Dr. Biren N. Shah
@snlpcp.ac.in
Professor
Shree Naranjibhai Lalbhai Patel College of Pharmacy, Umrakh
RESEARCH, TEACHING, or OTHER INTERESTS
Pharmaceutical Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
B.N. Shah, A.K. Seth, and K.M. Maheshwari
Science Alert
B.N. Shah, A.K. Seth, and R.V. Desai
Science Alert
SS Jalalpure, KR Alagawadi, CS Mahajanashetti, BN Shah, Salahuddin, Vijay Singh, and JK Patil
OMICS Publishing Group
Seed oils of Gynandropsis gynandra, Impatiens balsamina, Celastrus paniculata, Embelia ribes and Mucuna pruriens were investigated for their anthelmintic property against Pheritima posthuma . Three concentrations (10, 50 and 100 mg/ml) of each oil were studied in a bioassay, which involved the determination of time of paralysis and time of death of the worm. All the oils exhibited moderate to significant anthelmintic activity. Embelia ribes showed the best anthelmintic activity in both the parameters. Piperazine citrate (10 mg/ml) was included in the assay as standard reference drug.
Biren Shah, Ryan Benton, Zonghuan Wu, and Vijay Raghavan
IGI Global
When retrieving images, users may find it easier to express the desired semantic content with keywords than visual features. Accurate keyword retrieval can only occur when images are completely and accurately described. This can be achieved either through laborious manual effort or automated approaches. Current methods for automatically extracting semantic information from images can be classified into (a) text-based methods, which use metadata such as ontological descriptions and/or text associated with images, to assign and/or refine annotations, and (b) image-based methods, which focus on extracting semantic information directly from image content. The focus of this chapter is to create an awareness and understanding of research and advances in this field, by introducing them to basic concepts and theories and then by classifying, summarizing, and describing works from the published literature. It will also identify unsolved problems and offer suggestions for future research directions.
Biren Shah, Vijay Raghavan, Praveen Dhatric, and Xiaoquan Zhao
Wiley
The techniques of clustering and space transformation have been successfully used in the past to solve a number of pattern recognition problems. In this article, the authors propose a new approach to content-based image retrieval (CBIR) that uses (a) a newly proposed similarity-preserving space transformation method to transform the original low-level image space into a high-level vector space that enables efficient query processing, and (b) a clustering scheme that further improves the efficiency of our retrieval system. This combination is unique and the resulting system provides synergistic advantages of using both clustering and space transformation. The proposed space transformation method is shown to preserve the order of the distances in the transformed feature space. This strategy makes this approach to retrieval generic as it can be applied to object types, other than images, and feature spaces more general than metric spaces. The CBIR approach uses the inexpensive “estimated” distance in the transformed space, as opposed to the computationally inefficient “real” distance in the original space, to retrieve the desired results for a given query image. The authors also provide a theoretical analysis of the complexity of their CBIR approach when used for color-based retrieval, which shows that it is computationally more efficient than other comparable approaches. An extensive set of experiments to test the efficiency and effectiveness of the proposed approach has been performed. The results show that the approach offers superior response time (improvement of 1–2 orders of magnitude compared to retrieval approaches that either use pruning techniques like indexing, clustering, etc., or space transformation, but not both) with sufficiently high retrieval accuracy. © 2006 Wiley Periodicals, Inc.
S. Jalalpure, K. R. Alagawadi, C. S. Mahajanshetty, M. Salahuddin and B. Shah
Seed oils of Passiflora edulis, Jatropa curucas, Tinospora cordifolia, Sesbania grandiflora and Sapindus laurifolia were investigated for their antihelmintic property against Pheritima pasthuma. Three concentrations (10, 50 and 100 mg/ml) of each oil were studied in the bioassay, which involved the determination of time of paralysis and time of death of the worm. Sesbania grandiflora showed the highly significant antihelmintic activity in both the parameters (paralysis and death), while Tinospora cordifolia showed significant effect in case of time of death. Piperazine citrate (10 mg/ml) was included in the assay as standard reference drug.
Biren Shah, Karthik Ramachandran, and Vijay Raghavan
IGI Global
Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Static selection of views may materialize certain views that are not beneficial as the data and usage trends change over time. On the contrary, dynamic selection of views works better only for queries demanding a high degree of aggregation. These facts point to the need for a technique that combines the improved response time of the static approach and the automated tuning capability of the dynamic approach. In this article, we propose a hybrid approach for the selection of materialized views. The idea is to partition the collection of all views into a static and dynamic set such that views selected for materialization from the static set are persistent over multiple query (and maintenance) windows, whereas views selected from the dynamic set can be queried and/or replaced on the fly. Highly aggregated views are selected on the fly based on the query access patterns of users, whereas the more detailed static set of views plays a significant role in the efficient maintenance of the dynamic set of views and in answering certain detailed view queries. We prove that our proposed strategy satisfies the monotonicity requirements, which is essential in order for the greedy heuristic to deliver competitive solutions. Experimental results show that our approach outperforms Dynamat, a well-known dynamic view management system that is known to outperform optimal static view selection.
B. Shah, V. Raghavan, and P. Dhatric
IEEE Comput. Soc
A promising approach to content-based image retrieval, proposed by Choubey and Raghavan (1997), involves the representation of the original image space, in terms of low-level image features into a feature space, where images are represented as vectors of high-level features. The retrieval system based on that approach consists of three phases: database population; online addition; and image retrieval. Though their framework supports content-based retrieval of images, the issues that arise when database grows dynamically and user queries are different from those in database, were not investigated. In the current work, we: (i) experimentally investigate issues relating to online addition of new images and image retrieval; and (ii) provide a theoretical analysis of the complexity and effectiveness of our retrieval system by comparing it with the conventional approach. We have performed an extensive set of experiments to test the efficiency, effectiveness and scalability of our approach. The experimental results show that our approach is not only efficient but also effective in retrieving images even when the image database is dynamic and user queries are framed with images that are external to the database.
Biren Shah and Vijay Raghavan
Springer Berlin Heidelberg
Abhilash Gummadi, Jong P. Yoon, Biren Shah, and Vijay Raghavan
ACM
The information on the web is growing at a very fast pace. In this ever-accumulating data, the volume of information represented in XML format is on the rise in recent times. An organization that puts forth its information on the web in XML format has several issues to take into account such as limiting the view of intended audience to only relevant portions of the documents. To address this problem, we propose the concept of "Restricted views" to implement security in XML documents. This could be accomplished by defining "Access control" for the users trying to access the system. In this paper, we describe a bitmap-based approach to represent access control in XML documents. We also introduce a new three-dimensional bitmap called security cube, which efficiently represents access control on XML documents at their finest level of granularity.