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

Zeeshan Khan, Sandeep Kumar, Anurag jain, “A Review of Content Based Image Classification using Machine Learning Approach”, International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume2 Number-3 Issue-5 September-2012, pp 55-60.

Tina Vaz , Nagaraj Vernekar,”A Survey on Evaluating Handwritten Iterative Mathematical Expressions”, International Journal for Research in Applied Science & Engineering Technology (IJRASET) .

Anuj Dutt, AashiDutt,” Handwritten Digit Recognition Using Deep Learning”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 6, Issue 7, July 2017, ISSN: 2278 – 1323.

Abdul-Qawy, A. S., Pramod, P. J., Magesh, E., & Srinivasulu, T. (2015). The internet of things (iot): An overview. International Journal of Engineering Research and Applications, 1(5), 71-82.

Abdul-Qawy, A. S., Pramod, P. J., Magesh, E., & Srinivasulu, T. (2015). The internet of things (iot): An overview. International Journal of Engineering Research and Applications, 1(5), 71-82.

Shrivastava, A., Chakkaravarthy, M., Shah, M.A..A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches. In Cybernetics and Systems, 2022

Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 100219

Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–729

Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321

Boina, R., Ganage, D., Chincholkar, Y.D., .Chinthamu, N., Shrivastava, A., Enhancing Intelligence Diagnostic Accuracy Based on Machine Learning Disease Classification. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 765–774

Shrivastava, A., Pundir, S., Sharma, A., ...Kumar, R., Khan, A.K. Control of A Virtual System with Hand Gestures. In Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, 2023, pp. 1716–1721

Kim KH, Kim YW and Suh SW. Automatic visual inspections system to detect wrongly attached components. In: International conference on signal processing applications and technology (ICSPAT’98), Toronto, ON, Canada, 13–16 September 1998. ACM SIGPLAN.

Khandogin I, Kummert A and Maiwald D. DSP algorithms for the automatic inspection of fixing devices of railroad lines. In: International conference on signal processing applications and technology (ICSPAT’98), Toronto, ON, Canada, 13–16 September 1998. ACM SIGPLAN.

Zhou J, Kwan C and Ayhan B. A high performance missing pixel reconstruction algorithm for hyperspectral images. In: 2nd international conference on applied and theoretical information systems research, Taipei, Taiwan, 27–29 December 2012. ATISR.

Parolini L, Tolia N, Sinopoli B, et al. A cyber-physical systems approach to energy management in data centers. In: Proceedings of the 1st ACM/IEEE international conference on cyber-physical systems, Stockholm, 13–15 April 2012, pp.168–177. New York: ACM.

Zhang C, Chen X, Chen M, et al. A multiple instance learning approach for content based image retrieval using one class support vector machine. In: 2005 IEEE international conference on multimedia and expo (ICME 2005), Amsterdam, 6 July 2005, pp.1142–1145. New York: IEEE.

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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

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Research Article

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