Informatics Publishing Limited

R. Santhi ¹, K. Arun Prakash ²

Affiliations
1 Department of Mathematics Nallamuthu Gounder Mahalingam College, Pollachi-642 001, IN
2 Department of Mathematics, Kongu Engineering College, Perundurai-638 052, IN

Abstract

The purpose of this paper is to introduce and study the concepts of intuitionistic fuzzy completely semi-generalized continuous mappings in intuitionistic fuzzy topological space. Some interesting properties areinvestigated besides giving suitable examples.

AMS Mathematics Subject Classification: 54A40, 03F55

Keywords

Intuitionistic Fuzzy Topology, Intuitionistic Fuzzy Semi-generalized Closed Set, Intuitionistic Fuzzy Semi-generalized Continuous Mapping, Intuitionistic Fuzzy Completely Semi-generalized Continuous Mapping

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M. Sangeetha ¹, R. Natesan ¹, R. Santhi ¹

Affiliations
1 Department of Soil Science and Agricultural Chemistry, Tamil Nadu Agricultural University, Coimbatore, IN

Abstract

To study the influence of seasonal variation on the nitrate contamination of groundwater, water samples were collected during the pre and post-monsoon seasons from open wells, bore wells and hand pumps at different villages spread over in the 18 blocks of the Coimbatore district of Tamil Nadu. The water samples were analysed for pH, EC and NO₃-N content. Results indicated that the groundwater of different blocks of Coimbatore ranged from neutral to alkaline in reaction and saline in nature. The overall mean NO₃-N content of groundwater samples of different blocks varied from 5.20 to 9.52 mg L^-1 with the overall mean value of 6.74 mg L^-1 and it is below the critical level of 10.0 mg L^-1. The higher mean NO₃-N content was observed in groundwater samples collected from Gudimangalam block followed by Periyanaickenpalayam and Sultanpet blocks. The temporal variation in NO₃-N content of groundwater samples was observed, being higher in the post monsoon than in the pre monsoon season.

Keywords

Nitrate Contamination, Groundwater, Temporal Variation.

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U. Murugavel ¹, R. Santhi ¹

Affiliations
1 Department of Computer Science, Bharathiar University, IN

Abstract

The increase in unwanted spam email volumes created a clear need for more effective and robust anti-spam filters. Recent machine learning methods are employed to detect and process spam mails successfully. In this paper, we present a density based clustering for email classification problem using kNN algorithm. Initially, the relevant features for filtering the spam messages are extracted from the study and it acts as an antispam filter. It thereby generates the successful corpus list for detection of spam emails. The experiments are conducted on various email datasets and the results show that the proposed kNN density based clustering offers improved performance than the other methods. As shown by the test results, our methodology showed stronger prediction capabilities and better classifications based on in-depth learning techniques.

Keywords

Email, Spam Classification, Feature Extraction, Corpus.

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