Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Organizational Cynicism and Its Impact on Organizational Pride in Industrial Organizations.

Abstract: Organizational cynicism has been a topic of discussion and debate among employees and top management. The purpose of this study is to find out the relationship between organizational cynicism and organizational pride. Precisely, the objectives are to identify and measure organizational cynicism among employees in industrial organizations; to determine and measure the degree of organizational pride among employees in industrial organizations and to study the effect of organizational cynicism on the organizational pride of employees in industrial organizations. In this empirical research, the study population was employees of industrial organizations of Oman. Using a purposive sampling technique, nine industrial organizations from Oman were picked. With the help of structured questionnaire, data from 350 respondents was obtained. Structural equation modeling was used through Amos version 25.0 for data analysis. The results reveal that the two dimensions of organizational cynicism (affective cynicism and behavioral cynicism) have a significant and negative impact on emotional pride, while cognitive cynicism does not significantly effect emotional pride. The study results indicate that the one dimension of organizational cynicism (affective cynicism) has a significant impact on attitudinal pride, while the rest of the other dimensions (cognitive cynicism, behavioral cynicism) do not have a significant effect on attitudinal pride. The limitations and implications of the research are also discussed.

Evolution of mating isolation between populations of Drosophila ananassae.

Abstract: Prezygotic mating isolation has been a major interest of evolutionary biologists during the past several decades because it is likely to represent one of the first stages in the transition from populations to species. Mate discrimination is one of the most commonly measured forms of prezygotic isolation and appears to be relatively common among closely related species. In some cases, it has been used as a measure to distinguish populations from subspecies, races, and sister species, yet the influences of various evolutionary mechanisms that may generate mate discrimination are largely unknown. In this study, we measured the level and pattern of mate discrimination among 18 populations of a cosmopolitan drosophilid species, Drosophila ananassae, from throughout its geographical range and its sister species, Drosophila pallidosa, which has a restricted geographical distribution in the South Pacific Islands. In addition, we measured genetic differentiation between all 18 populations using mitochondrial DNA polymorphism data. Mate discrimination varies considerably throughout the species range, being higher among populations outside the ancestral Indonesian range, and highest in the South Pacific. Our results suggest that colonization and genetic differentiation may have an influence on the evolutionary origin of mate discrimination. Our phylogeographical approach clarifies the ancestral relationships of several populations from the South Pacific that show particularly strong mate discrimination and suggests that they may be in the early stages of speciation. Furthermore, both the genetic and behavioral results cast doubt on the status of D. pallidosa as a good species.

Data clustering using enhanced biogeography-based optimization

Abstract: Data clustering is one of the important tool in data analysis which partitions the dataset into different groups based on similarity and dissimilarity measures. Clustering is still a NP-hard problem for large dataset due to the presence of irrelevant, overlapping, missing and unknown features which leads to converge it into local optima. Therefore, this paper introduces a novel hybrid meta-heuristic data clustering approach which is based on K-means and biogeography-based optimization (BBO). The proposed method uses K-means to initialize the population of BBO. The simulation has been done on eleven dataset. Experimental and statistical results validate that proposed method outperforms the existing methods.

Multi-channel local ternary pattern for content-based image retrieval

Abstract: A feature based on a single modality such as color or texture is not sufficient to investigate the appearance variation across multiple images. In this paper, a novel feature referred to as multi-channel local ternary pattern is proposed for image retrieval. The proposed method captures cross-channel color–texture information through the combination of H–V, S–V and V–V channels obtained from HSV representation of the image. Not only texture statistics extracted in this manner contain color information but local texture information is also incorporated in such representations. The effectiveness of the proposed image retrieval method is measured by performing experiments on popular natural, face and texture databases including Corel 1000, Corel 10k, CMU-PIE, STex and MIT VisTex, and results are compared with the existing state-of-the-art techniques. Retrieval results clearly highlight the superior performance of the proposed approach in terms of average precision and average recall.