Project management in today’s intensely competitive environment has compelled organizations to adopt project management approach for better business results. Therefore, current research study aims to investigate the impact of servant leadership on project success with the mediating role of team motivation and team effectiveness in the software industry. A field survey was conducted, using a questionnaire as a survey tool. Data were collected from 219 respondents who have been working as team members of software development projects. The statistical results were obtained using the SPSS Process macro. The results show project managers need to exhibit a servant leadership style due to its strong influence on project success, albeit through team motivation and effectiveness. The findings from this study contribute to the field of leadership and project management along with the field of information systems and software engineering.
License plate recognition (LPR) is an integral part of the current intelligent systems that are developed to locate and identify various objects. Unfortunately, the LPR is a challenging task due to various factors, such as the numerous shapes and designs of the LPs, the non-following of standard LP templates, irregular outlines, angle variations, and occlusion. These factors drastically influence the LP appearance and significantly challenge the detection and recognition abilities of state-of-the-art detection and recognition algorithms. However, recent rising trends in the development of machine learning algorithms have yielded encouraging solutions. This paper presents a novel LPR method to address the aforesaid issues. The proposed method is composed of three distinct but interconnected steps. First, a vehicle that appears in an input image is detected using the Faster RCNN. Next, the LP area is located within the detected vehicle by using morphological operations. Finally, license plate recognition is accomplished using the deep learning network. Detailed simulations performed on the PKU, AOLP, and CCPD databases indicate that our developed approach produces mean license plate recognition accuracy of 99%, 96.0231%, and 98.7000% on the aforesaid databases.
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