A Visual Perception of Robotic Skills Using Domain-Adapted Machine Learning Technique
S.Ajaykrishna
, Dr., N. Pughazendi
Stochastic Neighbor Embedding with t-distribution, Contrastive Principal Component Analysis (c PCA), Ensemble Models, and Surgical Skills Evaluation (t -SNE)
In this research, I offer a domain- adapted end-to-end learning approach for executing and assessing surgical skills by various contributors using automated systems. It detects smoothness and retrieves training knowledge within a specified trajectory. The trajectories approach is more accurate in similar stochastic or machine learning modeling techniques, making it effective for extracting and understanding data features related to abilities. A JIGSAWS data set should be utilized to assess the suggested strategy. The t-SNE methodology is used in this approach, which visual mediums a low-dimensional representations of a variety of trials while emphasising sensitive information and returning odd or flawed trials as outliers separate from more typical skill or participant groups. The information trajectory can be utilised for analysis and learning in surgical assessments and training programmes
"A Visual Perception of Robotic Skills Using Domain-Adapted Machine Learning Technique", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.384-391, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001060.pdf
Volume 11
Issue 2,
February-2024
Pages : 384-391
Paper Reg. ID: TIJER_151088
Published Paper Id: TIJERB001060
Downloads: 000273
Research Area: Science and Technology
Country: Thiruvallur, TamilNadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
