ISRN Artificial Intelligence The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Study on the Effectiveness of the Investment Strategy Based on a Classifier with Rules Adapted by Machine Learning Mon, 03 Feb 2014 08:23:33 +0000 This paper examines two transactional strategies based on the classifier which opens positions using some rules and closes them using different rules. A rule set contains time-varying parameters that when matched allow making an investment decision. Researches contain the study of variability of these parameters and the relationship between learning period and testing (using the learned parameters). The strategies are evaluated based on the time series of cumulative profit achieved in the test periods. The study was conducted on the most popular currency pair EURUSD (Euro-Dollar) sampled with interval of 1 hour. An important contribution to the theory of algotrading resulting from presented research is specification of the parameter space (quite large, consisting of 11 parameters) that achieves very good results using cross validation. A. Wiliński, A. Bera, W. Nowicki, and P. Błaszyński Copyright © 2014 A. Wiliński et al. All rights reserved. Weighed Nonlinear Hybrid Neural Networks in Underground Rescue Mission Wed, 22 Jan 2014 16:37:35 +0000 In our previous work, a novel model called compact radial basis function (CRBF) in a routing topology control has been modelled. The computational burden of Zhang and Gaussian transfer functions was modified by removing the power parameters on the models. The results showed outstanding performance over the Zhang and Gaussian models. This study researched on several hybrids forms of the model where cosine (cos) and sine (sin) nonlinear weights were imposed on the two transfer functions such that . The purpose was to identify the best hybrid that optimized all of its parameters with a minimum error. The results of the nonlinear weighted hybrids were compared with a hybrid of Gaussian model. Simulation revealed that the negative nonlinear weights hybrids optimized all the parameters and it is substantially superior to the previous approaches presented in the literature, with minimized errors of 0.0098, 0.0121, 0.0135, and 0.0129 for the negative cosine (), positive cosine (HSCR-BF+cos), negative sine (), and positive sine (HSCR-BF+sin) hybrids, respectively, while sigmoid and Gaussian radial basis functions (HSGR-BF+cos) were 0.0117. The proposed hybrid could serve as an alternative approach to underground rescue operation. Hongxing Yao, Mary Opokua Ansong, and Jun Steed Huang Copyright © 2014 Hongxing Yao et al. All rights reserved. BPN Based Likelihood Ratio Score Fusion for Audio-Visual Speaker Identification in Response to Noise Wed, 08 Jan 2014 16:17:42 +0000 This paper deals with a new and improved approach of Back-propagation learning neural network based likelihood ratio score fusion technique for audio-visual speaker Identification in various noisy environments. Different signal preprocessing and noise removing techniques have been used to process the speech utterance and LPC, LPCC, RCC, MFCC, ΔMFCC and ΔΔMFCC methods have been applied to extract the features from the audio signal. Active Shape Model has been used to extract the appearance and shape based facial features. To enhance the performance of the proposed system, appearance and shape based facial features are concatenated and Principal Component Analysis method has been used to reduce the dimension of the facial feature vector. The audio and visual feature vectors are then fed to Hidden Markov Model separately to find out the log-likelihood of each modality. The reliability of each modality has been calculated using reliability measurement method. Finally, these integrated likelihood ratios are fed to Back-propagation learning neural network algorithm to discover the final speaker identification result. For measuring the performance of the proposed system, three different databases, that is, NOIZEUS speech database, ORL face database and VALID audio-visual multimodal database have been used for audio-only, visual-only, and audio-visual speaker identification. To identify the accuracy of the proposed system with existing techniques under various noisy environment, different types of artificial noise have been added at various rates with audio and visual signal and performance being compared with different variations of audio and visual features. Md. Rabiul Islam and Md. Abdus Sobhan Copyright © 2014 Md. Rabiul Islam and Md. Abdus Sobhan. All rights reserved. Multiobjective Stochastic Programming for Mixed Integer Vendor Selection Problem Using Artificial Bee Colony Algorithm Thu, 26 Dec 2013 14:06:16 +0000 It has been always critical and inevitable to select and assess the appropriate and efficient vendors for the companies such that all the aspects and factors leading to the importance of the select process should be considered. This paper studies the process of selecting the vendors simultaneously in three aspects of multiple criteria, random factors, and reaching efficient solutions with the objective of improvement. Thus, selecting the vendors is introduced in the form of a mixed integer multiobjective stochastic problem and for the first time it is converted by CCGC (min-max) model to a mixed integer nonlinear single objective deterministic problem. As the converted problem is nonlinear and solving it in large scale will be time-consuming then the artificial bee colony (ABC) algorithm is used to solve it. Also, in order to better understand ABC efficiency, a comparison is performed between this algorithm and the particle swarm optimization (PSO) and the imperialist competitive algorithm (ICA) and Lingo software output. The results obtained from a real example show that ABC offers more efficient solutions to the problem solving in large scale and PSO spends less time to solve the same problem. Mostafa Ekhtiari and Shahab Poursafary Copyright © 2013 Mostafa Ekhtiari and Shahab Poursafary. All rights reserved. A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules Thu, 19 Dec 2013 11:26:42 +0000 In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm. Binu Thomas and G. Raju Copyright © 2013 Binu Thomas and G. Raju. All rights reserved. 3D Gestural Interaction: The State of the Field Wed, 18 Dec 2013 17:28:06 +0000 3D gestural interaction provides a powerful and natural way to interact with computers using the hands and body for a variety of different applications including video games, training and simulation, and medicine. However, accurately recognizing 3D gestures so that they can be reliably used in these applications poses many different research challenges. In this paper, we examine the state of the field of 3D gestural interfaces by presenting the latest strategies on how to collect the raw 3D gesture data from the user and how to accurately analyze this raw data to correctly recognize 3D gestures users perform. In addition, we examine the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction. Finally, we present ideas for future research in this thriving and active research area. Joseph J. LaViola Jr. Copyright © 2013 Joseph J. LaViola Jr. All rights reserved. Comparison of Adaptive Information Security Approaches Wed, 02 Oct 2013 14:42:05 +0000 Dynamically changing environments and threat landscapes require adaptive information security. Adaptive information security makes it possible to change and modify security mechanisms at runtime. Hence, all security decisions are not enforced at design-time. This paper builds a framework to compare security adaptation approaches. The framework contains three viewpoints, that is, adaptation, security, and lifecycle. Furthermore, the paper describes five security adaptation approaches and compares them by means of the framework. The comparison reveals that the existing security adaptation approaches widely cover the information gathering. However, the compared approaches do not describe how to decide a method to perform a security adaptation. Similarly, means how to provide input knowledge for the security adaptation is not covered. Hence, these research areas have to be covered in the future. The achieved results are applicable for software developers when selecting a security adaptation approach and for researchers when considering future research items. Antti Evesti and Eila Ovaska Copyright © 2013 Antti Evesti and Eila Ovaska. All rights reserved. Probabilistic Multiagent Reasoning over Annotated Amalgamated F-Logic Ontologies Mon, 30 Sep 2013 16:53:28 +0000 In a multiagent system (MAS), agents can have different opinions about a given problem. In order to solve the problem collectively they have to reach consensus about the ontology of the problem. A solution to probabilistic reasoning in such an environment by using a social network of trust is given. It is shown that frame logic can be annotated and amalgamated by using this approach which gives a foundation for collective ontology development in MAS. Consider the following problem: a set of agents in a multiagent system (MAS) model a certain domain in order to collectively solve a problem. Their opinions about the domain differ in various ways. The agents are connected into a social network defined by trust relations. The problem to be solved is how to obtain consensus about the domain. Markus Schatten Copyright © 2013 Markus Schatten. All rights reserved. Belief Revision in the GOAL Agent Programming Language Thu, 05 Sep 2013 10:48:00 +0000 Agents in a multiagent system may in many cases find themselves in situations where inconsistencies arise. In order to properly deal with these, a good belief revision procedure is required. This paper illustrates the usefulness of such a procedure: a certain belief revision algorithm is considered in order to deal with inconsistencies and, particularly, the issue of inconsistencies, and belief revision is examined in relation to the GOAL agent programming language. Johannes Svante Spurkeland, Andreas Schmidt Jensen, and Jørgen Villadsen Copyright © 2013 Johannes Svante Spurkeland et al. All rights reserved. Yield Prediction for Tomato Greenhouse Using EFuNN Sun, 02 Jun 2013 13:23:23 +0000 In the area of greenhouse operation, yield prediction still relies heavily on human expertise. This paper proposes an automatic tomato yield predictor to assist the human operators in anticipating more effectively weekly fluctuations and avoid problems of both overdemand and overproduction if the yield cannot be predicted accurately. The parameters used by the predictor consist of environmental variables inside the greenhouse, namely, temperature, CO2, vapour pressure deficit (VPD), and radiation, as well as past yield. Greenhouse environment data and crop records from a large scale commercial operation, Wight Salads Group (WSG) in the Isle of Wight, United Kingdom, collected during the period 2004 to 2008, were used to model tomato yield using an Intelligent System called “Evolving Fuzzy Neural Network” (EFuNN). Our results show that the EFuNN model predicted weekly fluctuations of the yield with an average accuracy of 90%. The contribution suggests that the multiple EFUNNs can be mapped to respective task-oriented rule-sets giving rise to adaptive knowledge bases that could assist growers in the control of tomato supplies and more generally could inform the decision making concerning overall crop management practices. Kefaya Qaddoum, E. L. Hines, and D. D. Iliescu Copyright © 2013 Kefaya Qaddoum et al. All rights reserved. Health Monitoring for Elderly: An Application Using Case-Based Reasoning and Cluster Analysis Wed, 22 May 2013 16:30:27 +0000 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. PSO-Based PID Controller Design for a Class of Stable and Unstable Systems Tue, 21 May 2013 19:09:25 +0000 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. Gamma-Poisson Distribution Model for Text Categorization Thu, 04 Apr 2013 10:18:50 +0000 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. Bag-of-Words Representation in Image Annotation: A Review Thu, 29 Nov 2012 15:05:57 +0000 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. Model-Free, Occlusion Accommodating Active Contour Tracking Mon, 22 Oct 2012 09:55:15 +0000 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. SOM-Based Approach for the Analysis and Classification of Synchronous Impulsive Noise of an In-Ship PLC System Tue, 16 Oct 2012 09:32:38 +0000 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. Reasoning with Time Intervals: A Logical and Computational Perspective Sun, 14 Oct 2012 15:55:15 +0000 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. Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment Sun, 14 Oct 2012 15:18:59 +0000 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. Optimization of Swarm-Based Simulations Thu, 16 Aug 2012 11:02:14 +0000 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. Neural Network Implementations for PCA and Its Extensions Thu, 19 Jul 2012 15:54:32 +0000 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. Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization Sun, 08 Jul 2012 08:31:22 +0000 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. Evolutionary Computation for Label Layout on Unused Space of Stacked Graphs Wed, 21 Mar 2012 15:21:39 +0000 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. Neural Discriminant Models, Bootstrapping, and Simulation Tue, 13 Mar 2012 10:10:32 +0000 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. A Modular System Oriented to the Design of Versatile Knowledge Bases for Chatbots Mon, 05 Mar 2012 09:08:35 +0000 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. Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering Thu, 01 Mar 2012 10:33:58 +0000 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. Personalized Recommendation in Interactive Visual Analysis of Stacked Graphs Wed, 29 Feb 2012 11:58:09 +0000 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. A Smart Proofreader for All Natural Languages: Achieving Semantic Understanding by Majority Vote Sun, 19 Feb 2012 11:44:51 +0000 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. A Set of Geometric Features for Neural Network-Based Textile Defect Classification Tue, 07 Feb 2012 13:41:16 +0000 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. Application of Artificial Bee Colony Optimization Algorithm for Image Classification Using Color and Texture Feature Similarity Fusion Tue, 31 Jan 2012 08:00:21 +0000 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. Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets Wed, 18 Jan 2012 10:16:41 +0000 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.