Informatics Publishing Limited

G. P. Karthicka ¹, S. Dharani ¹

Affiliations
1 CSE Department, Dr.MGR University, Maduravoyal, Chennai-9, IN

Abstract

In Healthcare domain, there is a wide gap prevalent between health seekers and healthcare experts. This vocabulary gap is due to the presence of ambiguity in the natural language in which the users post their queries in online medical portals and therefore the emerging community generated health data is more colloquial, in terms of inconsistency, complexity and ambiguity. This poses challenges for data access and analytics. To bridge the vocabulary gap present, a new scheme has been introduced in this paper which combines two approaches namely local mining and global learning based on machine learning. Local mining extracts the individual medical concepts from medical records and map them to their appropriate medical terminologies. Global learning enhances the local mining database by jointly finding the missing key terms with the help of medical related resources.

Keywords

Healthcare, Machine Learning, Local Mining, Global Learning, Natural Language.

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S. Dharani ¹, B. Justus Rabi ², S. S. Darly ³, A. N. Nanda Kumar ⁴

Affiliations
1 Department of CSE, Dr.M.G,R. University, Chennai – 600095, Tamil Nadu, IN
2 Shri AndalAlagar College of Engineering, Mamandur- 603111, Tamil Nadu, IN
3 Department of EEE, Anna University, Chennai - 600025, Tamil Nadu, IN
4 R.L.Jalappa Institute of Technology, Bangalore – 561203, Karnataka, IN

Abstract

Objectives: In this paper, a simple and flexible partition algorithm has been proposed to mine frequent data item sets. This partition algorithm is different from other frequent pattern mining algorithm like Apriori algorithm, AprioriAllHybrid algorithm etc. Method: Partition algorithm concept has been proposed to increase the execution speed with minimum cost. Initially only for one time the database is scanned and separate partitions will be created for each sets of itemsets, which is 1-itemset, 2-itemsets, 3-itemsets etc. Findings: The scanning of whole database is not necessary to get the count of an itemset, it is enough to get the count of each data itemsets from its partition. This partition algorithm approach is implemented and evaluated against AprioriAllHybrid and Apriori algorithm. The candidate itemsets generated at each step is reduced and the scanning time is also reduced. The proposed methodology performance is significantly better than other algorithms and it promotes the faster execution time for mining frequent patterns. Applications: This proposed algorithm is used in areas like retail sales, production, universities, finance, banking systems and for business to plan and estimate the future values.

Keywords

AprioriAllHybrid, Apriori Algorithm, Data Mining, Frequent Pattern Mining, Partition.

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S. Ram Prasad ¹, K. Elango ¹, S. Daisy Chellakumari ¹, S. Dharani ¹

Affiliations
1 Department of Pharmaceutics, College of Pharmacy, Madras Medical College, Chennai-03, IN

Abstract

The present study deals with preparation of silver nanoparticles using green approach such as polysaccharides method. (Chitosan was used as reducing and stabilizing agent). The prepared silver nanoparticles were characterized by UV Visible Spectroscopy, Fourier Transform Infra- red spectroscopy, Photon Correlation Spectroscopy, Scanning Electron Microscope and ICPOES. UV- Visible spectrum shows the SPR band at 420nm which confirms the formation of silver nanoparticles. The average particle size of chitosan stabilized silver nanoparticles was found to be 127.3nm. The amount of silver present in the solution was found to be 1.259mg/L using ICPOES. Chitosan stabilized silver nanoparticles were studied for anti-inflammatory activity. In vitro toxicity studies were carried out in 3T3/NIH fibroblast cell line. Using the natural environmental benign polymer chitosan, the silver nanoparticles were prepared and characterized.

Keywords

Chitosan, Silver Nanoparticles, SPR Band, SEM, ICPOES, Membrane Stabilizing Activity.

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