Journal of Intelligent & Fuzzy Systems - Volume 24, issue 2

Authors: Lim, Chee Peng | Abeynayake, Canicious | Sato-Ilic, Mika | Jain, Lakhmi C.

Article Type: Other

Abstract: Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

Keywords: Computational Intelligence, image processing, information reasoning, fuzzy models, membrane computing, Bayesian Belief Network

DOI: 10.3233/IFS-2012-0546

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 199-200, 2013

Authors: Halder, Anisha | Mandal, Rajshree | Konar, Amit

Article Type: Research Article

Abstract: The paper aims at developing a hierarchical algorithm for matching a given template of m × n on an image of M × N pixels partitioned into equal sized blocks of m × n pixels. The algorithm employs a fuzzy metric to measure the dispersion of individual feature of a block with respect to that of the template. A fuzzy threshold, preset by the user, is employed to restrict less likely blocks from participation in the matching. A decision tree is used to test the feasibility of a block for matching with the template. The tree at each link examines …the condition for fuzzy thresholding for one feature of the image. If the block satisfies the condition, it is passed on to the next level in the tree for testing its feasibility of matching with respect to the next feature. If it fails, the block is discarded from the search space, and the next block from the partitioned image is passed on for examination. The process goes on until all the blocks pass through the decision tree. If a suitable block satisfies all the test conditions in the decision tree, the block is declared as the solution for the matching problem. The ordering of features to be examined by the tree is performed here by an entropy measure as used in classical decision tree. The time-complexity of the algorithm is of the order of MN/mn, and the elegance of the algorithm lies in its power of approximate matching using fuzzy conditions. The algorithm has successfully been implemented for template matching of human eyes in facial images carrying different emotions, and the classification accuracy is as high as 96%. Show more

Keywords: Template matching, decision tree, hierarchical search, entropy measure, fuzzy threshold

DOI: 10.3233/IFS-2012-0547

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 201-214, 2013

Authors: Sokolova, Marina V. | Serrano-Cuerda, Juan | Castillo, José Carlos | Fernández-Caballero, Antonio

Article Type: Research Article

Abstract: Fall detection, especially for elderly people, is a challenging problem which demands new products and technologies. In this paper a fuzzy model for fall detection and inactivity monitoring in infrared video is presented. The classification features proposed include geometric and kinematic parameters associated with more or less sudden changes in the tracked human-related regions of interest. A complete segmentation and tracking algorithm for infrared video as well as a fuzzy fall detection and confirmation algorithm are introduced. The proposed system is capable of identifying true and false falls, enhanced with inactivity monitoring aimed at confirming the need for medical assistance …and/or care. The fall indicators used as well as their fuzzy model is explained in detail. The fuzzy model has been tested for a wide number of static and dynamic falls, demonstrating exciting initial results. Show more

Keywords: Fall detection, video segmentation and tracking, infrared video, fuzzy system

DOI: 10.3233/IFS-2012-0548

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 215-228, 2013

Authors: Peng, Hong | Wang, Jun | Pérez-Jiménez, Mario J. | Shi, Peng

Article Type: Research Article

Abstract: Multi-level thresholding methods are a class of most popular image segmentation techniques, however, they are not computationally efficient since they exhaustively search the optimal thresholds to optimize the objective function. In order to eliminate the shortcoming, a novel multi-level thresholding method for image segmentation based on tissue P systems is proposed in this paper. The fuzzy entropy is used as the evaluation criterion to find optimal segmentation thresholds. The presented method can effectively search the optimal thresholds for multi-level thresholding based on fuzzy entropy due to parallel computing ability and particular mechanism of tissue P systems. Experimental results of both …qualitative and quantitative comparisons for the proposed method and several existing methods illustrate its applicability and effectiveness. Show more

Keywords: Image segmentation, thresholding method, membrane computing, tissue P systems, fuzzy entropy

DOI: 10.3233/IFS-2012-0549

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 229-237, 2013

Authors: Ooi, W.S. | Lim, C.P.

Article Type: Research Article

Abstract: In this paper, a multi-objective image segmentation approach with an Interactive Evolutionary Computation (IEC)-based framework is presented. Two objectives, i.e., the overall deviation and the connectivity measure, are optimized simultaneously using a multi-objective evolutionary algorithm to generate parameters used for segmentation. In addition, an IEC framework to allow users to participate in the parameters optimization process directly is devised. To demonstrate the effectiveness of the proposed IEC-based multi-objective image segmentation approach, a series of experiments is conducted, and the results are compared with those from other segmentation methods. The outcomes ascertain that the proposed approach is effective, as it compares …favorably with other classical approaches. Show more

Keywords: Multi-objective optimization, interactive evolutionary computation (IEC), image segmentation

DOI: 10.3233/IFS-2012-0550

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 239-249, 2013

Authors: Hasuike, Takashi | Ichimura, Takumi

Article Type: Research Article

Abstract: This paper proposes an approach to analyze the tourism information and data derived from the Web, particularly seat availability data of bullet trains in Japan, and to discover some useful knowledge for the tourism. For the fast development of information and communication technologies, the relation between the web data and tourism is inseparable. However, the Web data include various types of information such as numerical, linguistic, and graded data. Furthermore, the expert tourism planner's subjectivity is also an important factor to develop new favorable plans. A simplified fuzzy reasoning method, which is one of the useful approaches in Data mining, …is introduced in order to deal with these data mathematically. The analysis of the tourism data and the knowledge discovery are performed using actual data of bullet trains in Japan. Show more

Keywords: Tourism information, Web intelligence, fuzzy reasoning method, data mining

DOI: 10.3233/IFS-2012-0551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 251-259, 2013

Authors: Jee, Tze Ling | Tay, Kai Meng | Ng, Chee Khoon

Article Type: Research Article

Abstract: The use of a Fuzzy Inference System (FIS) as a part of Criterion-referenced Assessment (CRA) is not new. Nevertheless, there are several limitations in combining FIS models and CRA, as follows. (i) It is difficult to maintain the monotonicity property of the FIS-based CRA model; (ii) it is difficult and impractical to form a complete fuzzy rule base when the number of required rules is large, and (iii) reducing fuzzy rules may cause the “tomato classification” problem. In this paper, a practical solution to overcome these limitations is provided. We adopt the sufficient conditions (i.e., a mathematical foundation) as a …set of governing equations for designing fuzzy membership functions and fuzzy rules to preserve the monotonicity property. In this paper, our works in [21] is extended and a new procedure that comprises of the sufficient conditions, fuzzy rule reduction and a monotonicity-preserving similarity reasoning (SR) is proposed. The new framework reduces the number of fuzzy rules gathered from an assessor (i.e., selected rules) with a proposed fuzzy rule selection approach. Selected rules are identified in such that the unselected rules can be deduced via a monotonicity-preserving SR technique. We formulate the process of minimizing the number of selected rules as a constrained optimization problem and a genetic algorithm (GA) technique is implemented. Unselected rules are predicted with a proposed monotonicity-preserving SR scheme. The proposed approach contributes to a solution to reduce the number of fuzzy rules to be gathered from an assessor while maintaining the monotonicity property. Besides, this paper also contributes to a new application of SR in education assessment. The proposed approach is evaluated with a case study relating to a lab assessment in Universiti Malaysia Sarawak (UNIMAS). Show more

Keywords: Criterion-referenced assessment, fuzzy inference system, similarity reasoning, fuzzy rule reduction, monotonicity property, optimization

DOI: 10.3233/IFS-2012-0552

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 261-279, 2013

Authors: Loutchkina, Irena | Jain, Lakhmi C. | Nguyen, Thong | Nesterov, Sergey

Article Type: Research Article

Abstract: This paper presents an approach for modelling Systems Integration Technical Risks (SITR) assessment using Bayesian Belief Networks (BBN). SITR represent a significant part of project risks associated with a development of large software intensive systems. We propose conceptual modelling framework to address the problem of SITR assessment at early stages of a system life cycle. This framework includes a set of BBN models, representing the risk contributing factors, and complementing Parametric Models (PM), used for providing input data to the BBN models. In particular we describe SITR identification approach explaining corresponding BBN models' topologies and relevant conceptual model framework. This …framework includes a set of BBN models, representing the risk contributing factors, fused with complementary PMs providing input data to the BBN models. Heuristic approaches for easing Conditional Probabilities Tables (CPT) generation are described. We briefly discuss preliminary results of model testing. In conclusion we summarise benefits and constraints for SITR assessment based on BBN models, and provide suggestions for further research directions for model improvement. Show more

Keywords: Systems integration risks, systems integration risks modeling, Bayesian networks, risk assessment

DOI: 10.3233/IFS-2012-0553

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 281-296, 2013

Authors: Nakhaeinia, D. | Karasfi, B.

Article Type: Research Article

Abstract: This paper introduces a new behavior-based collision avoidance approach for mobile robot navigation in unknown and dynamic environments, which called Nearest Virtual-Target (NVT). The NVT approach was developed based on a modeling-planning-reaction configuration. In modeling module, sensory information is integrated to construct a local model of environment which represents obstacles distribution and free obstacle areas in a part of robot's work space. The planning module uses the “actual-virtual target switching strategy” to compute obstacle free paths towards the target. The robot motion generation is handled by the reaction module. The reaction module applies a fuzzy controller to control the robot's …rotational and translational velocities. The contribution of this approach is solving navigation difficulties presented in previous approaches for successful motion of the robot towards the target in troublesome scenarios such as narrow passages, very dense, cluttered and dynamic environments. Feasibility and effectiveness of the proposed approach are verified through simulation and real robot experiments. Eventually, advantages and limitations of this approach are discussed. Show more

Keywords: Mobile robots, behavior-based navigation, obstacle avoidance, virtual target, fuzzy control

DOI: 10.3233/IFS-2012-0554

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 299-311, 2013

Authors: Bělíček, T. | Kidéry, J. | Kukal, J. | Matěj, R. | Rusina, R.

Article Type: Research Article

Abstract: We propose a fuzzy image processing approach serving as a potential diagnostic tool for Alzheimer's disease. We have set up a sequence of several image-processing methods based on morphological fuzzy edge detection followed by a region-growing segmentation. We applied these operations to two sets of three-dimensional SPECT images of human brains: one set of images was composed of patients with Alzheimer's disease and the second was represented by control brains from non-demented patients. Then we undertook an analysis of nilpotent t-norms forming the edge detectors and the parameter of Gaussian filter, and in the end we carried out an evaluation …of segments that was computed after the final watershed segmentation. This article provides a detailed description of these methods as well as the analysis of evaluated data using Student's two-sample t-test. Traditional gradient magnitude edge detectors are included and compared with fuzzy detectors. Our goal was to demonstrate the usefulness of the Łukasiewicz BL-algebra in feature extraction of 3D biomedical images by using enhanced methods of image morphology. Show more

Keywords: Image processing, fuzzy logic, watershed transformation, SPECT, Alzheimer's disease

DOI: 10.3233/IFS-2012-0555

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 313-321, 2013

Authors: Mortaji, Seyed Taha Hossein | Bagherpour, Morteza | Noori, Siamak

Article Type: Research Article

Abstract: Earned Value Management (EVM) is a well-known project management technique to measure project performance and progress in a significant manner. The EVM has an ability to simultaneously and actively monitor and manage scope, schedule, and cost status via an integrated system. In this paper, firstly, we have formulated EVM in vagueness environment using L-R fuzzy numbers. It improves applicability of the EVM under real-life and uncertain conditions and leads to better planning and taking more appropriate managerial decisions. Also, it overcomes the typical fuzzy numbers' drawbacks. Besides, an efficient approach to calculate estimate at completion (EAC) has been developed. Finally, …an illustrative case proves successful implementation of the proposed method in reality. Show more

Keywords: Fuzzy earned value, uncertainty, L-R fuzzy number, fuzzy progress, estimate at completion

DOI: 10.3233/IFS-2012-0556

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 323-332, 2013

Authors: Mahmoudi, Maryam Tayefeh | Beheshti, Maedeh | Taghiyareh, Fattaneh | Badie, Kambiz | Lucas, Caro

Article Type: Research Article

Abstract: Content-based image retrieval (CBIR), as a well-known retrieval method, has been widely used in various applications. The basis of this method is on features like color, texture and shape. Color image histograms are very useful tools for this purpose. As these kinds of histograms results with large variations between neighboring bins, they seem so sensitive to any kind of changes such as noise, illumination, etc. To overcome this problem, fuzzy linking histogram based on OWA aggregation operator is proposed, which is capable of projecting 3-dimensional (L*a*b*) color histograms into single-dimension. The proposed method have been evaluated and compared with five …other methods in retrieving similar images from the common database. The experimental results, reveals better performance of the proposed method in comparison with the other mentioned methods. Show more

Keywords: Image retrieval, fuzzy color histogram, ordered weighted average (owa), fuzzy linking histogram

DOI: 10.3233/IFS-2012-0557

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 333-346, 2013

Authors: Derhami, Vali

Article Type: Research Article

Abstract: The sequential and uncontrolled punishments in social life may lead to what psychologists call learned helplessness or depression. Like learning in social life, agents based on Fuzzy Reinforcement Learning (FRL) sometimes cannot learn well. Experiments show that if an agent continuously performs actions that cause sequential punishments in the beginning of learning, then it does not usually behave well and often selects actions that evoke punishments. Therefore, the learning takes so long or not successful. In this paper, we address this issue called faulty learning in RL algorithm by exploiting learned helplessness. We demonstrate learned helplessness in the training of …an agent by FRL algorithm and analyze it. The result of analysis shows that since the action value function is approximated by a fuzzy system; continuous punishments lead all weight parameters of the approximator toward negative amounts. Hence, the agent cannot learn well. To prevent this problem, we propose a new reinforcement function. The proposed reinforcement function is adaptive and depends on the number of visit of the state. Simulation results show that new reinforcement function prevents learned helplessness and improves the learning in terms of learning speed and action quality. The proposed ideas can be used and extended to our social and psychology life. Show more

Keywords: Learned helplessness, fuzzy systems, reinforcement learning, Sarsa

DOI: 10.3233/IFS-2012-0558

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 347-354, 2013

Authors: Datta, Somak | Pratihar, D.K. | Bandyopadhyay, P.P.

Article Type: Research Article

Abstract: A novel architecture of hierarchical adaptive neuro-fuzzy inference systems was developed, which was tuned using a genetic algorithm and particle swarm optimization algorithm, separately. It was used to establish input-output relationships of a plasma spray coating process. The parameters, namely primary gas flow rate, stand-off distance, powder flow rate and arc current were considered as inputs of the process and the quality of coating was represented using three responses, such as its thickness, porosity and microhardness. Particle swarm optimization-based approach was found to perform better than the genetic algorithm-based approach on some test cases.

Keywords: Plasma spray coating, adaptive neuro-fuzzy inference system, hierarchical structure, genetic algorithm, particle swarm optimization

DOI: 10.3233/IFS-2012-0560

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 355-362, 2013

Authors: Hadi, Mahdipour | Morteza, Khademi | Hadi, Sadoghi Yazdi

Article Type: Research Article

Abstract: Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non-crisp (symbolic interval and fuzzy) numbers. Indeed, the VFCM method is a simple and general form of FCM that applies the FCM clustering method to various types of numbers (crisp and non-crisp) with different correspondent metrics in a single structure, and without any complex calculations …and exhaustive derivations. The VFCM maps the input (crisp or non-crisp) features to crisp ones and then applies the conventional FCM to the input numbers in the resulted crisp features' space. Finally, the resulted crisp prototypes in the mapped space would be influenced by inverse mapping to obtain the main prototypes' parameters in the input features' space. Equations of FCM applied to crisp, symbolic interval and fuzzy numbers (i.e., LR-type, trapezoidal-type, triangular-type and normal-type fuzzy numbers) are obtained in this paper, using the proposed VFCM method. Final resulted equations are the same as derived equations in [7, 9, 10, 13, 18, 19, 30, 38–40] (the FCM clustering method applying to non-crisp numbers directly and without using VFCM), while the VFCM obtains these equations using a single structure for all cases [7, 9, 10, 13, 18, 19, 30, 38–40] without any complex calculations. It is showed that VFCM is able to clustering of normal-type fuzzy numbers, too. Simulation results approve truly of normal-type fuzzy numbers clustering. Show more

Keywords: Vector fuzzy c-means, crisp, symbolic interval and fuzzy numbers, clustering

DOI: 10.3233/IFS-2012-0561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 363-381, 2013

Authors: Khooban, Mohammad Hassan | Soltanpour, Mohammad Reza

Article Type: Research Article

Abstract: This paper provides an optimal controlling approach for a class of nonlinear systems with structured and unstructured uncertainties using fuzzy sliding mode control. First known dynamics of the system are eliminated through feedback linearization and then optimal fuzzy sliding mode controller is designed using an intelligent fuzzy controller based on Sugeno-Type structure. The proposed controller is optimized by a novel heuristic algorithm namely Particle Swarm Optimization with random inertia Weight (RNW-PSO). In order to handle, the uncertainties Lyapunov method is used. There are no signs of the undesired chattering phenomenon in the proposed method. The globally asymptotic stability of the …closed-loop system is mathematically proved. Finally, this control method is applied to the inverted pendulum system as a case study. Simulation results show desirability of the system performance. Show more

Keywords: Nonlinear system, uncertainties, optimal, fuzzy, sliding mode control

DOI: 10.3233/IFS-2012-0569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 383-394, 2013

Authors: Wang, Rui-Zhi | Miao, Duo-Qian | Xu, Fei-Fei | Zhang, Hong-Yun

Article Type: Research Article

Abstract: The quantitative analysis of the degree of knowledge granularity poses theoretical challenges for the development of granular computing. Information-theoretic measures have been proposed to address this problem, which exhibit usefulness in complete information systems. However, mathematical analysis of relationships between these information-theoretic measures and knowledge granularity has not been done. In this paper, after introducing Shannon's entropy and mutual information into complete information systems, we prove, for the first time, that these information-theoretic measures decrease monotonously as partition becomes coarser under complete information systems. Moreover, we illustrate that their inverse relationships do not hold generally and present an additional condition …under which the inverse relationships are valid. By generalizing Shannon's entropy to incomplete information systems, we further discuss the relationship between the generalized Shannon's entropy (termed as rough information entropy) and knowledge granularity based on covering generalized rough sets. We find that in incomplete information systems, the rough information entropy varies nonmonotonously as covering becomes coarser. An illustrative example is given to verify the above observation result. Show more

Keywords: Granular computing, information entropy, rough sets, complete information systems, incomplete information systems

DOI: 10.3233/IFS-2012-0570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 395-404, 2013

Authors: Liu, Peide

Article Type: Research Article

Abstract: With respect to the multi-attribute group decision-making problems in which the attribute values and attribute weights take the form of the interval grey linguistic variables, the multi-attribute group decision making method based on the interval grey linguistic variables weighted aggregation operator is proposed. Firstly, the operation rules, the properties, and the comparing method of the interval grey linguistic variables are defined. Then some aggregation operators are defined, such as interval grey linguistic weighted aggregation (IGLWA) operator, interval grey linguistic ordered weighted aggregation (IGLOWA) operator, and interval grey linguistic hybrid weighted aggregation (IGLHWA) operator; the decision making methods based on these …operators are proposed to solve the group decision making problems. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple and effective. Show more

Keywords: Grey fuzzy number, interval grey linguistic variables, the interval grey linguistic variables hybrid weighted aggregation operator, multi-attribute group decision making

DOI: 10.3233/IFS-2012-0572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 2, pp. 405-414, 2013