Journal of Intelligent & Fuzzy Systems - Volume 36, issue 4

Authors: Pham, Van-Huy | Jo, Kang-Hyun | Hoang, Van-Dung

Article Type: Research Article

Abstract:  In recent years, action recognition techniques have played an increasingly important role in autonomous systems. However, the computational costs and precision of action recognition algorithms are still major challenges. Recently, a deep learning approach was proposed to obtain a higher accuracy, but large and deep neural networks have high computational costs. This paper presents a new approach that allows for a significant reduction in computational time while slightly increasing the accuracy. The contribution consists of two parts: a scalable feature extraction method (SFE) and a hybrid model of different classifiers. First, the SFE method is proposed for application to histogram …orientation-based feature descriptors, such as the histogram of orientated gradient (HOG), histogram of optical flow (HOF), and the motion boundary histogram (MBH). An advantage of SFE is its ability to quickly compute features. Scalable feature extraction enables accurate approximation of features extracted from traditional image pyramids by efficiently using only the original image. Our method is inspired by a special data structure used for storing basic information of optical flow and image gradients, which are computed from the original image and then used to extract features across multiple scales of the feature region without recomputing the image gradients and optical flow. Second, we focus on a hybrid classification method based on a linear support vector machine (SVM) and hidden conditional random field (HCRF) model that improves the recognition precision. This effort shows that a combination of SVM and HCRF models provides a better accuracy than the traditional approaches. Experimental results illustrate that the proposed approach allows for both a significant reduction in computational time and an improved accuracy. Show more

Keywords: Action recognition, hybrid classification, local feature descriptor, scalable feature extraction

DOI: 10.3233/JIFS-181085

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3357-3372, 2019

Authors: Wang, Kai | Shi, Fu-Gui

Article Type: Research Article

Abstract: In this paper we introduce the concept of strong L -convexity by making use of the fuzzy inclusion order on the fuzzy power set. And the category of strong L -convex spaces is topological and is a co-reflective subcategory of the category of bi-stratified L -convex spaces. It is apparently different from the strong L -topology in the sense of Zhang that is exactly bi-stratified L -topology. Finally, we present that strong L -convexities can be characterized by algebraic L -ordered closure operators.

Keywords: Fuzzy inclusion order, fuzzy convexity, strong L-convexity, algebraic L-ordered closure operator

DOI: 10.3233/JIFS-181103

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3373-3383, 2019

Authors: Bulbul, Mohammad Farhad | Islam, Saiful | Ali, Hazrat

Article Type: Research Article

Abstract: This paper introduces a method for identifying human actions in depth action videos. We first generate the corresponding Motion History Images (MHIs) and Static History Images (SHIs) to an action video by utilizing the so-called 3D Motion Trail Model (3DMTM). We then extract the Gradient Local Auto-Correlations (GLAC) features from the MHIs as well as SHIs to characterize the action video. Next, we concatenate the set of MHIs based GLAC features with the set of SHIs based GLAC features to gain a single action representation vector. Thus, the computed feature vectors in all action samples are passed to the l2-regularized …Collaborative Representation Classifier (l2-CRC) for recognizing multiple human actions effectively. Experimental evaluations on three action datasets, MSR-Action3D, DHA and UTD-MHAD, reveal that the proposed recognition system attains superiority over the state-of-the-art approaches considerably. In addition, the computational efficiency test indicates the real-time compatibility of the system. Show more

Keywords: Human action recognition, l2-regularized Collaborative Representation Classifier, motion history images, static history images, 3D Motion Trail Model

DOI: 10.3233/JIFS-181136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3385-3401, 2019

Authors: Sudha, K. | Suguna, N.

Article Type: Research Article

Abstract:  Anomaly detection in sentiment mining refers to detecting user’s abnormal sentiment patterns in a large collection of sentiment data. The anomalies detected may be due to rapid sentiment changes that are hidden in a huge amount of text. The anomaly of sentiment data sources is a foremost factor in affecting the efficiency of sentiment classification methods. Thus, analyzing sentiment data to identify abnormal sentiment patterns in a timely manner is a valuable topic of research. In this work, it is analyzed how anomaly detection and elimination can aid sentiment classification and hence enhance sentiment mining. This paper proposes a model …that combines the proposed anomaly detection method with meta-classification method to detect and eliminate anomalies and classify user’s sentiments. This paper also focuses on identifying the optimum percentage of data to be eliminated as anomalies after detection, so as to perform sentiment classification effectively on movie review data. The results exhibit the capabilities of the proposed method and offer better insight into this area of research. Show more

Keywords: Anomaly detection, sentiment analysis, machine learning, classification, sentiment classification, social media

DOI: 10.3233/JIFS-181138

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3403-3412, 2019

Authors: Rasham, Tahair | Mahmood, Qasim | Shahzad, Aqeel | Shoaib, Abdullah | Azam, Akbar

Article Type: Research Article

Abstract:  The purpose of this paper is to find out common fixed point results for two families of fuzzy mappings fulfilling generalized rational type A -dominated contractive conditions on a closed ball in complete dislocated b -metric space. Example is also given which shows the novelty of our results.

Keywords: Fixed point, closed ball, two families of fuzzy mapping, dislocated b-metric space

DOI: 10.3233/JIFS-181153

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3413-3422, 2019

Authors: Zhu, Xiaomin | Bai, Kaiyuan | Wang, Jun | Zhang, Runtong | Xing, Yuping

Article Type: Research Article

Abstract:  The recently proposed partitioned Bonferroni mean (PBM) can effectively deal with situations in which attributes are partitioned into several parts, and attributes in the same part are dependent, whereas the attributes in different parts are independent. The power average (PA) operator has the capacity of reducing the negative effects of decision makers’ unreasonable assessments on the decision result. To take advantages of PBM and PA, we propose a family of Pythagorean fuzzy aggregation operators based on the interaction operational laws of Pythagorean fuzzy numbers (PFNs), such as the Pythagorean fuzzy interaction power PBM (PFIPPBM), the Pythagorean fuzzy interaction power partitioned …geometric Bonferroni mean (PFIPPGBM), and the weighted forms of PFIPPBM and PFIPPGBM. The proposed operators can not only handle the situations where attributes are partitioned into several parts and attributes in the same part are interrelated, but also reduce the bad influence of unreasonable assessments on the decision result, and simultaneously consider the interactions between membership and non-membership degrees. Based on the proposed operators, a novel approach to multiple attribute group decision making (MAGDM) is proposed and a numerical instance as well as comparative analysis is conducted to demonstrate the validity and superiorities of the proposed approach. Show more

Keywords: Pythagorean fuzzy sets, multi-attribute group decision making, partitioned Bonferroni mean, power average operator, Pythagorean fuzzy interaction power partitioned Bonferroni mean

DOI: 10.3233/JIFS-181171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3423-3438, 2019

Authors: Aiyub, M. | Esi, A. | Subramanian, N.

Article Type: Research Article

Abstract: This paper is devoted to first, the definition of new rough statistical convergence with Poisson Fibonacci binomial matrix is given and some general properties of rough statistical convergence are examined. Second, approximation theory worked as a rate of the rough statistical convergence.

Keywords: Rough statistical convergence, natural density, triple sequences, Chi sequence, Korovkin type approximation theorems, poisson Fibonacci matrix, positive linear operator, 40F05, 40J05, 40G05

DOI: 10.3233/JIFS-181189

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3439-3445, 2019

Authors: Lin, Chih-Hong

Article Type: Research Article

Abstract:  A six-phase copper rotor induction motor (SCRIM) drive system causes a lot of nonlinear effects such as nonlinear uncertainties. To obtain better performance, the backstepping control system using switching function is firstly proposed for controlling the SCRIM drive system. To reduce chattering in control effort, the backstepping control system using revamped recurrent fuzzy neural network (RFNN) with mended ant colony optimization (ACO) is secondly proposed for controlling the SCRIM drive system to raise robustness of system. Furthermore, four variable learning rates of the weights in the revamped RFNN are adopted by using mended ACO to speed-up parameter’s convergence. Finally, comparative …performances through some experimental results are verified that the proposed backstepping control system by means of revamped RFNN with mended ACO has better control performances than the other methods for the SCRIM drive system. Show more

Keywords: Ant colony optimization, backstepping control, Lyapunov stability, recurrent fuzzy neural network, six-phase copper rotor induction motor

DOI: 10.3233/JIFS-181201

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3447-3459, 2019

Authors: Malik, Nosheen | Shabir, Muhammad

Article Type: Research Article

Abstract: The uncertainty in the data is a hurdle in decision-making problems. Rough set theory and fuzzy set theory are built to handle the uncertainty in data. We introduce the rough bipolar fuzzy sets as a hybridization of rough sets and the bipolar fuzzy sets. After that, we discuss a group decision making problem with the data having fuzziness endowed with bipolarity and iron out this problem by applying the rough bipolar fuzzy sets. We also propose an algorithm for this problem, which yields the best decision, as well as, the worst decision between some objects.

Keywords: Rough sets, bipolar fuzzy sets, approximation space, lower and upper approximations, group decision making

DOI: 10.3233/JIFS-181223

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3461-3470, 2019

Authors: Mehta, Brijesh | Rao, Udai Pratap | Gupta, Ruchika | Conti, Mauro

Article Type: Research Article

Abstract:  Various sources and sophisticated tools are used to gather and process the comparatively large volume of data or big data that sometimes leads to privacy disclosure (at broader or finer level) for the data owner. Privacy preserving data publishing approaches such as k -anonymity, l -diversity, and t -closeness are very well used to de-identify data, however, chances of re-identification of attributes always exist as data is collected from multiple sources such as public web, social media, Internet whereabouts, and sensors that are highly prone to data linkages. In literature, k -anonymity stands out amongst the most popular mainstream data …anonymization approaches that can also be used for large sized data. However, applying k -anonymization for variety of data (especially unstructured data) is difficult in the traditional way, due to the fact that it requires the given data to be classified into the personal data, the quasi identifiers, and the sensitive data. We identify existing approaches from the literature of Natural Language Processing(NLP) to convert the unstructured data to structured form in order to apply k -anonymization over the generated structured records. We adopt a two phase Conditional Random Field (CRF) based Named Entity Recognition (NER) approach to represent unstructured data into the structured form. Further, we propose an Improved Scalable k -Anonymization (ImSKA) to anonymize the well represented unstructured data that achieves privacy preserving unstructured big data publishing. We compare both of the propose approaches namely NER and ImSKA with existing approaches and the results show that our proposed solutions outperform the existing approaches in terms of F 1 score and Normalized Cardinality Penalty (NCP), respectively. Since, NER approaches are widely used for bio-medical datasets, we have also used a well-known Bio-NER dataset called GENIA corpus for measuring the performance. Show more

Keywords: Privacy preserving big data publishing, unstructured data privacy, named entity recognition, k-anonymity, scalable k-anonymization

DOI: 10.3233/JIFS-181231

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3471-3482, 2019