Informatics Publishing Limited

D. Rajasekar

Abstract

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D. Rajasekar

Abstract

No abstract

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D. Rajasekar

Abstract

No abstract

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D. Rajasekar ¹, S. K. Sandhya ¹, S. Nagarajan ¹, C. Dhanamani ¹

Affiliations
1 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641042, Tamil Nadu, IN

Abstract

Objective: The main objective of this research is to generate and provide an optimized itinerary planning for the users with flight and hotel booking as per the user requirement to reach their satisfaction level. Methods: In this research, a novel fuzzy c means clustering is introduced for grouping and generating an optimal itinerary planning for the group of users. This work intends to satisfy the needs of all the users who are requesting for the itinerary plans with the satisfaction of their QoS requirement. To achieve this goal, the point of interest (POI) graphs will be constructed with the help of mapRedcue application which will simplify the processing of large amount of data’s. FCM algorithm is proved to be better clustering algorithm which considers the membership values among the various data points. So that the same data points can be clustered in the different cluster to which its membership value belongs. In our work we make sue use of this algorithm to cluster the users with the similar requirements. So that the same user request may clustered in different groups. Among that the optimized cluster of users with unique requirements can be selected for the itinerary planning construction. Results: FCM clustering used in the proposed approach for effective construction of the itinerary planning with the consideration of the users with similar requirement whereas in the existing work user similarity requirements are not considered. The experimental tests conducted were proves that the proposed methodology provides the optimized itinerary with the less time complexity and burden than the existing methodology. This performance analysis is done with the two performance metrics namely processing time and the weight ratio from which it can be proved that the proposed methodology provides better result. Conclusion: The findings demonstrate that the itinerary planning construction using FCM is presented and this method has high clustering accuracy than the previous methodologies.

Keywords

Fuzzy C-means Clustering, Itinerary Plan, MapReduce, Point of Interest

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Meena Chandran ¹, Shiny George ¹, Kumaran Santhalingam ², Pallavi Gangwar ², D. Rajasekar ²

Affiliations
1 Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Vels University, Chennai, Tamil Nadu-600117, IN
2 Gensilico Biosolutions, Zamin Pallavarm, Chennai, Tamil Nadu-600043, IN

Abstract

Rheumatoid arthritis is characterised by persistent synovitis, systemic inflammation, and autoantibodies. It is best considered a clinical syndrome spanning several disease subsets. Molecular Docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex.The present study is mainly concentrating the theoretical synthesis of 3-methylquercetin (Isorhamnetin) is an O-methylated flavonol, contain choromone as the basic moiety and found in Tagetes lucida plant used as inhibitors of IL-6 protein which is involved in rheumatoid arthritis, docking was carried out in Argus Lab software. The IL-6 protein structure (PDB ID: 1ALU) was found from protein data bank. The parent compound 3-methylquercetin has shown best ligand pose -7.2703kcal/mol. All the derivatives have shown best ligand pose energy between -6.9293kcal/mol to -10.2862 kcal/mol. Out of the eight 3-methylquercetins derivaties, 1h posses beast ligand pose (-10.2862 kcal/mol).Thus from the theoretical studies imparts that 3, 5, 7-trihydroxy-2-(4-hydroxy-3-methoxyphenyl)-4h-chromen-4-one (3-methylquercetin) derivatives are active towards IL-6 protein and thus used for rheumatoid arthritis.

Keywords

Rheumatoid Arthritis, IL-6 Protein, 3-Methylquercetin, Argus Lab.

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