Volume-06 , Special Issue-04 , May - 2018 Go Back

The exponential growth as well as the availability of data has triggered the development of data analytics tools to harness the power of information hidden in data. These advancements have grabbed the attention of the researchers across the globe. The research frontiers of data analytics is expanding so rapidly, covering many fields including business intelligence, finance, market analysis, science, environmental studies, resource management, weather forecasting and outlier analysis. This paper describes the design of a decision-tree based classification technique to assess the degree of water contamination, based on the statistics of Government of India on water contamination, for six years from 2012-13 to 2017-18 (www.data.gov.in and indiawaters.gov.in). This statistics portrays the quality of water, based on the presence of Iron, Arsenic, Fluoride, Nitrate and Salinity. The proposed classification primarily classifies the pattern of contamination of each State in the India during specified period, in four point scale. Secondly, it classifies the quality of water into either potable or non-potable. This classification finds place in water quality assessment at regional and national level.

Data Analytics, Decision Tree, Classification, Water Quality Assessment

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P. Shanmugavadivu, P. Kavitha, S. Dhamodharan, "Change Detection Analytics on Water Contamination using Decision Tree based Classification", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.342-347, 2018.

This proposed work is aimed to develop an automatic method for brain tumor segmentation based on glowworm swarm optimization based fuzzy c-means clustering (GSOFCM) and region growing technique. The proposed method consists of three stages: Stage-1 is accelerating the FCM clustering for tissue segmentation process based on GSO. In Stage-2, is an abnormal detection process that helps to check the results of GSOFCM method by fuzzy symmetric measure (FSM). In Stage-3 is segment the tumor region from abnormal slices by region growing technique. The quantitative analysis of brain tumor segmentation process uses the parameters dice coefficient (DC), positive predictive value (PPV), and processing time. The proposed method is very efficient to segment the tumor region from MRI head scans.

Clustering, Fuzzy c-means, Glowwarm Swarm Optimization, Segmentation

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P. Nagaraja, T. Kalaiselvi, P. Sriramakrishnan, "Brain Tumor Segmentation from MRI Head Scans through GSO based FCM Clustering and Region Growing Technique", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.348-354, 2018.