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Search Results: 1 - 10 of 223660 matches for " Kamalraj R. Pardasani "
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A model for the evaluation of domain based classification of GPCR
Tannu Kumari,Bhaskar Pant,Kamalraj R. Pardasani
Bioinformation , 2009,
Abstract: G-Protein Coupled Receptors (GPCR) are the largest family of membrane bound receptor and plays a vital role in various biological processes with their amenability to drug intervention. They are the spotlight for the pharmaceutical industry. Experimental methods are both time consuming and expensive so there is need to develop a computational approach for classification to expedite the drug discovery process. In the present study domain based classification model has been developed by employing and evaluating various machine learning approaches like Bagging, J48, Bayes net, and Naive Bayes. Various softwares are available for predicting domains. The result and accuracy of output for the same input varies for these software’s. Thus, there is dilemma in choosing any one of it. To address this problem, a simulation model has been developed using well known five softwares for domain prediction to explore the best predicted result with maximum accuracy. The classifier is developed for classification up to 3 levels for class A. An accuracy of 98.59% by Na ve Bayes for level I, 92.07% by J48 for level II and 82.14% by Bagging for level III has been achieved.
A MATRIX MODEL FOR MINING FREQUENT PATTERNS IN LARGE DATABASES
DIVYA BHATNAGAR,KAMALRAJ PARDASANI
International Journal of Engineering Science and Technology , 2012,
Abstract: This paper proposes a model for mining frequent patterns in large databases by implementing a matrix approach. The whole database is scanned only once and the data is compressed in the form of a matrix. The frequent patterns are then mined from this compressed database which brings efficiency in data mining, as the number of database scans is effectively less than two. The computation time is reduced as some of the patterns are mined simultaneously and searching is minimized. Appropriate mathematical operations are designed and performed on matrices to achieve this efficiency.
ANTISPASMODIC STUDIES ON LEAF EXTRACT OF ERYTHRINA INDICA LAM
R. Kamalraj,G. Devdass
International Journal of Research in Ayurveda and Pharmacy , 2011,
Abstract: The aim of the present investigation to evaluate the antispasmodic activity of Erythrina indica leaf, an indigenous plant used in ayurvedic medicine in India. The antispasmodic effect of hexane extracts of Erythrina indica Leaf were studied in vitro in guinea pig ileum against three spasmogens; acetylcholine, histamine and barium chloride. The hexane extract produces a significant antispasmodic effect on the contractions of the guinea pig ileum induced by acetylcholine, histamine and barium chloride. The inhibitory concentration for each was determined. These results show that hexane extract of Erythrina indica Leaf possesses antispasmodic properties.
Efficient Method for Multiple-Level Association Rules in Large Databases
Pratima Gautam,K. R. Pardasani
Journal of Emerging Trends in Computing and Information Sciences , 2011,
Abstract: The problems of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging when some form of uncertainty in data or relationships in data exists. In this paper, we present a partition technique for the multilevel association rule mining problem. Taking out association rules at multiple levels helps in discovering more specific and applicable knowledge. Even in computing, for the number of occurrence of an item, we require to scan the given database a lot of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O cost and also reduce CPU overhead. In this paper, a new approach is introduced for solving the abovementioned issues. Therefore, this algorithm above all fit for very large size databases. We also use a top-down progressive deepening method, developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle
A Fast Algorithm for Mining Multilevel Association Rule Based on Boolean Matrix
Pratima Gautam,K. R. Pardasani
International Journal on Computer Science and Engineering , 2010,
Abstract: In this paper an algorithm is proposed formining multilevel association rules. A Boolean Matrix based approach has been employed to discover frequent itemsets, the item forming a rule come from different levels. It adopts Boolean relational calculus to discover maximum frequent itemsets at lower level. When using this algorithmfirst time, it scans the database once and will generate the association rules. Apriori property is used in prune the item sets. It is not necessary to scan the database again; it uses Boolean logical operation to generate the multilevel association rules and also use top-down progressive deepening method.
Rough Set Model for Discovering Hybrid Association Rules
Anjana Pandey,K. R. Pardasani
Computer Science , 2009,
Abstract: In this paper, the mining of hybrid association rules with rough set approach is investigated as the algorithm RSHAR.The RSHAR algorithm is constituted of two steps mainly. At first, to join the participant tables into a general table to generate the rules which is expressing the relationship between two or more domains that belong to several different tables in a database. Then we apply the mapping code on selected dimension, which can be added directly into the information system as one certain attribute. To find the association rules, frequent itemsets are generated in second step where candidate itemsets are generated through equivalence classes and also transforming the mapping code in to real dimensions. The searching method for candidate itemset is similar to apriori algorithm. The analysis of the performance of algorithm has been carried out.
A Novel Approach For Discovery Multi Level Fuzzy Association Rule Mining
Pratima Gautam,K. R. Pardasani
Computer Science , 2010,
Abstract: Finding multilevel association rules in transaction databases is most commonly seen in is widely used in data mining. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed fuzzy set concepts, multi-level taxonomy and different minimum supports to find fuzzy multilevel association rules in a given transaction data set. Apriori property is used in model to prune the item sets. The proposed model adopts a topdown progressively deepening approach to derive large itemsets. This approach incorporates fuzzy boundaries instead of sharp boundary intervals. An example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.
Recent advances in 1,4-benzoquinone chemistry
Abraham, Ignatious;Joshi, Rahul;Pardasani, Pushpa;Pardasani, R.T;
Journal of the Brazilian Chemical Society , 2011, DOI: 10.1590/S0103-50532011000300002
Abstract: 1,4-benzoquinones are ubiquitous in nature and can be synthesized by diverse strategies. recent developments on their synthetic methodologies, cycloaddition reactions, computational chemistry and pulse radiolytic studies are reported in this review. their chemical and biological significance as well as their derivates' are also covered.
Finite Element Model to Study Two Dimensional Unsteady State Cytosolic Calcium Diffusion in Presence of Excess Buffers
Shivendra G. Tewari,K. R. Pardasani
IAENG International Journal of Applied Mathematics , 2010,
Abstract:
A Model to Study Effect of Rapid Buffers and Na+ on Ca2+ Oscillations in Neuron Cell
Vikas Tewari,Shivendra Tewari,K. R. Pardasani
Journal of Mathematics Research , 2010, DOI: 10.5539/jmr.v2n1p74
Abstract: $Ca^{2+}$ plays a vital role in muscle mechanics, cardiac electrophysiology, secretion, hair cells, and adaptation in photoreceptors. It is a vital second messenger used in signal transduction. Calcium controls cell movement, cell differentiation, ciliary beating. Many cells exhibit oscillations in intracellular [Ca$^{2+}$] in response to agonist such as hormones and neurotransmitters. Many cells use oscillations in calcium concentration to transmit messages (Sneyd J. et al, 2006, p. 151-163). In this paper, an attempt has been made to develop a model to study calcium oscillations in neuron cells. This model incorporates the effect of variable Na$^{+}$ influx, sodium-calcium exchange (NCX) protein, Sarcolemmal Calcium ATPase (SL) pump, Sarco-Endoplasmic Reticulum CaATPase (SERCA) pump, sodium and calcium channels, and $IP_{3}R$ channel. The proposed mathematical model leads to a system of partial differential equations which has been solved numerically using Forward Time Centered Space (FTCS) approach. The numerical results have been used to study the relationships among different types of parameters such as buffer concentration, disassociation rate, calcium permeability, etc.
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