S. Gokila ¹, K. Ananda Kumar ², A. Bharathi ³

Affiliations
1 Bharathiar University, Coimbatore - 641 046, Tamil Nadu, IN
2 Department of MCA, Bannariamman Institute of Technology, Erode - 638401, Tamil Nadu, IN
3 Department of IT, Bannariamman Institute of Technology, Erode - 638401, Tamil Nadu, IN

Abstract

Objectives: Four seasons of Indian weather are interdependent. Prediction of seasonal weather supports many fields to work successfully. The objective of proposed model to work on Weather Pattern identification in Initial phase of prediction which has to include unequal weight of attributes. Methods: The projected space clustering model is suitable to handle the non-sequence patterns of data set. The existing projected space clustering eliminates the least weighted attribute. The framework suggested in this paper incorporate modified projected space cluster which work on complete set of attributes to form pattern wise clusters which is dynamic in number for each season. Next part of framework is seasonal weather prediction using ANN, works on dynamic set of clusters. Findings: Dynamic nature of clusters formed in modified projected space clustering completely eliminates the error rate arise because of fixed number of cluster. The extreme events patterns formed as a separate clusters are not eliminated as outline. The result of these clusters gives the study report of each season, like the changes of climate pattern, the frequency of extreme event and weather prediction of next season. Application/ Improvement: The modified projected space clustering work well on unequal complete set of attributes to form a cluster of different pattern. For each duration numbers of clusters are dynamic based on the pattern variation in climate data.

Keywords

Climate, Data Mining, Dynamic Clustering, Forecasting, Projected Space, Weather, Weather Season

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C. Thiyagarajan ¹, K. Anandha Kumar ², A. Bharathi ³

Affiliations
1 Bharathiar University, Coimbatore – 641046, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam – 638401, Tamil Nadu, IN
3 Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam – 638401, Tamil Nadu, IN

Abstract

Objectives: To design a classifier for the detection of Diabetes Mellitus with optimal cost and precise performance. Method and Analysis: The diagnosis and interpretation of the diabetes data is must because major problem occurs due to this data maintenance. Several researches are made with machine learning but still needs improvements. In this paper a new method is evaluated as hybrid Extreme Learning Machine (HELM) with African Buffalo Optimization (ABO). Findings: ELM is used to select the input data because of fast learning speed. Optimization technique is used for searching and classifying the good diabetic data. The ABO is a population based algorithm in which individual buffalos work together to identify the diabetics data by updating fitness value for best output solution. The proposed HELM technique is successfully implemented for diagnosing diabetes disease. By using this machine learning algorithm, the classification accuracy is achieved for classifying the diabetes patients by using much of the data set for training and few data sets for testing. In order to improve the quality as well as accuracy there is a need for algorithm. The combination of ELM-ABO classifier is applied in training dataset taken from PRIMA Indian dataset for classification and the experimental results are compared with SVM and other ELM classifiers applied on the same database. Improvement: It is observed that the HELM method obtained high accuracy in classification with less execution time along with performance evaluation of parameters such as recall, precision and F-Measure.

Keywords

African Buffalo Optimization (ABO), Diabetes Mellitus (DM), Extreme Learning Machine (ELM), Machine Learning Algorithms, Optimization

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