RCC Institute of Information Technology

About: RCC Institute of Information Technology is a based out in . It is known for research contribution in the topics: Image segmentation & Thresholding. The organization has 299 authors who have published 662 publications receiving 3322 citations.

Multilevel Image Thresholding Based on 2D Histogram and Maximum Tsallis Entropy— A Differential Evolution Approach

Abstract: Multilevel thresholding amounts to segmenting a gray-level image into several distinct regions. This paper presents a 2D histogram based multilevel thresholding approach to improve the separation between objects. Recent studies indicate that the results obtained with 2D histogram oriented approaches are superior to those obtained with 1D histogram based techniques in the context of bi-level thresholding. Here, a method to incorporate 2D histogram related information for generalized multilevel thresholding is proposed using the maximum Tsallis entropy. Differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, is employed to improve the computational efficiency of the proposed method. The performance of DE is investigated extensively through comparison with other well-known nature inspired global optimization techniques such as genetic algorithm, particle swarm optimization, artificial bee colony, and simulated annealing. In addition, the outcome of the proposed method is evaluated using a well known benchmark-the Berkley segmentation data set (BSDS300) with 300 distinct images.

S-shaped Binary Whale Optimization Algorithm for Feature Selection

Abstract: Whale optimization algorithm is one of the recent nature-inspired optimization technique based on the behavior of bubble-net hunting strategy. In this paper, a novel binary version of whale optimization algorithm (bWOA) is proposed to select the optimal feature subset for dimensionality reduction and classifications problem. The new approach is based on a sigmoid transfer function (S-shape). By dealing with the feature selection problem, a free position of the whale must be transformed to their corresponding binary solutions. This transformation is performed by applying an S-shaped transfer function in every dimension that defines the probability of transforming the position vectors’ elements from 0 to 1 and vice versa and hence force the search agents to move in a binary space. K-NN classifier is applied to ensure that the selected features are the relevant ones. A set of criteria are used to evaluate and compare the proposed bWOA-S with the native one over eleven different datasets. The results proved that the new algorithm has a significant performance in finding the optimal feature.

A group incremental feature selection for classification using rough set theory based genetic algorithm

Abstract: Display Omitted In proposed method, incremental learning technique has been investigated.A novel incremental feature selection algorithm is proposed for classification analysis.Incremental feature selection method is devised based on rough set theory and genetic algorithm.Experimental results show the effectiveness in terms of accuracy and time complexity. Data Mining is one of the most challenging tasks in a dynamic environment due to rapid growth of data with respect to time. Dimension reduction, the key process of relevant feature selection, is applied prior to extracting interesting patterns or information from large repositories of data. In a dynamic environment, newly generated group of data together with the information extracted from the previous data are analyzed to select the most relevant and important features of the entire data set. As a result, efficiency and acceptability of the incremental feature selection model increase in the field of data mining. In our paper, a group incremental feature selection algorithm is proposed using rough set theory based genetic algorithm for selecting the optimized and relevant feature subset, called reduct. The objective function of the genetic algorithm used for incremental feature selection is defined using the previously generated reduct and positive region of the target set, concepts of rough set theory. The method may be applied in a regular basis in the dynamic environment after small to moderate volume of data being added into the system and thus the computational time, the major issue of the genetic algorithm does not affect the proposed method. Experimental results on benchmark datasets demonstrate that the proposed method provides satisfactory results in terms of number of selected features, computation time and classification accuracies of various classifiers.