Journal of Intelligent & Fuzzy Systems - Volume 31, issue 2

Authors: Lv, Zhi-Ying | Zheng, Li-Wei | Liang, Xi-Nong | Liang, Xue-Zhang

Article Type: Research Article

Abstract: A fuzzy multiple attribute decision making method is investigated, there the weights are given by interval numbers, the qualitative attribute values are first given by linguistic terms and then are represented as the form of triangular fuzzy numbers, and the quantitative attribute values are given by the form of triangular fuzzy numbers. A possibility degree formula for the comparison between two trapezoidal fuzzy numbers is proposed. Then, using this possibility degree formula, possibility degree matrices are built and the central dominance of one alternative outranking all other alternatives is defined under one attribute. According to the ordered weighted average (OWA) …operator, an approach is presented to aggregate the possibility degree matrices based on attributes and then the most desirable alternative is selected. This fuzzy multiple attribute decision making method is used in the field of financial investment evaluation, and the set of attributes of the decision making program is built by financial analyses and accounting reports in the same industry. Finally, numerical example is provided to demonstrate the practicality and the feasibility of the proposed method. Show more

Keywords: Possibility degree, multiple attribution decision making, trapezoidal fuzzy number, investment options, OWA operators

DOI: 10.3233/JIFS-169010

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 787-794, 2016

Authors: Yu, Siyang | Li, Kenli | Li, Keqin | Qin, Yunchuan | Tong, Zhao

Article Type: Research Article

Abstract: SM4 is a block cipher proposed by the Chinese government. Strengthening the research and extension of SM4 is significant to the development and promotion of Chinese cryptography standards. To date, research relevant to SM4 is rare. Thus, we propose the implementation of an SM4 algorithm resistant to power analysis. Ideally, a secure masking scheme is used for the SM4 cipher, which is particularly suited for implementation in the application specific integrated circuit. Moreover, the mask scheme in our chip implementation process is improved to make SM4 safer. Simulation results confirm that the use of counteractive measures resistant to power analysis …is credible. Show more

Keywords: SM4, S-box, Galois Field, mask, zero attack

DOI: 10.3233/JIFS-169011

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 795-803, 2016

Authors: Demiriz, Ayhan | Ekizoğlu, Betül

Article Type: Research Article

Abstract: This article presents a novel approach for detecting fraudulent behaviors from automated teller machine (ATM) usage data by analyzing geo-behavioral habits of the customers and describe the use of a fuzzy rule-based system capable of classifying suspicious and non-suspicious financial transactions. Firstly, the geographic entropies of ATM cardholders are computed from the spatio-temporal ATM transactions data to form customer classes of mobility. ATM transactions exhibit spatio-temporal properties by inclusion of location information. The transition data can be generated by using transaction data from the current location to the next one. Once, the transition data are generated, statistical outlier detection techniques …can be utilized. On top of classical methods, crisp unsupervised methods can easily be used for detecting outliers in the transition data. In addition, fuzzy C-Means algorithm can be implemented to determine outliers. In this study, ATM usage dataset containing around two years’ worth of data, provided by a mid-size Turkish bank was analyzed. It was shown that a significant bulk of ATM users does not leave the vicinity of their living places. Some insightful business rules that can be extracted from geo-tagged ATM transaction data by means of using a fuzzy rule-based system were also presented. Show more

Keywords: Location intelligence, fraud detection, ATM fraud, spatio-temporal outlier

DOI: 10.3233/JIFS-169012

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 805-813, 2016

Authors: Xiang, Zhiyang | Xiao, Zhu | Wang, Dong | Georges, Hassana Maigary

Article Type: Research Article

Abstract: The semi-supervised learning (SSL) problems are often solved by graph based algorithms, semi-definite programmings etc. These methods always require high space complexities, and thus are not efficient for network intrusion detection systems. Apart from the space complexity challenge, a network intrusion detection system should be able to handle the distribution drifting of data flow as well. A common solution for this concept drift problem is by SSL. In this paper, an incremental SSL training framework is proposed to combine the low space complexity advantage of topology learning and SSL for network intrusion detection. First, the unsupervised self-organizing incremental neural network …is extended to process labeled and unlabeled information incrementally. Second, a kernel function is constructed from the training results of the previous step. Finally, a kernel machine is trained with the constructed kernel function. The proposed method reduces the space complexity of SSL to the magnitude similar to supervised learning. The experiments are carried out on the NSL-KDD datasets, and the results show that the proposed method outperforms the mainstream methods such as Transductive Support Vector Machine and Label Propagation. Show more

Keywords: Metric learning, nonlinear embedding, self-organizing incremental neural network, semi-supervised learning

DOI: 10.3233/JIFS-169013

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 815-823, 2016

Authors: Yue, Liu | Wangwei, Ju | Jianguo, Zhao | Junjun, Gao | Jiazhou, Zheng | aiping, Jiang

Article Type: Research Article

Abstract: Demand forecasting is one of the most essential components of supply chain management, which directly influences a company’s overall performance and competitiveness. However, it is difficult to accurately forecast the demand of fashion products with short life cycle and high volatility characteristics such as footwear and apparel products. An integrated demand forecasting method named Improved ABC-PF is proposed in this paper based on Product Life Cycle (PLC) theory considering the characteristics of fashion products. First, a PLC model based on cubic polynomial which is divided into two stages by the best-selling point, is established instead of traditional PLC modeling methods. …Second, an improved Artificial Bee Colony (ABC) algorithm is utilized to optimize the parameters of the two-stage PLC function, which is conducted by initial population selection, optimization function design and convergence rate improvement. After that, an inventory control strategy based on PLC analysis is studied and applied in the “Precise Order” mode. Finally, the proposed method is validated by real-world data from a Chinese footwear and apparel retailer. After being compared with the other demand forecasting methods such as Moving Average (MA), Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN), it is indicated that the proposed improved ABC-PF method can achieve higher prediction accuracy and lower safety inventory level, which improve the overall profitability of the company, therefore generate product demand management insights for footwear and apparel enterprises. Show more

Keywords: Demand forecasting, product life cycle, artificial bee colony algorithm

DOI: 10.3233/JIFS-169014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 825-836, 2016

Authors: Imran, Mohammad | Afzal, Muhammad Tanvir | Qadir, Muhammad Abdul

Article Type: Research Article

Abstract: In recent years the number of new malware threats has increased significantly, causing a damage of billions of dollars globally. To counter this aggressive malware attack, the anti-malware industry needs to be able to correctly classify malware in order to provide defense against them. Consequently, malware classification has been an active area of research, and a multitude of malware classification approaches have been proposed in the literature. This paper evaluates two methods of sequence classification based on Hidden Markov Model, namely the maximum likelihood and similarity-based methods, for classification of malware using a large and comprehensive dataset. System calls generated …by known malware during execution are used as observation sequences to train the Hidden Markov Models. Malware samples are evaluated against the trained models to produce similarity vectors, which are used in the maximum likelihood and similarity-based classification schemes to predict the family for an unknown malware sample. Comparison of the two schemes shows that combining the powerful statistical pattern analysis capability of Hidden Markov Models and discriminative classifiers in the similarity-based method results in a significantly better classification performance as compared to the maximum likelihood approach. Furthermore, evaluation of different classifiers in the similarity-based method demonstrates that Random Forest classifier performs better than other classifiers on malware similarity vectors. Show more

Keywords: Malware classification, Hidden Markov Model, sequence classification, machine learning

DOI: 10.3233/JIFS-169015

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 837-847, 2016

Authors: Zhou, Xu | Zhou, Yantao | Xiao, Guoqing | Zeng, Yifu | Zheng, Fei

Article Type: Research Article

Abstract: With the rapid growth of uncertain data available in many real life applications, a probabilistic skyline query, namely P-skyline query, has been developed and received widespread concern. However, the P-skyline query usually reports results, which have dominant relationship. This contradicts with the incomparable property of skyline queries. Motivated by this, we extend the P-skyline query and formulate an EP-skyline (EPS) query. Thereafter, to develop the processing performance of EPS query, we utilize an index, PR-tree, to organize uncertain datasets and employ efficient pruning strategies to reduce the search space. Moreover, an effective algorithm is developed for the EPS query. Extensive …experiments verify that our EPS query could always return better query results than P-skyline query with much less CPU cost, I/O cost and memory cost. Show more

Keywords: Data management, skyline query, uncertain data

DOI: 10.3233/JIFS-169016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 849-858, 2016

Authors: Shanmuganathan, Subana | Li, Yan

Article Type: Research Article

Abstract: The census of population and dwellings undertaken by national state institutions world over at regular time intervals, is a fantastic source of information. However, there are major challenges to overcome when transforming the census data that usually consists of a vast number of attributes, into useful knowledge. In this paper, an artificial intelligent (AI) based approach is investigated to select appropriate attribute features that indicate interesting patterns in Beppu census wards in 2000 and 2010. The results of the self-organising map or SOM (unsupervised artificial neural network) based clustering, GIS visualisation and machine learning (J48 and JRip functions of WEKA), …provide relevant discerning features, new patterns and new knowledge that can be of use to many professionals, such as urban/transport planers and resources management. Show more

Keywords: Self-organising map clustering (SOM), JRip and J48 (WEKA), GIS mapping

DOI: 10.3233/JIFS-169017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 859-872, 2016

Authors: Huang, Liping | Zhang, Bin | Yuan, Xun | Zhang, Changsheng | Ma, Anxiang

Article Type: Research Article

Abstract: The multi-objective service selection problem is a basic problem in Service Computing and it is NP-Hard. This paper proposes a novel Bi-Ant colony optimization (NBACO) algorithm for this problem. Two objective functions related to response time and cost attributes are considered. For each objective, a heuristic function and a pheromone updating policy are defined against the characteristics of this problem. Then, a combined state transition rule is designed based on them. It uses preposition skyline query (PSQ) algorithm for each service class to reduce the candidate services at the beginning of NBACO. The algorithm has been tested in nine cases …and compared to the related MOACO algorithm and Co-Evolution algorithm for this problem. The efficiency of NBACO is greatly improved by using PSQ. The result demonstrates that our approach is effective and better than MOACO and Co-Evolution. Show more

Keywords: Multi-objective, service selection, PSQ, NBACO

DOI: 10.3233/JIFS-169018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 873-884, 2016

Authors: Wang, Hai | He, Ping | Yu, Ming | Liu, Linfeng | Do, Manh Tuan | Kong, Huifang | Man, Zhihong

Article Type: Research Article

Abstract: This study develops a novel vehicle stability control (VSC) scheme using adaptive neural network sliding mode control technique for Steer-by-Wire (SbW) equipped vehicles. The VSC scheme is designed in two stages, i.e., the upper and lower level control stages. An adaptive sliding mode yaw rate controller is first proposed as the upper one to design the compensated steering angle for enabling the actual yaw rate to closely follow the desired one. Then, in the implementation of the yaw control system, the developed steering controller consists of a nominal control and a terminal sliding mode compensator where a radial basis function …neural network (RBFNN) is adopted to adaptively learn the uncertainty bound in the Lyapunov sense such that the actual front wheel steering angle can be driven to track the commanded angle in a finite time. The proposed novel stability control scheme possesses the following prominent superiorities over the existing ones: (i) No prior parameter information on the vehicle and tyre dynamics is required in stability control, which greatly reduces the complexity of the stability control structure. (ii) The robust stability control performance against parameter variations and road disturbances is obtained by means of ensuring the good tracking performance of yaw rate and steering angle and the strong robustness with respect to large and nonlinear system uncertainties. Simulation results are demonstrated to verify the superior control performance of the proposed VSC scheme. Show more

Keywords: Finite time convergence, radial basis function neural network, robustness, steer-by-wire

DOI: 10.3233/JIFS-169019

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 885-902, 2016