Impact of Image Processing and Deep Learning in IoT based Industrial Automation System

Authors

  • Bukka Shobharani Research Scholar, Department of English, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh- 522502, India
  • Sreelakshmy R. Associate Professor, Department of Electronics and Communication Engineering, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
  • V. Jyothsna Associate Dean (Academic Affairs) and Associate Professor, Department of Data Science, School of Computing, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College (Autonomous)), Tirupati- 517102, Andhra Pradesh, India
  • D. Rajendra Prasad Professor, Department of ECE, St. Ann's College of Engineering & Technology, Chirala- 523187, Andhra Pradesh, India
  • P. Chandra Sekhar Reddy Professor, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India
  • S. Farhad Associate Professor, Department of English, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh- 522502, India
  • Ankur Gupta Assistant Professor, Department of Computer Science and Engineering, Vaish College of Engineering, Rohtak, Haryana, India

Keywords:

Image Processing, Deep Learning, IoT, Industrial Automation System

Abstract

The integration of Image Processing and Deep Learning techniques within the realm of IoT-based Industrial Automation Systems has ushered in a transformative era for the manufacturing and industrial sectors. This synergy empowers these systems to perceive, interpret, and respond to visual data with unprecedented precision and efficiency. By harnessing the capabilities of image processing, IoT devices can capture, analyze, and transmit real-time visual information, enabling a comprehensive understanding of the production environment. Deep learning algorithms, in turn, provide the intelligence to make informed decisions, detect anomalies, and optimize processes autonomously. Consequently, this convergence has a profound impact on enhancing productivity, quality control, predictive maintenance, and overall operational excellence in industrial settings. As we move forward, the continued advancement of this integration promises to revolutionize the way industries operate, fostering greater automation, agility, and competitiveness.

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References

N. K. Pandey, K. Kumar, G. Saini, and A. K. Mishra, “Security issues and challenges in cloud of things-based applications for industrial automation,” Ann. Oper. Res., 2023, doi: 10.1007/s10479-023-05285-7.

P. Bedi, S. B. Goyal, A. S. Rajawat, P. Bhaladhare, A. Aggarwal, and A. Prasad, “Feature Correlated Auto Encoder Method for Industrial 4.0 Process Inspection Using Computer Vision and Machine Learning,” Procedia Comput. Sci., vol. 218, pp. 788–798, 2023, doi: 10.1016/j.procs.2023.01.059.

K. Sharifani and M. Amini, “Machine Learning and Deep Learning: A Review of Methods and Applications,” World Inf. Technol. Eng. J., vol. 10, no. 07, 2023.

A. Saberironaghi, J. Ren, and M. El-Gindy, “Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review,” Algorithms, vol. 16, no. 2, pp. 1–30, 2023, doi: 10.3390/a16020095.

R. Rosati et al., “From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0,” J. Intell. Manuf., vol. 34, no. 1, pp. 107–121, 2023, doi: 10.1007/s10845-022-01960-x.

S. Kumar et al., “Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control,” J. Intell. Manuf., vol. 34, no. 1, pp. 21–55, 2023, doi: 10.1007/s10845-022-02029-5.

M. Kor, I. Yitmen, and S. Alizadehsalehi, “An investigation for integration of deep learning and digital twins towards Construction 4.0,” Smart Sustain. Built Environ., vol. 12, no. 3, pp. 461–487, 2023, doi: 10.1108/SASBE-08-2021-0148.

M. S. Abdalzaher, M. M. Fouda, H. A. Elsayed, and M. M. Salim, “Toward Secured IoT-Based Smart Systems Using Machine Learning,” IEEE Access, vol. 11, no. November 2022, pp. 20827–20841, 2023, doi: 10.1109/ACCESS.2023.3250235.

M. I. Uddin, R. Damaševičius, and H. Jafari, “Deep learning solutions for service-enabled systems and applications in Internet of Things,” Serv. Oriented Comput. Appl., pp. 145–147, 2023, doi: 10.1007/s11761-023-00370-y.

M. S. Rahman, T. Ghosh, N. F. Aurna, M. S. Kaiser, M. Anannya, and A. S. M. S. Hosen, “Machine learning and internet of things in industry 4.0: A review,” Meas. Sensors, vol. 28, no. May, p. 100822, 2023, doi: 10.1016/j.measen.2023.100822.

A. M. Al Shahrani, M. A. Alomar, K. N. Alqahtani, M. S. Basingab, B. Sharma, and A. Rizwan, “Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things,” Sensors, vol. 23, no. 1, 2023, doi: 10.3390/s23010324.

S. K. Pandey et al., “Machine Learning-Based Data Analytics for IoT-Enabled Industry Automation,” Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/8794749.

Kumar, J. Rawat, N. Mohd, and S. Husain, “Opportunities of Artificial Intelligence and Machine Learning in the Food Industry,” J. Food Qual., vol. 2021, 2021, doi: 10.1155/2021/4535567.

Vijayakumar, “Computational intelligence, machine learning techniques, and IOT,” Concurr. Eng. Res. Appl., vol. 29, no. 1, pp. 3–5, 2021, doi: 10.1177/1063293X211001573.

Xing, Wenyu, Guannan Li, Chao He, Qiming Huang, Xulei Cui, Qingli Li, Wenfang Li, Jiangang Chen, and Dean Ta. "Automatic detection of A‐line in lung ultrasound images using deep learning and image processing." Medical Physics 50, no. 1 (2023): 330-343.

H. Sarker, “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,” SN Comput. Sci., vol. 2, no. 6, pp. 1–20, 2021, doi: 10.1007/s42979-021-00815-1.

P. Ambika, “Machine learning and deep learning algorithms on the Industrial Internet of Things (IIoT),”1st ed., vol. 117, no. 1. Elsevier Inc., 2020. doi: 10.1016/bs.adcom.2019.10.007.

H. Naeem et al., “Malware detection in industrial internet of things based on hybrid image visualization and deep learning model,” Ad Hoc Networks, vol. 105, p. 102154, 2020, doi: 10.1016/j.adhoc.2020.102154.

T. J. Sheng et al., “An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model,” IEEE Access, vol. 8, pp. 148793–148811, 2020, doi: 10.1109/ACCESS.2020.3016255.

U. Subbiah, D. K. Kumar, S. K. Thangavel, and L. Parameswaran, “Various Approaches to Object Detection using,” no. Icosec, pp. 183–194, 2020, doi: 10.22214/ijraset.2023.54966.

R. A. Khalil, N. Saeed, Y. M. Fard, T. Y. Al-Naffouri, and M.-S. Alouini, “Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications,” pp. 1–21, 2020, [Online]. Available: http://arxiv.org/abs/2008.06701

N. Rahmatov, A. Paul, F. Saeed, W. H. Hong, H. C. Seo, and J. Kim, “Machine learning–based automated image processing for quality management in industrial Internet of Things,” Int. J. Distrib. Sens. Networks, vol. 15, no. 10, 2019, doi: 10.1177/1550147719883551.

Yazdinejad, Abbas, Mostafa Kazemi, Reza M. Parizi, Ali Dehghantanha, and Hadis Karimipour. "An ensemble deep learning model for cyber threat hunting in industrial internet of things." Digital Communications and Networks 9, no. 1 (2023): 101-110.

Chander, Nenavath, and Mummadi Upendra Kumar. "Metaheuristic feature selection with deep learning enabled cascaded recurrent neural network for anomaly detection in Industrial Internet of Things environment." Cluster Computing 26, no. 3 (2023): 1801-1819.

S. Kumar, P. Tiwari, and M. Zymbler, “Internet of Things is a revolutionary approach for future technology enhancement: a review,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0268-2.

V. Talukdar, D. Dhabliya, B. Kumar, S. B. Talukdar, S. Ahamad, and A. Gupta, “Suspicious Activity Detection and Classification in IoT Environment Using Machine Learning Approach,” 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, Nov. 25, 2022. doi: 10.1109/pdgc56933.2022.10053312. Available: http://dx.doi.org/10.1109/PDGC56933.2022.10053312

P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya, and A. Gupta, “A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time,” 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM). IEEE, Feb. 22, 2023. doi: 10.1109/iciptm57143.2023.10118183. Available: http://dx.doi.org/10.1109/ICIPTM57143.2023.10118183.

M. Dhingra, D. Dhabliya, M. K. Dubey, A. Gupta, and D. H. Reddy, “A Review on Comparison of Machine Learning Algorithms for Text Classification,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Dec. 14, 2022. doi: 10.1109/ic3i56241.2022.10072502. Available: http://dx.doi.org/10.1109/IC3I56241.2022.10072502

D. Mandal, K. A. Shukla, A. Ghosh, A. Gupta, and D. Dhabliya, “Molecular Dynamics Simulation for Serial and Parallel Computation Using Leaf Frog Algorithm,” 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, Nov. 25, 2022. doi: 10.1109/pdgc56933.2022.10053161. Available: http://dx.doi.org/10.1109/PDGC56933.2022.10053161

P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya, and A. Gupta, “A Review on Application of Deep Learning in Natural Language Processing,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Dec. 14, 2022. doi: 10.1109/ic3i56241.2022.10073309. Available: http://dx.doi.org/10.1109/IC3I56241.2022.10073309

P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya, and A. Gupta, “Detection of Liver Disease Using Machine Learning Approach,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Dec. 14, 2022. doi: 10.1109/ic3i56241.2022.10073425. Available: http://dx.doi.org/10.1109/IC3I56241.2022.10073425

V. V. Chellam, S. Praveenkumar, S. B. Talukdar, V. Talukdar, S. K. Jain, and A. Gupta, “Development of a Blockchain-based Platform to Simplify the Sharing of Patient Data,” 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM). IEEE, Feb. 22, 2023. doi: 10.1109/iciptm57143.2023.10118194. Available: http://dx.doi.org/10.1109/ICIPTM57143.2023.10118194

P. Lalitha Kumari et al., “Methodology for Classifying Objects in High-Resolution Optical Images Using Deep Learning Techniques,” Lecture Notes in Electrical Engineering. Springer Nature Singapore, pp. 619–629, 2023. doi: 10.1007/978-981-19-8865-3_55. Available: http://dx.doi.org/10.1007/978-981-19-8865-3_55

N. Sindhwani et al., “Comparative Analysis of Optimization Algorithms for Antenna Selection in MIMO Systems,” Lecture Notes in Electrical Engineering. Springer Nature Singapore, pp. 607–617, 2023. doi: 10.1007/978-981-19-8865-3_54. Available: http://dx.doi.org/10.1007/978-981-19-8865-3_54

V. Jain, S. M. Beram, V. Talukdar, T. Patil, D. Dhabliya, and A. Gupta, “Accuracy Enhancement in Machine Learning During Blockchain Based Transaction Classification,” 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, Nov. 25, 2022. doi: 10.1109/pdgc56933.2022.10053213. Available: http://dx.doi.org/10.1109/PDGC56933.2022.10053213

Saini, D. J. B. ., & Qureshi, D. I. . (2021). Feature Extraction and Classification-Based Face Recognition Using Deep Learning Architectures. Research Journal of Computer Systems and Engineering, 2(1), 52:57. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/23

Shanthi, D. N. ., & J, S. . (2021). Machine Learning Architecture in Soft Sensor for Manufacturing Control and Monitoring System Based on Data Classification. Research Journal of Computer Systems and Engineering, 2(2), 01:05. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/24

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Published

10.11.2023

How to Cite

Shobharani, B. ., R., S. ., Jyothsna, V. ., Prasad, D. R. ., Reddy, P. C. S. ., Farhad, S. ., & Gupta, A. . (2023). Impact of Image Processing and Deep Learning in IoT based Industrial Automation System. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 801–807. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3865

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