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Meenatchi, V. T.
- Mining the Contact Lens Adhering Bacteria through Machine Learning and Clinical Analysis
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Authors
Affiliations
1 Department of Computer Science, Madurai Kamaraj University, Madurai - 625021, Tamil Nadu, IN
2 Department of CA and IT, Thiagarajar College, Madurai - 625009, Tamil Nadu, IN
3 Department of Zoology and Microbiology, Thiagarajar College, Madurai - 625009, Tamil Nadu, IN
1 Department of Computer Science, Madurai Kamaraj University, Madurai - 625021, Tamil Nadu, IN
2 Department of CA and IT, Thiagarajar College, Madurai - 625009, Tamil Nadu, IN
3 Department of Zoology and Microbiology, Thiagarajar College, Madurai - 625009, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 28 (2016), Pagination:Abstract
Objectives: Even when studies report most of the Contact Lens (CLs) wearers possess improved vision, there are some potential risks with the development of microbial keratitis. This is in turn creates research issue under public health concern. Methods/Analysis: The methodology of the work determines the culture sensitivity of the recovered isolates from three different CLs users: Daily disposable lens, monthly disposable lens and yearly disposable lens. Findings: Through the machine learning tool called Waikato Environment for Knowledge Analysis (WEKA) and extensive clinical laboratory analysis, the study provides information on prevalent Contact Lens adhering bacteria involved in causing keratitis and examine microbial biofilm formation using Scanning Electron Microscopic (SEM) analysis. The sample type of the lens with the bacterial infections were then statistically analyzed, so that the knowledge mined would aid the medical practitioners in the treatment of bacterial keratitis. Novelty/Improvement: The present study supports the treatment of bacterial keratitis associated with Contact Lens users to reduce or to prevent the adverse effects caused by bacterial pathogens.Keywords
Bacteria, Clinical, Contact Lens, Keratitis, Knowledge.Full Text
- Classification Algorithms with Attribute Selection:An Evaluation Study using WEKA
Abstract Views :207 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Raja Dorai Singam Govt Arts College, Sivagangai, IN
2 Department of Computer Science, Madurai kamaraj University, Madurai, IN
3 Department of CA & IT, Thiagarajar College, Madurai, IN
4 Department of CA, NIT, Tiruchi, IN
1 Department of Computer Science, Raja Dorai Singam Govt Arts College, Sivagangai, IN
2 Department of Computer Science, Madurai kamaraj University, Madurai, IN
3 Department of CA & IT, Thiagarajar College, Madurai, IN
4 Department of CA, NIT, Tiruchi, IN
Source
International Journal of Advanced Networking and Applications, Vol 9, No 6 (2018), Pagination: 3640-3644Abstract
Attribute or feature selection plays an important role in the process of data mining. In general the dataset contains more number of attributes. But in the process of effective classification not all attributes are relevant. Attribute selection is a technique used to extract the ranking of attributes. Therefore, this paper presents a comparative evaluation study of classification algorithms before and after attribute selection using Waikato Environment for Knowledge Analysis (WEKA). The evaluation study concludes that the performance metrics of the classification algorithm, improves after performing attribute selection. This will reduce the work of processing irrelevant attributes.Keywords
Attribute Filters, Attribute Selection, Classification, Data Mining, Weka.Full Text
References
- Meenatchi V.T, Gnanambal S, et.al, Comparative Study and Analysis of Classification Algorithms through Machine Learning, International Journal of Computer Engineering and Applications, 9(1),247-252,2018.
- Hany M. Harb1, Malaka A. Moustafa, Selecting optimal subset of features for student performance model, IJCSI , 9(5), 2012, 1694-0814
- Hwang, Young-Sup,Wrapper-based Feature Selection Using Support Vector Machine, Department of Computer Science and Engineering, Sun Moon University, Asan, Sunmoonro, Korea, Life Science Journal,11 (7), 221-70,2014.
- Wang Liping, Feature Selection Algorithm Based On Conditional Dynamic Mutual Information, International Journal Of Smart Sensing and Intelligent Systems,8(1), 2015.
- Qinbao Song, Jingjie Ni and Guangtao Wang, A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data, IEEE Transactions on Knowledge and Data Engineering, 25(1), 2013.
- Z.Zhao, H.Liu, On Similarity Preserving Feature Selection, IEEE Transactions on Knowledge and Data Engineering, 25(3), 2013.
- Sunita Beniwal and Jitender Arora, Classification and Feature Selection Techniques in Data Mining, International Journal of Engineering Research & Technology (IJERT), 1(6), 2012.
- Mital Doshi and Setu K Chaturvedi, Correlation Based Feature Selection (Cfs) Technique To Predict Student Perfromance, International Journal of Computer Networks & Communications (IJCNC),6(3),2014.
- M. Ramaswami and R. Bhaskaran,A Study on Feature Selection Techniques in Educational Data Mining, Journal Of Computing,1(1),December 2009.
- K.Sutha and J. Jebamalar Tamilselv, A Review of Feature Selection Algorithms for Data Mining Techniques, International Journal on Computer Science and Engineering (IJCSE), 7(6), ,2015.
- Gnanambal S and Thangaraj M, A new architectural framework for Rule Based Healthcare System using Semantic Web Technologies, International Journal of Computers in Healthcare, Inderscience, 2(1), 2014, 1-14. ISSN: 1755-3202.