S
Sakti Pramanik
Researcher at Michigan State University
Publications - 99
Citations - 3760
Sakti Pramanik is an academic researcher from Michigan State University. The author has contributed to research in topics: Tree (data structure) & Search engine indexing. The author has an hindex of 21, co-authored 99 publications receiving 3620 citations.
Papers
More filters
Journal ArticleDOI
A new version of the RDP (Ribosomal Database Project)
Bonnie L. Maidak,James R. Cole,Charles Thomas Parker,George M. Garrity,Niels Larsen,Bing Li,Timothy Lilburn,Michael J. McCaughey,Gary J. Olsen,Ross Overbeek,Sakti Pramanik,Thomas M. Schmidt,James M. Tiedje,Carl R. Woese +13 more
TL;DR: The Ribosomal Database Project (RDP-II), previously described by Maidak et al. (1997), is now hosted by the Center for Microbial Ecology at Michigan State University and will provide more rapid updating of data, better data accuracy and increased user access.
Journal ArticleDOI
The RDP (Ribosomal Database Project) continues.
Bonnie L. Maidak,James R. Cole,Timothy Lilburn,Charles Thomas Parker,Paul Saxman,Jason M. Stredwick,George M. Garrity,Bing Li,Gary J. Olsen,Sakti Pramanik,Thomas M. Schmidt,James M. Tiedje +11 more
TL;DR: The Ribosomal Database Project (RDP-II), previously described by Maidak et al., continued during the past year to add new rRNA sequences to the aligned data and to improve the analysis commands.
Proceedings ArticleDOI
Segmentation and histogram generation using the HSV color space for image retrieval
TL;DR: The feature extraction method has been applied for both image segmentation as well as histogram generation applications - two distinct approaches to content based image retrieval (CBIR), showing better identification of objects in an image.
Proceedings ArticleDOI
Similarity between Euclidean and cosine angle distance for nearest neighbor queries
TL;DR: This paper compares two commonly used distance measures in vector models, namely, Euclidean distance (EUD) and cosine angle distance (CAD), for nearest neighbor (NN) queries in high dimensional data spaces and shows that CAD works no worse than EUD.
Journal ArticleDOI
An efficient path computation model for hierarchically structured topographical road maps
Sungwon Jung,Sakti Pramanik +1 more
TL;DR: The authors' performance analysis of SPAH on grid graphs showed that it significantly reduces the search space over existing methods and Experimental results show that inter query shortest path problem provides more opportunity for scalable parallelism than the intra query shortest paths problem.