Journal of Intelligent & Fuzzy Systems - Volume 36, issue 6

Authors: Tan, Bo | Guo, Jingbo | Chang, Guang | Xu, Qingfeng

Article Type: Research Article

Abstract: The estimation and detection of a weak magnetic dipole signal is a critical problem in magnetic target detection. The difficulty arises due to a latent variable in the model, which affects the estimation and detection performance, especially at low signal-to-noise ratios (SNRs). A non-probability-distribution expectation maximization (NPD-EM) algorithm is proposed to estimate the magnetic dipole signal with the latent variable at low SNRs. A reasonable value of an intermediate variable instead of the optimal one is determined without any probability information in the iteration of the NPD-EM algorithm, which overcomes an unknown probability distribution appearing in the traditional expectation maximization …(EM) algorithm and reduces the calculated amount by 3 orders of magnitude compared with the traditional EM algorithm. A statistic based on the NPD-EM algorithm representing an unbiased estimator of the target signal energy is constructed to detect the magnetic dipole signal at low SNRs, and an innovative compensation in the detector is introduced so as to reduce the noise influence on the statistic. The experiment results show that, the constructed detector is comparable to the ideal matching filter due to the attractive performance of the NPD-EM algorithm and the outstanding statistic. Show more

Keywords: EM Algorithm, estimation and detection, weak magnetic signal, latent variable, low signal-to-noise ratio

DOI: 10.3233/JIFS-18803

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6669-6684, 2019

Authors: Liu, Qian | Yang, Feng | Li, Ce

Article Type: Research Article

Abstract: Binarized normed gradients (BING) can be utilized as a preprocessing step for generic object proposal generation, and has attracted great attention because of its fast running and appropriate generalization performance. Recently, although some modified schemes were presented to improve the proposal localization quality, the mechanism of enhancing the performance is still an open problem. In this paper, Adaptive weighted binary normed gradients plus (AWBING Plus) algorithm is proposed, based on the BING method, which replaces the support vector machine (SVM) with adaptive weighted extreme learning machine (Adaptive WELM) to reduce the number of proposals, as well as comparable performance, by …using the multi-thresholding straddling expansion (MTSE) as the post-processing stage to enhance the localization quality. We explain the methodology of WELM applied to BING, and analyzed the effect of the improved WELM algorithm, which is named Adaptive WELM. The experimental results from PASCAL VOC2007, Microsoft COCO2014 and ILSVRC2013 show that the proposed approach achieved superior performance compared with other advanced methods on generic object proposal generation, and it runs faster as well. Show more

Keywords: generic object proposal generation, imbalanced data, BING, WELM

DOI: 10.3233/JIFS-18810

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6685-6701, 2019

Authors: Santamaria, Jorge L. | Valentin, Vanessa | Ross, Timothy J.

Article Type: Research Article

Abstract:  Concrete is influenced by affecting conditions during its production and construction processes. The literature reveals that there is limited understanding about the effect of unstructured factors (i.e., construction site factors) on concrete product. Crew experience, compaction method, mixing time, curing humidity, and curing temperature were selected to quantify their impact on concrete compressive strength, fabrication cost, and production rates. Each response was measured and utilized for fuzzy modeling. A Sugeno-type fuzzy inference system (FIS) was obtained for quantifying each response. Model validation was accomplished by plotting predicted versus experimental data while sensitivity analysis used Monte Carlo simulation and Spearman’s correlation …coefficients. Curing temperature was identified as the most influential factor for concrete compressive strength while mixing time was identified to have the largest impact on concrete cost and production rates. All FISs can be used as supporting tools to discern desired concrete operating conditions. Show more

Keywords: Construction site factors, concrete strength

DOI: 10.3233/JIFS-18950

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6703-6715, 2019

Authors: Guler Bayazit, Nilgun | Bayazit, Ulug

Article Type: Research Article

Abstract:  In the conventional fuzzy k-NN classification rule, the vote cast by each nearest neighboring known (labelled) sample on the class membership grades of the unknown (unlabelled) sample is formed by weighting the nearest neighbor’s class membership grades by the inverse of the nearest neighbor’s distance to the unknown sample. This paper proposes a modification of the weight (distance) used for each nearest neighbor by employing the geometrical relation among the nearest neighbor, its most informative known neighbor of the same class and the unknown sample. It is also proposed that this modification be only (conditionally) applied when the feature vector …of the unknown sample lies outside the convex hull of the feature vectors of the known samples of each class. Results on a large number of datasets from the UCI and KEEL repositories and synthetically generated datasets show that, in return for a modest increase in classification complexity over the original fuzzy k-NN rule, the proposed fuzzy k-NN rule offers a better classification accuracy than the accuracies of the original fuzzy kNN rule and most other nearest neighbor type algorithms. Show more

Keywords: k-nearest-neighbor, fuzzy, classification, distance, convex hull

DOI: 10.3233/JIFS-18974

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6717-6729, 2019

Authors: Kuo, R.J. | Cheng, W.C.

Article Type: Research Article

Abstract:  In this study, an intuitionistic fuzzy neural network (IFNN) with Gaussian membership function and Yager-generating function is proposed. Since intuitionistic fuzzy logic (IFL) considers membership, non-membership and hesitation values simultaneously, the incorporation of the concept of IFL into a fuzzy neural network (FNN) can enhance the performance of an FNN. A back-propagation learning algorithm is developed to optimize the IFNN parameters and weights. The proposed IFNN is applied to ten problems, including nonlinear control and prediction problems. The computational results indicate that the proposed IFNN is more efficient than conventional algorithms, such as artificial neural networks (ANN), fuzzy neural networks …(FNN), and a support vector regression (SVR). Show more

Keywords: Fuzzy neural network, intuitionistic fuzzy sets, fuzzy systems

DOI: 10.3233/JIFS-18998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6731-6741, 2019

Authors: de Campos Souza, Paulo Vitor | Rezende, Thiago Silva | Guimaraes, Augusto Junio | Araujo, Vanessa Souza | Batista, Lucas Oliveira | da Silva, Gabriel Adriano | Silva Araujo, Vinicius Jonathan

Article Type: Research Article

Abstract: The growth of the computerization of processes and services has changed human relations and, as a consequence, have created new forms of attacks and frauds for users of digital equipment. Because many people use computers, smartphones, and e-mail to perform day-to-day tasks, various data traffic is susceptible to attack. This can undermine the competitiveness of a company that may have breached strategic information. Therefore, security and information management are fundamental factors for companies to keep due control and management of their business knowledge. Cyber attacks are represented by a growing worldwide scale of secrecy breach of relevant information and are …characterized as one of the significant challenges of the contemporary world. This article aims to propose a computational system based on intelligent hybrid models, which through fuzzy rules allows the construction of expert systems in attacks on cybernetic data of diverse natures. The tests were carried out with real bases of attacks on the database of governmental computerized devices. The model proposed in this paper uses fuzzy evolving data grouping concepts. The extreme learning machine performs the training and the logical neurons of the unineuron type are responsible for creating fuzzy rules capable of transforming the knowledge acquired by the model into a database for employee training in companies, construction of other computer systems and awareness of elements which may harm the integrity of the data of individuals and companies. The novelty of the intelligent technique presented in the paper is that the nature of cyber attacks defines the structure of the model because the techniques of fuzzification and regularization are based entirely on the complexity of the cybernetic invasions. The binary pattern classification tests confronted with traditional models of the literature prove that the proposal of this paper can maintain the accuracy of detection of cyber attacks and still manages to construct a set of rules that serve as knowledge for the companies that wish to protect their information from attacks devices. Show more

Keywords: Evolving fuzzy neural network, cyber attack, cyber protection, knowledge management

DOI: 10.3233/JIFS-190229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6743-6763, 2019