Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

Misclassifying parts in the small-medium manufacturing enterprise can lead to serious consequences. Manual inspection, as currently practiced, allows for compromises in product traceability. Due to this condition, inspection of the part's number is not digitally visible. Due to a lack of modern traceability, customers receive incorrect parts, and the same incidents continue to occur. It is essential to transform manual inspections into digital and automated ones. AI-based technologies have recently been employed to enable a smart and intelligent recognition system for industrial machining parts. Convolutional Neural Networks (CNN) are widely used for image recognition tasks and are gaining popularity as deep learning algorithms. In this paper, a CNN model is used to perform binary recognition on two similar industrial machining parts. The model has been trained to recognise two classes of machining parts: Parts A and B. The dataset used to train the model includes both original and augmented images, with a total of 2447 images for both classes. The performance metrics have been measured during the training process, and 10 experiments have been conducted to evaluate the performance of the model. The test results reveal that the CNN model achieves 98% mean accuracy, 97.1% precision for Part A, 99% precision for part B and 0.982 AUC value. The results demonstrate the effectiveness of the CNN-based recognition of parts. It offers an effective alternative and is a compelling method for quality assurance in small-medium manufacturing enterprises.

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