The increasing global population at a rapid pace makes road traffic dense; managing such massive traffic is challenging. In developing countries like Pakistan, road traffic accidents (RTA) have the highest mortality percentage among other Asian countries. The main reasons for RTAs are road cracks and potholes. Understanding the need for an automated system for the detection of cracks and potholes, this study proposes a decision support system (DSS) for an autonomous road information system for smart city development with the use of deep learning. The proposed DSS works in layers where initially the image of roads is captured and coordinates attached to the image with the help of global positioning system (GPS), communicated to the decision layer to find about the cracks and potholes in the roads, and eventually, that information is passed to the road management information system, which gives information to drivers and the maintenance department. For the decision layer, we projected a CNN-based model for pothole crack detection (PCD). Aimed at training, a K-fold cross-validation strategy was used where the value of K was set to 10. The training of PCD was completed with a self-collected dataset consisting of 6000 images from Pakistani roads. The proposed PCD achieved 98% of precision, 97% recall, and accuracy while testing on unseen images. The results produced by our model are higher than the existing model in terms of performance and computational cost, which proves its significance.
Design specification and requirement analysis, during development process involved in transformation of real world problems to software system are subjected to severe issues owing to involvement of semantics. Though, for design and specification of object-oriented systems, Unified Modeling Language (UML) is now recognized as standard language however, its structures have numerous drawbacks which include lack of semantics definition and unidentified deadlocks. The research work proposes a model to avoid deadlocks, specifically in composite structure of UML. Verification of system models by formal methods holds significance, particularly, at requirement specification and design level, to ensure the accuracy of models and high light the design problems before implementation. The paper proposes the rules that allow software engineers to formalize the behavior of UML 2.0 composite structure using Colored petri nets. Using these rules, the research shall analyze the correspondent Colored petri nets and conclude the properties of the original work flow, using theoretical outcomes in the Colored petri nets domain.
In this research, we extended our existing work by applying the concept of strong mobility of mobile agent to make service oriented systems. Initially, an algorithm is developed on the model, based on strong mobility between the clients and server using mobile agent technology via VSAT (very small aperture technology). Where clients are deployed in critical regions which calculate flood discharge based on speed of flood. The proposed algorithm has been verified through the mobile Petri net, a formal language, which is used to formalize it. Reachability tree method of analysis is adopted to verify this model. For practical implementation, a critical area "Kalabagh" is selected and a Java based system is developed of this model. The system is verified by using the historical data values of this critical area and the results verify the model.
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