Informatics Publishing Limited

B.P. Ravi ¹, C. Suresh Kumar ¹, Dhanalakshmi ¹, Kshamma ¹, Prashant ¹

Affiliations
1 Sri Chamarajendra Zoological Gardens, Mysore

Abstract

No abstract given

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K. Kandasamy ¹, C. Suresh Kumar ²

Affiliations
1 Bharathiyar University, Coimbatore, Tamil Nadu, IN
2 J.K.K. Nattraja College of Engineering and Technology, Kumarapalayam Tamil Nadu, IN

Abstract

Objective: To find the optimal time interval for predicting mobile user behavior in the location-based services. Methods: An innovative technique called Modified Particle Swarm Optimization based Optimal Time Interval Identification is introduced for predicting Mobile User Behavior (MPSO-OTI2-PMB)-in Location-Based Services. The MPSO algorithm intends to find the optimal time interval in the prediction of mobile user behavior for the location-based services. This MPSO algorithm aims to search the best time interval with less computation complexity. Contrast to the PSO algorithm, the MPSO algorithm decreases the search range during the optimization process. Results: MPSO-OTI2-PMB shows higher rate when compared to the existing Cluster-based Temporal Mobile Sequential Pattern Mine (CTMSP-Mine) method. In the MPSO algorithm, randomly generate the initial population with the search space and find the fitness value. Instead of fixed search range, the search range is decreased based on the fitness value during the optimization process. If the network size is 6, the precision rate in MPSO-OTI2-PMB is 0.92, the recall in MFAMGS is 0.9 and the F-Measure is 0.89. According to the comparison and the results from the experiment shows that the proposed method has high efficiency in terms of precision, recall and F-Measure. Conclusion: MPSO-OTI2-PMB is presented and this method has high efficiency for predicting mobile user behavior in the location-based services.

Keywords

Data Mining, Mobile Environments, Mining Methods and Algorithms, Particle Swarm Optimization.

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D. Revathi ¹, C. Suresh Kumar ²

Affiliations
1 Bharathiyar University, Coimbatore - 641 046, Tamil Nadu, IN
2 J.K.K. Nattraja College of Engineering and Technology, Kumarapalayam, Namakkal - 638 183, Tamil Nadu, IN

Abstract

Objective: To remove the noises occurred in the bio medical pictures with less computation time. Methods: An innovative technique called Parallel Fuzzy Inference System (PFIS) is introduced for image denoising in the medical images. This method takes input training images, process a dissimilar neighbourhood association between the center pixel and generates the fuzzy inference rules. These rules are distributed to the nodes for simultaneous execution. Then, the type-1 interval fuzzy set is submitted to the resultant defuzzifier module which will decode it into scalar value. If it is impulse pixel, the noise filter is used to reduce the noise. Results: The improvement of the PFIS technique is evaluated by using the medical images. PFIS method shows high efficiency when matched with the existing Type-2 Fuzzy Logic (TFL) based impulse detector for impulse noise removal. In the PFIS method, the fuzzy rules are generated analyzing the medical images and these rules are processed simultaneously. If the noise density is 75%, the Mean Squared Error (MSE) in PFIS is 59.48, the False Classification Ratio (FCR) is 3.25 and the computation time is 740 ms. According to the comparison and the results from the experiment shows that the proposed method has high efficiency. Conclusion: The findings demonstrate that the PFIS is presented and this method has high efficiency in terms of MSE, FCR and computation time.

Keywords

Fuzzy Logic, Image Processing, Impulse Noise, Noise Filter, Parallel Fuzzy Inference System

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