IoT Enabled Stress Detection Based on Image Processing with Ensembling Machine Learning Approach
Keywords:
Image Processing, Machine Learning, Internet of ThingsAbstract
Once a product, or more particularly, a central processing unit system, has been constructed, the objective of this article is to automate the quality control process in order to make it more effective than it now is. In order to facilitate quality control, improve productivity, and accelerate the production process, it is essential to develop a model that automatically rejects anomalous goods. This will help reduce the number of defective products. Image processing, which relies on the use of specialised cameras or imaging systems positioned inside the production line, is one of the most common techniques for accomplishing this objective and has become one of the most popular approaches in recent years. In this piece, we provide a method that is both very effective and highly productive for automating the production lines of central processing units in a particular industry. This method may be found in this article. The model analyses photographs of the manufacturing lines, searches for deviations in the way their components are put together, and then summarises the findings. After that, this information is sent through a network that is part of a cyber-physical cloud system to the administrator of the system.
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