Applied Computational Intelligence and Soft Computing The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Long Term Solar Radiation Forecast Using Computational Intelligence Methods Thu, 11 Dec 2014 00:10:29 +0000 The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR) filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour. João Paulo Coelho and José Boaventura-Cunha Copyright © 2014 João Paulo Coelho and José Boaventura-Cunha. All rights reserved. Lyapunov-Based Controller for a Class of Stochastic Chaotic Systems Wed, 10 Dec 2014 00:10:19 +0000 This study presents a general control law based on Lyapunov’s direct method for a group of well-known stochastic chaotic systems. Since real chaotic systems have undesired random-like behaviors which have also been deteriorated by environmental noise, chaotic systems are modeled by exciting a deterministic chaotic system with a white noise obtained from derivative of Wiener process which eventually generates an Ito differential equation. Proposed controller not only can asymptotically stabilize these systems in mean-square sense against their undesired intrinsic properties, but also exhibits good transient response. Simulation results highlight effectiveness and feasibility of proposed controller in outperforming stochastic chaotic systems. Hossein Shokouhi-Nejad, Amir Rikhtehgar Ghiasi, and Saeed Pezeshki Copyright © 2014 Hossein Shokouhi-Nejad et al. All rights reserved. Script Identification from Printed Indian Document Images and Performance Evaluation Using Different Classifiers Sun, 07 Dec 2014 14:16:50 +0000 Identification of script from document images is an active area of research under document image processing for a multilingual/ multiscript country like India. In this paper the real life problem of printed script identification from official Indian document images is considered and performances of different well-known classifiers are evaluated. Two important evaluating parameters, namely, AAR (average accuracy rate) and MBT (model building time), are computed for this performance analysis. Experiment was carried out on 459 printed document images with 5-fold cross-validation. Simple Logistic model shows highest AAR of 98.9% among all. BayesNet and Random Forest model have average accuracy rate of 96.7% and 98.2% correspondingly with lowest MBT of 0.09 s. Sk Md Obaidullah, Anamika Mondal, Nibaran Das, and Kaushik Roy Copyright © 2014 Sk Md Obaidullah et al. All rights reserved. A Comparative Study of EAG and PBIL on Large-Scale Global Optimization Problems Sun, 07 Dec 2014 07:26:40 +0000 Estimation of Distribution Algorithms (EDAs) use global statistical information effectively to sample offspring disregarding the location information of the locally optimal solutions found so far. Evolutionary Algorithm with Guided Mutation (EAG) combines global statistical information and location information to sample offspring, aiming that this hybridization improves the search and optimization process. This paper discusses a comparative study of Population-Based Incremental Learning (PBIL), a representative of EDAs, and EAG on large-scale global optimization problems. We implemented PBIL and EAG to build an experimental setup upon which simulations were run. The performance of these algorithms was analyzed in terms of solution quality and computational cost. We found that EAG performed better than PBIL in attaining a good quality solution, but the latter performed better in terms of computational cost. We also compared the performance of EAG and PBIL with MA-SW-Chains, the winner of CEC’2010, and found that the overall performance of EAG is comparable to MA-SW-Chains. Imtiaz Hussain Khan Copyright © 2014 Imtiaz Hussain Khan. All rights reserved. Identification of a Multicriteria Decision-Making Model Using the Characteristic Objects Method Thu, 27 Nov 2014 00:10:02 +0000 This paper presents a new, nonlinear, multicriteria, decision-making method: the characteristic objects (COMET). This approach, which can be characterized as a fuzzy reference model, determines a measurement standard for decision-making problems. This model is distinguished by a constant set of specially chosen characteristic objects that are independent of the alternatives. After identifying a multicriteria model, this method can be used to compare any number of decisional objects (alternatives) and select the best one. In the COMET, in contrast to other methods, the rank-reversal phenomenon is not observed. Rank-reversal is a paradoxical feature in the decision-making methods, which is caused by determining the absolute evaluations of considered alternatives on the basis of the alternatives themselves. In the Analytic Hierarchy Process (AHP) method and similar methods, when a new alternative is added to the original alternative set, the evaluation base and the resulting evaluations of all objects change. A great advantage of the COMET is its ability to identify not only linear but also nonlinear multicriteria models of decision makers. This identification is based not on a ranking of component criteria of the multicriterion but on a ranking of a larger set of characteristic objects (characteristic alternatives) that are independent of the small set of alternatives analyzed in a given problem. As a result, the COMET is free of the faults of other methods. Andrzej Piegat and Wojciech Sałabun Copyright © 2014 Andrzej Piegat and Wojciech Sałabun. All rights reserved. A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization Tue, 25 Nov 2014 00:00:00 +0000 This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO), for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD) data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM) model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO) algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1) the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2) the GRCM-PSO method is able to handle the real HPLC-DAD data set. Lizhi Cui, Zhihao Ling, Josiah Poon, Simon K. Poon, Junbin Gao, and Paul Kwan Copyright © 2014 Lizhi Cui et al. All rights reserved. Frequent Pattern Mining of Eye-Tracking Records Partitioned into Cognitive Chunks Sun, 23 Nov 2014 09:20:09 +0000 Assuming that scenes would be visually scanned by chunking information, we partitioned fixation sequences of web page viewers into chunks using isolate gaze point(s) as the delimiter. Fixations were coded in terms of the segments in a mesh imposed on the screen. The identified chunks were mostly short, consisting of one or two fixations. These were analyzed with respect to the within- and between-chunk distances in the overall records and the patterns (i.e., subsequences) frequently shared among the records. Although the two types of distances were both dominated by zero- and one-block shifts, the primacy of the modal shifts was less prominent between chunks than within them. The lower primacy was compensated by the longer shifts. The patterns frequently extracted at three threshold levels were mostly simple, consisting of one or two chunks. The patterns revealed interesting properties as to segment differentiation and the directionality of the attentional shifts. Noriyuki Matsuda and Haruhiko Takeuchi Copyright © 2014 Noriyuki Matsuda and Haruhiko Takeuchi. All rights reserved. The Mixed Type Splitting Methods for Solving Fuzzy Linear Systems Tue, 18 Nov 2014 08:50:18 +0000 We consider a class of fuzzy linear systems (FLS) and demonstrate some of the existing methods using the embedding approach for calculating the solution. The main aim in this paper is to design a class of mixed type splitting iterative methods for solving FLS. Furthermore, convergence analysis of the method is proved. Numerical example is illustrated to show the applicability of the methods and to show the efficiency of proposed algorithm. H. Saberi Najafi, S. A. Edalatpanah, and S. Shahabi Copyright © 2014 H. Saberi Najafi et al. All rights reserved. Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System Thu, 13 Nov 2014 08:28:35 +0000 Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA) interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP), radial basis function (RBF) neural network, and adaptive neurofuzzy inference system (ANFIS) has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented. Nandkumar Wagh and D. M. Deshpande Copyright © 2014 Nandkumar Wagh and D. M. Deshpande. All rights reserved. A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence Sun, 09 Nov 2014 11:16:44 +0000 This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction . The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR’s tuning parameters, the incorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that the has achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction. Nhat-Duc Hoang, Anh-Duc Pham, and Minh-Tu Cao Copyright © 2014 Nhat-Duc Hoang et al. All rights reserved. Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches Mon, 27 Oct 2014 12:02:03 +0000 The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs. Manjunath Patel Gowdru Chandrashekarappa, Prasad Krishna, and Mahesh B. Parappagoudar Copyright © 2014 Manjunath Patel Gowdru Chandrashekarappa et al. All rights reserved. 2-Layered Architecture of Vague Logic Based Multilevel Queue Scheduler Thu, 09 Oct 2014 10:07:54 +0000 In operating system the decisions which CPU scheduler makes regarding the sequence and length of time the task may run are not easy ones, as the scheduler has only a limited amount of information about the tasks. A good scheduler should be fair, maximizes throughput, and minimizes response time of system. A scheduler with multilevel queue scheduling partitions the ready queue into multiple queues. While assigning priorities, higher level queues always get more priorities over lower level queues. Unfortunately, sometimes lower priority tasks get starved, as the scheduler assures that the lower priority tasks may be scheduled only after the higher priority tasks. While making decisions scheduler is concerned only with one factor, that is, priority, but ignores other factors which may affect the performance of the system. With this concern, we propose a 2-layered architecture of multilevel queue scheduler based on vague set theory (VMLQ). The VMLQ scheduler handles the impreciseness of data as well as improving the starvation problem of lower priority tasks. This work also optimizes the performance metrics and improves the response time of system. The performance is evaluated through simulation using MatLab. Simulation results prove that the VMLQ scheduler performs better than the classical multilevel queue scheduler and fuzzy based multilevel queue scheduler. Supriya Raheja, Reena Dadhich, and Smita Rajpal Copyright © 2014 Supriya Raheja et al. All rights reserved. Merging Agents and Cloud Services in Industrial Applications Tue, 19 Aug 2014 00:00:00 +0000 A novel idea to combine agent technology and cloud computing for monitoring a plant floor system is presented. Cloud infrastructure has been leveraged as the main mechanism for hosting the data and processing needs of a modern industrial information system. The cloud offers unlimited storage and data processing in a near real-time fashion. This paper presents a software-as-a-service (SaaS) architecture for augmenting industrial plant-floor reporting capabilities. This reporting capability has been architected using networked agents, worker roles, and scripts for building a scalable data pipeline and analytics system. Francisco P. Maturana, Juan L. Asenjo, Neethu S. Philip, and Shweta Chatrola Copyright © 2014 Francisco P. Maturana et al. All rights reserved. Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip Tue, 12 Aug 2014 13:10:15 +0000 This paper proposes a multiobjective application mapping technique targeted for large-scale network-on-chip (NoC). As the number of intellectual property (IP) cores in multiprocessor system-on-chip (MPSoC) increases, NoC application mapping to find optimum core-to-topology mapping becomes more challenging. Besides, the conflicting cost and performance trade-off makes multiobjective application mapping techniques even more complex. This paper proposes an application mapping technique that incorporates domain knowledge into genetic algorithm (GA). The initial population of GA is initialized with network partitioning (NP) while the crossover operator is guided with knowledge on communication demands. NP reduces the large-scale application mapping complexity and provides GA with a potential mapping search space. The proposed genetic operator is compared with state-of-the-art genetic operators in terms of solution quality. In this work, multiobjective optimization of energy and thermal-balance is considered. Through simulation, knowledge-based initial mapping shows significant improvement in Pareto front compared to random initial mapping that is widely used. The proposed knowledge-based crossover also shows better Pareto front compared to state-of-the-art knowledge-based crossover. Yin Zhen Tei, Yuan Wen Hau, N. Shaikh-Husin, and M. N. Marsono Copyright © 2014 Yin Zhen Tei et al. All rights reserved. Individual Identification Using Linear Projection of Heartbeat Features Sun, 10 Aug 2014 12:46:58 +0000 This paper presents a novel method to use the electrocardiogram (ECG) signal as biometrics for individual identification. The ECG characterization is performed using an automated approach consisting of analytical and appearance methods. The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace. We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects. Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts. The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU) database. These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU) database, respectively. Yogendra Narain Singh Copyright © 2014 Yogendra Narain Singh. All rights reserved. Image Enhancement under Data-Dependent Multiplicative Gamma Noise Sun, 01 Jun 2014 11:21:11 +0000 An edge enhancement filter is proposed for denoising and enhancing images corrupted with data-dependent noise which is observed to follow a Gamma distribution. The filter is equipped with three terms designed to perform three different tasks. The first term is an anisotropic diffusion term which is derived from a locally adaptive p-laplacian functional. The second term is an enhancement term or a shock term which imparts a shock effect at the edge points making them sharp. The third term is a reactive term which is derived based on the maximum a posteriori (MAP) estimator and this term helps the diffusive term to perform a Gamma distributive data-dependent multiplicative noise removal from images. And moreover, this reactive term ensures that deviation of the restored image from the original one is minimum. This proposed filter is compared with the state-of-the-art restoration models proposed for data-dependent multiplicative noise. Jidesh Pacheeripadikkal and Bini Anattu Copyright © 2014 Jidesh Pacheeripadikkal and Bini Anattu. All rights reserved. Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures Tue, 15 Apr 2014 07:15:36 +0000 Malware detection done at the network infrastructure level is still an open research problem ,considering the evolution of malwares and high detection accuracy needed to detect these threats. Content based classification techniques have been proven capable of detecting malware without matching for malware signatures. However, the performance of the classification techniques depends on observed training samples. In this paper, a new detection method that incorporates Snort malware signatures into Naive Bayes model training is proposed. Through experimental work, we prove that the proposed work results in low features search space for effective detection at the packet level. This paper also demonstrates the viability of detecting malware at the stateless level (using packets) as well as at the stateful level (using TCP byte stream). The result shows that it is feasible to detect malware at the stateless level with similar accuracy to the stateful level, thus requiring minimal resource for implementation on middleboxes. Stateless detection can give a better protection to end users by detecting malware on middleboxes without having to reconstruct stateful sessions and before malwares reach the end users. Ismahani Ismail, Sulaiman Mohd Nor, and Muhammad Nadzir Marsono Copyright © 2014 Ismahani Ismail et al. All rights reserved. Novel Adaptive Bacteria Foraging Algorithms for Global Optimization Tue, 25 Mar 2014 11:42:46 +0000 This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in the motion provides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order to overcome such problems, novel linear and nonlinear mathematical relationships based on the index of iteration, index of bacteria, and fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested with several unimodal and multimodal benchmark functions in comparison with the original BFA. Moreover, the application of the proposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform the predecessor algorithm in all test functions and acquire better model for the twin rotor system. Ahmad N. K. Nasir, M. O. Tokhi, and N. Maniha Abd. Ghani Copyright © 2014 Ahmad N. K. Nasir et al. All rights reserved. Feature Fusion Based Audio-Visual Speaker Identification Using Hidden Markov Model under Different Lighting Variations Wed, 05 Mar 2014 11:39:48 +0000 The aim of the paper is to propose a feature fusion based Audio-Visual Speaker Identification (AVSI) system with varied conditions of illumination environments. Among the different fusion strategies, feature level fusion has been used for the proposed AVSI system where Hidden Markov Model (HMM) is used for learning and classification. Since the feature set contains richer information about the raw biometric data than any other levels, integration at feature level is expected to provide better authentication results. In this paper, both Mel Frequency Cepstral Coefficients (MFCCs) and Linear Prediction Cepstral Coefficients (LPCCs) are combined to get the audio feature vectors and Active Shape Model (ASM) based appearance and shape facial features are concatenated to take the visual feature vectors. These combined audio and visual features are used for the feature-fusion. To reduce the dimension of the audio and visual feature vectors, Principal Component Analysis (PCA) method is used. The VALID audio-visual database is used to measure the performance of the proposed system where four different illumination levels of lighting conditions are considered. Experimental results focus on the significance of the proposed audio-visual speaker identification system with various combinations of audio and visual features. Md. Rabiul Islam and Md. Abdus Sobhan Copyright © 2014 Md. Rabiul Islam and Md. Abdus Sobhan. All rights reserved. Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features Wed, 05 Mar 2014 06:36:44 +0000 People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images. Keranmu Xielifuguli, Akira Fujisawa, Yusuke Kusumoto, Kazuyuki Matsumoto, and Kenji Kita Copyright © 2014 Keranmu Xielifuguli et al. All rights reserved. Application of DEO Method to Solving Fuzzy Multiobjective Optimal Control Problem Thu, 27 Feb 2014 15:50:41 +0000 In the present paper a problem of optimal control for a single-product dynamical macroeconomic model is considered. In this model gross domestic product is divided into productive consumption, gross investment, and nonproductive consumption. The model is described by a fuzzy differential equation (FDE) to take into account imprecision inherent in the dynamics that may be naturally conditioned by influence of various external factors, unforeseen contingencies of future, and so forth. The considered problems are characterized by four criteria and by several important aspects. On one hand, the problem is complicated by the presence of fuzzy uncertainty as a result of a natural imprecision inherent in information about dynamics of real-world systems. On the other hand, the number of the criteria is not small and most of them are integral criteria. Due to the above mentioned aspects, solving the considered problem by using convolution of criteria into one criterion would lead to loss of information and also would be counterintuitive and complex. We applied DEO (differential evolution optimization) method to solve the considered problem. Latafat A. Gardashova Copyright © 2014 Latafat A. Gardashova. All rights reserved. An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery Wed, 26 Feb 2014 06:36:30 +0000 This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the expansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through numerical experiments. Shigeaki Sakurai, Kyoko Makino, and Shigeru Matsumoto Copyright © 2014 Shigeaki Sakurai et al. All rights reserved. Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges Thu, 20 Feb 2014 16:55:20 +0000 Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification. Mita K. Dalal and Mukesh A. Zaveri Copyright © 2014 Mita K. Dalal and Mukesh A. Zaveri. All rights reserved. Assessment of Haar Wavelet-Quasilinearization Technique in Heat Convection-Radiation Equations Wed, 05 Feb 2014 14:43:01 +0000 We showed that solutions by the Haar wavelet-quasilinearization technique for the two problems, namely, (i) temperature distribution equation in lumped system of combined convection-radiation in a slab made of materials with variable thermal conductivity and (ii) cooling of a lumped system by combined convection and radiation are strongly reliable and also more accurate than the other numerical methods and are in good agreement with exact solution. According to the Haar wavelet-quasilinearization technique, we convert the nonlinear heat transfer equation to linear discretized equation with the help of quasilinearization technique and apply the Haar wavelet method at each iteration of quasilinearization technique to get the solution. The main aim of present work is to show the reliability of the Haar wavelet-quasilinearization technique for heat transfer equations. Umer Saeed and Mujeeb ur Rehman Copyright © 2014 Umer Saeed and Mujeeb ur Rehman. All rights reserved. Subspace Clustering of High-Dimensional Data: An Evolutionary Approach Tue, 31 Dec 2013 17:12:32 +0000 Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full-dimensional space. In this paper, we have presented a robust multi objective subspace clustering (MOSCL) algorithm for the challenging problem of high-dimensional clustering. The first phase of MOSCL performs subspace relevance analysis by detecting dense and sparse regions with their locations in data set. After detection of dense regions it eliminates outliers. MOSCL discovers subspaces in dense regions of data set and produces subspace clusters. In thorough experiments on synthetic and real-world data sets, we demonstrate that MOSCL for subspace clustering is superior to PROCLUS clustering algorithm. Additionally we investigate the effects of first phase for detecting dense regions on the results of subspace clustering. Our results indicate that removing outliers improves the accuracy of subspace clustering. The clustering results are validated by clustering error (CE) distance on various data sets. MOSCL can discover the clusters in all subspaces with high quality, and the efficiency of MOSCL outperforms PROCLUS. Singh Vijendra and Sahoo Laxman Copyright © 2013 Singh Vijendra and Sahoo Laxman. All rights reserved. Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences Tue, 31 Dec 2013 17:09:43 +0000 We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a user’s Kansei through the interaction between the user and machine. However, especially in the domain of product recommendations, there may be numerous optimum points. Therefore, the purpose of this study is to develop a new iGA crossover method that concurrently searches for multiple optimum points for multiple user preferences. The proposed method estimates the locations of the optimum area by a clustering method and then searches for the maximum values of the area by a probabilistic model. To confirm the effectiveness of this method, two experiments were performed. In the first experiment, a pseudouser operated an experiment system that implemented the proposed and conventional methods and the solutions obtained were evaluated using a set of pseudomultiple preferences. With this experiment, we proved that when there are multiple preferences, the proposed method searches faster and more diversely than the conventional one. The second experiment was a subjective experiment. This experiment showed that the proposed method was able to search concurrently for more preferences when subjects had multiple preferences. Misato Tanaka, Yasunari Sasaki, Mitsunori Miki, and Tomoyuki Hiroyasu Copyright © 2013 Misato Tanaka et al. All rights reserved. An Algorithm for Extracting Intuitionistic Fuzzy Shortest Path in a Graph Mon, 02 Dec 2013 15:22:07 +0000 We consider an intuitionistic fuzzy shortest path problem (IFSPP) in a directed graph where the weights of the links are intuitionistic fuzzy numbers. We develop a method to search for an intuitionistic fuzzy shortest path from a source node to a destination node. We coin the concept of classical Dijkstra’s algorithm which is applicable to graphs with crisp weights and then extend this concept to graphs where the weights of the arcs are intuitionistic fuzzy numbers. It is claimed that the method may play a major role in many application areas of computer science, communication network, transportation systems, and so forth. in particular to those networks for which the link weights (costs) are ill defined. Siddhartha Sankar Biswas, Bashir Alam, and M. N. Doja Copyright © 2013 Siddhartha Sankar Biswas et al. All rights reserved. Parallel Swarms Oriented Particle Swarm Optimization Tue, 05 Nov 2013 09:53:00 +0000 The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution. In the multi-stage of evolution, individual subswarms evolve independently in parallel, and in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages of evolution demonstrate better performance on test functions, especially of higher dimensions. The attractive feature of the PSO-PSO version of the algorithm is that it does not introduce any new parameters to improve its convergence performance. The strategy maintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO. Tad Gonsalves and Akira Egashira Copyright © 2013 Tad Gonsalves and Akira Egashira. All rights reserved. Numerical Solution of Uncertain Beam Equations Using Double Parametric Form of Fuzzy Numbers Sun, 20 Oct 2013 13:19:04 +0000 Present paper proposes a new technique to solve uncertain beam equation using double parametric form of fuzzy numbers. Uncertainties appearing in the initial conditions are taken in terms of triangular fuzzy number. Using the single parametric form, the fuzzy beam equation is converted first to an interval-based fuzzy differential equation. Next, this differential equation is transformed to crisp form by applying double parametric form of fuzzy number. Finally, the same is solved by homotopy perturbation method (HPM) to obtain the uncertain responses subject to unit step and impulse loads. Obtained results are depicted in terms of plots to show the efficiency and powerfulness of the methodology. Smita Tapaswini and S. Chakraverty Copyright © 2013 Smita Tapaswini and S. Chakraverty. All rights reserved. Development of Product Data Model for Maintenance in Concrete Highway Bridges Tue, 27 Aug 2013 08:34:59 +0000 The primary objective of this paper is to develop the product data models, in which systematic information is defined for accumulating, exchanging, and sharing in the maintenance of concrete highway bridges. The information requirement and existing issues and solutions were analyzed based on the life cycle and the standardization for sharing. The member data models and business data models that defined design and construction information and accumulated results information were developed. The maintenance business process in which project participants utilize the product data model was described as utilization scenario. The utilization frameworks which define information flow were developed. Satoshi Kubota and Ichizou Mikami Copyright © 2013 Satoshi Kubota and Ichizou Mikami. All rights reserved.