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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Naderian, Sobhan | Salemnia, Ahmad
Article Type: Research Article
Abstract: This paper aims to develop a new idea for the classification of power quality disturbances. The method is based on Stockwell’s-Transform (ST) and Type-2 Fuzzy Kernel Support Vector Machine (T2FK-SVM). Through the introduction of ST and its properties, we propose a classification plan for nine types of power quality disturbances. Firstly, features of disturbance signals extracted through the ST. Secondly, features extracted by using the ST are applied as input to T2FK-SVM classifier for automatic classification of the power quality (PQ) disturbances. Design of Kernel is a main part of many kernel based methods such as Support Vector Machine (SVM), …so by using of Type-2 Fuzzy sets as a kernel of SVM, the total accuracy of classification enhanced.This method can reduce the features of the disturbance signals significantly, and so less time and memory is required for classification by the T2FK-SVM method. Six single event and two complex event as well normal voltage selected as reference are considered for the classification. The simulation results showed accurate classification, fast learning and execution in the detection and classification of PQ events. Results are compared with other methods and the robustness of proposed method evaluated under noisy conditions. Finally, proposed method is also implemented on real time PQ disturbances to confirm the validity of this method in practical conditions. Show more
Keywords: Detection, classification, power quality (PQ), S-transform (ST), Type-2 fuzzy kernel (T2FK), Support Vector Machine (SVM)
DOI: 10.3233/JIFS-152560
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5115-5124, 2019
Authors: Kozae, A.M. | El Atik, A.A. | Elrokh, A. | Atef, M.
Article Type: Research Article
Abstract: In this article, a technique to construct a new type of topological structures by graphs called topological graphs is introduced. We use the concept of a homeomorphism between topological graphs as a topological property to prove the isomorphic between graphs. We construct a computer program that builds graphs and its topological graphs. This model helps in studying many topological properties such as continuity, connectedness, compactness and separation axioms on graphs. Finally, we present some examples of different types of graphs and their topological graphs and study some of their topological and algebraic properties.
Keywords: Graphs, a topological graph, a topological structure, homeomorphic, isomorphic
DOI: 10.3233/JIFS-171561
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5125-5134, 2019
Authors: Barzegar, Behnam | Motameni, Homayun | Movaghar, Ali
Article Type: Research Article
Abstract: Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS. The first phase focuses on a smart combination …of duplication and clustering strategy to reduce makespan and energy consumed by processors in an effort to execute Directed Acyclic Graph (DAG) while satisfying the throughput constraint. The main idea behind EATSDCD intended to minimize energy consumption in the second phase. After determining the critical path and specifying a set of dependent tasks in non-critical paths, the slack time for each task in non-critical paths was distributed among all dependent tasks in that path. Then, the frequency of DVFS-enabled processors is scaled down to execute non-critical tasks as well as idle and communication phases, without extending the execution time of tasks. Finally, a testbed is developed and different parameters are tested on the randomly generated DAG to evaluate and illustrate the effectiveness of EATSDCD. It was also compared against duplication and clustering-based algorithms and DVFS-based algorithms. In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against Power Aware List-based Scheduling (PALS) and Power Aware Task Clustering (PATC) algorithms, respectively. Furthermore, there is 16% improvement over Parallel Pipeline Latency Optimization (PaPilo) algorithm with Encur = 1.2Enmin (G). In comparison with Reliability Aware Scheduling with Duplication (RASD) algorithm, the execution time has been reduced in heterogeneous environments. Show more
Keywords: Green computing, cloud data centers, dynamic voltage and frequency scaling (DVFS), task duplication, energy consumption, slack time, throughput
DOI: 10.3233/JIFS-171927
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5135-5152, 2019
Authors: Shi, Yi | Shi, Fu-Gui
Article Type: Research Article
Abstract: In this paper, the concepts of (L , M )-fuzzy internal relations and (L , M )-fuzzy enclosed relations between two L -fuzzy sets are introduced. They are defined respectively to be M -fuzzy subsets of L X × L X satisfying a set of axioms. A categorical approach is provided to present these relations. It is proved that the category of (L , M )-fuzzy internal relation spaces, the category of (L , M )-fuzzy enclosed relation spaces and the category of (L , M )-fuzzy topological spaces are isomorphic. In addition, some (L , M )-fuzzy …internal relations and (L , M )-fuzzy enclosed relations are naturally constructed from (L , M )-fuzzy quasi-uniformities and (L , M )-fuzzy S-quasi-proximities. Show more
Keywords: (L, M)-fuzzy topology; (L, M)-fuzzy internal relation; (L, M)-fuzzy enclosed relation; (L, M)-fuzzy IRDP mapping; (L, M)-fuzzy ERDP mapping
DOI: 10.3233/JIFS-172129
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5153-5165, 2019
Authors: Chang, Furong | Zhang, Bofeng | Li, Haiyan | Huang, Mingqing | Li, Bingchun | Zhao, Yue
Article Type: Research Article
Abstract: Methods for detecting overlapping communities are essential for understanding complex network structures and extracting implied information. Traditional community detection algorithms have been proven to be unsatisfactory when the network community structure is relatively fuzzy. In this paper, we proposed a novel overlapping community discovery algorithm (ENFI) to address this problem on the micro level using ego-nets. The ENFI approach exploits the micro-characteristics of ego-nets, extracts the ego-net’s local community by calculating the friend intimacy, and then forms the overlapping communities of the network. We conducted experiments on both synthetic and real-world social networks using normalized mutual information (NMI) and overlapping …community modularity as evaluation criteria. The results demonstrated that the proposed ENFI algorithm can detect community structures in complex networks more efficiently and accurately than existing state-of-the-art algorithms. Show more
Keywords: Ego-net, overlapping community, friend intimacy, complex network
DOI: 10.3233/JIFS-172242
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5167-5175, 2019
Authors: Wu, Huanrong | Zhou, Xiangnan | Li, Qingguo | Zhang, Huarong
Article Type: Research Article
Abstract: In this paper, we redefine the concept of prime L -fuzzy ideals of an ordered semigroup so that the prime L -fuzzy ideals are not necessarily 2-valued. Then a topological space, called the spectrum of prime L -fuzzy ideals of an ordered semigroup, has been obtained and some topological properties like separation axioms, compactness, connectedness are researched. Further, a contravariant functor from the category of commutative ordered semigroups into the category of compact and connected topological spaces is gotten. Finally, we focus on the subspace which is defined in the set of all minimal prime ideals in an ordered semigroup …S and show that if S is commutative, then this subspace is Hausdorff, totally disconnected and completely regular. Show more
Keywords: Ordered semigroup, L-fuzzy ideal, Spectrum, Minimal prime L-fuzzy ideal, Contravariant functor
DOI: 10.3233/JIFS-172286
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5177-5187, 2019
Authors: Kumar, Sushil | Tripathi, Bipin Kumar
Article Type: Research Article
Abstract: There are various high-dimensional engineering and scientific applications in communication, control, robotics, computer vision, biometrics, etc.; where researchers are facing predicament to fabricate an intelligent and robust neural system which can process higher dimensional information efficiently. In various literatures, the conventional neural networks based only on real valued, are tried to solve the problem associated with high-dimensional parameters, but these neural network structures possess high complexity and are very time consuming and weak to noise. These networks are also not able to learn magnitude and phase values simultaneously in space. The quaternion is the number, which possesses the magnitude in …all four directions and phase information is embedded within it. This paper presents a learning machine with a quaternionic domain neural network that can finely process magnitude and phase information of high dimension data without any hassle. The learning and generalization capability of the proposed learning machine is performed through chaotic time series predictions (Lorenz system and Chua’s circuit), 3D linear transformations, and 3D face recognition as benchmark problems, which demonstrate the significance of the work. Show more
Keywords: Quaternion, quaternionic domain neural network, 3D motion, 3D imaging, chaotic time series prediction
DOI: 10.3233/JIFS-17461
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5189-5202, 2019
Authors: Sumathi, R. | Sujatha, R. | Sundareswaran, R.
Article Type: Research Article
Abstract: — In this paper, new concepts are introduced to enhance the process of block identification in fuzzy graphs using sum distance metric and special type of cycles, which is called a strongest strong cycle, locamin cycle. An Algorithm is developed for retrieving information from Big-Data using block identification.
Keywords: Sum distance metric, strongest strong cycle (SSC), θ-fuzzy graph, Big-Data
DOI: 10.3233/JIFS-17707
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5203-5211, 2019
Authors: Fallah, M. | Tavakkoli-Moghaddam, R. | Alinaghian, M. | Salamatbakhsh-Varjovi, A.
Article Type: Research Article
Abstract: The purpose of this paper is to examine and evaluate a new mathematical model of vehicle routing problem in order to optimize fuel consumption and maximize commercial profitability under the conditions of uncertainty of distributor service to customers using robust approach under scenario. According to the real world, distribution companies are interested in minimizing consumption of fuel in the distribution of goods for two reasons: the first reason is that reducing the consumption of fuel will reduce the current costs of distribution companies and ultimately increase their profits. The second reason is that reducing fuel consumption will reduce the harmful …effects of greenhouse gases and air pollution. Other words, distribution companies operate in a competitive environment that has more than one distributor in the distribution network, and start time for serving customers has a significant impact on the profitability of the distributors. To calculate the efficiency of the proposed model, we used differential evolution (DE) algorithm and Particle swarm optimization (PSO), and the results were compared in small and medium scales with the results of the exact solution method. To verify proceeds of proposed algorithms in large scales, a number of sample problems were created in large scales and the figures were evaluated. The computational results indicate that DE algorithm has a better computational function, but the PSO algorithm has better computational time. Show more
Keywords: Green periodic VRP, robust optimization, Fuel consumption, Particle swarm optimization algorithm
DOI: 10.3233/JIFS-179323
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5213-5225, 2019
Authors: Shukla, Amit K. | Muhuri, Pranab K.
Article Type: Research Article
Abstract: The Decision making has been a major research topic in the computing literature for so long due to its vast significance in many real-world applications. Traditional fuzzy decision making (FDM) approaches have limitations due to the inability of the type-1 fuzzy sets (T1 FSs) in modeling higher order uncertainties. Since, the membership function (MF) of an interval type-2 fuzzy set (IT2 FS) is also fuzzy, as superior to T1 FSs, researchers considered IT2 FSs to model higher level of uncertainties in FDM and proposed a number of IT2 FDM methods. However, unlike IT2 FSs, general type-2 fuzzy sets (GT2 FSs) …do not consider equal secondary membership values for all its primary membership functions. Hence, GT2 FSs offer more suitability in modelling uncertainties that exist in real-world scenarios. Thus, this paper proposes a more efficient decision making method called the “GT2 Fuzzy Decision Making (GT2 FDM)”, which considers GT2 FSs to model the fuzzy goals and fuzzy constraints in a problem. The working of the proposed approach is demonstrated using an example of room temperature selection. Then we have applied it to the problem of convenient travel time selection using a real-time traffic data set. It is observed that the proposed GT2 FDM approach offers more flexibility to the decision makers in choosing an optimal solution from a much wider solution space and hence is found to be more efficient than the IT2 FDM and classical FDM approaches. Show more
Keywords: Decision making, General type-2 fuzzy sets, Centroid defuzzification, Bibliographic analysis, Real-time traffic dataset
DOI: 10.3233/JIFS-18071
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5227-5244, 2019
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