Informatics Publishing Limited

Toran Verma ¹, Lokesh K. Sharma ², Sourabh Rungta ²

Affiliations
1 Department of Computer Science and Engineering, Rungta College of Engineering and Technology, Bhilai-4900245, CG, IN
2 Department of Computer Science and Engineering, Rungta College of Engineering and Technology, Bhilai-490024, CG, IN

Abstract

Data mining is a process to extract useful information for decision making from available set of data. Blast is a fungal disease of rice and occurs in all the rice-growing regions. It causes complete destruction of the rice crop. Present day applications require automation of process to interprets and analyze the information by using various kinds of images and pictures as source of information for interpretation and analysis. The fuzzy set theory is incorporated to handle uncertainties and fuzzy clustering is a powerful method of data mining. FIS is an expert system to approximate input-output mapping according to defined rules. In this proposed approach, leaf blast infected image in rice crop is acquired by digital camera. Image's RGB features of pixels has been extracted and further used for clustering. This clustered data is categorized as Not Affected (NA), Medium Affected (MA) and Highly Affected (HA) by leaf blast disease according to colour information and previous acquired knowledge. For each of these three categories, minimum, medium and maximum RGB ranges has been evaluated. For that linguistic term RMIN, RMED, RMAX, GMIN, GMED, GMAX, BMIN, BMID and BMAX assumed and according to range of linguistic term, triangular membership function has been taken as an input in FIS. For easier implementation of FIS, range of NA, MA and HA is also assumed and taken in form of triangular membership function. According to evaluated data, assumed data and 14 fuzzy inference rules , FIS with three input Red, Green and Blue and any one possible outcome from NA, MA and HA has been implemented. After implementation, random 60 pixels RGB information of another image has been send as input in FIS for testing and it is successfully categorized by FIS, which help to approximate loss caused by leaf blast disease in rice crop.

Keywords

Rice Blast Disease, Image Acquisition, RGB, Fuzzy Logic, Fuzzy Inference System.

Full Text

     

Ajaya K. Akaspau ¹, P. Srinivasa Rao ², Lokesh K. Sharma ³

Affiliations
1 Rungta College of Engineering and Technology, Bhilai (CG), IN
2 Department of Computer Science and Systems Engineering, Andhra University, Visakhapatnam, IN
3 Department of Computer Science and Engineering, Rungta College of Engineering and Technology, Bhilai (CG), IN

Abstract

Trajectory data mining is an emerging area of research, having a large variety of applications. This paper proposes an efficient nearest neighbour based trajectory data classification. The nearest neighbour classification is simplest method. The main issue of a Nearest Neighbour classifier is measuring the distance between two items, and this becomes more complicated for Trajectory Data. The closeness between objects is determined using a distance measure. Despite its simplicity, Nearest Neighbour also has some drawbacks: 1) it suffers from expensive computational cost in training when the training set contains millions of objects; 2) its classification time is linear to the size of the training set. The larger the training set, the longer it takes to search for the nearest neighbors. To improve the efficiency of algorithm an R-tree data structure is used. Extensive experiments were conducted using real datasets of moving vehicles in Milan (Italy) and London (UK). Our experimental investigation yields output as classified test trajectories, significant in terms of correctly classified success rate being 98.2%, the results are discussed with the summaries of confusion matrix. To measure the agreement between predicted and observed categorization of the dataset is carried out using Kappa statistics.

Keywords

Trajectory Data Mining, Trajectory Classification, Mobility Data, Nearest Neighbour.

Full Text

     

Lokesh K. Sharma ¹, Sourabh Rungta ¹

Affiliations
1 Department of Computer Science and Engineering, Rungta College of Engineering and Technology Bhilai, Chhattisgarh, IN

Abstract

Computation analysis of the biological data obtained in genome sequencing and other projects is essential for understanding cellular function and the discovery of new drug and therapies. Data mining become an important tool for researchers of various field including bioinformatics. Protein fold recognition is an important approach to structure discovery in bioinformatics. In this paper the protein fold recognition methods are studied. Supervised learning methods of data mining are carried out and tested for multi-class protein fold recognition. The accuracy is measured by various statistics parameters and the results are reported in this paper. In the result we found that Bayesian Network classifier works better compare as other methods in the cross validation test. The Bayesian Network and Multi Layer Perceptron are reasonably comparable in independent test data supply; accuracy of both methods relatively similar. It is also observed that one-versus-other and all-versus-all mechanisms improve the accuracy as individual parameters.

Keywords

Protein Structure Recognition, Bioinformatics, Data Mining and Supervised Learning.

Full Text