Applied Computational Intelligence and Soft Computing http://www.hindawi.com The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. Low-Rank Kernel-Based Semisupervised Discriminant Analysis Wed, 20 Jul 2016 16:43:21 +0000 http://www.hindawi.com/journals/acisc/2016/2783568/ 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 http://www.hindawi.com/journals/acisc/2016/5160460/ 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 http://www.hindawi.com/journals/acisc/2016/8508329/ 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 http://www.hindawi.com/journals/acisc/2016/3403150/ 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 http://www.hindawi.com/journals/acisc/2016/6928080/ 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 http://www.hindawi.com/journals/acisc/2016/1709827/ 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 http://www.hindawi.com/journals/acisc/2016/7950348/ 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 http://www.hindawi.com/journals/acisc/2016/2580169/ 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 http://www.hindawi.com/journals/acisc/2016/6304915/ 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 http://www.hindawi.com/journals/acisc/2016/3916942/ 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 http://www.hindawi.com/journals/acisc/2016/4102156/ 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 http://www.hindawi.com/journals/acisc/2016/3684238/ 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 http://www.hindawi.com/journals/acisc/2016/8546108/ 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 http://www.hindawi.com/journals/acisc/2016/2796863/ 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 http://www.hindawi.com/journals/acisc/2016/3713918/ 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 http://www.hindawi.com/journals/acisc/2016/1861247/ 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 http://www.hindawi.com/journals/acisc/2016/9569161/ 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 http://www.hindawi.com/journals/acisc/2016/1465810/ 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 http://www.hindawi.com/journals/acisc/2016/5919717/ 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 http://www.hindawi.com/journals/acisc/2016/6798905/ 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 http://www.hindawi.com/journals/acisc/2016/5983469/ 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 http://www.hindawi.com/journals/acisc/2016/5859080/ 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 http://www.hindawi.com/journals/acisc/2016/7658207/ 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. Constrained Fuzzy Predictive Control Using Particle Swarm Optimization Sun, 28 Jun 2015 09:09:59 +0000 http://www.hindawi.com/journals/acisc/2015/437943/ A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach. Oussama Ait Sahed, Kamel Kara, and Mohamed Laid Hadjili Copyright © 2015 Oussama Ait Sahed et al. All rights reserved. Hiding Information in Reversible English Transforms for a Blind Receiver Sun, 31 May 2015 11:55:24 +0000 http://www.hindawi.com/journals/acisc/2015/387985/ This paper proposes a new technique for hiding secret messages in ordinary English text. The proposed technique exploits the redundancies existing in some English language constructs. Redundancies result from the flexibility in maneuvering certain statement constituents without altering the statement meaning or correctness. For example, one can say “she went to sleep, because she was tired” or “Because she was tired, she went to sleep.” The paper provides a number of such transformations that can be applied concurrently, while keeping the overall meaning and grammar intact. The proposed data hiding technique is blind since the receiver does not keep a copy of the original uncoded text (cover). Moreover, it can hide more than three bits per statement, which is higher than that achieved in the prior work. A secret key that is a function of the various transformations used is proposed to protect the confidentiality of the hidden message. Our security analysis shows that even if the attacker knows how the transforms are employed, the secret key provides enough security to protect the confidentiality of the hidden message. Moreover, we show that the proposed transformations do not affect the inconspicuousness of the transformed statements, and thus unlikely to draw suspicion. Salma Banawan and Ibrahim Kamel Copyright © 2015 Salma Banawan and Ibrahim Kamel. All rights reserved. A Software Tool for Assisting Experimentation in Dynamic Environments Wed, 22 Apr 2015 11:43:10 +0000 http://www.hindawi.com/journals/acisc/2015/302172/ In real world, many optimization problems are dynamic, which means that their model elements vary with time. These problems have received increasing attention over time, especially from the viewpoint of metaheuristics methods. In this context, experimentation is a crucial task because of the stochastic nature of both algorithms and problems. Currently, there are several technologies whose methods, problems, and performance measures can be implemented. However, in most of them, certain features that make the experimentation process easy are not present. Examples of such features are the statistical analysis of the results and a graphical user interface (GUI) that allows an easy management of the experimentation process. Bearing in mind these limitations, in the present work, we present DynOptLab, a software tool for experimental analysis in dynamic environments. DynOptLab has two main components: (1) an object-oriented framework to facilitate the implementation of new proposals and (2) a graphical user interface for the experiment management and the statistical analysis of the results. With the aim of verifying the benefits of DynOptLab’s main features, a typical case study on experimentation in dynamic environments was carried out. Pavel Novoa-Hernández, Carlos Cruz Corona, and David A. Pelta Copyright © 2015 Pavel Novoa-Hernández et al. All rights reserved. Concise and Accessible Representations for Multidimensional Datasets: Introducing a Framework Based on the D-EVM and Kohonen Networks Sun, 01 Mar 2015 11:22:05 +0000 http://www.hindawi.com/journals/acisc/2015/676780/ A new framework intended for representing and segmenting multidimensional datasets resulting in low spatial complexity requirements and with appropriate access to their contained information is described. Two steps are going to be taken in account. The first step is to specify ()D hypervoxelizations, , as Orthogonal Polytopes whose th dimension corresponds to color intensity. Then, the D representation is concisely expressed via the Extreme Vertices Model in the -Dimensional Space (D-EVM). Some examples are presented, which, under our methodology, have storing requirements minor than those demanded by their original hypervoxelizations. In the second step, 1-Dimensional Kohonen Networks (1D-KNs) are applied in order to segment datasets taking in account their geometrical and topological properties providing a non-supervised way to compact even more the proposed -Dimensional representations. The application of our framework shares compression ratios, for our set of study cases, in the range 5.6496 to 32.4311. Summarizing, the contribution combines the power of the D-EVM and 1D-KNs by producing very concise datasets’ representations. We argue that the new representations also provide appropriate segmentations by introducing some error functions such that our 1D-KNs classifications are compared against classifications based only in color intensities. Along the work, main properties and algorithms behind the D-EVM are introduced for the purpose of interrogating the final representations in such a way that it efficiently obtains useful geometrical and topological information. Ricardo Pérez-Aguila and Ricardo Ruiz-Rodríguez Copyright © 2015 Ricardo Pérez-Aguila and Ricardo Ruiz-Rodríguez. All rights reserved. On the Performance Improvement of Devanagari Handwritten Character Recognition Sun, 22 Feb 2015 06:06:49 +0000 http://www.hindawi.com/journals/acisc/2015/193868/ The paper is about the application of mini minibatch stochastic gradient descent (SGD) based learning applied to Multilayer Perceptron in the domain of isolated Devanagari handwritten character/numeral recognition. This technique reduces the variance in the estimate of the gradient and often makes better use of the hierarchical memory organization in modern computers. -weight decay is added on minibatch SGD to avoid overfitting. The experiments are conducted firstly on the direct pixel intensity values as features. After that, the experiments are performed on the proposed flexible zone based gradient feature extraction algorithm. The results are promising on most of the standard dataset of Devanagari characters/numerals. Pratibha Singh, Ajay Verma, and Narendra S. Chaudhari Copyright © 2015 Pratibha Singh et al. All rights reserved. Cascade Support Vector Machines with Dimensionality Reduction Thu, 15 Jan 2015 06:18:45 +0000 http://www.hindawi.com/journals/acisc/2015/216132/ Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities. Oliver Kramer Copyright © 2015 Oliver Kramer. All rights reserved. Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing Tue, 06 Jan 2015 08:26:19 +0000 http://www.hindawi.com/journals/acisc/2015/578601/ We put forward architecture of a framework for integration of data from moving objects related to urban transportation network. Most of this research refers to the GPS outdoor geolocation technology and uses distributed cloud infrastructure with big data NoSQL database. A network of intelligent mobile sensors, distributed on urban network, produces congestion traffic patterns. Congestion predictions are based on extended simulation model. This model provides traffic indicators calculations, which fuse with the GPS data for allowing estimation of traffic states across the whole network. The discovery process of congestion patterns uses semantic trajectories metamodel given in our previous works. The challenge of the proposed solution is to store patterns of traffic, which aims to ensure the surveillance and intelligent real-time control network to reduce congestion and avoid its consequences. The fusion of real-time data from GPS-enabled smartphones integrated with those provided by existing traffic systems improves traffic congestion knowledge, as well as generating new information for a soft operational control and providing intelligent added value for transportation systems deployment. A. Boulmakoul, L. Karim, M. Mandar, A. Idri, and A. Daissaoui Copyright © 2015 A. Boulmakoul et al. All rights reserved.