Applied Computational Intelligence and Soft Computing https://www.hindawi.com The latest articles from Hindawi © 2017 , Hindawi Limited . All rights reserved. The Prefiltering Techniques in Emotion Based Place Recommendation Derived by User Reviews Sun, 22 Oct 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/5680398/ Context-aware recommendation systems attempt to address the challenge of identifying products or items that have the greatest chance of meeting user requirements by adapting to current contextual information. Many such systems have been developed in domains such as movies, books, and music, and emotion is a contextual parameter that has already been used in those fields. This paper focuses on the use of emotion as a contextual parameter in a tourist destination recommendation system. We developed a new corpus that incorporates the emotion parameter by employing semantic analysis techniques for destination recommendation. We review the effectiveness of incorporating emotion in a recommendation process using prefiltering techniques and show that the use of emotion as a contextual parameter for location recommendation in conjunction with collaborative filtering increases user satisfaction. U. A. Piumi Ishanka and Takashi Yukawa Copyright © 2017 U. A. Piumi Ishanka and Takashi Yukawa. All rights reserved. Representation for Action Recognition Using Trajectory-Based Low-Level Local Feature and Mid-Level Motion Feature Thu, 19 Oct 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/4019213/ The dense trajectories and low-level local features are widely used in action recognition recently. However, most of these methods ignore the motion part of action which is the key factor to distinguish the different human action. This paper proposes a new two-layer model of representation for action recognition by describing the video with low-level features and mid-level motion part model. Firstly, we encode the compensated flow (-flow) trajectory-based local features with Fisher Vector (FV) to retain the low-level characteristic of motion. Then, the motion parts are extracted by clustering the similar trajectories with spatiotemporal distance between trajectories. Finally the representation for action video is the concatenation of low-level descriptors encoding vector and motion part encoding vector. It is used as input to the LibSVM for action recognition. The experiment results demonstrate the improvements on J-HMDB and YouTube datasets, which obtain 67.4% and 87.6%, respectively. Xiaoqiang Li, Dan Wang, and Yin Zhang Copyright © 2017 Xiaoqiang Li et al. All rights reserved. Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression Wed, 19 Jul 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/7487438/ Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile’s configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs. Ali Ghorbani and Mostafa Firouzi Niavol Copyright © 2017 Ali Ghorbani and Mostafa Firouzi Niavol. All rights reserved. A Regular -Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise Wed, 12 Jul 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/2520301/ The removal of mixed Gaussian-impulse noise plays an important role in many areas, such as remote sensing. However, traditional methods may be unaware of promoting the degree of the sparsity adaptively after decomposing into low rank component and sparse component. In this paper, a new problem formulation with regular spectral -support norm and regular -support norm is proposed. A unified framework is developed to capture the intrinsic sparsity structure of all two components. To address the resulting problem, an efficient minimization scheme within the framework of accelerated proximal gradient is proposed. This scheme is achieved by alternating regular -shrinkage thresholding operator. Experimental comparison with the other state-of-the-art methods demonstrates the efficacy of the proposed method. Han Pan, Zhongliang Jing, Lingfeng Qiao, and Minzhe Li Copyright © 2017 Han Pan et al. All rights reserved. On the Horizontal Deviation of a Spinning Projectile Penetrating into Granular Systems Tue, 06 Jun 2017 07:52:10 +0000 http://www.hindawi.com/journals/acisc/2017/8971353/ The absence of a general theory that describes the dynamical behavior of the particulate materials makes the numerical simulations the most current powerful tool that can grasp many mechanical problems relevant to the granular materials. In this paper, based on a two-dimensional soft particle discrete element method (DEM), a numerical approach is developed to investigate the consequence of the orthogonal impact into various granular beds of projectile rotating in both clockwise (CW) and counterclockwise (CCW) directions. Our results reveal that, depending on the rotation direction, there is a significant deviation of the -coordinate of the final stopping point of a spinning projectile from that of its original impact point. For CW rotations, a deviation to the right occurs while a left deviation has been recorded for CCW rotation case. Waseem Ghazi Alshanti Copyright © 2017 Waseem Ghazi Alshanti. All rights reserved. Sliding Window Based Machine Learning System for the Left Ventricle Localization in MR Cardiac Images Sun, 04 Jun 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/3048181/ The most commonly encountered problem in vision systems includes its capability to suffice for different scenes containing the object of interest to be detected. Generally, the different backgrounds in which the objects of interest are contained significantly dwindle the performance of vision systems. In this work, we design a sliding windows machine learning system for the recognition and detection of left ventricles in MR cardiac images. We leverage on the capability of artificial neural networks to cope with some of the inevitable scene constraints encountered in medical objects detection tasks. We train a backpropagation neural network on samples of left and nonleft ventricles. We reformulate the left ventricles detection task as a machine learning problem and employ an intelligent system (backpropagation neural network) to achieve the detection task. We treat the left ventricle detection problem as binary classification tasks by assigning collected left ventricle samples as one class, and random (nonleft ventricles) objects are the other class. The trained backpropagation neural network is validated to possess a good generalization power by simulating it with a test set. A recognition rate of 100% and 88% is achieved on the training and test set, respectively. The trained backpropagation neural network is used to determine if the sampled region in a target image contains a left ventricle or not. Lastly, we show the effectiveness of the proposed system by comparing the manual detection of left ventricles drawn by medical experts and the automatic detection by the trained network. Abdulkader Helwan and Dilber Uzun Ozsahin Copyright © 2017 Abdulkader Helwan and Dilber Uzun Ozsahin. All rights reserved. Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval Wed, 24 May 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/9635348/ Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN) search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI), to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications. Lijuan Duan, Chongyang Zhao, Jun Miao, Yuanhua Qiao, and Xing Su Copyright © 2017 Lijuan Duan et al. All rights reserved. Multiscale Convolutional Neural Networks for Hand Detection Mon, 22 May 2017 09:26:42 +0000 http://www.hindawi.com/journals/acisc/2017/9830641/ Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs) in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN) model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge. Shiyang Yan, Yizhang Xia, Jeremy S. Smith, Wenjin Lu, and Bailing Zhang Copyright © 2017 Shiyang Yan et al. All rights reserved. Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map Mon, 15 May 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/3508189/ The paper presents the use of a self-organizing feature map (SOFM) for determining damage in reinforced concrete frames with shear walls. For this purpose, a concrete frame with a shear wall was subjected to nonlinear dynamic analysis. The SOFM was optimized using the genetic algorithm (GA) in order to determine the number of layers, number of nodes in the hidden layer, transfer function type, and learning algorithm. The obtained model was compared with linear regression (LR) and nonlinear regression (NonLR) models and also the radial basis function (RBF) of a neural network. It was concluded that the SOFM, when optimized with the GA, has more strength, flexibility, and accuracy. Mehdi Nikoo, Łukasz Sadowski, Faezehossadat Khademi, and Mohammad Nikoo Copyright © 2017 Mehdi Nikoo et al. All rights reserved. Failure Effects Evaluation for ATC Automation System Tue, 02 May 2017 10:05:35 +0000 http://www.hindawi.com/journals/acisc/2017/8304236/ ATC (air traffic control) automation system is a complex system, which helps maintain the air traffic order, guarantee the flight interval, and prevent aircraft collision. It is essential to ensure the safety of air traffic. Failure effects evaluation is an important part of ATC automation system reliability engineering. The failure effects evaluation of ATC automation system is aimed at the effects of modules or components which affect the performance and functionality of the system. By analyzing and evaluating the failure modes and their causes and effects, some reasonable improvement measures and preventive maintenance plans can be established. In this paper, the failure effects evaluation framework considering performance and functionality of the system is established on the basis of reliability theory. Some algorithms for the quantitative evaluation of failure effects on performance of ATC automation system are proposed. According to the algorithms, the quantitative evaluation of reliability, availability, maintainability, and other assessment indicators can be calculated. Rui Li, Zili Zhou, Yansong Cheng, and Jianqiang Wang Copyright © 2017 Rui Li et al. All rights reserved. Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning Tue, 04 Apr 2017 07:20:18 +0000 http://www.hindawi.com/journals/acisc/2017/9897078/ This study investigates an adaptive-weighted instanced-based learning, for the prediction of the ultimate punching shear capacity (UPSC) of fiber-reinforced polymer- (FRP-) reinforced slabs. The concept of the new method is to employ the Differential Evolution to construct an adaptive instance-based regression model. The performance of the proposed model is compared to those of Artificial Neural Network (ANN) and traditional formula-based methods. A dataset which contains the testing results of FRP-reinforced concrete slabs has been collected to establish and verify new approach. This study shows that the investigated instance-based regression model is capable of delivering the prediction result which is far more accurate than traditional formulas and very competitive with the black-box approach of ANN. Furthermore, the proposed adaptive-weighted instanced-based learning provides a means for quantifying the relevancy of each factor used for the prediction of UPSC of FRP-reinforced slabs. Nhat-Duc Hoang, Duy-Thang Vu, Xuan-Linh Tran, and Van-Duc Tran Copyright © 2017 Nhat-Duc Hoang et al. All rights reserved. Machine Learning and Visual Computing Sun, 19 Mar 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/7571043/ Lei Zhang, Yu Cao, Fei Yang, and Qiushi Zhao Copyright © 2017 Lei Zhang et al. All rights reserved. Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting Wed, 08 Mar 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/5134962/ Forecasting in big datasets is a common but complicated task, which cannot be executed using the well-known parametric linear regression. However, nonparametric and semiparametric methods, which enable forecasting by building nonlinear data models, are computationally intensive and lack sufficient scalability to cope with big datasets to extract successful results in a reasonable time. We present distributed parallel versions of some nonparametric and semiparametric regression models. We used MapReduce paradigm and describe the algorithms in terms of SPARK data structures to parallelize the calculations. The forecasting accuracy of the proposed algorithms is compared with the linear regression model, which is the only forecasting model currently having parallel distributed realization within the SPARK framework to address big data problems. The advantages of the parallelization of the algorithm are also provided. We validate our models conducting various numerical experiments: evaluating the goodness of fit, analyzing how increasing dataset size influences time consumption, and analyzing time consumption by varying the degree of parallelism (number of workers) in the distributed realization. Jelena Fiosina and Maksims Fiosins Copyright © 2017 Jelena Fiosina and Maksims Fiosins. All rights reserved. Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems Wed, 08 Mar 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/7974218/ This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems. Ali Wagdy Mohamed and Abdulaziz S. Almazyad Copyright © 2017 Ali Wagdy Mohamed and Abdulaziz S. Almazyad. All rights reserved. Research on Simulink/Fluent Collaborative Simulation Zooming of Marine Gas Turbine Thu, 02 Mar 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/8324810/ Based on the detailed analysis of collaborative running interface of Simulink/Fluent, a system simulation for the rated working condition as well as variable working condition of marine gas turbine has been achieved, which can improve the simulation efficiency of marine gas turbine by developing simulation model of combustor with Fluent and simulation models of other components with Simulink. The result shows that the Simulink/Fluent collaborative simulation zooming can make the inner working conditions of combustor be observed specifically, based on the overall performance matching analysis; thus an effective technical means for the structural optimization design of combustor has been provided. Zhi-tao Wang, Jian Li, Tie-lei Li, and Shu-ying Li Copyright © 2017 Zhi-tao Wang et al. All rights reserved. Deep Learning in Visual Computing and Signal Processing Sun, 19 Feb 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/1320780/ Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks. Danfeng Xie, Lei Zhang, and Li Bai Copyright © 2017 Danfeng Xie et al. All rights reserved. Multiband Operation and Performance Enhancement of the PIFA Antenna by Using Particle Swarm Optimization and Overlapping Method Wed, 15 Feb 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/3481709/ Recently, the demand for wireless devices that support multiband frequency has increased. The integration of such technology in mobile communication system has led to a great demand in developing small size antenna with multiband operation, which is able to operate in the required system. In this paper, a novel type planar inverted F antenna (PIFA) with gridded ground plane structure and overlapping cells is presented. By controlling the overlapping size, we improve the characteristics of the proposed antenna. This antenna is developed to achieve multiband operation with small size and good performance. The particle swarm optimization (PSO) is employed to a PIFA antenna to get rid of the limitations of single band operation by searching the optimal localization and length of linear slots on the ground plane to give triband operation. This PIFA antenna can be integrated to operate for several mobile applications as Bluetooth/WLAN, WIMAX, and 4G (UMTS2100, LTE). The optimized antenna is simulated by both Ansoft HFSS and computer simulation technology microwave studio (CSTMWS) in terms of -parameters. A good agreement between simulated performances by both software types is achieved. A parametric study is made to analyze the effect of different PIFA parameters on the operating frequency and the reflection coefficient in order to enhance the antenna performances. In these frequency bands, the antenna has nearly omnidirectional radiation pattern. L. Wakrim, S. Ibnyaich, and M. M. Hassani Copyright © 2017 L. Wakrim et al. All rights reserved. Parallel Evolutionary Peer-to-Peer Networking in Realistic Environments Thu, 26 Jan 2017 06:33:08 +0000 http://www.hindawi.com/journals/acisc/2017/4169152/ In the present paper we first conduct simulations of the parallel evolutionary peer-to-peer (P2P) networking technique (referred to as P-EP2P) that we previously proposed using models of realistic environments to examine if P-EP2P is practical. Environments are here represented by what users have and want in the network, and P-EP2P adapts the P2P network topologies to the present environment in an evolutionary manner. The simulation results show that P-EP2P is hard to adapt the network topologies to some realistic environments. Then, based on the discussions of the results, we propose a strategy for better adaptability of P-EP2P to the realistic environments. The strategy first judges if evolutionary adaptation of the network topologies is likely to occur in the present environment, and if it judges so, it actually tries to achieve evolutionary adaptation of the network topologies. Otherwise, it brings random change to the network topologies. The simulation results indicate that P-EP2P with the proposed strategy can better adapt the network topologies to the realistic environments. The main contribution of the study is to present such a promising way to realize an evolvable network in which the evolution direction is given by users. Kei Ohnishi Copyright © 2017 Kei Ohnishi. All rights reserved. Mining Key Skeleton Poses with Latent SVM for Action Recognition Mon, 23 Jan 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/5861435/ Human action recognition based on 3D skeleton has become an active research field in recent years with the recently developed commodity depth sensors. Most published methods analyze an entire 3D depth data, construct mid-level part representations, or use trajectory descriptor of spatial-temporal interest point for recognizing human activities. Unlike previous work, a novel and simple action representation is proposed in this paper which models the action as a sequence of inconsecutive and discriminative skeleton poses, named as key skeleton poses. The pairwise relative positions of skeleton joints are used as feature of the skeleton poses which are mined with the aid of the latent support vector machine (latent SVM). The advantage of our method is resisting against intraclass variation such as noise and large nonlinear temporal deformation of human action. We evaluate the proposed approach on three benchmark action datasets captured by Kinect devices: MSR Action 3D dataset, UTKinect Action dataset, and Florence 3D Action dataset. The detailed experimental results demonstrate that the proposed approach achieves superior performance to the state-of-the-art skeleton-based action recognition methods. Xiaoqiang Li, Yi Zhang, and Dong Liao Copyright © 2017 Xiaoqiang Li et al. All rights reserved. An Automated Structural Optimisation Methodology for Scissor Structures Using a Genetic Algorithm Wed, 18 Jan 2017 12:52:10 +0000 http://www.hindawi.com/journals/acisc/2017/6843574/ We developed a fully automated multiobjective optimisation framework using genetic algorithms to generate a range of optimal barrel vault scissor structures. Compared to other optimisation methods, genetic algorithms are more robust and efficient when dealing with multiobjective optimisation problems and provide a better view of the search space while reducing the chance to be stuck in a local minimum. The novelty of this work is the application and validation (using metrics) of genetic algorithms for the shape and size optimisation of scissor structures, which has not been done so far for two objectives. We tested the feasibility and capacity of the methodology by optimising a 6 m span barrel vault to weight and compactness and by obtaining optimal solutions in an efficient way using NSGA-II. This paper presents the framework and the results of the case study. The in-depth analysis of the influence of the optimisation variables on the results yields new insights which can help in making choices with regard to the design variables, the constraints, and the number of individuals and generations in order to obtain efficiently a trade-off of optimal solutions. Aushim Koumar, Tine Tysmans, Rajan Filomeno Coelho, and Niels De Temmerman Copyright © 2017 Aushim Koumar et al. All rights reserved. Reidentification of Persons Using Clothing Features in Real-Life Video Wed, 11 Jan 2017 00:00:00 +0000 http://www.hindawi.com/journals/acisc/2017/5834846/ Person reidentification, which aims to track people across nonoverlapping cameras, is a fundamental task in automated video processing. Moving people often appear differently when viewed from different nonoverlapping cameras because of differences in illumination, pose, and camera properties. The color histogram is a global feature of an object that can be used for identification. This histogram describes the distribution of all colors on the object. However, the use of color histograms has two disadvantages. First, colors change differently under different lighting and at different angles. Second, traditional color histograms lack spatial information. We used a perception-based color space to solve the illumination problem of traditional histograms. We also used the spatial pyramid matching (SPM) model to improve the image spatial information in color histograms. Finally, we used the Gaussian mixture model (GMM) to show features for person reidentification, because the main color feature of GMM is more adaptable for scene changes, and improve the stability of the retrieved results for different color spaces in various scenes. Through a series of experiments, we found the relationships of different features that impact person reidentification. Guodong Zhang, Peilin Jiang, Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita Copyright © 2017 Guodong Zhang et al. All rights reserved. The Performance of LBP and NSVC Combination Applied to Face Classification Sun, 25 Dec 2016 12:47:37 +0000 http://www.hindawi.com/journals/acisc/2016/8272796/ The growing demand in the field of security led to the development of interesting approaches in face classification. These works are interested since their beginning in extracting the invariant features of the face to build a single model easily identifiable by classification algorithms. Our goal in this article is to develop more efficient practical methods for face detection. We present a new fast and accurate approach based on local binary patterns (LBP) for the extraction of the features that is combined with the new classifier Neighboring Support Vector Classifier (NSVC) for classification. The experimental results on different natural images show that the proposed method can get very good results at a very short detection time. The best precision obtained by LBP-NSVC exceeds 99%. Mohammed Ngadi, Aouatif Amine, Bouchra Nassih, Hanaa Hachimi, and Adnane El-Attar Copyright © 2016 Mohammed Ngadi et al. All rights reserved. A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems Mon, 14 Nov 2016 12:33:21 +0000 http://www.hindawi.com/journals/acisc/2016/5241279/ Many industrial processes are inherently distributed in space and time and are called spatially distributed dynamical systems (SDDSs). Sensor placement affects capturing the spatial distribution and then becomes crucial issue to model or control an SDDS. In this study, a new data-driven based sensor placement method is developed. SVR algorithm is innovatively used to extract the characteristics of spatial distribution from a spatiotemporal data set. The support vectors learned by SVR represent the crucial spatial data structure in the spatiotemporal data set, which can be employed to determine optimal sensor location and sensor number. A systematic sensor placement design scheme in three steps (data collection, SVR learning, and sensor locating) is developed for an easy implementation. Finally, effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzy controlled spatially distributed systems. Xian-xia Zhang, Zhi-qiang Fu, Wei-lu Shan, Bing Wang, and Tao Zou Copyright © 2016 Xian-xia Zhang et al. All rights reserved. Local Community Detection Algorithm Based on Minimal Cluster Mon, 07 Nov 2016 07:10:49 +0000 http://www.hindawi.com/journals/acisc/2016/3217612/ In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks. Yong Zhou, Guibin Sun, Yan Xing, Ranran Zhou, and Zhixiao Wang Copyright © 2016 Yong Zhou et al. All rights reserved. Lexicon-Based Sentiment Analysis of Teachers’ Evaluation Thu, 13 Oct 2016 09:33:04 +0000 http://www.hindawi.com/journals/acisc/2016/2385429/ The end of the course evaluation has become an integral part of education management in almost every academic institution. The existing automated evaluation method primarily employs the Likert scale based quantitative scores provided by students about the delivery of the course and the knowledge of the instructor. The feedback is subsequently used to improve the quality of the teaching and often for the annual appraisal process. In addition to the Likert scale questions, the evaluation form typically contains open-ended questions where students can write general comments/feedback that might not be covered by the fixed questions. The textual feedback, however, is usually provided to teachers and administration and due to its nonquantitative nature is frequently not processed to gain more insight. This paper aims to address this aspect by applying several text analytics methods on students’ feedback. The paper not only presents a sentiment analysis based metric, which is shown to be highly correlated with the aggregated Likert scale scores, but also provides new insight into a teacher’s performance with the help of tag clouds, sentiment score, and other frequency-based filters. Quratulain Rajput, Sajjad Haider, and Sayeed Ghani Copyright © 2016 Quratulain Rajput et al. All rights reserved. Generative Power and Closure Properties of Watson-Crick Grammars Mon, 29 Aug 2016 16:35:21 +0000 http://www.hindawi.com/journals/acisc/2016/9481971/ We define WK linear grammars, as an extension of WK regular grammars with linear grammar rules, and WK context-free grammars, thus investigating their computational power and closure properties. We show that WK linear grammars can generate some context-sensitive languages. Moreover, we demonstrate that the family of WK regular languages is the proper subset of the family of WK linear languages, but it is not comparable with the family of linear languages. We also establish that the Watson-Crick regular grammars are closed under almost all of the main closure operations. Nurul Liyana Mohamad Zulkufli, Sherzod Turaev, Mohd Izzuddin Mohd Tamrin, and Azeddine Messikh Copyright © 2016 Nurul Liyana Mohamad Zulkufli et al. All rights reserved. Low-Rank Kernel-Based Semisupervised Discriminant Analysis Wed, 20 Jul 2016 16:43:21 +0000 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.