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Big data had accumulated a massive amount of stored data for applications including robotics, internet of things (IoT), and healthcare system. Although the IoT-based healthcare system plays a vital role in big data industry, in some case, the sensing may be difficult to predict the accurate result. The proposed system with artificial intelligence and IoT for Parkinson's disease can enhance the gait performance tremendously. This research clearly defines the role of robots in Parkinson's disease and how they interact with big data analytics. To process the research scheme, data are collected from big data. Moreover, Laser scanned scheme with piecewise linear Gaussian dynamic time warp machine learning is introduced. In order to scan the path for obstacle and safe place, laser scan system is used. The main role of robot is to predict the walker motion and give physical training to the patient. To predict the walker motion of patient, robot has to walk along with patient since the sensors are fixed in both the patient and the robot. Finally, the performance of proposed methodology is evaluated with existing works.

The Internet of Things (IoT) is an emerging domain in recent days as they provided a huge number of applications in day-to-day lives. In contrast to the agricultural sector, the automatic techniques for recognizing plant disease have different benefits and pose several issues. In addition, inappropriate diagnoses are ineffectual in treating the disease and may affect the crop yield. This paper presents a novel technique for plant health monitoring by estimating sulphur dioxide. Here, the simulation of IoT was performed for improved functioning. After that, the cluster head selection and routing are performed using the proposed invasive water cycle (IWC) algorithm, which is devised by integrating the water cycle algorithm (WCA) and invasive weed optimization (IWO) algorithm. Here, the fitness function is newly modeled using certain factors involving Energy, intra and intercluster distance, and delay. After the cluster head selection and routing, the sulphur dioxide content from the soil is estimated. For sulphur dioxide estimation, the soil data is considered the input data, and then the data transformation is performed to transform the data. After that, the feature selection is performed by Mahalanobis distance, and then sulphur dioxide from the soil is estimated using Deep Q-Network, where training is performed using the proposed IWC algorithm. The proposed IWC-based Deep Q-Network offered improved performance with the highest accuracy of 0.941, and the smallest root mean square error (RMSE) of 0.242. In addition, the minimal Energy and highest Throughput are computed by the proposed IWC-based Deep Q-Network.

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Summary The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti‐corona virus‐Henry gas solubility optimization‐based deep maxout network (ACV‐HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV‐HGSO is designed by incorporating anti‐corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi‐objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension‐reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively.