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Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

Integration of city systems is needed to provide flexibility, agility and access to real-time information for the creation and delivery of efficient services in a smart and sustainable city. Consequently, City Process Modelling (CPMo) becomes an essential element of connecting various city sectors. However, to date, there has been limited research on the requirements of an ideal CPMo approach and the usefulness of available Business Process Modelling (BPMo) approaches. This research develops a framework for CPMo to guide smart city developers when modelling city processes.

Data from literature analysis was gathered to derive capabilities of existing BPMo techniques. Then, semi-structured interviews were conducted to thematically and qualitatively explore the requirements, challenges and success factors of CPMo.

The interview findings offered 17 requirements to be addressed by a CPMo approach, along with several challenges and success factors to be considered when implementing CPMo approaches. Then, the paper presents the results of mapping these requirements against 12 existing BPMo capabilities, identified from the literature, concluding that a significant number of requirements (which are mainly related to inputs and visualisation) have been left unfulfilled by existing BPMo approaches. Hence, developing an innovative CPMo approach is necessary to address the components of unfulfilled requirements.

The innovative framework presented in this paper justifies the CPMo requirements, which are unexplored in existing SCD frameworks. Moreover, it will act as a guide for smart city developers, to model cross-sectoral city processes, helping them progress their SCD road map and make their cities smart.