Publications

In this study, we are concerned with the role of information granulation in data mining in databases. By their nature, data mining pursuits are very much oriented towards end-users and imply that any results need to be easily interpretable. Granulation of information promotes this interpretability and channels all pursuits of data mining (that are otherwise computationally intensive and thus highly prohibitive) towards more efficient and feasible processing. First, we discuss the essence of information granulation and afterwards elaborate on the main approaches to the design of information granules. We distinguish between user-driven, data-driven and hybrid methods of information granulation. Several main classes of membership functions of information granules-fuzzy sets are investigated and contrasted in terms of some selection criteria such as parametric flexibility and sensitivity of the ensuing information granules. We propose two fundamental concepts in data mining: associations and rules. Associations are direction-free constructs that capture the most essential components of the overall structure in database. The relevance of associations is expressed by the cardinality of the data embraced by the Cartesian products of the information granules contributing to the construction of the associations. The proposed methodology of data mining comprises two phases. First, associations are built and the most essential (relevant) ones are collected in the form of a data mining agenda. Second, some associations can be converted into direction-driven constructs (rules). The idea of consistency of the rules is discussed in detail.

Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one of the methods for solving NESs, given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, problem domain knowledge of NESs is investigated in this study, where we propose the incorporation of a variable reduction strategy (VRS) into EAs to solve NESs. The VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through variable relationships that exist in the equation systems. It enables the reduction of partial variables and equations and shrinks the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper mainly integrates the VRS into two existing state-of-the-art EA methods (i.e., MONES and DR-JADE) according to the integration framework of the VRS and EA, respectively. Experimental results show that, with the assistance of the VRS, the EA methods can produce better results than the original methods and other compared methods. Furthermore, extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.

Drones play a pivotal role in various industries of Industry 4.0. For achieving the application of drones in a dynamic environment, finding a clear path for their autonomous flight requires more research. This paper addresses the problem of finding a navigation path for an autonomous drone based on visual scene information. A deep learning-based object detection approach can localize obstacles detected in a scene. Considering this approach, we propose a solution framework that includes masking with a color-based segmentation method to identify an empty area where the drone can fly. The scene is described using segmented regions and localization points. The proposed approach can be used to remotely guide drones in dynamic environments that have poor coverage from global positioning systems. The simulation results show that the proposed framework with object detection and the proposed masking technique support drone navigation in a dynamic environment based only on the visual input from the front field of view.

In this paper, the fundamental idea of Linguistic Models (LM) introduced by Pedrycz is followed and their genetically oriented design framework is developed. The LM is designed by the use of fuzzy granulation realized via Context-based Fuzzy C-Means (CFCM) clustering. This clustering technique builds information granules in the form of fuzzy sets and develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. However, it is difficult to optimize the number of linguistic context produced in the output space, the number of cluster generated by each context, and fuzzification factor related to fuzzy clustering. Thus, we perform simultaneous optimization of design parameters linking information granules in the input and output spaces based on Genetic Algorithm (GA). The experimental results on coagulant dosing process in a water purification plant reveal that the proposed method shows a good performance in comparison with Linear Regression (LR), Neural Networks (NN), Radial Basis Function Networks (RBFN), and LM itself.

Information granules, as encountered in natural language, are implicit in nature. To make them fully operational so they can be effectively used to analyze and design intelligent systems, information granules need to be made explicit. An emerging discipline, granular computing focuses on formalizing information granules and unifying them to create a coherent methodological and developmental environment for intelligent system design and analysis. Granular Computing: Analysis and Design of Intelligent Systems presents the unified principles of granular computing along with its comprehensive algorithmic framework and design practices. Introduces the concepts of information granules, information granularity, and granular computing Presents the key formalisms of information granules Builds on the concepts of information granules with discussion of higher-order and higher-type information granules Discusses the operational concept of information granulation and degranulation by highlighting the essence of this tandem and its quantification in terms of the associated reconstruction error Examines the principle of justifiable granularity Stresses the need to look at information granularity as an important design asset that helps construct more realistic models of real-world systems or facilitate collaborative pursuits of system modeling Highlights the concepts, architectures, and design algorithms of granular models Explores application domains where granular computing and granular models play a visible role, including pattern recognition, time series, and decision making Written by an internationally renowned authority in the field, this innovative book introduces readers to granular computing as a new paradigm for the analysis and synthesis of intelligent systems. It is a valuable resource for those engaged in research and practical developments in computer, electrical, industrial, manufacturing, and biomedical engineering. Building from fundamentals, the book is also suitable for readers from nontechnical disciplines where information granules assume a visible position.

This paper introduces an approximate reasoning system for assessing software quality and introduces the application of two computational intelligence methods in designing a software quality decision system, namely, granulation from fuzzy sets and rule-derivation from rough sets. This research is part of a computational intelligent systems approach to software quality evaluation, which includes a fuzzy-neural software quality factor-criteria selection model with learning and a rough-fuzzy-neural software quality decision system. Overall, computational intelligence results from a synergy of various combinations of genetic, fuzzy, rough and neural computing in designing engineering systems. Based on observations concerning software quality and the granulations of measurements in an extended form of the McCall software quality measurement framework, an approach to deriving rules about software quality is given. Quality decision rules express relationships between evaluations of software quality criteria measurements. A quality decision table is constructed relative to the degree of membership of each software quality measurement in particular granules. Decision-tables themselves are as a collection of sensors, which "sense" inputs and output conditions for rules. Rosetta is used to generate quality decision rules. The approach described in this paper illustrates the combined application of fuzzy sets and rough sets in developing a software quality decision system.

Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GAFCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub-convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.