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The existing healthcare system based on traditional management involves the storage and processing of large quantities of medical data. The incorporation of the Internet of Things (IoT) and its gradual maturation has led to the evolution of IoT-enabled healthcare with extraordinary data processing capability and massive data storage. Due to the advancement in the Industrial Internet of Things (IIoT), the resulting system is aimed at building an intelligent healthcare system that can monitor the medical health of the patient by means of a wearable device that is monitored remotely. The data that is gathered by the wearable IoT module is stored in the cloud server which is subject to privacy leakage and attacks by unauthorized users and attackers. To address this security issue, an IoT-based deep learning-based privacy preservation and data analytics system is proposed in this work. Data is collected from the user, and the sensitive information is segregated and separated. Using a convolutional neural network (CNN), the health-related information is analyzed in the cloud, devoid of users’ privacy information. Thus, a secure access control module is introduced that works based on the user attributes for the IoT-Healthcare system. A relationship between the users’ trust and attributes is discovered using the proposed work. The precision, recall, and F1 score of the proposed CNN classifier are achieved at 95%. With the increase in the size of the training set, higher performance is attained. When data augmentation is added, the system performs better without data augmentation. Further, the accuracy of around 98% is achieved with an increased user count. Experimental analysis indicates the robustness and effectiveness of the proposed system with respect to low privacy leakage and high data integrity.

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The wear behaviour of hot pressed AA 2618 aluminium alloy matrix composites reinforced through nano Si3N4 elements (1 percent and 2 percent) has been investigated in this paper. Temperatures of 50°C, 150°C, and 250°C were used to examine the tribological characteristics of the models under a range of loads and pressures. The best wear performance was found in AA 2618/2wt percent Si3N4. Under a load of 30 N and temperature of 250°C, it was discovered that Si3N4-enriched AA 2618 alloy was 35.7% more wear-resistant than unreinforced AA 2618 alloy. Metal flow and plain delamination were the most common wear mechanisms at higher temperatures. Delamination is the most common wear mechanism at temperatures between 50 and 250 degrees Celsius. In the analysis of variance, the wear rate was influenced by temperature, load, and the presence of Si3N4 by 47.2%. In order to predict the wear rate, regression equations (linear and nonlinear) were developed by Taguchi method. Using a high determination coefficient, the nonlinear regression was the preeminent success rate (92.8 percent).