A methodology for CIM modelling and its transformation to PIM

Developing with Model Driven Architecture is nowadays widely used starting with a CIM that can be transformed to models of low abstraction (PIM, PSM) that can be used to generate the code. The CIM represents the highest level of abstraction of the approach which allowing modeling system’s requirement. However, there is no standard method to build this type of model or how to transform it to lower level of abstraction (PIM) which is considered the final objective of building such model. This paper provides an approach to build the CIM that can be transformed (semi-) automatically later to lower levels of abstraction in PIMs.  Thereby, the proposed architecture represents both the static and dynamic view of the system based on the business process model. Meanwhile, the PIM level is represented by the Domain Diagram class and Sequence Diagram of Systems External behavior. Thus, the proposal helps bridging the gap between those that are experts about the domain and its requirements, and those that are experts of the system design and development. Keywords: CIM to PIM transformation; MDA; software process

Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19

Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability

Getting Relational Database from Legacy Data-MDRE Approach

The previous management information systems turning on traditional mainframe environment are often written in COBOL and store their data in files; they are usually large and complex and known as legacy systems. These legacy systems need to be maintained and evolved due to several causes, including correction of anomalies, requirements change, management rules change, new reorganization, etc. But, the maintenance of legacy systems becomes over years extremely complex and highly expensive, In this case, a new or an improved system must replace the previous one. However, replacing those systems completely from scratch is also very expensive and it represents a huge risk. Nevertheless, they should be evolved by profiting from the valuable knowledge embedded in them. This paper proposes a reverse engineering process based on Model Driven engineering that presents a solution to provide a normalized relational database which includes the integrity constraints extracted from legacy data. A CASE tool CETL: (COBOL Extract Transform Load) is developed to support the proposal. Keywords: legacy data, reverse engineering, model driven engineering, COBOL metamodel, domain class diagram, relational database

An improved Arabic text classification method using word embedding

Feature selection (FS) is a widely used method for removing redundant or irrelevant features to improve classification accuracy and decrease the model’s computational cost. In this paper, we present an improved method (referred to hereafter as RARF) for Arabic text classification (ATC) that employs the term frequency-inverse document frequency (TF-IDF) and Word2Vec embedding technique to identify words that have a particular semantic relationship. In addition, we have compared our method with four benchmark FS methods namely principal component analysis (PCA), linear discriminant analysis (LDA), chi-square, and mutual information (MI). Support vector machine (SVM), k-nearest neighbors (K-NN), and naive Bayes (NB) are three machine learning based algorithms used in this work. Two different Arabic datasets are utilized to perform a comparative analysis of these algorithms. This paper also evaluates the efficiency of our method for ATC on the basis of performance metrics viz accuracy, precision, recall, and F-measure. Results revealed that the highest accuracy achieved for the SVM classifier applied to the Khaleej-2004 Arabic dataset with 94.75%, while the same classifier recorded an accuracy of 94.01% for the Watan-2004 Arabic dataset