This article studies the relationship between perceived organizational support (POS), person–organization fit (P–O fit) and organizational commitment (OC). Primary data were collected from 430 corporate executives of Indian manufacturing companies through a questionnaire survey. Correlation and hierarchical regression analysis were performed to study the relationship among variables. Results show that POS significantly affects OC. Findings also establish a significant relationship between P–O fit and OC. Further, the moderating impact of P–O fit affecting the relationship between POS and OC has been established. This study highlights the importance of providing organizational support to employees in order to foster their commitment to the organization. Moreover, results provide evidence that if there is fit between the values of the employees with that of the organization, it enhances the commitment level of the employees. In addition to the establishment of a significantly positive relation between POS and OC, this study also determines P–O fit as a factor that can moderate the relationship between POS and OC.

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Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming. Hence, there is need to develop an automated tool for teeth segmentation. Recently, few deep models have been developed for dental image segmentation. But, such models possess large number of training parameters, thus making the segmentation a very complex task. Also, these models are based only on conventional CNN and lacks in exploiting multimodal CNN features for dental image segmentation. Thus, to address these issues, a novel encoder-decoder model based on multimodal-feature extraction for automatic segmentation of teeth area is proposed. The encoder has three different CNN based architectures: conventional CNN, atrous-CNN, and separable CNN to encode rich contextual information. Whereas decoder contains a single stream of deconvolutional layers for segmentation. The proposed model is tested on 1500 panoramic X-ray images and uses very less parameters when compared to state-of-the-art methods. Besides this, the precision and recall are 95.01% and 94.06%, which out performs the state-of-the art methods.

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