Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients and their families, researchers, and decision-makers. Thus, any innovations that improve the diagnostic process while maintaining quality and safety are crucial to the development of the healthcare field. Many diseases can be tentatively diagnosed during their initial stages. In this study, all developed techniques were applied to tuberculosis (TB). Thus, we propose an optimized machine learning-based model that extracts optimal texture features from TB-related images and selects the hyper-parameters of the classifiers. Increasing the accuracy rate and minimizing the number of characteristics extracted are our goals. In other words, this is a multitask optimization issue. A genetic algorithm (GA) is used to choose the best features, which are then fed into a support vector machine (SVM) classifier. Using the ImageCLEF 2020 data set, we conducted experiments using the proposed approach and achieved significantly higher accuracy and better outcomes in comparison with the state-of-the-art works. The obtained experimental results highlight the efficiency of modified SVM classifier compared with other standard ones.
The ever-increasing number of mobile platforms constitutes a challenge for application developers, who must develop efficient mobile applications for multiple platforms. Recently, a specific interest is being taken in the Model Based User Interface Development (MBUID) by Software Engineering Community. In such paradigm, an application's user interface is obtained by defining models and transformations of those models. This paper aim at adopting MBUID paradigm to address the problem of mobile application development. As such, we propose a new approach and its support system for the automatic generation of mobile user interfaces. The approach and the system are based on a set of standards and relevant technologies such as EMF, GMF, ATL, and Xpand. A case study is presented, in the paper, with the aim of verifying the usefulness of this approach.
Model transformation plays a key role in the model-driven engineering (MDE) approach. In fact, it describes the process of converting one model into another of the same system. Considering a source model, there may be several ways to transform it into target models. Although alternative target models may be equivalent from the functional viewpoint, they may differ in their usability attributes. One of the key challenges for an automated transformation process is to identify which model transformations will produce a target model with the desired usability attributes. The present paper addresses this issue and provides a parameterized transformation to deal with usability driven in an MDE approach. Specifically, it focuses on how to associate usability attributes with the different alternative transformations and how this can be taken into account in an automated transformation process to obtain user interface model with the desired usability attributes.
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