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1.
Application of fuzzy AHP approach to selection of organizational structure with consideration to contextual dimensions
Alireza Aslani, Feryal Aslani, 2012, original scientific article

Abstract: The literature of organizational structure design is relatively rich along with conceptual and complex patterns. This complexity arising from the number of elements and numerous relations in addition to the nature of variables. Thereby, the lack of operational decision-making models is felt to propose adequate structural designs in practice. In this article, the researchers employ a fuzzy multi attribute decision making model (FMADM) to select the most suitable organizational structure based on expert’s judgments and by deploying contextual dimensions of the organization. Since the organizational changes especially in the structural levels are along with resistances among involved staffs, the implementation of this model is a supportive tool in addition to help the managers to make a qualified decision and change.
Keywords: organizational structure designing, business process reengineering, development management, integration, fuzzy ahpimage analysis, probabilistic modeling, signal processing, license plate recognition
Published in DKUM: 28.11.2017; Views: 1054; Downloads: 333
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2.
An overview of image analysis algorithms for license plate recognition
Khalid Aboura, Rami Al-Hmouz, 2017, original scientific article

Abstract: Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate. Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates. Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%. Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.
Keywords: image analysis, probabilistic modeling, signal processing, license plate recognition
Published in DKUM: 28.11.2017; Views: 984; Downloads: 333
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