CoChat
Sign Up
Cited by 27Robotics and Automated SystemsRobotic Process Automation ApplicationsArtificial Intelligence Applications

Wireless Communication for Robotic Process Automation Using Machine Learning Technique

Open access
Murugamani, C., Sahoo, Santosh Kumar, Kshirsagar, Pravin R., Prathap, Boppuru Rudra, Islam, Saiful, Noorulhasan Naveed, Quadri, Hussain, Mohammad Rashid, Hung, Bui Thanh +1 more·2022·Wireless Communications and Mobile Computing
DOI 10.1155/2022/4723138
Open this paper in CoChat — free accountOpen PDF

Abstract

Machine intelligence is what has been generated by programming computers with certain aspects of human intellect, like training, solving problems, and priority setting. A machine can solve a number of complicated issues using these capabilities. In major industries, such as customer support and manufacturing, machine intelligence is now being employed. The growth and quick development of digital technology and artificial intelligence (AI) technologies are becoming more and more difficult. At now, sophisticated manufacturing, the world of invention, and broad acceptance are undergoing a fast transition. Robotics is much more vital as it may now be related to the human brain by the connection between machine and brain, as AI develops. The world’s economy faces substantial difficulties by increasing productivity in the manufacturing industry. This study examines the present progress of robotic communication styles of artificial intelligence (AI). In many specific applications, communication between members of a robotic group or even people becomes vital. The paper solves the problem of implementation of an independent industry mobile robot in all fields in the major business, live interactive, planning, mobile robot technologies, and intending. In order to identify the best solution to this issue, a mixed integer robotic model has been developed.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1155/2022/4723138

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI, ML, and DL technologies are enabling robots during assembly process to work smarter, faster, and more efficiently than ever before, and are helping manufacturers to improve quality, reduce costs, and increase productivity.

    “Assembly robots: AI, ML, and DL technologies are enabling robots during assembly process to work smarter, faster, and more efficiently than ever before, and are helping manufacturers to improve quality, reduce costs, and increase productivity [67] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [67]

    The Introduction says AI can improve robots’ image recognition and precision in assembly. Section 4 links human–robot work to higher production standards and cheaper prices, while §7.1 reports improved productivity and operating efficiency. The copy does not establish that ML and DL specifically produce all these benefits during assembly. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 24

  1. V. Sharma and R. Kumar, "Cooperative frameworks and net- work models for flying ad hoc networks: a survey," In Concur- rencyand computation: practice and experience, vol. 29, no. 4, 2017.
  2. K. C. Morris, C. Schlenoff, and V. Srinivasan, "Guest editorial a remarkable resurgence of artificial intelligence and its impact on automation and autonomy," IEEE Transactions on Auto- mation Science and Engineering, vol. 14, no. 2, pp. 407-409, 2017.
  3. M. Wazid, A. K. Das, and J. H. Lee, "Authentication protocols for the internet of drones: taxonomy, analysis and future direc- tions," Journal of Ambient Intelligence and Humanized Com- puting, 2018.
  4. P. R. Kshirsagar, A. N. Rakhonde, and P. Chippalkatti, "MRI image based brain tumor detection using machine learning," Test Engineering and Management, vol. 81, pp. 3672-3680, 2020.
  5. P. Kshirsagar, "Operational collection strategy for monitoring SMART waste management system using shortest PATH algo- rithm," Journal of Environmental Protection and Ecology, vol. 22, no. 2, pp. 566-577, 2021.
  6. V. Scilimati, A. Petitti, P. Boccadoro et al., "Industrial internet of things at work: a case study analysis in robotic-aided envi- ronmental monitoring," In IET wireless sensor systems, vol. 7, no. 5, pp. 155-162, 2017.
  7. M. A. Messous, H. Sedjelmaci, and S. M. Senouci, "Imple- menting an emerging mobility model for a fleet of uavs based on a fuzzy logic inference system," Pervasive and Mobile Com- puting, vol. 42, pp. 393-410, 2017.
  8. P. T. Zacharia, S. A. Tsirkas, G. Kabouridis, A. C. Yiannopou- los, and G. I. Giannopoulos, "Genetic-based optimization of the manufacturing process of a robotic arm under fuzziness," Mathematical Problems in Engineering, vol. 2018, 12 pages, 2018.
  9. M. E. Boudella, E. Sahin, and Y. Dallery, "A mathematical model to assess the performance of a robotic kitting system in an assembly plant," in In: 11th international conference on modeling, optimization and simulation-MOSIM'16, 2016.
  10. E. Fernandez Macias and J. Hurley, "Routine-biased technical change and job polarization in Europe," Socio-Economic Review, vol. 15, no. 3, pp. 563-585, 2016.
  11. P. R. Kshirsagar, P. P. Chippalkatti, and S. M. Karve, "Perfor- mance Optimization of Neural Network using GA Incorpo- rated PSO," Journal of Adv Research in Dynamical & Control Systems, vol. 10, pp. 156-169, 2018.
  12. B. Zhang, C. H. Liu, J. Tang, Z. Xu, J. Ma, and W. Wang, "Learning-based energy-efficient data collection by unmanned vehicles in smart cities," IEEE Transactions on Industrial Infor- matics, vol. 14, no. 4, pp. 1666-1676, 2018.
  13. S. Akojwar and P. Kshirsagar, "A novel probabilistic-PSO based learning algorithm for optimization of neural networks for benchmark problems," WSEAS Transactions on Electron- ics, vol. 7, pp. 79-84, 2016.
  14. L. Zhang, X. Diao, and O. Ma, "A preliminary study on a robot's prediction of human intention," in In: 2017 IEEE 7th annualinternational conference on CYBER technology in auto- mation,control, and intelligent systems (CYBER), pp. 1446- 1450, Honolulu, HI, USA, 2017.
  15. P. Valente Klaine, M. A. Imran, O. Onireti, and R. D. Souza, "A survey of machine learning techniques applied to Self- Organizing cellular networks," IEEE Communications Surveys andTutorials., vol. 19, no. 4, pp. 2392-2431, 2017.
  16. O. Vermesan, A. Bröring, E. Tragos, M. Serrano, D. Bacciu, and S. Chessa, "Internet of robotic things: converging sens- ing/actuating, hypoconnectivity, artificial intelligence and IoT platforms," in Cognitivehyperconnected digital transfor- mation: Internet of things intelligenceevolution, O. Vermesan and J. Bacquet, Eds., pp. 1-35, River Publishers, 2017.
  17. S. Oza, "IoT: The Future for Quality of Services," in Lecture notes in electrical engineering, A. Kumar and S. Mozar, Eds., vol. 570, Springer, Singapore, 2020.
  18. P. Kshirsgar, V. More, V. Hendre, P. Chippalkatti, and K. Paliwal, "IOT Based Baby Incubator for Clinic," in Lecture Notes in Electrical Engineering, A. Kumar and S. Mozar, Eds., vol. 570, Springer, Singapore, 2019.
  19. J. Lee, B. Bagheri, and H.-A. Kao, "A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems," Manufacturing Letters, vol. 3, pp. 18-23, 2015.
  20. S. Sundaramurthy, C. Saravanabhavan, and P. Kshirsagar, "Prediction and classification of rheumatoid arthritis using ensemble machine learning approaches," in 2020 International Conference on Decision Aid Sciences and Application (DASA), pp. 17-21, Sakheer, Bahrain, 2020.
  21. X. W. Wang, Industry 4.0: Road to the Future Industry of Ger- man Manufacturing Industry 2025 Diagram, China Machine Press, Beijing, 2015.
  22. M. R. Pedersen, L. Nalpantidis, R. S. Andersen et al., "Robot skills for manufacturing: from concept to industrial deploy- ment," Robotics and Computer-Integrated Manufacturing, vol. 37, no. 37, pp. 282-291, 2016.
  23. A. Keisner, J. Raffo, and S. Wunsch-Vincent, "Robotics: break- through technologies, innovation, intellectual property," Fore- sight and STI Governance, vol. 10, no. 20, pp. 7-27, 2016.
  24. M. Faccio, M. Bottin, and G. Rosati, "Collaborative and tradi- tional robotic assembly: a comparison model," The Interna- tional Journal of Advanced Manufacturing Technology, vol. 102, no. 5-8, pp. 1355-1372, 2019.

Cited by

Sorted by citations

Authors
Murugamani, C., Sahoo, S. K., Kshirsagar, P. R., Prathap, B. R., Islam, S., Noorulhasan Naveed, Q., Hussain, M. R., Hung, B. T., & Teressa, D. M.
Affiliations
  • Raisoni Group of Institutions (IN)
  • Christ University (IN)
  • King Khalid University (SA)
  • Addis Ababa Science and Technology University (ET)
Corresponding author
Kshirsagar, Pravin R.
ORCID
Venue
Wireless Communications and Mobile Computing
Publisher
Wiley
Published
2022-01
Volume
2022
Issue
1
ISSN
  • 1530-8669 (ISSN-L)
  • 1530-8677
DOI
10.1155/2022/4723138
OpenAlex
W4223908961
Cited by
27
References
0
Citations by year
2025: 17 · 2024: 8 · 2023: 1 · 2022: 1
FWCI
2.72
Type
journal article
Language
en
Open access
Licence
Locations
  • Wireless Communications and Mobile Computing (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
King Khalid University (RGP. 1/184/42)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Robotic Process Automation Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
robotic process automation, wireless communication, mobile robots, industrial robots, human-robot communication, mixed integer programming, robot planning, machine learning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review