on the gathered inputted data. The IDA-DNN classifies '2' classes (presence of disease and absence of disease) for every type of data. The proposed method's outcomes are examined as well as contrasted with the other prevailing techniques to corroborate that the proposed IDA-DNN detects the COVID-19 more efficiently.

Clouds Proportionate Medical Data Stream Analytics for Internet of Things-Based Healthcare Systems.

Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.

Internet of things for secure surveillance for sewage wastewater treatment systems.

IoT is a secure communication technology used to transfer data from a physical entity to a device with intelligent analysis tools through a wireless channel. The wastewater treatment method extracts pollutants and transforms them into effluents added to the water supply with minimal environmental effects or recovered directly. The major issue is monitoring the disposal of sewage in the treatment plants. Hence, this paper, Surveillance-based Sewage Wastewater Monitoring System (SSWMS) with IoT, has been proposed for monitoring wastewater treatment and improving water quality. A smart water sensor enabled by IoT monitors water quality, water pressure, and water temperature and quantifies water dynamics to map water flow through the entire treatment facility. The proposed method calculates the wastewater treatment facility's effectiveness and ensures that chemical releases are maintained below allowable levels. Thus, the experimental results show the improved recycling water quality level is raised to 97.98%, enhancing secure communication and less moisture content when compared to other methods.

Internet of things-based intelligent physical support framework using future internet of things.

BACKGROUND: Physical exercise programs are required to improve students' physical ability, physical fitness, self-responsibility, and satisfaction to remain physically active for a lifetime. The supporting system's demanding characteristics include lack of school leadership support, and lack of communication skills among students is considered an essential factor in the physical education system. OBJECTIVE: In this paper, an Internet of Things (IoT)-based intelligent physical support framework (IoT-IPSF) has been proposed to encourage education leadership and student social interaction in the physical education system. METHOD: Training service analysis is introduced to improve adequate leadership support, helping in the physical education system's growth. Self-determination analysis is integrated with IoT-IPSF to enhance effective communication among school teachers, educational experts, and curriculum officers in the physical education system. RESULTS: The simulation results show that the proposed method achieves a high accuracy ratio of 98.7%, an efficiency ratio of 95.6, student performance 97.8%, fitness level 82.3%, activity involvement 94.5% compared to other existing models.

Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals.

In this paper, MODWT is used to decompose the Electrocardiography (ECG) signals and to identify the changes of R waves in the noisy input ECG signal. The MODWT is used to handle the arbitrary changes in the input signal. The R wave's detctected by the proposed framework is used by the doctors and careholders to take necessary action for the patients. MATLAB simulink model is used to develop the simulation model for the MODWT method. The performance of the MODWT based remote health monitoring system method is comparatively analyzed with other ECG monitoring approaches such as Haar Wavelet Transformation (HWT) and Discrete Wavelet Transform (DWT). Sensitivity, specificity, and Receiver Operating Characteristic (ROC) curve are calculated to evaluate the proposed Internet of Things with MODWT based ECG monitoring system. We have used MIT-BIH Arrythmia Database to perform the experiments.