Internet‐of‐Things (IoT) enabled cyber‐physical systems (CPS) is a system in which communication between the physical devices and the cyber environment runs independently without any user interaction. Several optimization algorithms have been used for determining the optimal solutions that can reduce the production cost and/or enhance the production efficiency with in limited time‐periods. However, existing optimization approaches have failed to solve the issues in the complex manufacturing process. To overcome this issue, a novel technique called directed acyclic graph theory based multiobjective oppositional learnt artificial ant colony resource optimization (DAGT‐MOLAACRO) technique has been introduced in this study for solving the complex manufacturing process in the industry. Initially, IoT devices are used in the industrial sector for sensing and collecting data. Then the collected data is sent to the cyberspace of the CPS system with the least latency. Then, the CPS system collects the data generated from the industrial IoT devices that is stored in cyberspace with lesser memory consumption. MOLAACRO is applied to find the optimal solution among the population that satisfies the resource constraints by constructing the directed acyclic graph. In this way, the DAGT‐MOLAACRO technique reduces the time complexity with minimal latency and computation overhead. For verification purposes, our experimental work has been carried out using different performance metrics such as data latency, time complexity, and computation overhead with respect to the number of IoT devices and the amount of data collected. The results show that the DAGT‐MOLAACRO technique has better performance with reductions in terms of time complexity by 10%, latency by 17%, and the computation overhead by 11% against the existing works in literature.

Purpose The paper is to study a review of the employment of deep learning (DL) techniques inside the healthcare sector, together with the highlight of the strength and shortcomings of existing methods together with several research ultimatums. Our study lays the foundation for healthcare professionals and government with present-day inclinations in DL-based data analytics for smart healthcare. Methods A deep learning-based technique is designed to extract sensor displacement effects and predict abnormalities for activity recognition via Artificial Intelligence (AI). The presented technique minimizes the vanishing gradient issue of Recurrent Neural Networks (RNN), thereby reducing the time for detecting abnormalities with consideration of temporal and spatial factors. Proposed Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN) introduced. Results Experimental results show the feasibility of the proposed method. The results show that the proposed method improves accuracy by 95% and reduces execution time by 18%. Conclusion MAR-ERNN performs well in the activity recognition of health status. Collectively, this IoT-enabled smart healthcare system is utilized by enhancing accuracy, and minimizing time and overhead reduction.

RHCM is expanding in acceptance as both patients and healthcare office workers desire health to be kept track of the exterior of clinical environments. RHCM is also named remote patient observation of the exterior of clinical settings. In other words, RHCM refers to the procedure of utilizing technology to observe patients in nonclinical settings, like inside the house.

The widening of coronavirus disease (COVID-19) across the globe has put both the government and humanity at risk. The funds of part of the biggest recessions are stressed out due to the severe infectivity rates and highly communicable nature of this disease. Due to the expanding consequence of cases being registered and their successive significance on the civic body administration and health professionals, certain prediction methods are intended to be necessitated to predict the number of cases in the future. In this paper, nonlinear cosine-based time series learning (NCTL) is introduced for the prediction and analysis of COVID-19 in India. First, the nonlinear least squares regressive feature selection (NLS-RFS) model is used for choosing the relevant features by considering both the active cases with less prediction error. Next, the cosine-based neighborhood filter algorithm is applied to attain the optimum filtered features to select relevant features with minimum prediction time. Finally, cosine neighborhood-based LSTM is used for the prediction of the number of COVID-19 cases being registered in India to the fore and consequence of precautionary measures like social distancing, lockdown, and declaring containment zones on the outspread of COVID-19. The existing deep learning methods’ prediction accuracy was not enhanced with lesser time. In order to overcome the issue, the nonlinear cosine-based time series learning (NCTL) method has been introduced. The aim of the proposed NCTL method is to predict the number of COVID-19 cases with less prediction time and prediction error. This helps to enhance the prediction accuracy for considering the time series with accurate prediction results. The experiment of the NCTL method is conducted using metrics such as accuracy, prediction error, prediction accuracy, and prediction time with respect to diverse samples. The simulation result illustrates that the NCTL method increases the prediction accuracy by 8%, reduces the prediction time by 18%, and minimizes the prediction error by 31% compared to state-of-the-art works in a computationally efficient manner.

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