Applied Computational Intelligence and Soft Computing The latest articles from Hindawi Publishing Corporation © 2017 , Hindawi Publishing Corporation . All rights reserved. An Automated Structural Optimisation Methodology for Scissor Structures Using a Genetic Algorithm Wed, 18 Jan 2017 12:52:10 +0000 We developed a fully automated multiobjective optimisation framework using genetic algorithms to generate a range of optimal barrel vault scissor structures. Compared to other optimisation methods, genetic algorithms are more robust and efficient when dealing with multiobjective optimisation problems and provide a better view of the search space while reducing the chance to be stuck in a local minimum. The novelty of this work is the application and validation (using metrics) of genetic algorithms for the shape and size optimisation of scissor structures, which has not been done so far for two objectives. We tested the feasibility and capacity of the methodology by optimising a 6 m span barrel vault to weight and compactness and by obtaining optimal solutions in an efficient way using NSGA-II. This paper presents the framework and the results of the case study. The in-depth analysis of the influence of the optimisation variables on the results yields new insights which can help in making choices with regard to the design variables, the constraints, and the number of individuals and generations in order to obtain efficiently a trade-off of optimal solutions. Aushim Koumar, Tine Tysmans, Rajan Filomeno Coelho, and Niels De Temmerman Copyright © 2017 Aushim Koumar et al. All rights reserved. Reidentification of Persons Using Clothing Features in Real-Life Video Wed, 11 Jan 2017 00:00:00 +0000 Person reidentification, which aims to track people across nonoverlapping cameras, is a fundamental task in automated video processing. Moving people often appear differently when viewed from different nonoverlapping cameras because of differences in illumination, pose, and camera properties. The color histogram is a global feature of an object that can be used for identification. This histogram describes the distribution of all colors on the object. However, the use of color histograms has two disadvantages. First, colors change differently under different lighting and at different angles. Second, traditional color histograms lack spatial information. We used a perception-based color space to solve the illumination problem of traditional histograms. We also used the spatial pyramid matching (SPM) model to improve the image spatial information in color histograms. Finally, we used the Gaussian mixture model (GMM) to show features for person reidentification, because the main color feature of GMM is more adaptable for scene changes, and improve the stability of the retrieved results for different color spaces in various scenes. Through a series of experiments, we found the relationships of different features that impact person reidentification. Guodong Zhang, Peilin Jiang, Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita Copyright © 2017 Guodong Zhang et al. All rights reserved. The Performance of LBP and NSVC Combination Applied to Face Classification Sun, 25 Dec 2016 12:47:37 +0000 The growing demand in the field of security led to the development of interesting approaches in face classification. These works are interested since their beginning in extracting the invariant features of the face to build a single model easily identifiable by classification algorithms. Our goal in this article is to develop more efficient practical methods for face detection. We present a new fast and accurate approach based on local binary patterns (LBP) for the extraction of the features that is combined with the new classifier Neighboring Support Vector Classifier (NSVC) for classification. The experimental results on different natural images show that the proposed method can get very good results at a very short detection time. The best precision obtained by LBP-NSVC exceeds 99%. Mohammed Ngadi, Aouatif Amine, Bouchra Nassih, Hanaa Hachimi, and Adnane El-Attar Copyright © 2016 Mohammed Ngadi et al. All rights reserved. A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems Mon, 14 Nov 2016 12:33:21 +0000 Many industrial processes are inherently distributed in space and time and are called spatially distributed dynamical systems (SDDSs). Sensor placement affects capturing the spatial distribution and then becomes crucial issue to model or control an SDDS. In this study, a new data-driven based sensor placement method is developed. SVR algorithm is innovatively used to extract the characteristics of spatial distribution from a spatiotemporal data set. The support vectors learned by SVR represent the crucial spatial data structure in the spatiotemporal data set, which can be employed to determine optimal sensor location and sensor number. A systematic sensor placement design scheme in three steps (data collection, SVR learning, and sensor locating) is developed for an easy implementation. Finally, effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzy controlled spatially distributed systems. Xian-xia Zhang, Zhi-qiang Fu, Wei-lu Shan, Bing Wang, and Tao Zou Copyright © 2016 Xian-xia Zhang et al. All rights reserved. Local Community Detection Algorithm Based on Minimal Cluster Mon, 07 Nov 2016 07:10:49 +0000 In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks. Yong Zhou, Guibin Sun, Yan Xing, Ranran Zhou, and Zhixiao Wang Copyright © 2016 Yong Zhou et al. All rights reserved. Lexicon-Based Sentiment Analysis of Teachers’ Evaluation Thu, 13 Oct 2016 09:33:04 +0000 The end of the course evaluation has become an integral part of education management in almost every academic institution. The existing automated evaluation method primarily employs the Likert scale based quantitative scores provided by students about the delivery of the course and the knowledge of the instructor. The feedback is subsequently used to improve the quality of the teaching and often for the annual appraisal process. In addition to the Likert scale questions, the evaluation form typically contains open-ended questions where students can write general comments/feedback that might not be covered by the fixed questions. The textual feedback, however, is usually provided to teachers and administration and due to its nonquantitative nature is frequently not processed to gain more insight. This paper aims to address this aspect by applying several text analytics methods on students’ feedback. The paper not only presents a sentiment analysis based metric, which is shown to be highly correlated with the aggregated Likert scale scores, but also provides new insight into a teacher’s performance with the help of tag clouds, sentiment score, and other frequency-based filters. Quratulain Rajput, Sajjad Haider, and Sayeed Ghani Copyright © 2016 Quratulain Rajput et al. All rights reserved. Generative Power and Closure Properties of Watson-Crick Grammars Mon, 29 Aug 2016 16:35:21 +0000 We define WK linear grammars, as an extension of WK regular grammars with linear grammar rules, and WK context-free grammars, thus investigating their computational power and closure properties. We show that WK linear grammars can generate some context-sensitive languages. Moreover, we demonstrate that the family of WK regular languages is the proper subset of the family of WK linear languages, but it is not comparable with the family of linear languages. We also establish that the Watson-Crick regular grammars are closed under almost all of the main closure operations. Nurul Liyana Mohamad Zulkufli, Sherzod Turaev, Mohd Izzuddin Mohd Tamrin, and Azeddine Messikh Copyright © 2016 Nurul Liyana Mohamad Zulkufli et al. All rights reserved. Low-Rank Kernel-Based Semisupervised Discriminant Analysis Wed, 20 Jul 2016 16:43:21 +0000 Semisupervised Discriminant Analysis (SDA) aims at dimensionality reduction with both limited labeled data and copious unlabeled data, but it may fail to discover the intrinsic geometry structure when the data manifold is highly nonlinear. The kernel trick is widely used to map the original nonlinearly separable problem to an intrinsically larger dimensionality space where the classes are linearly separable. Inspired by low-rank representation (LLR), we proposed a novel kernel SDA method called low-rank kernel-based SDA (LRKSDA) algorithm where the LRR is used as the kernel representation. Since LRR can capture the global data structures and get the lowest rank representation in a parameter-free way, the low-rank kernel method is extremely effective and robust for kinds of data. Extensive experiments on public databases show that the proposed LRKSDA dimensionality reduction algorithm can achieve better performance than other related kernel SDA methods. Baokai Zu, Kewen Xia, Shuidong Dai, and Nelofar Aslam Copyright © 2016 Baokai Zu et al. All rights reserved. Research on E-Commerce Platform-Based Personalized Recommendation Algorithm Wed, 20 Jul 2016 09:05:29 +0000 Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms. Zhijun Zhang, Gongwen Xu, and Pengfei Zhang Copyright © 2016 Zhijun Zhang et al. All rights reserved. Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation Tue, 28 Jun 2016 08:00:29 +0000 One challenge of unsupervised MRI brain image segmentation is the central gray matter due to the faint contrast with respect to the surrounding white matter. In this paper, the necessity of supervised image segmentation is addressed, and a soft Mumford-Shah model is introduced. Then, a framework of semisupervised image segmentation based on soft Mumford-Shah model is developed. The main contribution of this paper lies in the development a framework of a semisupervised soft image segmentation using both Bayesian principle and the principle of soft image segmentation. The developed framework classifies pixels using a semisupervised and interactive way, where the class of a pixel is not only determined by its features but also determined by its distance from those known regions. The developed semisupervised soft segmentation model turns out to be an extension of the unsupervised soft Mumford-Shah model. The framework is then applied to MRI brain image segmentation. Experimental results demonstrate that the developed framework outperforms the state-of-the-art methods of unsupervised segmentation. The new method can produce segmentation as precise as required. Hong-Yuan Wang and Fuhua Chen Copyright © 2016 Hong-Yuan Wang and Fuhua Chen. All rights reserved. Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study Wed, 15 Jun 2016 12:37:20 +0000 We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature. Fisnik Dalipi, Sule Yildirim Yayilgan, and Alemayehu Gebremedhin Copyright © 2016 Fisnik Dalipi et al. All rights reserved. A Dynamic and Heuristic Phase Balancing Method for LV Feeders Tue, 31 May 2016 12:05:50 +0000 Due to the single-phase loads and their stochastic behavior, the current in the distribution feeders is not balanced. In addition, the single-phase loads are located in different positions along the LV feeders. So the amount of the unbalanced load and its location affect the feeder losses. An unbalanced load causes the feeder losses and the voltage drop. Because of time-varying behavior of the single-phase loads, phase balancing is a dynamic and combinatorial problem. In this research, a heuristic and dynamic solution for the phase balancing of the LV feeders is proposed. In this method, it is supposed that the loads’ tie could be connected to all phases through a three-phase switch. The aim of the proposed method is to make the feeder conditions as balanced as possible. The amount and the location of single-phase loads are considered in the proposed phase balancing method. Since the proposed method needs no communication interface or no remote controller, it is inexpensive, simple, practical, and robust. Applying this method provides a distributed and dynamic phase balancing control. In addition, the feasibility of reducing the used switches is investigated. The ability of the proposed method in the phase balancing of the LV feeders is approved by carrying out some simulations. Samad Taghipour Boroujeni, Mohammad Mardaneh, and Zhale Hashemi Copyright © 2016 Samad Taghipour Boroujeni et al. All rights reserved. Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB2 Superconductors Using Room Temperature Resistivity Mon, 23 May 2016 14:02:56 +0000 Doping and fabrication conditions bring about disorder in MgB2 superconductor and further influence its room temperature resistivity as well as its superconducting transition temperature (). Existence of a model that directly estimates of any doped MgB2 superconductor from the room temperature resistivity would have immense significance since room temperature resistivity is easily measured using conventional resistivity measuring instrument and the experimental measurement of wastes valuable resources and is confined to low temperature regime. This work develops a model, superconducting transition temperature estimator (STTE), that directly estimates of disordered MgB2 superconductors using room temperature resistivity as input to the model. STTE was developed through training and testing support vector regression (SVR) with ten experimental values of room temperature resistivity and their corresponding using the best performance parameters obtained through test-set cross validation optimization technique. The developed STTE was used to estimate of different disordered MgB2 superconductors and the obtained results show excellent agreement with the reported experimental data. STTE can therefore be incorporated into resistivity measuring instruments for quick and direct estimation of of disordered MgB2 superconductors with high degree of accuracy. Taoreed O. Owolabi, Kabiru O. Akande, and Sunday O. Olatunji Copyright © 2016 Taoreed O. Owolabi et al. All rights reserved. Modified Grey Wolf Optimizer for Global Engineering Optimization Wed, 04 May 2016 13:29:30 +0000 Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO), which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO (mGWO) is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms. Nitin Mittal, Urvinder Singh, and Balwinder Singh Sohi Copyright © 2016 Nitin Mittal et al. All rights reserved. A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization Wed, 27 Apr 2016 12:17:29 +0000 Hybrid computational intelligence is defined as a combination of multiple intelligent algorithms such that the resulting model has superior performance to the individual algorithms. Therefore, the importance of fusing two or more intelligent algorithms to achieve better performance cannot be overemphasized. In this work, a novel homogenous hybridization scheme is proposed for the improvement of the generalization and predictive ability of support vector machines regression (SVR). The proposed and developed hybrid SVR (HSVR) works by considering the initial SVR prediction as a feature extraction process and then employs the SVR output, which is the extracted feature, as its sole descriptor. The developed hybrid model is applied to the prediction of reservoir permeability and the predicted permeability is compared to core permeability which is regarded as standard in petroleum industry. The results show that the proposed hybrid scheme (HSVR) performed better than the existing SVR in both generalization and prediction ability. The outcome of this research will assist petroleum engineers to effectively predict permeability of carbonate reservoirs with higher degree of accuracy and will invariably lead to better reservoir. Furthermore, the encouraging performance of this hybrid will serve as impetus for further exploring homogenous hybrid system. Kabiru O. Akande, Taoreed O. Owolabi, Sunday O. Olatunji, and AbdulAzeez Abdulraheem Copyright © 2016 Kabiru O. Akande et al. All rights reserved. Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images Sun, 24 Apr 2016 11:52:30 +0000 Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN) to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN) and the Cat Swarm Optimization (CSO) is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR), have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods. M. Kumar, S. K. Mishra, and S. S. Sahu Copyright © 2016 M. Kumar et al. All rights reserved. An Efficient Chaotic Map-Based Authentication Scheme with Mutual Anonymity Thu, 14 Apr 2016 16:47:15 +0000 A chaotic map-based mutual authentication scheme with strong anonymity is proposed in this paper, in which the real identity of the user is encrypted with a shared key between the user and the trusted server. Only the trusted server can determine the real identity of a user during the authentication, and any other entities including other users of the system get nothing about the user’s real identity. In addition, the shared key of encryption can be easily computed by the user and trusted server using the Chebyshev map without additional burdensome key management. Once the partnered two users are authenticated by the trusted server, they can easily proceed with the agreement of the session key. Formal security analysis demonstrates that the proposed scheme is secure under the random oracle model. Yousheng Zhou, Junfeng Zhou, Feng Wang, and Feng Guo Copyright © 2016 Yousheng Zhou et al. All rights reserved. Synthesis of Thinned Planar Antenna Array Using Multiobjective Normal Mutated Binary Cat Swarm Optimization Mon, 11 Apr 2016 07:49:04 +0000 The process of thinned antenna array synthesis involves the optimization of a number of mutually conflicting parameters, such as peak sidelobe level, first null beam width, and number of active elements. This necessitates the development of a multiobjective optimization approach which will provide the best compromised solution based on the application at hand. In this paper, a novel multiobjective normal mutated binary cat swarm optimization (MO-NMBCSO) is developed and proposed for the synthesis of thinned planar antenna arrays. Through this method, a high degree of flexibility is introduced to the realm of thinned array design. A Pareto-optimal front containing all the probable designs is obtained in this process. Targeted solutions may be chosen from the Pareto front to satisfy the different requirements demonstrating the superiority of the proposed approach over multiobjective binary particle swarm optimization method (MO-BPSO). A comparative study is carried out to quantify the performance of the two algorithms using two performance metrics. Lakshman Pappula and Debalina Ghosh Copyright © 2016 Lakshman Pappula and Debalina Ghosh. All rights reserved. Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems Wed, 30 Mar 2016 07:37:42 +0000 Integration of solar photovoltaic (PV) generation with power distribution networks leads to many operational challenges and complexities. Unintentional islanding is one of them which is of rising concern given the steady increase in grid-connected PV power. This paper builds up on an exploratory study of unintentional islanding on a modeled radial feeder having large PV penetration. Dynamic simulations, also run in real time, resulted in exploration of unique potential causes of creation of accidental islands. The resulting voltage and current data underwent dimensionality reduction using principal component analysis (PCA) which formed the basis for the application of statistic control charts for detecting the anomalous currents that could island the system. For reducing the false alarm rate of anomaly detection, Kullback-Leibler (K-L) divergence was applied on the principal component projections which concluded that statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and a -nearest neighbor (-NN) binomial classifier was then trained for identification and classification of potential islanding precursors from other power system transients. The three-phase short-circuit fault case was successfully identified as statistically different from islanding symptoms. Shashank Vyas, Rajesh Kumar, and Rajesh Kavasseri Copyright © 2016 Shashank Vyas et al. All rights reserved. Design of Optimal Proportional Integral Derivative Based Power System Stabilizer Using Bat Algorithm Tue, 15 Mar 2016 12:34:18 +0000 The design of a proportional, derivative, and integral (PID) based power system stabilizer (PSS) is carried out using the bat algorithm (BA). The design of proposed PID controller is considered with an objective function based on square error minimization to enhance the small signal stability of nonlinear power system for a wide range of operating conditions. Three benchmark power system models as single-machine infinite-bus (SMIB) power system, two-area four-machine ten-bus power system, and IEEE New England ten-machine thirty-nine-bus power system are considered to examine the effectiveness of the designed controller. The BA optimized PID based PSS (BA-PID-PSS) controller is applied to these benchmark systems, and the performance is compared with controllers reported in literature. The robustness is tested by considering eight plant conditions of each system, representing the wide range of operating conditions. It includes unlike loading conditions and system configurations to establish the superior performance with BA-PID-PSS over-the-counter controllers. Dhanesh K. Sambariya and Rajendra Prasad Copyright © 2016 Dhanesh K. Sambariya and Rajendra Prasad. All rights reserved. A Study of Moment Based Features on Handwritten Digit Recognition Thu, 10 Mar 2016 10:00:59 +0000 Handwritten digit recognition plays a significant role in many user authentication applications in the modern world. As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem. A lot of work has been done for various non-Indic scripts particularly, in case of Roman, but, in case of Indic scripts, the research is limited. This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely, Indo-Arabic, Bangla, Devanagari, Roman, and Telugu. A 130-element feature set which is basically a combination of six different types of moments, namely, geometric moment, moment invariant, affine moment invariant, Legendre moment, Zernike moment, and complex moment, has been estimated for each digit sample. Finally, the technique is evaluated on CMATER and MNIST databases using multiple classifiers and, after performing statistical significance tests, it is observed that Multilayer Perceptron (MLP) classifier outperforms the others. Satisfactory recognition accuracies are attained for all the five mentioned scripts. Pawan Kumar Singh, Ram Sarkar, and Mita Nasipuri Copyright © 2016 Pawan Kumar Singh et al. All rights reserved. An Efficient Two-Objective Hybrid Local Search Algorithm for Solving the Fuel Consumption Vehicle Routing Problem Mon, 07 Mar 2016 12:09:49 +0000 The classical model of vehicle routing problem (VRP) generally minimizes either the total vehicle travelling distance or the total number of dispatched vehicles. Due to the increased importance of environmental sustainability, one variant of VRPs that minimizes the total vehicle fuel consumption has gained much attention. The resulting fuel consumption VRP (FCVRP) becomes increasingly important yet difficult. We present a mixed integer programming model for the FCVRP, and fuel consumption is measured through the degree of road gradient. Complexity analysis of FCVRP is presented through analogy with the capacitated VRP. To tackle the FCVRP’s computational intractability, we propose an efficient two-objective hybrid local search algorithm (TOHLS). TOHLS is based on a hybrid local search algorithm (HLS) that is also used to solve FCVRP. Based on the Golden CVRP benchmarks, 60 FCVRP instances are generated and tested. Finally, the computational results show that the proposed TOHLS significantly outperforms the HLS. Weizhen Rao, Feng Liu, and Shengbin Wang Copyright © 2016 Weizhen Rao et al. All rights reserved. Retrieval Architecture with Classified Query for Content Based Image Recognition Mon, 29 Feb 2016 16:41:44 +0000 The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology of feature extraction by image binarization technique for enhancing identification and retrieval of information using content based image recognition. The proposed algorithm was tested on two public datasets, namely, Wang dataset and Oliva and Torralba (OT-Scene) dataset with 3688 images on the whole. It has outclassed the state-of-the-art techniques in performance measure and has shown statistical significance. Rik Das, Sudeep Thepade, Subhajit Bhattacharya, and Saurav Ghosh Copyright © 2016 Rik Das et al. All rights reserved. Angle Modulated Artificial Bee Colony Algorithms for Feature Selection Mon, 29 Feb 2016 09:46:03 +0000 Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. The objective of the feature subset selection is to eliminate the irrelevant and noisy feature in order to select optimum feature subsets and increase accuracy. The large number of features in a dataset increases the computational complexity thus leading to performance degradation. In this paper, to overcome this problem, angle modulation technique is used to reduce feature subset selection problem to four-dimensional continuous optimization problem instead of presenting the problem as a high-dimensional bit vector. To present the effectiveness of the problem presentation with angle modulation and to determine the efficiency of the proposed method, six variants of Artificial Bee Colony (ABC) algorithms employ angle modulation for feature selection. Experimental results on six high-dimensional datasets show that Angle Modulated ABC algorithms improved the classification accuracy with fewer feature subsets. Gürcan Yavuz and Doğan Aydin Copyright © 2016 Gürcan Yavuz and Doğan Aydin. All rights reserved. Online Incremental Learning for High Bandwidth Network Traffic Classification Thu, 25 Feb 2016 11:32:49 +0000 Data stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such as Field Programmable Gate Array (FPGA). This paper proposes an algorithm for online network traffic classification based on the concept of incremental -means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incremental -means (Euclidean and Manhattan) distance are analyzed to measure their impact on the network traffic classification in the presence of concept drift. The experimental results on real datasets show that the proposed algorithm exhibits consistency, up to 94% average accuracy for both distance measures, even in the presence of concept drifts. The proposed incremental -means classification using Manhattan distance can classify network traffic 3 times faster than Euclidean distance at 671 thousands flow instances per second. H. R. Loo, S. B. Joseph, and M. N. Marsono Copyright © 2016 H. R. Loo et al. All rights reserved. A Semisupervised Cascade Classification Algorithm Mon, 22 Feb 2016 09:58:23 +0000 Classification is one of the most important tasks of data mining techniques, which have been adopted by several modern applications. The shortage of enough labeled data in the majority of these applications has shifted the interest towards using semisupervised methods. Under such schemes, the use of collected unlabeled data combined with a clearly smaller set of labeled examples leads to similar or even better classification accuracy against supervised algorithms, which use labeled examples exclusively during the training phase. A novel approach for increasing semisupervised classification using Cascade Classifier technique is presented in this paper. The main characteristic of Cascade Classifier strategy is the use of a base classifier for increasing the feature space by adding either the predicted class or the probability class distribution of the initial data. The classifier of the second level is supplied with the new dataset and extracts the decision for each instance. In this work, a self-trained NBC4.5 classifier algorithm is presented, which combines the characteristics of Naive Bayes as a base classifier and the speed of C4.5 for final classification. We performed an in-depth comparison with other well-known semisupervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique has better accuracy in most cases. Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis, and Kyriakos Sgarbas Copyright © 2016 Stamatis Karlos et al. All rights reserved. Towards Utilization of Neurofuzzy Systems for Taxonomic Identification Using Psittacines as a Case Study Sun, 21 Feb 2016 16:39:54 +0000 Demonstration of the neurofuzzy application to the task of psittacine (parrot) taxonomic identification is presented in this paper. In this work, NEFCLASS-J neurofuzzy system is utilized for classification of parrot data for 141 and 183 groupings, using 68 feature points or qualities. The reported results display classification accuracies of above 95%, which is strongly tied to the setting of certain parameters of the neurofuzzy system. Rule base sizes were in the range of 1,750 to 1,950 rules. Shahram Rahimi, Cynthia R. Spiess, Bidyut Gupta, and Elham Sahebkar Copyright © 2016 Shahram Rahimi et al. All rights reserved. Bacteria Foraging Algorithm in Antenna Design Sun, 14 Feb 2016 11:15:46 +0000 A simple design procedure to realize an optimum antenna using bacteria foraging algorithm (BFA) is proposed in this paper. The first antenna considered is imaginary. This antenna is optimized using the BFA along with a suitable fitness function formulated by considering some performance parameters and their best values. To justify the optimum design approach, one 12-element Yagi-Uda antenna is considered for an experiment. The optimized result of this antenna obtained using the optimization algorithm is compared with nonoptimized (conventional) result of the same antenna to appreciate the importance of optimization. Biswa Binayak Mangaraj, Manas Ranjan Jena, and Saumendra Kumar Mohanty Copyright © 2016 Biswa Binayak Mangaraj et al. All rights reserved. Application of Bipolar Fuzzy Sets in Graph Structures Wed, 10 Feb 2016 13:10:24 +0000 A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we apply the concept of bipolar fuzzy sets to graph structures. We introduce certain notions, including bipolar fuzzy graph structure (BFGS), strong bipolar fuzzy graph structure, bipolar fuzzy -cycle, bipolar fuzzy -tree, bipolar fuzzy -cut vertex, and bipolar fuzzy -bridge, and illustrate these notions by several examples. We study -complement, self-complement, strong self-complement, and totally strong self-complement in bipolar fuzzy graph structures, and we investigate some of their interesting properties. Muhammad Akram and Rabia Akmal Copyright © 2016 Muhammad Akram and Rabia Akmal. All rights reserved. Prediction of Defective Software Modules Using Class Imbalance Learning Sun, 07 Feb 2016 07:13:28 +0000 Software defect predictors are useful to maintain the high quality of software products effectively. The early prediction of defective software modules can help the software developers to allocate the available resources to deliver high quality software products. The objective of software defect prediction system is to find as many defective software modules as possible without affecting the overall performance. The learning process of a software defect predictor is difficult due to the imbalanced distribution of software modules between defective and nondefective classes. Misclassification cost of defective software modules generally incurs much higher cost than the misclassification of nondefective one. Therefore, on considering the misclassification cost issue, we have developed a software defect prediction system using Weighted Least Squares Twin Support Vector Machine (WLSTSVM). This system assigns higher misclassification cost to the data samples of defective classes and lower cost to the data samples of nondefective classes. The experiments on eight software defect prediction datasets have proved the validity of the proposed defect prediction system. The significance of the results has been tested via statistical analysis performed by using nonparametric Wilcoxon signed rank test. Divya Tomar and Sonali Agarwal Copyright © 2016 Divya Tomar and Sonali Agarwal. All rights reserved.