Education•Jabalpur, Madhya Pradesh, India•
About: Indian Institute of Information Technology, Design and Manufacturing, Jabalpur is a education organization based out in Jabalpur, Madhya Pradesh, India. It is known for research contribution in the topics: Cuckoo search & Wavelet transform. The organization has 600 authors who have published 1249 publications receiving 13259 citations. The organization is also known as: IIITD&M & IIITD&M Jabalpur.
Papers published on a yearly basis
TL;DR: The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.
Abstract: The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur's entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur's entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.
TL;DR: A two phase hybrid model for cancer classification is being proposed, integrating Correlation-based Feature Selection (CFS) with improved-Binary Particle Swarm Optimization (iBPSO), which achieves up to 100% classification accuracy for seven out of eleven datasets with a very small sized prognostic gene subset.
Abstract: DNA microarray technology has emerged as a prospective tool for diagnosis of cancer and its classification. It provides better insights of many genetic mutations occurring within a cell associated with cancer. However, thousands of gene expressions measured for each biological sample using microarray pose a great challenge. Many statistical and machine learning methods have been applied to get most relevant genes prior to cancer classification. A two phase hybrid model for cancer classification is being proposed, integrating Correlation-based Feature Selection (CFS) with improved-Binary Particle Swarm Optimization (iBPSO). This model selects a low dimensional set of prognostic genes to classify biological samples of binary and multi class cancers using Naive–Bayes classifier with stratified 10-fold cross-validation. The proposed iBPSO also controls the problem of early convergence to the local optimum of traditional BPSO. The proposed model has been evaluated on 11 benchmark microarray datasets of different cancer types. Experimental results are compared with seven other well known methods, and our model exhibited better results in terms of classification accuracy and the number of selected genes in most cases. In particular, it achieved up to 100% classification accuracy for seven out of eleven datasets with a very small sized prognostic gene subset (up to
TL;DR: This review provides an overview of different guanine lesions formed due to reactions of Guanine with different reactive species, including involvement of these lesions in inter- and intra-strand crosslinks, DNA–protein crosslinks and mutagenesis.
Abstract: DNA is continuously attacked by reactive species that can affect its structure and function severely. Structural modifications to DNA mainly arise from modifications in its bases that primarily occur due to their exposure to different reactive species. Apart from this, DNA strand break, inter- and intra-strand crosslinks and DNA–protein crosslinks can also affect the structure of DNA significantly. These structural modifications are involved in mutation, cancer and many other diseases. As it has the least oxidation potential among all the DNA bases, guanine is frequently attacked by reactive species, producing a plethora of lethal lesions. Fortunately, living cells are evolved with intelligent enzymes that continuously protect DNA from such damages. This review provides an overview of different guanine lesions formed due to reactions of guanine with different reactive species. Involvement of these lesions in inter- and intra-strand crosslinks, DNA–protein crosslinks and mutagenesis are discussed. How certain enzymes recognize and repair different guanine lesions in DNA are also presented.
TL;DR: Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.
Abstract: A modified ABC algorithm based fast satellite image segmentation has been presented.ABC, PSO and GA methods are compared with this proposed method.The experimental results demonstrate better performance of MABC based technique.The proposed MABC based approach is much faster (CPU time is less).The validity of the proposed technique is reported both qualitatively and quantitatively. In this paper, a modified artificial bee colony (MABC) algorithm based satellite image segmentation using different objective function has been presented to find the optimal multilevel thresholds. Three different methods are compared with this proposed method such as ABC, particle swarm optimization (PSO) and genetic algorithm (GA) using Kapur's, Otsu and Tsallis objective function for optimal multilevel thresholding. The experimental results demonstrate that the proposed MABC algorithm based segmentation can efficiently and accurately search multilevel thresholds, which are very close to optimal ones examined by the exhaustive search method. In MABC algorithm, an improved solution search equation is used which is based on the bee's search only around the best solution of previous iteration to improve exploitation. In addition, to improve global convergence when generating initial population, both chaotic system and opposition-based learning method are employed. Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.
TL;DR: In this article, an improved method for the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI) is presented. And the simulation results show that the NDVI is highly useful in detecting the surface features of the visible area which are extremely beneficial for municipal planning and management.
Abstract: This paper presents an improved method for the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI). The method employs the multi-spectral remote sensing data technique to find spectral signature of different objects such as vegetation index, land cover classification, concrete structure, road structure, urban areas, rocky areas and remaining areas presented in the image. For land cover classification, some band combinations of the remote sensed data are exploited and the spatial distribution such as road, urban area, agriculture land and water resources are easily interpreteted by computing their normalized difference vegetation index. Different values of threshold of NDVI are used for generating the false colour composite of the classified objects. The simulation results show that the NDVI is highly useful in detecting the surface features of the visible area which are extremely beneficial for municipal planning and management. The vegetation analysis can be used for the situation of unfortunate natural disasters to provide humanitarian aid, damage assessment and furthermore to device new protection strategies.
Showing all 636 results
|Ashish Kumar Bhandari||23||69||2012|
|Pavan Kumar Kankar||22||98||2020|
|Prashant K. Jain||20||98||1229|
|Rajesh K. Pandey||20||80||1252|
|Pravin N. Kondekar||18||118||1278|
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