Journal of Intelligent & Fuzzy Systems - Volume 39, issue 1

Authors: Dheenathayalan, S. | Paramasivan, B.

Article Type: Research Article

Abstract: Delay Tolerant Network (DTN) is an array of protocols which work together to enable a standardized way to communicate between nodes is store-carry-and-forward approach. In DTN, nodes help to convey message even with connectivity problem. Instead of using the own resource to pass a message to other nodes, a relay node is preferred which discovers the sink node with a greater probability of transferring messages on its own. A condition named node’s selfishness is thus raised and it surely degrades network performance, which is more obvious in urban environment. To handle such problem, an incentive-based routing algorithm is generated by …using the concept Selfishness-Enhanced Reliable Forwarding (SERF) to remove node selfishness and Direct Diffusion to omit duplicate messages present in relay node. This algorithm finds the probability of messages received by the relay node with reference to the usage of resources of sender node. Concurrently, buffer management policy is maintained, by setting the threshold of message copies according to the resource consumption of the source node when it generates a message. The result reveals that the proposed algorithm is better to the existing algorithms with regard to the performance metrics such as, delivery ratio, the average delay, and network overhead. Show more

Keywords: DTN, buffer management, incentive mechanism, Direct Diffusion, SERF

DOI: 10.3233/JIFS-191409

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 407-419, 2020

Authors: Al-Kasassbeh, Mouhammd | Almseidin, Mohammad | Alrfou, Khaled | Kovacs, Szilveszter

Article Type: Research Article

Abstract: Recently, the Internet of Things (IoT) has been used in technology for different aspects to increase the efficiency and comfort of human life. Protecting the IoT infrastructure is not a straightforward task. There is an urgent need to handle different attack scenarios within the IoT smart environment. Attackers continuously targeted the modern aspects of technology, and trying abusing these technologies using complex attack scenarios such as Botnet attacks. Botnet attacks considered a serious challenge faces of the IoT smart environment. In this paper, we introduce a novel idea that capable of supporting the detecting of IoT-Botnet attack and in meanwhile …to avoid the issues associated with the deficiencies of the knowledge-based representation and the binary decision. This paper aims to introduce a detection approach for the IoT-BotNet attack by using the Fuzzy Rule Interpolation (FRI). The FRI reasoning methods added a benefit to enhance the robustness of fuzzy systems and effectively reduce the system’s complexity. These benefits help the Intrusion Detection System (IDS) to generate more realistic and comprehensive alerts. The proposed approach was applied to an open-source BoT-IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber. The proposed approach was tested, evaluated and obtained a 95.4% detection rate. Moreover, it effectively smooth the boundary between normal and IoT-BotNet traffics because of its fuzzy-nature, as well as, it had the ability to generate the required IDS alert in case of the deficiencies of the knowledge-based representation. Show more

Keywords: Internet of things, fuzzy rule interpolation, botnet attack, intrusion detection system

DOI: 10.3233/JIFS-191432

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 421-431, 2020

Authors: Niandong, Liao | Yanqi, Song | Sheng, Su | Xianshen, Huang | Haoliang, Ma

Article Type: Research Article

Abstract: Aiming at the problems of excessive dependence on manual work, low detection accuracy and poor real-time performance of current probe flow anomaly detection in power system network security detection, a detection method for calculating information entropy of probe flow and random forest classification is proposed. Firstly, the network probe stream data are captured and aggregated in real-time to extract network stream metadata. Secondly, by calculating Pearson correlation coefficient and maximum mutual information coefficient, feature selection of network metadata is carried out. Finally, the information entropy and stochastic forest algorithm are combined to detect the anomaly of probe traffic based on …the selected key feature groups, and the probe traffic is accurately classified by multiple incremental learning. The results show that the proposed method can quickly locate the abnormal position of probe traffic and analyze the abnormal points, which greatly reduces the workload of application platform for power system security monitoring, and has high detection accuracy. It effectively improves the reliability and early warning ability of power system network security. Show more

Keywords: Power system, flow detection, network probe, random forest algorithms

DOI: 10.3233/JIFS-191448

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 433-447, 2020

Authors: Holdon, Liviu-Constantin

Article Type: Research Article

Abstract: The variety of De Morgan residuated lattices includes important subvarieties of residuated lattices such as Boolean algebras, MV-algebras, BL-algebras, Stonean residuated lattices, MTL-algebras and involution residuated lattices (see L.C. Holdon [7 ]). X. Zhu, J. Yang and A. Borumand Saeid [16 ] used a special family of extreme fuzzy filters F on a BL-algebra L , they constructed a uniform structure ( L , K ) , and then the part K induced a uniform topology τ F in …L  . Also, they proved that the pair ( L , τ F ) is a topological BL-algebra, and some properties of ( L , τ F ) were investigated. Inspired by their study, in this paper, we define the family of extreme fuzzy ideals I on a De Morgan residuated lattice L , we construct a uniform structure (L , K ) , and then the part K induce a uniform topology τ I in L  . We prove that the pair ( L , τ I ) is a Topological De Morgan Residuated lattice, and some properties of ( L , τ I ) are investigated. In particular, we show that ( L , τ I ) is a first-countable, zero-dimensional, disconnected and completely regular space. Finally, we give some characterizations of topological properties of ( L , τ I ) . We note that, since ideals and filters are dual in BL-algebras (see C. Lele and J. B. Nganou [12 ]), a study on extreme fuzzy ideals in BL-algebras follows by duality, but in the framework of De Morgan residuated lattices, which is a larger class than BL-algebras, the duality between ideals and filters does not hold, so the study of extreme fuzzy ideals in De Morgan residuated lattices becomes interesting from algebraic and topological point of view, and the results of X. Zhu, J. Yang and A. Borumand Saeid [16 ] become particular cases of our theory. Show more

Keywords: De Morgan residuated lattice, obstinate ideal, extreme fuzzy ideal

DOI: 10.3233/JIFS-191474

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 449-461, 2020

Authors: Ahmad, Niaz | Mehmood, Nayyar | Kumam, Poom

Article Type: Research Article

Abstract:  In this article we introduce the notion of fuzzy measure of noncompactness. We define the fuzzy condensing and fuzzy k -set contractions using fuzzy measure of noncompactness. An extension of Stanislaw Szulfa’s fixed point theorem for a self-operator on a closed bounded and convex subset of a Banach space has been proved. Using the defined multivalued k -set contractions, generalizations of Kakutani-Fan and Krasnoselskii type theorems have been proved. For applications the existence result for solutions of a fractional Caputo-Fabrizio anti-periodic boundary value problem has been proved. We give some examples to validate our results.

Keywords: fuzzy measure of noncompactness, Szulfa’s theorem, K-Fan theorem, multivalued fuzzy condensing operators, multivalued fuzzy k-set contractions, Krasnoselskii’s theorem, fixed points

DOI: 10.3233/JIFS-191496

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 463-474, 2020

Authors: Liu, Meng Ke | Xin, Xiao Long

Article Type: Research Article

Abstract: In this paper, we study the generating formula of filters in pseudo equality algebras, also we introduce prelinear pseudo equality algebras and divisible pseudo equality algebras, and then we investigate some characterizations of them. We focus on algebraic structures of the set F  (X ) of all filters in pseudo equality algebras and obtain that F  (X ) can form a Heyting algebra. Moreover, we give the notions of some types of filters ((positive) implicative filters, fantastic filters) in pseudo equality algebras and investigate their properties. Finally, we discuss the relations among these filters.

Keywords: Pseudo equality algebra, prelinear pseudo equality algebra, divisible pseudo equality algebra, (positive) implicative filter, fantastic filter, strong normal filter

DOI: 10.3233/JIFS-191512

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 475-487, 2020

Authors: Kalanat, Nasrin | Khanshan, Alireza | Khanjari, Eynollah

Article Type: Research Article

Abstract: Knowledge discovery and data mining provide an array of solutions for real-world problems. When facing business requirements, the ultimate goal of knowledge discovery is not the knowledge itself but rather making the gained knowledge practical. Consequently, the models and patterns found by the mining methods often require post-processing. To this end, actionable knowledge discovery has been introduced which is developed to extract actionable knowledge from data. The output of actionable knowledge discovery is a set of actions that help the domain expert to gain the desired outcome. Such a process where a set of actions are extracted is called action …extraction. One of the challenges of action extraction is to incorporate causal dependencies among the variables to find actions with higher effectiveness compared to when no such dependencies are used. The goal of this paper is to dive into the lesser studied subject of “action discovery in social networks” and intends to extract actions by utilizing the casual structures discovered from such data. Furthermore, in order to capture the underlying information within a social network, we extract the corresponding structural features. We propose a method called SF-ICE-CREAM (Social Features included Inductive Causation Enabled Causal Relationship-based Economical Action Mining) to overcome the challenges introduced above. This method uses structural features to find the underlying causal structures within a social network and incorporates them into the action extraction process. Show more

Keywords: Actionable knowledge discovery, action extraction, causal network, feature extraction

DOI: 10.3233/JIFS-191519

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 489-501, 2020

Authors: Mahmoodirad, Ali | Niroomand, Sadegh | Shafiee, Mehdi

Article Type: Research Article

Abstract: A new closed loop supply chain network design problem is considered in this study. In comparison to the literature, the problem considers a more complete set of stages in the network e.g. simultaneous consideration of supplier, plant, distribution center, customer, and collection, recovering, recycling, and disposal centers. The problem makes location, capacity, allocation, demand mode and material/product flow decisions for optimizing net benefit including sales revenue, fixed establishing cost of facilities, transportation cost, material purchasing cost, production cost, and inventory holding cost. As a novelty, for the first time multi-mode demand satisfaction is considered in a closed loop supply chain …problem. As another contribution, in order to be close to real-world situations, the problem is tackled in a fuzzy environment by using trapezoidal fuzzy parameters which yield a trapezoidal fuzzy objective function value. As solution methodology, considering the fuzzy objective function, the problem is reformulated as a multi-objective fuzzy mixed integer linear problem and is crisped using credibility measure of the fuzzy constraints. Finally, the crisp multi-objective version of the problem is solved by several hybrid fuzzy programming approaches to obtain a good efficient solution. Applying several numerical test problems, the SO method performs better than other approaches. Show more

Keywords: Closed loop supply chain, fuzzy set theory, multi-objective optimization, credibility constrained modeling

DOI: 10.3233/JIFS-191528

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 503-524, 2020

Authors: Li, Guang | Mahmoudi, Mohammad Reza | Qasem, Sultan Noman | Tuan, Bui Anh | Pho, Kim-Hung

Article Type: Research Article

Abstract: During the last decade, ensemble clustering has been the subject of many researches in data mining. In ensemble clustering, several basic partitions are first generated and then a function is used for the clustering aggregation in order to create a final partition that is similar to all of the basic partitions as much as possible. Ensemble clustering has been proposed to enhance efficiency, strength, reliability, and stability of the clustering. A common slogan concerning the ensemble clustering techniques is that “the model combining several poorer models is better than a stronger model”. Here at this paper, an ensemble clustering method …is proposed using the basic k-means clustering method as its base clustering algorithm. Also, this study could raise the diversity of consensus by adopting some measures. Although our clustering ensemble approach has the strengths of kmeans, such as its efficacy and low complexity, it lacks the drawbacks which the kmeans suffers from; such as its problem in detection of clusters that are not uniformly distributed or in the circular shape. In the empirical studies, we test the proposed ensemble clustering algorithm as well as the other up-to-date cluster ensembles on different data-sets. Based on the experimental results, our cluster ensemble method is stronger than the recent competitor cluster ensemble algorithms and is the most up-to-date clustering method available. Show more

Keywords: Graph representation, cluster ensemble, kmeans clustering, small cluster

DOI: 10.3233/JIFS-191530

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 525-542, 2020

Authors: Malarvel, Muthukumaran | Nayak, Soumya Ranjan

Article Type: Research Article

Abstract: Edge detection and segmentation are the two main approaches being used since last three decades for successful image analysis in remote sensing domain. Although many intensive studies were undertaken, they all were limited to high-resolution aerial images and none addressed this problem exhaustively. The purpose of this study was to investigate both edge detection and segmentation by employing a novel hybrid method combining probability density function and partial differential equation to obtain accurate estimations. The newly proposed method is implemented in two phases: the first phase deals with smoothening that include improved kernel density estimation (KDE) with anisotropic diffusion coefficient …function kernel with both adaptive bandwidth and constant threshold selection using Shannon entropy, in addition to a weighting parameter of 3 × 3 window for lower probability of the whole image in diffusion function; whereas in the second phase, edge detection and segmentation are dealt with by incorporating two prominent techniques, namely diffusion coefficient equation and six-sigma control limit. We carried out a cross-sectional analysis using different datasets such as SIPI database and ground truth images for smoothing, edging and segmentation. Afterward, the results were compared with the other state-of-the-art techniques. Finally, the performance measures of the implemented technique were evaluated by means of entropy, fractal dimension, and an equivalent number of looks for smoothened images, by the Pratt metric for edge detection, and in the case of segmentation, misclassification error was considered. The experimental results demonstrated that the proposed scheme outperforms its counterparts in all aspects. Hence, the proposed hybrid scheme is better and robust, and results in accurate estimation for the given datasets. Show more

Keywords: Kernel density estimation, anisotropic diffusion, fractal dimension, aerial image, image smoothing, segmentation, edge detection

DOI: 10.3233/JIFS-191547

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 543-560, 2020