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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: Qin, Yechen | Langari, Reza | Wang, Zhenfeng | Xiang, Changle | Dong, Mingming
Article Type: Research Article
Abstract: Inspired by unsupervised feature learning and deep learning, this paper provides a novel classification method for advanced suspension system based on Deep Neural Networks (DNNs). Sparse autoencoder and softmax regression are chosen to form deep structure and the parameters are trained by deep learning. Aiming at showing the superiority of DNNs based road classification method, a simulation of a B-class vehicle with skyhook control is performed in CarSim, and three measurable system responses, i.e., centre of gravity (C.G.) of sprung mass acceleration, rattle space and unsprung mass acceleration are chosen and three independent classifiers are established. Simulation results show that …the classifier using unsprung mass acceleration has the highest accuracy and better performance than existing methods. Because of the adaptive learning ability and the deep structure, the proposed method can save work and provide higher classification accuracy. Show more
Keywords: Deep Neural Networks (DNNs), road classification, semi-active suspension system, Deep Learning (DL)
DOI: 10.3233/JIFS-161860
Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1907-1918, 2017
Authors: Li, Zhengfei | Liu, Peide | Qin, Xiyou
Article Type: Research Article
Abstract: Since linguistic intuitionistic fuzzy numbers (LIFNs) can more easily express the fuzzy information in a qualitative setting, it is necessary to extend their operational rules and develop the decision making method based on the LIFNs. In this paper, we propose some new operational laws such as the subtraction and division, score function and accuracy function for (LIFNs), and we develop the linguistic intuitionistic fuzzy entropy, which can measure not only the fuzziness but also the intuitionism. Then we propose a method for determining the objective weight of attributes by the proposed entropy. In addition, because the VIKOR (from Serbian: VIseKriterijumska …Optimizacija I Kompromisno Resenje) method is an effective decision making tool which can select the optimal alternative by the maximizing “group utility” and minimizing “individual regret”, we extended the VIKOR method to process the linguistic intuitionistic fuzzy information (LIFI) based on the new operational laws, and proposed an extended VIKOR method for the multiple attribute decision making (MADM) problems with LIFI. An illustrative example shows the effectiveness and advantages of the proposed approach. Show more
Keywords: Multiple attribute decision making, linguistic intuitionistic fuzzy numbers, new operational laws, linguistic intuitionistic fuzzy entropy, VIKOR method
DOI: 10.3233/JIFS-17488
Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1919-1931, 2017
Authors: Kalirajan, K. | Sudha, M.
Article Type: Research Article
Abstract: This article presents a novel moving object detection algorithm using median-based scale invariant local ternary pattern for intelligent video surveillance system. Both the texture and color local features are extracted from the incoming frames independently and they are combined at the classification level to improve the object detection results. Here, each incoming image frames are subdivided into several regions and the median-based scale invariant local ternary pattern (MD-SILTP) is obtained for each sub-region. Based on the MD-SILTP patterns, the texture histograms are computed and matched with the background model using the histogram intersection method. Furthermore, the color features are extracted …through color histogram matching technique. The background model is then updated based on the best matching texture and color histograms. Finally, the color and texture information are combined for final feature classification. Experiment results illustrate that the fusion of MD-SILTP texture with the color features is stable than the others under smooth surface regions, image noises due to illumination changes, moving cast shadow, and scaling problems. Show more
Keywords: Moving object detection, median-SILTP pattern, color histogram, video surveillance, background modelling
DOI: 10.3233/JIFS-162231
Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1933-1943, 2017
Authors: Piasecki, Krzysztof
Article Type: Research Article
Abstract: The entropy measure is predefined as a mapping, increasing with an increase in the fuzziness of evaluated set. The relation “is less fuzzy than” is strictly modeled by an inequality fulfilled by membership functions. This inequality is systematically repeated across the literature. In this paper we show that it is incorrect. We prove that the relation “is less fuzzy than” should be modeled by an inclusion. Obtained conclusions are applied in a more precise definition of entropy measure.
Keywords: Fuzzy set, intuitionistic fuzzy set, entropy measure, ambiguity, indistinctness, undecidability
DOI: 10.3233/JIFS-15364
Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1945-1952, 2017
Authors: Wang, Meng-Xian | Wang, Jian-Qiang | Li, Lin
Article Type: Research Article
Abstract: WEB 2.0 facilitates the bidirectional communication capabilities of online review, causing the personalization and asymmetry of online review. Despite the problem of online personalized recommendation system, the influence of consumer characteristics on consumer repurchase intention is insufficiently examined in the extant literature. To address this issue, this study proposes a new online personalized recommendation approach based on the perceived value of consumer characteristics. Two aspects of the proposed framework are addressed. The first aspect is the linguistic information transformation model, which converts online reviews to unbalanced linguistic label cloud. The second aspect is an online recommendation approach based on the …linguistic information trans-formation model. A series of experiments are conducted based on a set of hotel assessment data from four cities and the electronic consumer record of four consumers selected randomly. Results show that the proposed cluster method is useful for identifying consumer characteristics and gives personalized recommendation. Overall, this method reduces computation and provides a reference point based on consumer characteristics. Show more
Keywords: Consumer heterogeneity, cloud model, cluster method, perceived value, unbalanced linguistic term set
DOI: 10.3233/JIFS-17034
Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1953-1968, 2017
Authors: Sadrykia, Mansoureh | Delavar, Mahmoud Reza | Zare, Mehdi
Article Type: Research Article
Abstract: Seismic vulnerability assessment is a critical topic in disaster management. It is a complex uncertain spatial decision making problem due to lack of complete data, vagueness of experts’ comments in addition to uncertainties in the numerical data/relations. This paper presents a new Geospatial Information System (GIS)-based multi-criterion decision-making (MCDM) method developed for predicting building damages prior to the occurrence of a potential earthquake scenario which considers different sources of uncertainty to make realistic assessments. The developed method suggests an approximate reasoning approach through using Fuzzy Sets theory (FST) and enhanced Dempster-Shafer theory (DST). FST handles the vagueness of the heuristic …knowledge on ‘importance weights of the selected criteria’ and ‘the relationship of the criteria with physical seismic vulnerability (PSV)’. The enhanced DST is used for fusion of the information by taking into account the reliability of the adopted criteria. The proposed method’s applicability is tested on existing buildings of a municipality district of Tabriz, a historical and earthquake prone city in Iran. The implementation results confirm that the proposed method is a pragmatic, rational and simple model which reduces uncertainties of PSVA to provide realistic predictions essential for assisting planners and administrators with reducing future earthquake losses in urban areas. Show more
Keywords: Seismic vulnerability assessment, uncertainty, fuzzy sets, Dempster-Shafer theory of evidence, multi-criteria decision making (MCDM)
DOI: 10.3233/JIFS-17075
Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1969-1981, 2017
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