Applied Computational Intelligence and Soft Computing
http://www.i-scholar.in/index.php/AciSc
Applied Computational Intelligence and Soft Computing is a peer-reviewed, open access journal that focuses on the disciplines of computer science, engineering, and mathematics.en-USacisc@hindawi.com (Dr. Shyi-Ming Chen)acisc@hindawi.com (Dr. Shyi-Ming Chen)Wed, 27 Apr 2016 10:53:25 +0000OJS 2.4.2.0http://blogs.law.harvard.edu/tech/rss60Online Incremental Learning for High Bandwidth Network Traffic Classification
http://www.i-scholar.in/index.php/AciSc/article/view/98088
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 k-means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incremental k-means (Euclidean andManhattan) 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 k-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, M. N. Marsonohttp://www.i-scholar.in/index.php/AciSc/article/view/98088Retrieval Architecture with Classified Query for Content Based Image Recognition
http://www.i-scholar.in/index.php/AciSc/article/view/98091
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, Saurav Ghoshhttp://www.i-scholar.in/index.php/AciSc/article/view/98091A Study of Moment Based Features on Handwritten Digit Recognition
http://www.i-scholar.in/index.php/AciSc/article/view/98095
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 <em>Roman</em>, but, in case of <em>Indic</em> 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, <em>Indo-Arabic, Bangla, Devanagari, Roman</em>, and <em>Telugu</em>. A 130-element feature set which is basically a combination of six different types of moments, namely, geometric moment, moment invariant, affine moment invariant, Legendremoment, Zernikemoment, and complexmoment, 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, Mita Nasipurihttp://www.i-scholar.in/index.php/AciSc/article/view/98095Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems
http://www.i-scholar.in/index.php/AciSc/article/view/98097
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 Q 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 Q statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and a K-nearest neighbor (K-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, Rajesh Kavasserihttp://www.i-scholar.in/index.php/AciSc/article/view/98097An Efficient Two-Objective Hybrid Local Search Algorithm for Solving the Fuel Consumption Vehicle Routing Problem
http://www.i-scholar.in/index.php/AciSc/article/view/98102
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 ofVRPs that minimizes the total vehicle fuel consumption has gainedmuch 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, Shengbin Wanghttp://www.i-scholar.in/index.php/AciSc/article/view/98102An Efficient Chaotic Map-Based Authentication Scheme with Mutual Anonymity
http://www.i-scholar.in/index.php/AciSc/article/view/98105
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, Feng Guohttp://www.i-scholar.in/index.php/AciSc/article/view/98105Synthesis of Thinned Planar Antenna Array Using Multiobjective Normal Mutated Binary Cat Swarm Optimization
http://www.i-scholar.in/index.php/AciSc/article/view/98108
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, Debalina Ghoshhttp://www.i-scholar.in/index.php/AciSc/article/view/98108Application of Bipolar Fuzzy Sets in Graph Structures
http://www.i-scholar.in/index.php/AciSc/article/view/98109
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 N<sub>i</sub>-cycle, bipolar fuzzy N<sub>i</sub>-tree, bipolar fuzzy N<sub>i</sub>-cut vertex, and bipolar fuzzy N<sub>i</sub>-bridge, and illustrate these notions by several examples. We study Φ-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, Rabia Akmalhttp://www.i-scholar.in/index.php/AciSc/article/view/98109A Semisupervised Cascade Classification Algorithm
http://www.i-scholar.in/index.php/AciSc/article/view/98111
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.Themain 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 NB∇C4.5 classifier algorithm is presented, which combines the characteristics ofNaive 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, Kyriakos Sgarbashttp://www.i-scholar.in/index.php/AciSc/article/view/98111Bacteria Foraging Algorithm in Antenna Design
http://www.i-scholar.in/index.php/AciSc/article/view/98115
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, Saumendra Kumar Mohantyhttp://www.i-scholar.in/index.php/AciSc/article/view/98115Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images
http://www.i-scholar.in/index.php/AciSc/article/view/98117
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 optimumweight 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, S. S. Sahuhttp://www.i-scholar.in/index.php/AciSc/article/view/98117Towards Utilization of Neurofuzzy Systems for Taxonomic Identification Using Psittacines as a Case Study
http://www.i-scholar.in/index.php/AciSc/article/view/98122
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, Elham Sahebkarhttp://www.i-scholar.in/index.php/AciSc/article/view/98122Prediction of Defective Software Modules Using Class Imbalance Learning
http://www.i-scholar.in/index.php/AciSc/article/view/98124
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, Sonali Agarwalhttp://www.i-scholar.in/index.php/AciSc/article/view/98124Design of Optimal Proportional Integral Derivative Based Power System Stabilizer Using Bat Algorithm
http://www.i-scholar.in/index.php/AciSc/article/view/98126
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, Rajendra Prasadhttp://www.i-scholar.in/index.php/AciSc/article/view/98126Angle Modulated Artificial Bee Colony Algorithms for Feature Selection
http://www.i-scholar.in/index.php/AciSc/article/view/98127
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.Gurcan Yavuz, Dogan Aydinhttp://www.i-scholar.in/index.php/AciSc/article/view/98127