Natural Language processing used in Surgery Implementing with Robot

Authors

  • Nandagopal Reddy, M. Swapna

Keywords:

Natural language Processing, Dynamic Speech Recognition, Hypothesis

Abstract

This article liberalize this machine learning features as it is utilizes within the emerging edge and as feature highlighter to speech recognition approaches on present-day surgical robots. The desire is to advance the event of medical robots among the machine learning and speech recognition liberal that has opened up from the purpose of view of health care services in social protection. The machine learning hypotheses and models are used for pattern recognition structures combined with speech synthesis model with advanced robotic options in medical field. Machine learning is displayed within the comprehension of speech recognition components and its influence in biomedical robots for surgeries. Topical advances of machine learning and intelligent algorithms, further accentuations on their vast hugeness within the improvement of speech recognition in medical surgical applications

Downloads

Download data is not yet available.

References

Davis S and Mermelstein P, “Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences”, IEEE Trans. on ASSP, vol. 28, pp. 357-366.

Hiroaki Sakoe and Seibi Chiba, “Dynamic ProgrammingAlgorithms Optimization for Spoken Word Recoginition”, IEEE Trans on acoustics, speech and signal processing, vol.ASSP-26, no.1, december 1978.

L.R.Rabiner and B.H.Juang, “Fundamentals of Speech Recognition”, Prentice Hall, New Jersey, 1993.

Rabiner L R, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recog- nition” Proc. IEEE, vol. 77, 1989, pp. 257-286.

M.K.Ravishankar, ”Efficient algorithms for Speech Recognition”, PhD thesis: CMU-CS-96-143.

Samudravijaya K, “Hindi Speech Recognition”, J. Acoust. Soc. India, 29(1), pp. 385-393, 2001.

H.Aust, M.Oerder, F.Seide and V.Steinbiss “The Philips automatic train timetable informationsystem”, Speech Commun. 17 (1995) 249-262.

E.Noth and A.Homdasch, “Experiences with Commercial Telephone-based Dialogue Systems”, ITInformation Technology, 46 (2004) 6, pp.315-321.

http://www.irctc.co.in/voice.html

http://www.au-kbc.org/dfki/

Samudravijaya K, Rao P V S and Agrawal S S, “Hindi speech database”, in Proc. Int. Conf. Spoken Language Processing ICSLP00, Beijing, 2000; CDROM: 00192.pdf.

http://speech.tifr.res.in/slp in india.html

http://ltrc.iiit.net/icon2005; http://www.ncst.ernet.in/kbcs2004

http://tdil.mit.gov.in

B. Challacombe and D. Stoianovici, “The basic science of robotic surgery,” Urologic Robotic Surgery in Clinical Practice (2009): 1–23.

https://link.springer.com/chapter/10.1007/978-1-84800-243-2_2

P. S. Jagadeesh Kumar, Mingmin Pan, Xianpei Li and Yanmin Yuan, “Modern Machine Learning Conniptions for Automatic Speech Recognition”, International Journal of Information and Communication Technology, 44.10 (2017):150-160.

https://publications.waset.org/abstracts/71467/modern-machine-learning- c onniptions-for-automatic-speech-recognition

H. Kenngott, L. Fischer, F. Nickel, J. Rom, J. Rassweiler, and B. Muller-Stich, “Status of robotic assistance: a less traumatic and more accurate minimally invasive surgery?” Langenbeck's Archives of Surgery, 397. 3 (2012): 1–9.

https://link.springer.com/article/10.1007/s00423-011-0859-

P. Gomes, “Surgical robotics: reviewing the past, analysing the present, imagining the future,”Robotics and Computer-Integrated Manufacturing, 27.2(2011):261–266.

https://www.scopus.com/record/display.uri?eid=2-s2.0-

78649330405&origin=inward&txGid=f22033877a0ad0701db86425ee4412d9

P. S. Jagadeesh Kumar, “Deep Learning Based Cognitive Self-Supervised Robots in Medical Engineering”, 6th World Convention on Robots, Autonomous Vehicles and Deep Learning, Smart Robotics Congress 2018, September 10-11, (2018).

Downloads

Published

19.04.2025

How to Cite

Nandagopal Redd. (2025). Natural Language processing used in Surgery Implementing with Robot. International Journal of Intelligent Systems and Applications in Engineering, 13(1), 09–13. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7442

Issue

Vol. 13 No. 1 (2025)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.