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

Authors: Wang, Ting | Xu, Rui | Han, Xianhua | Chen, Yen-Wei | Ishizaki, Yoshitomo | Miyamoto, Masaru | Hattori, Tomohito

Article Type: Research Article

Abstract: The automatic inspection of throw-away tips is very important for quality control in precision cutting. We proposed an image processing based method for automatic inspection of the processing wear of throw-away tips. After image denoising, the proposed method utilized image-patch based principal component analysis method to enhance the cutting worn region while suppress the background region. Then the enhanced worn region was automatically segmented by a simple thresholding method followed by post-processing. The area of the segmented worn region was used as a measure of cutting wear degree. We collected three datasets of time-series images that recorded the processing of …throw-away tips on a product line. One dataset was used to choose optimal parameters of the proposed method, and the other two datasets were used for evaluate its performances. Experimental results showed that the proposed method was able to inspect the cutting wear with high accuracy. Additionally, it was also showed that the proposed method outperformed the conventional thresholding based method. Show more

Keywords: Principal component analysis, segmentation, worn region, throw-away tips, automatic inspection

DOI: 10.3233/JIFS-169020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 903-913, 2016

Authors: Cheng, Ching-Hsue | Yang, Jun-He

Article Type: Research Article

Abstract: Rainfall forecasting has been a popular research topic. Precise rainfall prediction can not only assist water management in region of water scarcity, but it can also warn or alleviate the effects of excess or insufficient rainfall. As a result of the advancement in information technology, current prediction methods are more diverse and sophisticated; however they require significant amounts of resources, and time are costly, and the forecast outcomes are still very uncertain. Therefore, this study proposed a novel rainfall forecast model, which combined the proposed integrated non-linear attributes selection method with support vector regression (SVR) to enhance the forecast performance. …First, the proposed integrated non-linear attribute selection method was employed to determine the important attributes that affect rainfall in the mountainous region of Taiwan, and then, the selected attribute data were input into the SVR model to train the rainfall forecast model. To assess the prediction performance of the proposed model, this study collected rainfall data from 2005 to 2014 at monitoring stations in the Taiwanese mountains, and compared the proposed model results with those of the listing models. Experimental results show that the proposed model outperforms the listing models in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Show more

Keywords: Component, feature selection, rainfall forecast, SVR, time series

DOI: 10.3233/JIFS-169021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 915-925, 2016

Authors: Jiang, Shengyi | Wang, Lianxi

Article Type: Research Article

Abstract: With the extensive increase of the amount of data, such as text categorization, genomic microarray data, bio-informatics and digital images, there are more and more challenges in feature selection. Recently, feature selection has been widely studied in supervised learning, but there is significantly less work in unsupervised learning because of the absence of class information and explicit search criteria. In this work, we introduce a new measure to assess the importance of features in terms of feature separability. A clustering-based feature selection algorithm is then introduced to conduct the feature selection. The proposed algorithm with nearly linear time complexity selects …final feature subset through a ranking procedure based on the separabilities of features and it is applicable to datasets of mixed nature. Experimental results on UCI datasets show that our method, by retaining relevant features, can obtain similar or even better results of classification and clustering for most datasets, and it outperforms other traditional supervised and unsupervised feature selection methods in terms of dimensionality reduction and classification accuracy. Show more

Keywords: Feature selection, feature separability, clustering, unsupervised learning

DOI: 10.3233/JIFS-169022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 927-937, 2016

Authors: Ekker, Knut

Article Type: Research Article

Abstract: An emergency management tool developed for training police, fire and ambulance teams in Norway and Sweden provided the data for this paper. The teams communicated with representatives of the local power companies and county and municipality officials in responding to various emergency scenarios in a web-based training tool. The project generated rich textual data of the content of the communications as well as a range of quantitative data on who communicated with whom, how often and with what type of information. The author analyzed the qualitative data using the NVivo software package and the quantitative analysis used the R statistics …package and the social network analysis (SNA) module. The textual analysis shows distinct patterns of concepts and terms used by the various emergency response agencies. The quantitative analysis illustrates the flow of communication among the participants of the emergency management training (EMT). Visual representation of both the qualitative and quantitative data from the project provides a thorough insight into processes of communication among emergency response personnel in role-playing training sessions. The data visualization enhances the debriefing session following emergency response training. The research group at Mid Sweden University and NORD University recently received funding for a three-year continuation of the project. The new project will emphasize the demand side (community stakeholders) in addition to the supply side (the emergency personnel). Show more

Keywords: Emergency management, crisis communication, training software, qualitative data, content analysis, quantitative data, R statistics, social network analysis, SNA

DOI: 10.3233/JIFS-169023

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 939-948, 2016

Authors: Zhang, Jieqiong | Yang, Kongyu

Article Type: Research Article

Abstract: Clustering analysis as one of the key components of data mining has been widely applied. This paper aimed to apply a clustering algorithm to classify and evaluate securities investment funds. It established a fund evaluation index system by researching the indexes that are influenced by the performance of funds. It drew upon domestic and foreign mature funds evaluation theory and used the data mining function of Excel to establish a clustering analysis model. Finally, this paper used 40 equity funds as sample data to conduct an empirical research. The cluster results would be beneficial in evaluating funds’ performance and guiding …the decision making on rational investment. Show more

Keywords: Clustering analysis, evaluation, securities investment funds

DOI: 10.3233/JIFS-169024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 949-956, 2016

Authors: Zheng, Weihua | Xiao, Shenping | Li, Kenli | Li, Keqin | Jiang, Weijin

Article Type: Research Article

Abstract: Discrete Fourier transform (DFT) finds various applications in signal processing, image processing, artificial intelligent, and fuzzy logic etc. DFT is often computed efficiently with Fast Fourier transform (FFT). The modified split radix FFT (MSRFFT) algorithm implements a length-N =2m DFT achieving a reduction of arithmetic complexity compared to split-radix FFT (SRFFT). In this paper, a simplified algorithm is proposed for the MSRFFT algorithm, reducing the number of real coefficients evaluated from 5/8N  - 2 to 15/32N  - 2 and the number of groups of decomposition from 4 to 3. A implementation approach is also presented. The approach makes data-path of …the MSRFFT regular similar to that of the radix-2 FFT algorithm. The experimental results show that (1) MSRFFT consumes less time on central processing units (CPUs) with sufficient cache than existing algorithms; (2) the proposed implementation method can save execution time on CPUs and general processing units (GPUs). Show more

Keywords: Fast Fourier transform (FFT), general processing unit (GPU) parallelism, modified split-radix (MSR), split-radix (SR)

DOI: 10.3233/JIFS-169025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 957-965, 2016

Authors: Luo, Jiawei | Lin, Dingyu | Cao, Buwen

Article Type: Research Article

Abstract: With the increasing of available protein-protein interaction (PPI) data, many computational methods have been explored to identify protein complexes from PPI networks. Majority of algorithms employ the feature of local neighbors to detect local dense subgraphs which correspond to protein complexes. Those approaches neglect the inherent core-attachment structure of protein complexes, which to an extent affect the protein complexes of prediction accuracy. In this paper, we propose a new algorithm for predicting protein complexes, deriving from the framework of the core-attachment. The proposed method first obtains the triangular structures of the core of protein complexes, name as cells, in which …the edge-clustering coefficient is used. And then the cells are expanded to protein complex cores based on the closeness. Finally, the attachments are added to their corresponding cores to form the final protein complexes. The experimental results on two yeast PPI data show our method outperform the existing algorithms in terms of matched protein complexes and biological significance using two benchmark data sets. Show more

Keywords: Core-attachment, protein complex, protein-protein interaction, triangular structure

DOI: 10.3233/JIFS-169026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 967-978, 2016

Authors: Deng, Xiaoheng | Pan, Yan | Shen, Hailan | Gui, Jingsong

Article Type: Research Article

Abstract: Influence maximization is a problem of identifying a small set of highly influential individuals such that obtaining the maximum value of influence spread in social networks. How to evaluate the influence is essential to solve the influence maximization problem. Meanwhile, finding out influence propagation paths is one of key factors in the assessment of influence spread. However, since nodes’ degrees are utilized by most of existent models and algorithms to estimate the activation probabilities on edges, node features are always ignored in the evaluation of influence ability for different users. In this paper, besides the node features, the Credit Distribution …(CD) model is extended to incorporate the time-critical aspect of influence in online social networks. After assigning credit along with the action propagation paths, we pick up the node which has maximal marginal gain in each iteration to form the seed set. The experiments we performed on real datasets demonstrate that our approach is efficient and reasonable for identifying seed nodes, and the influence spread prediction by our approach is more accurate than that of original method which disregards node features in the influence evaluation and diffusion process. Show more

Keywords: Online social networks, influence evaluation, influence maximization, credit distribution, greedy algorithm

DOI: 10.3233/JIFS-169027

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 979-990, 2016

Authors: Liu, Chang | Luo, Juan | Song, Yanchao

Article Type: Research Article

Abstract: Environment monitoring is one of the typical application scenarios of the wireless sensor networks. As an energy limited system, most of the energy consumption is for the data transmission. As a well-known principle, the difference among the physical parameters of adjacent nodes is approximate a constant. Eliminating these data to be transmitted will lead to remarkable energy saving. A correlative pattern based data aggregation mechanism following this principle is proposed in this paper, which is named the Correlative Pattern based Data Aggregation (CPDA). CPDA mines the correlations of every adjacent nodes pair, and generates a correlation graph of the network, …then builds an aggregation routing tree for each connected component of correlation graph based on the shortest path methodology. Following the CPDA algorithm, a node’s sensed data will be suppressed when the data and the children’s match the restriction that is defined by CPDA. When the aggregated data arrive at the Sink node, all the data can be recovered. The recovery error will be limited within a specified small error threshold based on the reversed mechanism. The simulations based on the data set of Berkeley lab show that CPDA has excellent performance in aggregation degree and average error. Further more, a real established temperature sensing experiment also gives the same conclusion. Show more

Keywords: Constant correlation, data aggregation, shortest path, wireless sensor networks

DOI: 10.3233/JIFS-169028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 991-999, 2016

Authors: Park, Min-Kee

Article Type: Research Article

Abstract: When a train is delayed because of a disturbance, the time interval between successive trains increases, and high-frequency metro lines can become unstable. Time interval control is therefore necessary in preventing such instabilities. In this paper, we propose a traffic regulation algorithm that is easy to implement and guarantees system stability. In the proposed method, controlled trains are determined from time interval deviations between successive trains, and the control algorithm for both staying time and running time is designed using a discrete traffic model to ensure an optimal time interval between successive trains. The results of a computer simulation are …also given to demonstrate the validity of the proposed algorithm. Show more

Keywords: Traffic regulation, traffic control, traffic model, time interval, stability

DOI: 10.3233/JIFS-169029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1001-1008, 2016

Authors: Jiang, Bin | Yang, Chao | Wang, Lei | Li, Renfa

Article Type: Research Article

Abstract: Information exchange among people via social network service has produced a mass of communication data, which have been widely used in research on user interaction and information propagation on virtual social networks. The focus of this paper is to investigate the multiplex power-law distributions and retweeting patterns on Twitter platform. To achieve this goal, we analyze the multiplex power-law distributions from relationship network based on unidirectional and bidirectional follow connections and interaction network based on user and tweet entities. Further, we explain the observed features on each network. Additionally, we also explore the emergent pattern of tweet retweeting path and …analyze their generative mechanisms. The observed results show that mining Twitter data from various angles could obtain more interesting discoveries in social networks. Show more

Keywords: Information propagation, social network, power-law distribution

DOI: 10.3233/JIFS-169030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1009-1016, 2016

Authors: Lv, Qi | Niu, Xin | Dou, Yong | Xu, Jiaqing | Xia, Fei

Article Type: Research Article

Abstract: This paper proposes a classification approach for hyperspectral image using the local receptive fields based random weights networks (RWN). Considering the local correlations of spectral features, it is promising to improve the performance of hyperspectral image (HSI) classification by introducing the local receptive fields (LRF). It is the first time to apply such LRF-based RWN structure to HSI classification. The proposed classification framework consists of four layers, i.e., input layer, convolution layer, pooling layer, and output layer. The convolution and pooling layer are used for feature extracting and the last layer is used as the classifier. Experimental results on two …real hyperspectral image datasets have confirmed the effectiveness of the proposed HSI classification method. Show more

Keywords: Hyperspectral image classification, random weights networks, local receptive field

DOI: 10.3233/JIFS-169031

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1017-1028, 2016

Authors: Zhan, Yu | Pan, Haiwei | Xie, Xiaoqin | Zhang, Zhiqiang | Li, Wenbo

Article Type: Research Article

Abstract: The high incidence of brain tumor has increased significantly in recent years. It is becoming more and more concernful to discover knowledge through mining medical brain image to aid doctors’ diagnosis. Clustering medical images for Intelligent Decision Support is an important part in the field of medical image mining because there are several technical aspects which make this problem challenging. In this paper, we propose a medical brain image clustering method to find similar pathology images that can assist doctors to analyze the specific disease, discover its potential cause and make more accurate treatment. Firstly, this method represents medical brain …image dataset as a weighted, undirected and complete graph. Secondly, this graph is sparsified so as to describe the similarity of medical images very well. Last but not the least, a graph entropy based clustering method for this sparsified graph is proposed to cluster these medical images. The experimental results show that this method can cluster medical images efficiently and run well in time complexity. The clustering results can better describe the similarity of medical images. Show more

Keywords: Medical image, graph entropy, sparsification, clustering

DOI: 10.3233/JIFS-169032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1029-1039, 2016

Authors: Ouyang, Aijia | Peng, Xuyu | Wang, Qian | Wang, Ya | Truong, Tung Khac

Article Type: Research Article

Abstract: Considering the problems of slow convergence and easily getting into local optimum of invasive weed optimization (IWO) algorithm in finding the optimal solution to large scale global optimization (LSGO) problems, we have proposed an improved IWO (IIWO) algorithm on the basis of the basic IWO algorithm. Concrete adjustments include setting the newborn weed seeds per plant to a fixed number of parameters, changing the initial step and final step to adaptive step, and re-initializing the solution which exceeds the limit value. Meanwhile, through applying the IIWO algorithm to the GPU platform, a parallel IIWO (PIIWO) based on GPU is obtained. …The algorithm not only improves the convergence rate, but also strikes a balance between the global and local search capabilities. The simulation results of solving on the LSGO problems (CEC’ 2010 high-dimensional functions), have shown that, compared with other algorithms, our designed IIWO can yield better performance, faster convergence speed and higher accuracy; whilst the PIIWO has fewer iterations, higher computing accuracy and significant speedup than the serial algorithm IIWO. Show more

Keywords: Adaptive step, fixed population, invasive weed optimization, GPU, large scale global optimization, speedup

DOI: 10.3233/JIFS-169033

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1041-1051, 2016

Authors: Zhao, Tong | Jing, Mei

Article Type: Research Article

Abstract: Task scheduling is an important component of parallel and distributed computing. Therefore, it is of theoretical significance and practical value to develop an effective task scheduling algorithm and implement it. For the task scheduling in cloud computing environment, it means that a group of tasks consisting a working load are distributed to a number of computational nodes as per certain implementing time sequence based on scheduling discipline and strategy to short the time needed by the whole task scheduling and to achieve good implementation performance. Divisible task scheduling is one of the important roles in the parallel computation and distributed …computation. In this paper, we studies on a classical algorithm: Uniform Multi-Round (UMR), based on which an improved multi-path divisible task scheduling algorithm: MSUMR (Master Service Uniform Multi-Round) Algorithm is proposed. Such an algorithm could not only ensure the scheduling efficiency when the bandwidth is sufficient but also maximizes the computing efficiency of working node when the available bandwidth is limited. According to the experimental result, this algorithm, compared with such scheduling algorithms as UMR, Multi-Installment (MI) and eXtended Multi-Installment (XMI), is improved in the two aspects of dividing algorithm and task allocation principles, thus short down the number of unused computing nodes during task implementation and making full use of computing resources, indicating batter practical application value. Show more

Keywords: Scheduling algorithm, cloud computing, bandwidth-aware, divisible task

DOI: 10.3233/JIFS-169034

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1053-1063, 2016

Authors: Deng, Zuojie | Zhou, Jingli

Article Type: Research Article

Abstract: Nowadays, cloud storage has become an attractive storage scheme for a user to store his files. When a user stores his files on a remote cloud storage system, he cannot make sure whether his files are intact, so he must use some protocol to check the integrity of his files in the cloud storage. To guarantee high availability, some cloud storage servers provide a kind of highly-available service, which stores multiple copies of user files in the cloud storage, and the file owner cannot make sure whether all these copies are intact as well. Some cloud storage servers allow his …users to operate their files online. As the file owner cannot always be online, he must entrust a trusted public data auditor to check his files in the cloud storage. In this work, we investigate the above issues about provable data possession with multi-copy and data dynamics supporting public verification in a cloud storage. We design a kind of authenticated 2-3 tree with ordered leaves and use this kind of tree to organize file block tags. We design a privacy preserving provable data possession scheme with multi-copy and data dynamics which supports public verification, and use a kind of RSA tag to construct this scheme. We apply our scheme to a cloud file backup system. Our theoretical proofs and experiments show that our scheme is feasible and reasonable. Show more

Keywords: Cloud storage, data dynamics, multi-copy, provable data possession, privacy preserving

DOI: 10.3233/JIFS-169035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1065-1078, 2016

Authors: Tang, Xiaoyong | Yang, Xiaopan | Liao, Guiping | Zhu, Xinghui

Article Type: Research Article

Abstract: In the past few years, multi-core processors incorporating four, six, eight, or more cores on a single die have become ubiquitous. Those cores, having their own private caches, often share a higher level cache memory, which leads to compete among different tasks. This can seriously affect the average performance of multi-core systems as the probability of cache hit could be lowered. In realizing this, we study the problem of scheduling bag-of-tasks (BoT) applications with shared cache constraint on multi-core systems. We first use cache space isolation techniques to divide shared caches into partitions. Then, we give a motivational example and …outline the shared cache aware scheduling problem of multi-core systems. Finally, to provide an optimum solution for this problem, we propose a heuristic shared cache contention aware scheduling (SCAS) algorithm on multi-core systems. Our extensive simulation performance evaluation study clearly demonstrate that our proposed SCAS algorithm outperforms the existing traditional scheduling algorithm Min-min and the modified algorithm MSCAS in terms of schedule length and average response time. Show more

Keywords: Cache, Multi-core, task scheduling, schedule length, average response time

DOI: 10.3233/JIFS-169036

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1079-1088, 2016

Authors: Pham, Tuan D.

Article Type: Research Article

Abstract: The quantitative categorization of textures according to their visual appearances is an important area of research in computer vision and image understanding, because texture analysis and its applications are found useful in many areas of health, medicine, sciences, and engineering. For the first time, the theory of chaos and fuzzy sets are applied in this paper to measure the spatial dynamics of the texture spectrum. Experiments carried out on the well-known Brodatz texture database suggest the promising application of the method proposed for texture quantification.

Keywords: Texture categorization, spatial dynamics, metric entropy, fuzzy sets

DOI: 10.3233/JIFS-169038

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1089-1097, 2016

Authors: Fei, Xiongwei | Li, Kenli | Yang, Wangdong

Article Type: Research Article

Abstract: In the open environment of cloud computing, a large amount of user data needs to be encrypted/decrypted fast to maintain confidentiality and provide high quality of service. Advanced Encryption Standard (AES), the standard encryption algorithm, has better security and efficiency compared to its competitive algorithms, so it is widely used in cloud computing and other fields. However, the implementation of AES based on software still has the problem of low efficiency; whereas the implementation of AES based on hardware needs to purchase special purpose devices. Adopting the method of special instruction sets can resolve the above two drawbacks. Therefore, we …propose a fast parallel cryptographic algorithm, NIPAES, which is based on the AES-NI (New Instructions) instruction set and CPU multiple cores. NIPAES makes use of the block property of AES and the parallel property of Counter (CTR) model, adopts OpenMP to evenly distribute workloads to each thread, which performs AES-NI instructions to complete encryption/decryption. Compared to CPU serial AES based on lookup tables, CPU parallel AES, and serial AES based on AES-NI, NIPAES has significant improvement on performance. The experimental results show that NIPAES achieves the average speedups of 3197.78x, 196.12x, and 7.71x, compared to the other aforementioned algorithms, respectively. Show more

Keywords: Advanced Encryption Standard New Instruction, counter mode, encryption speed, encryption speedup, encryption time, OpenMP, parallel encryption, performance

DOI: 10.3233/JIFS-169039

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1099-1107, 2016

Authors: Xiao, Guoqing | Wu, Fan | Zhou, Xu | Li, Keqin

Article Type: Research Article

Abstract: Query processing over uncertain data is very important in many applications due to the existence of uncertainty in real-world data. In this paper, we propose a novel and important query for uncertain data, namely probabilistic top-(k , l ) range (PTR) query, which retrieves l uncertain tuples that are expected to meet score range constraint [s 1 , s 2 ] and have the maximum top-k probabilities but no less than a given probability threshold q . In order to accelerate the PTR query, we present some effective pruning techniques to reduce the search space of PTR query, …which are integrated seamlessly into an efficient PTR query procedure. Extensive experiments over both real-world and synthetic datasets verify the efficiency and effectiveness of our proposed approaches. Show more

Keywords: Data management, probabilistic top-k query, query processing, range query, uncertain data

DOI: 10.3233/JIFS-169040

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1109-1120, 2016

Authors: Wang, Nini | Xia, Jun | Yin, Jianchuan | Liu, Xiaodong

Article Type: Research Article

Abstract: Detecting temporal and spatial trends of annual and seasonal land surface temperature (LST) can contribute to study the effect of climate change and climate variability on temperature behaviors both in time and space. The temporal and spatial gridded dataset of monthly mean LST series produced by Berkeley Earth were chosen for the analysis of LST trends. The dynamic programming (DP) based segmentation algorithm is a fast and efficient time series segmentation algorithm which can identify multiple change points in a given time series. Multiple change points of annual and seasonal LST average anomaly time series during the period 1880–2013 (reference …to the 20th century average) were identified by the DP based time series segmentation algorithm. Schwarz’s Bayesian information criterion (BIC) was applied to automatically determine the optimal segmentation order. BIC selected one change point for annual and seasonal average LST except for the autumn season’s. Regardless of the number of change points, at the first segment, trends are always increasing and at the last, they are sharply increasing except for the winter season. Moreover, all the change points locate around El Nin ˜ o years, La Nin ˜ a years, and phase transition years of the Pacific decadal oscillation (PDO). Based on optimal time series segmentation results selected by BIC, the spatial distributions of linear trends (slope estimates) of annual and seasonal LST anomalies corresponding to different homogeneous periods are showed. Comparing to adjacent segment, the recent warming trends not only appear in Greenland and the surrounding area, but also dominate most parts of land surface, and significant warming trends appear in high latitude regions of the Northern Hemisphere. Show more

Keywords: Time series segmentation, temporal-spatial variability, trend analysis, climate change

DOI: 10.3233/JIFS-169041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1121-1131, 2016

Authors: Yu, Jianhang | Zhang, Xiaoyan | Zhao, Zhenhua | Xu, Weihua

Article Type: Research Article

Abstract: The graded rough set and multi-granulation rough set are two significant generalized rough set models which be constructed on the indiscernibility relation. They solve the issues that the degree of overlap between the equivalence class and basic set in different view points of quantitative information. The purpose of this study is that research the good points of graded rough set in the multi-granulation environment which in different granules have different grades based on dominance relation. Three new types of multi-granulation with different grades rough set models are proposed, which include the optimistic, pessimistic and mean multi-granulation with different grades rough …set. Then, their principal structure, basic properties and serval kinds of uncertainty measure methods are investigated as well. Furthermore, an experimental evaluation about urban investment is utilized to verify the proposed properties, which is valuable for applying these theories to deal with practical issues. Show more

Keywords: Different grades, dominance relation, graded rough set, multi-granulation rough set, uncertainty measures

DOI: 10.3233/JIFS-169042

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1133-1144, 2016

Authors: Li, Chao | Chu, Xiaogeng | Chen, Yingwu | Xing, Lining

Article Type: Research Article

Abstract: Genetic Algorithms are efficient for the travelling salesman problem, but they have a premature convergence problem resulting in suboptimal solutions. As the initialization step has a profound impact on the algorithm’s performance, this study proposes a knowledge-based initialization technique to learn the patterns of evolved populations, and integrates four heuristic strategies to generate the initial population. Advanced initial solutions and high quality gene blocks can be quickly created with this method. Instances in the TSPLIB library are used to set the parameters and test different initialization methods. The results show that this proposed technique can improve the initial population and …optimization performance of genetic algorithms. Show more

Keywords: Genetic algorithm, travelling salesman problem, initial population, heuristic technique

DOI: 10.3233/JIFS-169043

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1145-1152, 2016

Article Type: Research Article

DOI: 10.3233/JIFS-169044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1153-1153, 2016