Legacy system program transformation by Lyee methodology

Based on the comparative review of several approaches to legacy system conversion and revitalization, the Lyee methodology application for the issue is presented to clarify its idea, the associated procedure, and the implemented tools. It could be said that with the tools and manual developed by ICBSM&T, the mechanical transformation of the conventional program to a Lyee-structured one becomes possible as long as the programs are made in a procedure-oriented language. In addition to the program structure conversion, the Lyee methodology permits people to choose any application language in the transformed program. At the same time, quite a new approach related to the system conversion is introduced, in which the chunk of data extracted from an old program is edited to make a new conventional structure program that has a logical sequence instead of a Lyee type of declarative program. These features can be realized through the concept of LyeeBELT, which is a set of word-information about the attributes, formulae, and conditions for an independent data item.

The overall workflow of the legacy program transformation is shown in the following.

A critical part in its implementation is the feasibility study (pre-analysis) stage where necessary information is supposed to be secured, and an appropriate plan and policy about the system to be revitalized in the new system environment should be clarified so as to customize the tools accordingly. If the initial process is completed, the mechanical legacy system conversion will be realized by registering the parameters in the tool, and the reestablishment of business knowledge in the LyeeBELT will be enabled. With the regulated business logic on the LyeeBELT, the program maintenance afterwards becomes drastically simplified and stable without the ‘spaghetti’ problem, so that software evolution can be possible.     

Editorial for the 30th anniversary special issue

Applied Intelligence -     

Using artificial intelligence techniques for COVID-19 genome analysis

Applied Intelligence - The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province,...     

Enhancing PROMETHEE method with intuitionistic fuzzy soft sets

The notion of intuitionistic fuzzy soft sets (IFSSs) provides an effective tool for solving multiple attribute decision making with intuitionistic fuzzy information. The most crucial issue in decision making based on IFSSs is how to derive the ranking of alternatives from the information quantified in terms of intuitionistic fuzzy values. In this study, we propose a new extension of the preference ranking organization method for enrichment evaluation (PROMETHEE), by taking advantage of IFSSs. In addition to presenting a myriad of new notions, such as intuitionistic fuzzy membership (or nonmembership) deviation matrices, intuitionistic fuzzy membership (or nonmembership) preference matrices, and aggregated intuitionistic fuzzy preference matrices, we put more emphasis on the construction of three distinct preference structures and related utility functions on the corresponding weakly ordered sets by considering the positive, negative, and net flows of the alternatives based on the aggregated intuitionistic fuzzy preference matrix. We present a new algorithm for solving multiple attribute decision-making problems with the extended PROMETHEE method based on IFSSs. Moreover, a benchmark problem concerning risk investment is investigated to give a comparative analysis and show the feasibility of our approach.     

SpringBoard: game-agnostic tool for scenario editing with meta-programming support

Although we have recently seen an increase of good, free game engine editors, general purpose scenario (level) editors are still lagging behind in terms of functionalities and ease of use. Using them to create game scenarios can be difficult as they often expose general engine capabilities instead of limiting the toolset to fit game-specific requirements. They often require programming skills to use, which introduce additional user skill requirements, and configuring them for a specific game can be equally difficult. In this paper we have developed SpringBoard, an open source scenario editor for games using the SpringRTS engine. Extending it to support game and level requirements is achieved with multi-level meta-programming, while still providing a system that is integrated with the GUI editor and therefore intuitive to use. Our meta-programming system has support for trigger elements (events, functions and actions), custom (composite) data types, scoped data access, higher order functions and actions, and data synchronization mechanics. This novel approach allows us to have the full expressiveness of the underlying programming language, while exposing a user-friendly GUI that consists of terminology familiar to the domain expert.     

Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals

Electrocardiogram is widely used to diagnose the congestive heart failure (CHF). It is the primary noninvasive diagnostic tool that can guide in the management and follow-up of patients with CHF. Heart rate variability (HRV) signals which are nonlinear in nature possess the hidden signatures of various cardiac diseases. Therefore, this paper proposes a nonlinear methodology, empirical mode decomposition (EMD), for an automated identification and classification of normal and CHF using HRV signals. In this work, HRV signals are subjected to EMD to obtain intrinsic mode functions (IMFs). From these IMFs, thirteen nonlinear features such as approximate entropy \( (E_{\text{ap}}^{x} ) \), sample entropy \( (E_{\text{s}}^{x} ) \), Tsallis entropy \( (E_{\text{ts}}^{x} ) \), fuzzy entropy \( (E_{\text{f}}^{x} ) \), Kolmogorov Sinai entropy \( (E_{\text{ks}}^{x} ) \), modified multiscale entropy \( (E_{{{\text{mms}}_{y} }}^{x} ) \), permutation entropy \( (E_{\text{p}}^{x} ) \), Renyi entropy \( (E_{\text{r}}^{x} ) \), Shannon entropy \( (E_{\text{sh}}^{x} ) \), wavelet entropy \( (E_{\text{w}}^{x} ) \), signal activity \( (S_{\text{a}}^{x} ) \), Hjorth mobility \( (H_{\text{m}}^{x} ) \), and Hjorth complexity \( (H_{\text{c}}^{x} ) \) are extracted. Then, different ranking methods are used to rank these extracted features, and later, probabilistic neural network and support vector machine are used for differentiating the highly ranked nonlinear features into normal and CHF classes. We have obtained an accuracy, sensitivity, and specificity of 97.64, 97.01, and 98.24 %, respectively, in identifying the CHF. The proposed automated technique is able to identify the person having CHF alarming (alerting) the clinicians to respond quickly with proper treatment action. Thus, this method may act as a valuable tool for increasing the survival rate of many cardiac patients.

     

A process algebra to formalize the Lyee methodology

Over the last years, various methodologies and techniques have been elaborated and proposed to improve one or many aspects related to the software development life cycle. However, despite the great effort in this research field, the production of clearly understood and modifiable systems still an ambitious goal and far from reached. This is due, on one hand, to the complexity and the subtlety of software themselves and, on the other hand, to the limitations of the current methodologies. Recently, a new and very promising methodology, called Lyee, has been proposed. Intended to deal efficiently with a wide range of software problems related to different field, Lyee allows the development of software by simply defining their requirements.

Nevertheless, since both the semantics of Lyee generated software together with the process of automatic generation of software from requirements are described using informal language, difficulties and confusions may arise when trying to understand and study this methodology.

The main purpose of this paper is to formalize, using a process algebra, the process of automatic generation of softwares together with the semantics of Lyee generated softwares. Actually, process algebra naturally supports many concepts of the Lyee methodology and consequently formalize them simply and elegantly. It offers to the Lyee methodology an abstract machine more suitable than the Von-Newman one. In fact, this new abstract machine consider a program as chemical solution when molecules (different vectors of the lyee methodology) interact together to reach a collective goal.     

Fuzzy rankings for preferences modeling in group decision making

Although fuzzy preference relations (FPRs) are among the most commonly used preference models in group decision making (GDM), they are not free from drawbacks. First of all, especially when dealing with many alternatives, the definition of FPRs becomes complex and time consuming. Moreover, they allow to focus on only two options at a time. This facilitates the expression of preferences but let experts lose the global perception of the problem with the risk of introducing inconsistencies that impact negatively on the whole decision process. For these reasons, different preference models are often adopted in real GDM settings and, if necessary, transformation functions are applied to obtain equivalent FPRs. In this paper, we propose fuzzy rankings, a new approximate preference model that offers a higher level of user‐friendliness with respect to FPRs while trying to maintain an adequate level of expressiveness. Fuzzy rankings allow experts to focus on two alternatives at a time without losing the global picture so reducing inconsistencies. Conversion algorithms from fuzzy rankings to FPRs and backward are defined as well as similarity measures, useful when evaluating the concordance between experts’ opinion. A comparison of the proposed model with related works is reported as well as several explicative examples.     

Big data-driven large-scale group decision-making under uncertainty (BiGDM-U)

Applied Intelligence -     

Efficiently mining erasable stream patterns for intelligent systems over uncertain data

Data mining is a method for extracting useful information that is necessary for a system from a database. As the types of data processed by the system are diversified, the transformed pattern mining techniques for processing these type of data have been proposed. Unlike the traditional pattern mining methods, erasable pattern mining is a technique for finding the patterns that can be removed by coming with a small profit. Erasable pattern mining should be able to process data by considering both the environment that the data are generated from and the characteristics of the data. An uncertain database is a database that is composed of uncertain data. Since erasable patterns discovered from uncertain data contain significant information, these patterns need to be extracted. In addition, databases gradually increase, because the data from various fields is generated and accumulated over data streams. Data streams should be processed as intelligently as possible to provide the useful data to the system in real time. In this paper, we propose an efficient erasable pattern mining algorithm that processes uncertain data that is generated over data streams. The uncertain erasable patterns discovered through the suggested technique are more meaningful information by considering the probability of the item and the profit. Moreover, the proposed method can perform efficient mining operations by using both tree and list structures. The performance of the suggested algorithm is verified through the performance tests compared with state-of-the-art algorithms using real data sets and synthetic data sets.