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Jayanthi, B.
- A New Approach to Discover Frequent Patterns Using FP-Graph Model
Abstract Views :189 |
PDF Views:1
Authors
B. Jayanthi
1,
K. Duraiswamy
2
Affiliations
1 P.G.Department of Computer Science, Kongu Arts and Science College, Erode, Tamilnadu, IN
2 K.S. Rangasamy College of Technology, Tiruchengode, Tamilnadu, IN
1 P.G.Department of Computer Science, Kongu Arts and Science College, Erode, Tamilnadu, IN
2 K.S. Rangasamy College of Technology, Tiruchengode, Tamilnadu, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 5 (2011), Pagination: 297-299Abstract
In this paper an algorithm is proposed for mining frequent itemsets. This paper proposes a new framework to generate frequent Itemsets/Patterns. First, a partitioning technique is used to divide a transaction database TDB into n non-overlapping partitions. Second we use fp-graph model to discover frequent itemsets for each partition. Example illustrating the proposed approach is given. The characteristics of the algorithm are discussed.Keywords
Data Mining, Frequent Patterns, Frequent Itemset, Partitioning Technique, FP-Graph, Association Rule.- Utilization of Mixed Leaves Litter for Converting into Vermicompost by Using an Epigeic Earthworm Eudrilus eugeniae
Abstract Views :132 |
PDF Views:0
Authors
Affiliations
1 Centre for Vermibiotechnology, Department of Zoology, Nehru Memorial College (Autonomous), Puthanampatti-621 007, Dist. Tiruchirappalli, T.N., IN
1 Centre for Vermibiotechnology, Department of Zoology, Nehru Memorial College (Autonomous), Puthanampatti-621 007, Dist. Tiruchirappalli, T.N., IN
Source
Nature Environment and Pollution Technology, Vol 9, No 4 (2010), Pagination: 763-766Abstract
The processed mixed leaves litter with cured cow dung was mixed in different proportions viz., 50:50, 60:40 and 70:30 (each concentration in triplicates) and filled in the plastic trays, individually. Hundred Eudrilus eugeniae adult earthworms were introduced into each of these trays. Simultaneously, a control for each of these concentrations was prepared and maintained without earthworms. The conversion ratio of mixed leaves litter into vermicompost was found to be more or less similar in all the concentrations. However, the cocoons and young ones production was found to be little higher in 50:50 proportions than the other two proportions. Further, vermicompost obtained from all the three concentrations has desired level of plant nutrients for uptake. The results of the present study suggest that the mixed leaves litter with cured cow dung at anyone of these three concentrations can be used for converting into value added vermicompost by utilizing the earthworm E. eugeniae.Keywords
Mixed Leaves Litter, Vermicompost, Eudrilus eugeniae, Plant Nutrients.- Bio-Conversion of Korai Waste (Cyperus rotundus) into Vermicompost Utilizing a Temperate Earthworm, Eisenia foetida
Abstract Views :185 |
PDF Views:0
Authors
Affiliations
1 P.G. and Research Department of Zoology, Nehru Memorial College (Autonomous), Puthanpatti, Trichy (T.N.), IN
2 P.G. Department of Zoology, A.A. Govt. Arts College, Musiri, Trichy (T.N.), IN
1 P.G. and Research Department of Zoology, Nehru Memorial College (Autonomous), Puthanpatti, Trichy (T.N.), IN
2 P.G. Department of Zoology, A.A. Govt. Arts College, Musiri, Trichy (T.N.), IN
Source
An Asian Journal of Soil Science, Vol 4, No 2 (2010), Pagination: 194-197Abstract
The vermibeds were prepared by mixing the processed korai wastes with cured cow dung in different proportions viz., 50:50, 60:40, and 70:30 (each concentration in triplicates) and they were filled in the plastic trays, individually. Simultaneously, a control for each of these concentrations was prepared and maintained. Sixty Eisenia foetida adult worms were introduced into each of these trays excepting the controls. The conversion ratio of wastes into vermicompost was found to be high in 50:50 proportions (68%). The cocoons and young ones production was found to be higher in 50:50 proportion than the other two proportions. Further the results also reveal that 50:50 concentrations retained all the adult worms (60) when compared to the other two concentrations. Further, vermicompost obtained from 50:50 concentrations had desired level of chemical nutrients when compared to the other two concentrations viz., 60:40 and 70:30. The results of the present study obviously suggest that the korai wastes with cow dung at 50:50 concentrations can very well be used for converting into value added vermicompost by utilizing E. foetida.Keywords
Vermicompost, Korai Waste, Cyperus rotundus, Eisenia foetida, Vermicast, Macro and Micronutrients.- Analysis And Review On Feature Selection And Classification Methods On Cervical Cancer
Abstract Views :163 |
PDF Views:1
Authors
Affiliations
1 School of Computer Studies, Rathnavel Subramaniam College of Arts and Science, IN
1 School of Computer Studies, Rathnavel Subramaniam College of Arts and Science, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 2 (2022), Pagination: 2551-2558Abstract
Cervical cancer is one of the most widely recognized gynecologic malignancies on the world and it is demanding since this malignant growth happens with no signs. As per World Health Organization (WHO), cervical cancer is the fourth most recurrent disease which is higher death rate that influenced women everywhere in the world. It has demonstrated that early discovery of any cancer when followed up with suitable diagnosis and treatment can expand the patient survival rate. But the existing techniques have problem with imbalanced dataset and feature selection-based classification accuracy. To conquer the previously mentioned issues, the existing strategies are analyzed different procedures of data mining and feature selection techniques which can be applied to bring out hidden information from the cervical cancer dataset. In this review, classification process and feature selection-based classification are performed to improve the given cervical cancer dataset accuracy significantly. In the classification process, the imbalanced data and redundant features are not handled effectively. Hence the feature selection-based classification is required to improve the cervical cancer classification accuracy. This survey is also analyzed the merits and shortcomings of each method applied to application. The comparative analysis is done using various classification techniques like Support Vector Machine (SVM), K Nearest Neighbor (KNN), Convolution Neural Network (CNN) and Synthetic Minority Oversampling Technique + Random Forest with Recursive Feature Elimination (SMOTE+RFE+RF) approach. The experimental result shows that the SMOTE+RFE+RF approach provides better performance in terms of higher accuracy, specificity, Positive Predicted Accuracy (PPA) and Negative Predicted Accuracy (NPA) and sensitivity rather than the other existing methods.Keywords
Cervical Cancer, Imbalanced Data, Classification, Early Detection, Machine Learning, Feature SelectionReferences
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