IoT Enabled Stress Detection Based on Image Processing with Ensembling Machine Learning Approach

Authors

  • Puja S. Gholap Assistant Professor, Department of Computer Engineering, Sharadchandra Pawar College of Engineering, Savitribai Phule Pune University
  • Gunjan Sharma Assistant Professor, Institute of Business Management, GLA University, Mathura
  • A. Deepak Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • Parul Madan Assistant. Professor, Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun
  • Rajeev Sharma Assistant Professor, Department of Electronics and Communication Engineering, The Technological Institute of Textile & Sciences, Bhiwani (Haryana)
  • Meenakshi Sharma Professor, RNB Global University, Bikaner
  • Anurag Shrivastava Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu

Keywords:

Image Processing, Machine Learning, Internet of Things

Abstract

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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References

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Published

12.01.2024

How to Cite

Gholap, P. S. ., Sharma, G. ., Deepak, A. ., Madan, P. ., Sharma, R. ., Sharma, M. ., & Shrivastava, A. . (2024). IoT Enabled Stress Detection Based on Image Processing with Ensembling Machine Learning Approach. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 760–768. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4577

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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