http://www.hindawi.com The latest articles from Hindawi Publishing Corporation © 2013 , Hindawi Publishing Corporation . All rights reserved. Wed, 22 May 2013 16:30:27 +0000 http://www.hindawi.com/isrn/ai/2013/380239/ This paper presents a framework to process and analyze data from a pulse oximeter which remotely measures pulse rate and blood oxygen saturation from a set of individuals. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to their similarity. Record collection has been performed using a personalized health profiling approach in which participants wore a pulse oximeter sensor for a fixed period of time and performed specific activities for pre-determined intervals. Using a variety of feature extraction methods in time, frequency, and time-frequency domains, as well as data processing techniques, the data is fed into a CBR system which retrieves most similar cases and generates an alarm according to the case outcomes. The system has been compared with an expert's classification, and a 90% match is achieved between the expert's and CBR classification. Again, considering the clustered measurements, the CBR approach classifies 93% correctly both for the pulse rate and oxygen saturation. Along with the proposed methodology, this paper provides a basis for which the system can be used in the analysis of continuous health monitoring and can be used as a suitable method in home/remote monitoring systems. Mobyen Uddin Ahmed, Hadi Banaee, and Amy Loutfi Copyright © 2013 Mobyen Uddin Ahmed et al. All rights reserved. Tue, 21 May 2013 19:09:25 +0000 http://www.hindawi.com/isrn/ai/2013/543607/ Nonlinear processes are very common in process industries, and designing a stabilizing controller is always preferred to maximize the production rate. In this paper, tuning of PID controller for a class of time delayed stable and unstable process models using Particle Swarm Optimization (PSO) algorithm is discussed. The dimension of the search space is only three (, , and ); hence, a fixed weight is assigned for the inertia parameter. A comparative study is presented between various inertia weights such as 0.5, 0.75, and 1. From the result, it is evident that the proposed method helps to attain better controller settings with reduced iteration number. The efficacy of the proposed scheme has been validated through a comparative study with classical controller tuning methods and heuristic methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Finally, a real-time implementation of the proposed method is carried on a nonlinear spherical tank system. From the simulation and real-time results, it is evident that the PSO algorithm performs well on the stable and unstable process models considered in this work. The PSO tuned controller offers enhanced process characteristics such as better time domain specifications, smooth reference tracking, supply disturbance rejection, and error minimization. K. Latha, V. Rajinikanth, and P. M. Surekha Copyright © 2013 K. Latha et al. All rights reserved. Thu, 04 Apr 2013 10:18:50 +0000 http://www.hindawi.com/isrn/ai/2013/829630/ We introduce a new model for describing word frequency distributions in documents for automatic text classification tasks. In the model, the gamma-Poisson probability distribution is used to achieve better text modeling. The framework of the modeling and its application to text categorization are demonstrated with practical techniques for parameter estimation and vector normalization. To investigate the efficiency of our model, text categorization experiments were performed on 20 Newsgroups, Reuters-21578, Industry Sector, and TechTC-100 datasets. The results show that the model allows performance comparable to that of the support vector machine and clearly exceeding that of the multinomial model and the Dirichlet-multinomial model. The time complexity of the proposed classifier and its advantage in practical applications are also discussed. Hiroshi Ogura, Hiromi Amano, and Masato Kondo Copyright © 2013 Hiroshi Ogura et al. All rights reserved. Thu, 29 Nov 2012 15:05:57 +0000 http://www.hindawi.com/isrn/ai/2012/376804/ Content-based image retrieval (CBIR) systems require users to query images by their low-level visual content; this not only makes it hard for users to formulate queries, but also can lead to unsatisfied retrieval results. To this end, image annotation was proposed. The aim of image annotation is to automatically assign keywords to images, so image retrieval users are able to query images by keywords. Image annotation can be regarded as the image classification problem: that images are represented by some low-level features and some supervised learning techniques are used to learn the mapping between low-level features and high-level concepts (i.e., class labels). One of the most widely used feature representation methods is bag-of-words (BoW). This paper reviews related works based on the issues of improving and/or applying BoW for image annotation. Moreover, many recent works (from 2006 to 2012) are compared in terms of the methodology of BoW feature generation and experimental design. In addition, several different issues in using BoW are discussed, and some important issues for future research are discussed. Chih-Fong Tsai Copyright © 2012 Chih-Fong Tsai. All rights reserved. Mon, 22 Oct 2012 09:55:15 +0000 http://www.hindawi.com/isrn/ai/2012/672084/ This study investigates tracking in monocular image sequences by a model-free, occlusion accommodating active contour method. The objective functional contains a model-free shape tracking term to constrain the active curve in a frame to have a shape which approximates as closely as possible the shape of the active curve in the preceding frame. It complements a kernel photometric tracking term which constrains the active curve in a frame to enclose an intensity profile that matches as closely as possible the profile within the curve in the preceding frame. This data term is in kernel form so as to forgo image modeling. The method, which is exclusively driven by the curve/level set evolution equations derived from the objective functional Euler-Lagrange conditions, can track several objects independently. Experimental validation includes examples with infrared imaging, occlusion, clutter, and articulated motion. Mohamed Ben Salah and Amar Mitiche Copyright © 2012 Mohamed Ben Salah and Amar Mitiche. All rights reserved. Tue, 16 Oct 2012 09:32:38 +0000 http://www.hindawi.com/isrn/ai/2012/105694/ The interest in wideband data transmission over power line communications has increased rapidly. This technology offers a convenient and inexpensive medium to transmit data, reducing the number of cables. This advantage is particularly appealing in many fields, like the railway, naval, and aeronautical ones. Nevertheless, several problems have to be faced to obtain a high data rate. In particular, the presence of noise makes the transmission difficult, degrading the quality of received signals and prohibiting the full application of these communication frameworks. In this paper the behaviour of an in-ship powerline communication system is analyzed in the presence of synchronous periodic impulsive noise. Such noise is modelled at source and its effects on the transmission of wideband signals are evaluated by means of a simulation circuit model. The obtained results allow to identify the characteristics of the channel and the critical conditions due to noise. Subsequently, an unsupervised technique based on principal component analysis and fuzzy c-mean classifier detects the presence and classifies the specific noises. Numerical results show that the proposed approach enables to achieve this target accurately under different operating conditions, proving to be an effective tool to enhance the performances of the considered technology. G. Acciani, V. Amoruso, G. Fornarelli, and A. Giaquinto Copyright © 2012 G. Acciani et al. All rights reserved. Sun, 14 Oct 2012 15:55:15 +0000 http://www.hindawi.com/isrn/ai/2012/616087/ The role of time in artificial intelligence is extremely important. Interval-based temporal reasoning can be seen as a generalization of the classical point-based one, and the first results in this field date back to Hamblin (1972) and Benhtem (1991) from the philosophical point of view, to Allen (1983) from the algebraic and first-order one, and to Halpern and Shoham (1991) from the modal logic one. Without purporting to provide a comprehensive survey of the field, we take the reader to a journey through the main developments in modal and first-order interval temporal reasoning over the past ten years and outline some landmark results on expressiveness and (un)decidability of the satisfiability problem for the family of modal interval logics. Guido Sciavicco Copyright © 2012 Guido Sciavicco. All rights reserved. Sun, 14 Oct 2012 15:18:59 +0000 http://www.hindawi.com/isrn/ai/2012/178658/ A new algorithm for solving sequence alignment problem is proposed, which is named SAPS (Simulated Annealing with Previous Solutions). This algorithm is based on the classical Simulated Annealing (SA). SAPS is implemented in order to obtain results of pair and multiple sequence alignment. SA is a simulation of heating and cooling of a metal to solve an optimization problem. In order to select randomly a current solution, SAPS algorithm chooses a solution from solutions that have been previously generated within the Metropolis Cycle. This simple change has led to increase the quality of the solution to the problem of aligning genomic sequences with respect to the classical Simulated Annealing algorithm. The parameters of SAPS, for certain instances, are tuned by an analytical method, and some parameters have experimentally been tuned. SAPS has generated high-quality results in comparison with the classical SA. The instances used are specific genes of the AIDS virus. Ernesto Liñán-García and Lorena Marcela Gallegos-Araiza Copyright © 2012 Ernesto Liñán-García and Lorena Marcela Gallegos-Araiza. All rights reserved. Thu, 16 Aug 2012 11:02:14 +0000 http://www.hindawi.com/isrn/ai/2012/365791/ In computational swarms, large numbers of reactive agents are simulated. The swarm individuals may coordinate their movements in a “search space” to create efficient routes, to occupy niches, or to find the highest peaks. From a more general perspective though, swarms are a means of representation and computation to bridge the gap between local, individual interactions, and global, emergent phenomena. Computational swarms bear great advantages over other numeric methods, for instance, regarding their extensibility, potential for real-time interaction, dynamic interaction topologies, close translation between natural science theory and the computational model, and the integration of multiscale and multiphysics aspects. However, the more comprehensive a swarm-based model becomes, the more demanding its configuration and the more costly its computation become. In this paper, we present an approach to effectively configure and efficiently compute swarm-based simulations by means of heuristic, population-based optimization techniques. We emphasize the commonalities of several of our recent studies that shed light on top-down model optimization and bottom-up abstraction techniques, culminating in a postulation of a general concept of self-organized optimization in swarm-based simulations. Sebastian von Mammen, Abbas Sarraf Shirazi, Vladimir Sarpe, and Christian Jacob Copyright © 2012 Sebastian von Mammen et al. All rights reserved. Thu, 19 Jul 2012 15:54:32 +0000 http://www.hindawi.com/isrn/ai/2012/847305/ Many information processing problems can be transformed into some form of eigenvalue or singular value problems. Eigenvalue decomposition (EVD) and singular value decomposition (SVD) are usually used for solving these problems. In this paper, we give an introduction to various neural network implementations and algorithms for principal component analysis (PCA) and its various extensions. PCA is a statistical method that is directly related to EVD and SVD. Minor component analysis (MCA) is a variant of PCA, which is useful for solving total least squares (TLSs) problems. The algorithms are typical unsupervised learning methods. Some other neural network models for feature extraction, such as localized methods, complex-domain methods, generalized EVD, and SVD, are also described. Topics associated with PCA, such as independent component analysis (ICA) and linear discriminant analysis (LDA), are mentioned in passing in the conclusion. These methods are useful in adaptive signal processing, blind signal separation (BSS), pattern recognition, and information compression. Jialin Qiu, Hui Wang, Jiabin Lu, Biaobiao Zhang, and K.-L. Du Copyright © 2012 Jialin Qiu et al. All rights reserved. Sun, 08 Jul 2012 08:31:22 +0000 http://www.hindawi.com/isrn/ai/2012/609718/ Correct diagnosis of a disease is one of the most important problems in medicine. Hepatitis disease is one of the most dangerous diseases that affect millions of people every year and take man’s life. In this paper, the combination of two methods of PSO and CBR (case-based reasoning) has been used to diagnose hepatitis disease. First, a case-based reasoning method is workable to preprocess the data set therefore a weight vector for every one feature is extracted. A particle swarm optimization model is then practical to assemble a decision-making system based on the selected features and diseases recognized. Many researchers have tried to have a more accurate diagnosis of the disease through the use of various methods. The data used has been taken from the site UCI called hepatitis disease. This database has 155 records and 19 fields. This method was compared with five other classification methods and given the results of the proposed method (CBR-PSO), better results were achieved. The proposed method could diagnose hepatitis disease with the accuracy of 93.25%. Mehdi Neshat, Mehdi Sargolzaei, Adel Nadjaran Toosi, and Azra Masoumi Copyright © 2012 Mehdi Neshat et al. All rights reserved. Wed, 21 Mar 2012 15:21:39 +0000 http://www.hindawi.com/isrn/ai/2012/139603/ Placing numerous objects and their corresponding labels in the stacked graph visualization is a challenging problem. In the stacked graph, different combinations of initial parameters and filtering effects yield views with hidden information, illegible labels, and unused space. The result is a tool that does not take advantage on the unused space to reveal information to the user for further investigation. We present an automatic method for label layout on the unused space in a stacked graph. An evolutionary computation (EC) is used to optimize the best label position according to legibility requirements, as well as requirements for keeping semantic relationships between labels and their representative visual objects. A number of EC experiments, as well as a usability study on label legibility, show that our proposed solution looks promising, as compared to the traditional solutions. Alejandro Toledo, Kingkarn Sookhanaphibarn, Ruck Thawonmas, and Frank Rinaldo Copyright © 2012 Alejandro Toledo et al. All rights reserved. Tue, 13 Mar 2012 10:10:32 +0000 http://www.hindawi.com/isrn/ai/2012/820364/ This paper considers the feed-forward neural network models for data of mutually exclusive groups and a set of predictor variables. We take into account the bootstrapping based on information criterion when selecting the optimum number of hidden units for a neural network model and the deviance in order to summarize the measure of goodness-of-fit on fitted neural network models. The bootstrapping is also adapted in order to provide estimates of the bias of the excess error in a prediction rule constructed with training samples. Simulated data from known (true) models are analyzed in order to interpret the results using the neural network. In addition, the thyroid disease database, which compares estimated measures of predictive performance, is examined in both a pure training sample study and in a test sample study, in which the realized test sample apparent error rates associated with a constructed prediction rule are reported. Apartment house data of the metropolitan area station with four-class classification are also analyzed in order to assess the bootstrapping by comparing leaving-one-out cross-validation (CV). Masaaki Tsujitani, Katsuhiro Iba, and Yusuke Tanaka Copyright © 2012 Masaaki Tsujitani et al. All rights reserved. Mon, 05 Mar 2012 09:08:35 +0000 http://www.hindawi.com/isrn/ai/2012/363840/ The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base for a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations. The modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According to this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into account particular features of the dialogue or the user behavior. We illustrate the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation methodologies and capable of managing different conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate. Giovanni Pilato, Agnese Augello, and Salvatore Gaglio Copyright © 2012 Giovanni Pilato et al. All rights reserved. Thu, 01 Mar 2012 10:33:58 +0000 http://www.hindawi.com/isrn/ai/2012/923946/ The segmentation of leukocytes and their components acts as the foundation for all automated image-based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Since the diagnostic information content of the segmented images is plentiful, suitable segmentation routines need to be developed for better disease recognition. Clustering is an essential image segmentation procedure which segments an image into desired regions. A judicious integration of rough sets and fuzzy sets is suitably employed towards leukocyte segmentation in a clustering framework. In this study, the goodness of fuzzy sets and rough sets is suitably integrated to achieve improved segmentation performance. The membership concept of fuzzy sets endow is efficient handling of overlapping partitions, and the rough sets provide a reasonable solution to deal with uncertainty, vagueness, and incompleteness in data. Such synergistic combination gives the proposed scheme an edge over standard cluster-based segmentation techniques, that is, K-means, K-medoid, fuzzy c-means, and rough c-means. Comparative analysis reveals that the hybrid rough fuzzy c-means algorithm is robust in segmenting stained blood microscopic images. The accomplished segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which leads to automated leukemia detection. Subrajeet Mohapatra, Dipti Patra, and Kundan Kumar Copyright © 2012 Subrajeet Mohapatra et al. All rights reserved. Wed, 29 Feb 2012 11:58:09 +0000 http://www.hindawi.com/isrn/ai/2012/389540/ We present a system which combines interactive visual analysis and recommender systems to support insight generation for the user. Our approach combines a stacked graph visualization with a content-based recommender algorithm, where promising views can be revealed to the user for further investigation. By exploiting both the current user navigational data and view properties, the system allows the user to focus on visual space in which she or he is interested. After testing with more than 30 users, we analyze the results and show that accurate user profiles can be generated based on user behavior and view property data. Alejandro Toledo, Kingkarn Sookhanaphibarn, Ruck Thawonmas, and Frank Rinaldo Copyright © 2012 Alejandro Toledo et al. All rights reserved. Sun, 19 Feb 2012 11:44:51 +0000 http://www.hindawi.com/isrn/ai/2012/918362/ The language tools offered in common word processors use dictionaries and simple grammatical rules. They cannot detect errors such as a wrong preposition, interchanged words, or typos that result in a dictionary word. However, by comparing the user's text to a large repository, it is possible to detect many of these errors and also to suggest alternatives. By looking at full sentences, it is often possible to get the correct context. This is important in detecting errors and in order to offer valuable suggestions. These ideas have been implemented in a prototype system. We present examples in English and Norwegian, but the method, that of following a “majority vote,” can be applied to any written language. Kai A. Olsen Copyright © 2012 Kai A. Olsen. All rights reserved. Tue, 07 Feb 2012 13:41:16 +0000 http://www.hindawi.com/isrn/ai/2012/643473/ A significant attention of researchers has been drawn by automated textile inspection systems in order to replace manual inspection, which is time consuming and not accurate enough. Automated textile inspection systems mainly involve two challenging problems, one of which is defect classification. The amount of research done to solve the defect classification problem is inadequate. Scene analysis and feature selection play a very important role in the classification process. Inadequate scene analysis results in an inappropriate set of features. Selection of an inappropriate feature set increases the complexities of the subsequent steps and makes the classification task harder. By taking into account this observation, we present a possibly appropriate set of geometric features in order to address the problem of neural network-based textile defect classification. We justify the features from the point of view of discriminatory quality and feature extraction difficulty. We conduct some experiments in order to show the utility of the features. Our proposed feature set has obtained classification accuracy of more than 98%, which appears to be better than reported results to date. Md. Tarek Habib and M. Rokonuzzaman Copyright © 2012 Md. Tarek Habib and M. Rokonuzzaman. All rights reserved. Tue, 31 Jan 2012 08:00:21 +0000 http://www.hindawi.com/isrn/ai/2012/426957/ With the advancement in image capturing device, the image data is being generated in high volumes. The challenging and important problem in image mining is to reveal useful information by grouping the images into meaningful categories. Image retrieval is extensively required in recent decades because CBIR is regarded as one of the most effective ways of accessing visual data. Conventionally, the way of searching the collections of digital image database is by matching keywords with image caption, descriptions and labels. Keyword based searching method provides very high computational complexity and user has to remember the exact keywords used in the image database. Instead, our paper proposes image retrieval system with Artificial Bee Colony optimization algorithm by fusing similarity score based on color and texture features of an image thereby achieving very high classification accuracy and minimum retrieval time. In this scheme, the color is described by color histogram method in HSV space and texture represented by contrast, energy, entropy, correlation and local stationary over the region in an image. The proposed Comprehensive Image Retrieval scheme fuses the color and texture feature based similarity score between query and all the database images. The experimental results show that the proposed method is superior to keywords based retrieval and content based retrieval schemes with individual low-level features of image. D. Chandrakala and S. Sumathi Copyright © 2012 D. Chandrakala and S. Sumathi. All rights reserved. Wed, 18 Jan 2012 10:16:41 +0000 http://www.hindawi.com/isrn/ai/2012/929085/ Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant unsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which fuzzy c-means (FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved versions of FCM with rather different objective functions are proposed in recent years. Generalized Improved fuzzy partitions FCM (GIFP-FCM) is one of them, which uses 𝐿𝑝 norm distance measure and competitive learning and outperforms the previous algorithms in this field. In this paper, we present a novel FCM clustering method with improved fuzzy partitions that utilizes shadowed sets and try to improve GIFP-FCM in noisy data sets. It enhances the efficiency of GIFP-FCM and improves the clustering results by correctly eliminating most outliers during steps of clustering. We name the novel fuzzy clustering method shadowed set-based GIFP-FCM (SGIFP-FCM). Several experiments on vessel segmentation in retinal images of DRIVE database illustrate the efficiency of the proposed method. Seyed Mohsen Zabihi and Mohammad-R Akbarzadeh-T Copyright © 2012 Seyed Mohsen Zabihi and Mohammad-R Akbarzadeh-T. All rights reserved. Tue, 03 Jan 2012 11:57:59 +0000 http://www.hindawi.com/isrn/ai/2012/576873/ This paper proposes the generalized projective synchronization for chaotic heavy symmetric gyroscope systems versus external disturbances via sliding rule-based fuzzy control. Because of the nonlinear terms of the gyroscope, the system exhibits complex and chaotic motions. Based on Lyapunov stability theory and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic synchronization are attained. The fuzzy rules are directly constructed subject to a common Lyapunov function such that the error dynamics of two identical chaotic motions of symmetric gyros satisfy stability in the Lyapunov sense. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the Eigen values of the Jacobian matrix. It is a systematic procedure for synchronization of chaotic systems. It can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. It needs only one controller to realize synchronization no matter how much dimensions the chaotic system contains, and the controller is easy to be implemented. The designed controller is robust versus model uncertainty and external disturbances. Numerical simulation results demonstrate the validity and feasibility of the proposed method. Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Mohammad Ali Nekoui, and Mohammad Teshnehlab Copyright © 2012 Faezeh Farivar et al. All rights reserved. Tue, 03 Jan 2012 11:57:19 +0000 http://www.hindawi.com/isrn/ai/2012/309687/ A novel structure of fuzzy logic controller is presented for trajectory tracking and vibration control of a flexible joint manipulator. The rule base of fuzzy controller is divided into two sections. Each section includes two variables. The variables of first section are the error of tip angular position and the error of deflection angle, while the variables of second section are derivatives of mentioned errors. Using these structures, it would be possible to reduce the number of rules. Advantages of proposed fuzzy logic are low computational complexity, high interpretability of rules, and convenience in fuzzy controller. Implementing of the fuzzy logic controller on Quanser flexible joint reveals efficiency of proposed controller. To show the efficiency of this method, the results are compared with LQR method. In this paper, experimental validation of proposed method is presented. Mojtaba Rostami Kandroodi, Mohammad Mansouri, Mahdi Aliyari Shoorehdeli, and Mohammad Teshnehlab Copyright © 2012 Mojtaba Rostami Kandroodi et al. All rights reserved. Thu, 08 Dec 2011 08:27:27 +0000 http://www.hindawi.com/isrn/ai/2012/486361/ Conjugate gradient methods constitute excellent neural network training methods characterized by their simplicity, numerical efficiency, and their very low memory requirements. In this paper, we propose a conjugate gradient neural network training algorithm which guarantees sufficient descent using any line search, avoiding thereby the usually inefficient restarts. Moreover, it achieves a high-order accuracy in approximating the second-order curvature information of the error surface by utilizing the modified secant condition proposed by Li et al. (2007). Under mild conditions, we establish that the proposed method is globally convergent for general functions under the strong Wolfe conditions. Experimental results provide evidence that our proposed method is preferable and in general superior to the classical conjugate gradient methods and has a potential to significantly enhance the computational efficiency and robustness of the training process. Ioannis E. Livieris and Panagiotis Pintelas Copyright © 2012 Ioannis E. Livieris and Panagiotis Pintelas. All rights reserved. Mon, 05 Dec 2011 10:18:24 +0000 http://www.hindawi.com/isrn/ai/2012/628496/ This study examines the potential of two soft computing techniques, namely, support vector machines (SVMs) and genetic programming (GP), to predict ultimate bearing capacity of cohesionless soils beneath shallow foundations. The width of footing (𝐵), depth of footing (𝐷), the length-to-width ratio (𝐿/𝐵) of footings, density of soil (𝛾 or 𝛾), angle of internal friction (Φ), and so forth were used as model input parameters to predict ultimate bearing capacity (𝑞𝑢). The results of present models were compared with those obtained by three theoretical approaches, artificial neural networks (ANNs), and fuzzy inference system (FIS) reported in the literature. The statistical evaluation of results shows that the presently applied paradigms are better than the theoretical approaches and are competing well with the other soft computing techniques. The performance evaluation of GP model results based on multiple error criteria confirms that GP is very efficient in accurate prediction of ultimate bearing capacity cohesionless soils when compared with other models considered in this study. S. Adarsh, R. Dhanya, G. Krishna, R. Merlin, and J. Tina Copyright © 2012 S. Adarsh et al. All rights reserved. Thu, 17 Nov 2011 15:38:40 +0000 http://www.hindawi.com/isrn/ai/2012/289721/ This paper presents a human gait recognition algorithm based on a leg gesture separation. Main innovation in this paper is gait recognition using leg gesture classification which is invariant to covariate conditions during walking sequence and just focuses on underbody motions and a neuro-fuzzy combiner classifier (NFCC) which derives a high precision recognition system. At the end, performance of the proposed algorithm has been validated by using the HumanID Gait Challenge data set (HGCD), the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition, and time. And it has been compared to recent algorithm of gait recognition. Hadi Sadoghi Yazdi, Hessam Jahani Fariman, and Jaber Roohi Copyright © 2012 Hadi Sadoghi Yazdi et al. All rights reserved. Tue, 15 Nov 2011 14:54:24 +0000 http://www.hindawi.com/isrn/ai/2012/723516/ DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper, an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes and sizes that differ in local density. Our proposed method VMDBSCAN first finds out the “core” of each cluster—clusters generated after applying DBSCAN. Then, it “vibrates” points toward the cluster that has the maximum influence on these points. Therefore, our proposed method can find the correct number of clusters. Mohammad T. Elbatta, Raed M. Bolbol, and Wesam M. Ashour Copyright © 2012 Mohammad T. Elbatta et al. All rights reserved. Sun, 13 Nov 2011 14:30:09 +0000 http://www.hindawi.com/isrn/ai/2012/714245/ Many planning applications must address conflicting plan objectives, such as cost, duration, and resource consumption, and decision makers want to know the possible tradeoffs. Traditionally, such problems are solved by invoking a single-objective algorithm (such as A*) on multiple, alternative preferences of the objectives to identify nondominated plans. The less-popular alternative is to delay such reasoning and directly optimize multiple plan objectives with a search algorithm like multiobjective A* (MOA*). The relative performance of these two approaches hinges upon the number of 𝑓-values computed for individual search nodes. A* may revisit a node several times and compute a different 𝑓-value each time. MOA* visits each node once and may compute some number of 𝑓-values (each estimating the value of a different nondominated solution constructed from the node). While A* does not share 𝑓-values between searches for different solutions, MOA* can sometimes find multiple solutions while computing a single 𝑓-value per node. The results of extensive empirical comparison show that (i) the performance of multiple invocations of a single-objective A* versus a single invocation of MOA* is often worse in time and quality and (ii) that techniques for balancing per node cost and exploration are promising. Daniel Bryce Copyright © 2012 Daniel Bryce. All rights reserved. Tue, 01 Nov 2011 18:08:34 +0000 http://www.hindawi.com/isrn/ai/2012/271878/ Experts predict that in the next 10 to 100 years scientists will succeed in creating human-level artificial general intelligence. While it is most likely that this task will be accomplished by a government agency or a large corporation, the possibility remains that it will be done by a single inventor or a small team of researchers. In this paper, we address the question of safeguarding a discovery which could without hesitation be said to be worth trillions of dollars. Specifically, we propose a method based on the combination of zero knowledge proofs and provably AI-complete CAPTCHA problems to show that a superintelligent system has been constructed without having to reveal the system itself. Roman V. Yampolskiy Copyright © 2012 Roman V. Yampolskiy. All rights reserved.