ALBERT

Description: We present here a work that applies an automatic construction of ensembles based on the Clustering and Selection (CS) algorithm for time series forecasting. The automatic method, called CSELM, initially finds an optimum number of clusters for training data set and subsequently designates an Extreme Learning Machine (ELM) for each cluster found. For model evaluation, the testing data set are submitted to clustering technique and the nearest cluster to data input will give a supervised response through its associated ELM. Self-organizing maps were used in the clustering phase. Adaptive differential evolution was used to optimize the parameters and performance of the different techniques used in the clustering and prediction phases. The results obtained with the CSELM method are compared with results obtained by other methods in the literature. Five well-known time series were used to validate CSELM. Content Type Journal Article Pages 191-203 DOI 10.3233/HIS-130176 Authors Tiago P.F. Lima, Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil Teresa B. Ludermir, Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 4 / 2013

Description: A recovery monitoring system, based on hybrid computational intelligent techniques, is presented for post anterior cruciate ligament (ACL) injured/reconstructed subjects. The case based reasoning methodology has been combined with fuzzy and neuro-fuzzy techniques in order to develop a knowledge base and a learning model for classification of recovery stages and monitoring the progress of ACL-reconstructed subjects during the convalescence regimen. The system records kinematics and neuromuscular parameters from lower limbs of healthy and ACL-reconstructed subjects using body-mounted wireless sensors and a combined feature set is generated by performing data transformation and feature reduction techniques. In order to classify the recovery stage of subjects, fuzzy k-nearest neighbor technique and adaptive neuro-fuzzy inference system have been applied and results have been compared. The system was successfully tested on a group of healthy and post-operated athletes for analyzing their performance during ambulation and single leg balance testing activities. A semi-automatic process has been employed for case adaptation and retention, requiring input from the physiotherapists and physiatrists. The system can be utilized by physiatrists, physiotherapists, sports trainers and clinicians for multiple purposes including maintaining athletes' profile, monitoring progress of recovery, classifying recovery status, adapting recovery protocols and predicting athletes' sports performance Content Type Journal Article Pages 215-235 DOI 10.3233/HIS-130178 Authors S.M.N. Arosha Senanayke, Universiti Brunei Darussalam, Gadong, BE, Brunei Darussalam Owais Ahmed Malik, Universiti Brunei Darussalam, Gadong, BE, Brunei Darussalam Pg. Mohammad Iskandar, Universiti Brunei Darussalam, Gadong, BE, Brunei Darussalam Dansih Zaheer, Sports Medicine and Research Center, Hassan Bolkiah National Stadium, Berakas, Brunei Darussalam Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 4 / 2013

Description: In this paper, Hybrid Elite Genetic Algorithm and Tabu Search (HEGATS) is proposed to automatically generate Fuzzy rule base for fuzzy inference systems. The algorithm is used to simultaneously optimize the premise and consequent parameters of the fuzzy rules for the appropriate design of fuzzy system for Takagi-Sugeno zero-order. After finale selection of the new generation calculated by genetic algorithm, elitist solution is saved. In this step, tabu search is introduced to find the better neighboring of the elitist solution which will be introduced in the new generation. This hybridization of global and local optimization algorithms minimizes the number of iterations and computation time while maintaining accuracy and minimum response time. To demonstrate the effectiveness of the proposed algorithm, several numerical examples given in the literature for control and modeling systems are examined. Results prove the effectiveness of the proposed algorithm. Content Type Journal Article Pages 205-214 DOI 10.3233/HIS-130177 Authors N. Talbi, Faculty of Science and Technology, Jijel University, Jijel, Algeria K. Belarbi, Faculty of Engineering, Mentoury University, Constantine, Algeria Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 4 / 2013

Description: Goal of feature selection is to find a suitable feature subset that produces higher accuracy for classifier in the user end. Hybrid methods for feature selection comprised of combination of filter and wrapper approaches have recently been emerged as strong techniques for the problem in this domain. In this paper we have presented a novel approach for feature selection based on feature clustering using well known k-means philosophy for the high dimensional gene expression data. Also we have proposed three simple hybrid approaches for reducing data dimensionality while maintaining classification accuracy which combine our basic feature selection through feature clustering (FSFC) approach to other standard approaches of feature selection in different orientation. We have employed popular Box and Whisker plot and ROC curve analysis to evaluate experimental outcome. Our experimental results clearly show suitability of our methods in hybrid approaches of feature selection in micro-array gene expression domain. Content Type Journal Article Pages 165-178 DOI 10.3233/HIS-130172 Authors Choudhury Muhammad Mufassil Wahid, School of Information and Communication Technology, CQUniversity, Rockhampton, QLD, Australia A.B.M. Shawkat Ali, School of Information and Communication Technology, CQUniversity, Rockhampton, QLD, Australia Kevin S. Tickle, School of Information and Communication Technology, CQUniversity, Rockhampton, QLD, Australia Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 4 / 2013

Description: Intelligent computing has attracted many scientists, decision makers, and practicing researchers in recent years as powerful and novel intelligent techniques for solving unlimited number of complex real-world problems, particularly related to research area of optimization. Under the unpredictable and fuzzy environment, classical and traditional approaches are unable to obtain a complete solution with satisfaction for the practical optimization problems. Therefore, new global optimization methods are required to properly handle these issues. One of such methods is hybrid evolutionary computation, which is a generic, flexible, robust, and versatile method for solving the complex global optimization problems as well as implementing to practical applications. Content Type Journal Article Pages 179-190 DOI 10.3233/HIS-130175 Authors Pandian Vasant, Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Malaysia. E-mail: pvasant@gmail.com Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 4 / 2013

Description: Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on experiments generated from the simulation of evolutionary robots and on dynamic optimization problems generated by the Moving Peaks generator. Content Type Journal Article Pages 155-168 DOI 10.3233/HIS-2011-0136 Authors Renato Tinós, Department of Computing and Mathematics, FFCLRP, University of São Paulo (USP), 14040-901, Ribeirão Preto, SP, Brazil Shengxiang Yang, Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, UK Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 3 / 2011

Description: The main aim of biometric-based identification systems is to automatically recognize individuals based on their physiological and/or behavioural characteristics such as fingerprint, face, hand-geometry, among others. These systems offer several advantages over traditional forms of identity protection. However, there are still some important aspects that need to be addressed in these systems. The main questions are concerned with the security of biometric authentication systems since it is important to ensure the integrity and public acceptance of these systems. In order to avoid the problems arising from compromised biometric templates, the concept of cancellable biometrics has recently been introduced. The concept is to transform a biometric trait into a new representation for enrolment and matching. Although cancellable biometrics were proposed to solve privacy concerns, the concept raises new issues, since they make the authentication problem more complex and difficult to solve. Thus, more effective authentication structures are needed to perform these tasks. In this paper, we investigate the use of ensemble systems in cancellable biometrics, using fingerprint-based identification to illustrate the possible benefits accruing. In order to increase the effectiveness of the proposed ensemble systems, three feature selection methods will be used to distribute the attributes among the individual classifiers of an ensemble. The main aim of this paper is to analyse the performance of such well-established structures on transformed biometric data to determine whether they have a positive effect on the performance of this complex and difficult task. Content Type Journal Article Pages 143-154 DOI 10.3233/HIS-2011-0135 Authors Anne M.P. Canuto, Department of Informatics and Applied Mathematics, Federal University of RN, Natal Brazil Michael C. Fairhurst, School of Engineering and Digital Arts, University of Kent, Canterbury, UK Fernando Pintro, Department of Informatics and Applied Mathematics, Federal University of RN, Natal Brazil João C. Xavier Junior, Computing and Automation Engineering Department, Federal University of RN, Natal Brazil Antonino Feitosa Neto, Department of Informatics and Applied Mathematics, Federal University of RN, Natal Brazil Luis Marcos G. Gonçalves, Computing and Automation Engineering Department, Federal University of RN, Natal Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 3 / 2011

Description: Many real-world problems, like microchip design, can be modeled by means of the well-known traveling salesman problem (TSP). Many instances of this problem can be found in the literature. Although several optimization algorithms have been applied to TSP instances, the selection of the more promising algorithm is, in practice, a difficult decision. In this paper, a new meta-learning-based approach is investigated for the selection of optimization algorithms for TSP instances. Essentially, a learning model is trained with TSP instances for which the performance of a set of optimization algorithms is known a priori. Then, the learned model is used to predict the best algorithm for a new TSP instance. Each instance is described by meta-features that capture characteristics of the TSP that affect the performance of the optimization algorithms. Given that the best solution for a given TSP instance can be obtained by several algorithms, the meta-learning problem is considered here to be a multi-label classification problem. Several experiments illustrate the performance of the proposed approach, with promising results. Content Type Journal Article Pages 117-128 DOI 10.3233/HIS-2011-0133 Authors Jorge Kanda, Instituto de Ciencias Matematicas e de Computacao, Universidade de Sao Paulo, Sao Carlos, Brazil Andre Carvalho, Instituto de Ciencias Matematicas e de Computacao, Universidade de Sao Paulo, Sao Carlos, Brazil Eduardo Hruschka, Instituto de Ciencias Matematicas e de Computacao, Universidade de Sao Paulo, Sao Carlos, Brazil Carlos Soares, LIAAD-INESC Porto LA/Faculdade de Economia, Universidade do Porto, Porto, Portugal Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 3 / 2011

Description: Content Type Journal Article Category Guest-editorial Pages 115-116 DOI 10.3233/HIS-2011-0132 Authors Teresa B. Ludermir, Federal University of Pernambuco, Recife, Brazil Ricardo B.C. Prudêncio, Federal University of Pernambuco, Recife, Brazil Cleber Zanchettin, Federal University of Pernambuco, Recife, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 3 / 2011

Description: A time series is a sequence of observations of a random variable. Hence, it is a stochastic process. Forecasting time series data is important component of operations research because these data often provide the foundation for decision models. This models are used to predict data points before they are measured based on known past events. Researches in this subject have been done in many areas like economy, energy production, ecology and others. To improve the process of time series forecasting it is important to identify which of past values will be considered to be used in the models by eliminating redundant or irrelevant attributes. Two hybrid systems Harmony Search with Neural Networks (HS) and Temporal Memory Search with Neural Networks (TMS) are improved and a new one is proposed: the Temporal Memory Search Limited with Neural Networks (TMSL). The performance of the techniques is investigated through an empirical evaluation on twenty real-world time series. Content Type Journal Article Pages 129-141 DOI 10.3233/HIS-2011-0134 Authors Ivna Valença, Informatics Center, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, 50740-560, Recife-PE, Brazil Tarcísio Lucas, Informatics Center, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, 50740-560, Recife-PE, Brazil Teresa Ludermir, Informatics Center, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, 50740-560, Recife-PE, Brazil Mêuser Valença, Department of Systems and Computer, University of Pernambuco, Benfica, 455, Madalena, Recife-PE, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 3 / 2011

Description: Particle Swarm Optimization (PSO) is a well known technique for solving various kinds of combinatorial optimization problems including scheduling, resource allocation and vehicle routing. However, basic PSO suffers from premature convergence problem. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GAs) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use dynamic parameterization when applying the GA operators. In this work, dynamic parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that all the PSO hybrids with dynamic probability have shown satisfactory performance in finding the best distance of the Vehicle Routing Problem With Time Windows. Content Type Journal Article Pages 13-25 DOI 10.3233/HIS-140202 Authors S. Masrom, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perak, Malaysia Siti Z.Z. Abidin, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Malaysia N. Omar, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Malaysia K. Nasir, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Malaysia A.S. Abd Rahman, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Perak, Malaysia Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2015

Description: This paper investigates the performance of an adaptive controller using a multi-layer quantum neural network (QNN) comprising qubit-inspired neurons as information processing units. The control system is a self-tuning controller whose control parameters are tuned online by the QNN to track plant output relative to the desired plant output generated by a reference model. A proportional-integral-derivative (PID) controller is utilized as a conventional controller, with its parameters tuned by the QNN. Computational experiments to control a single-input single-output discrete-time non-linear plant are conducted to evaluate the capability and characteristics of the quantum neural self-tuning controller. The experiment results demonstrate the feasibility and effectiveness of the proposed controller. Content Type Journal Article Pages 41-52 DOI 10.3233/HIS-140204 Authors Kazuhiko Takahashi, Information Systems Design, Doshisha University, Kyoto, Japan Yuka Shiotani, Information Systems Design, Doshisha University, Kyoto, Japan Masafumi Hashimoto, Intelligent Information Engineering and Science, Doshisha University, Kyoto, Japan Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2015

Description: Reversals are operations of great biological significance for the analysis of the evolutionary distance between organisms. Genome rearrangement through reversals, consists in finding the shortest sequence of reversals to transform one genome represented as a signed or unsigned permutation into another. When genes are non oriented and correspondingly permutations are unsigned, sorting by reversals came arise as a challenging problem in combinatorics of permutations. In fact, this problem is known to be NP-hard, but the question whether it is NP-complete remains open for more than twenty years. When permutations are signed and correspondingly genes are oriented, the problem is known to be in P. A parallelization of a standard GA (Genetic Algorithm) is proposed for the problem of sorting unsigned permutations. This GA was previously reported in the literature as the most competitive regarding precision for which as control mechanism an 1.5-approximation algorithm was used. For the parallelization, the MPI Library of the C language was used and experiments were performed for calculating the execution time and precision. By increasing the number of individuals, experiment showed improvement in relation to previous approaches. Additionally, a virtualization of the GA using a MicroBlaze processor from Xilinx was performed on OVP for which the average number of executed instructions was of approximately 1.40 Giga instruction per second. In this extended version of this works originally presented in NaBIC 2013 biological data was generated and it was shown how the parallelization can be applied for their analysis. Specifically, the evolutionary distances between different pairs of organism were computed based on the set of non common genes in their mitochondrial DNA genome and the reversal distance between the sequences of common genes. Content Type Journal Article Pages 53-64 DOI 10.3233/HIS-140205 Authors José Luis Soncco-Álvarez, Departments of Computer Science, University of Brasilia, Brasilia, Brazil Gabriel Marchesan Almeida, Institute for Information Processing Technology, Karlsruhe Institute of Technology, Karlsruhe, Germany Juergen Becker, Institute for Information Processing Technology, Karlsruhe Institute of Technology, Karlsruhe, Germany Mauricio Ayala-Rincón, Departments of Computer Science, University of Brasilia, Brasilia, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2015

Description: Recently, it was proposed a novel hybrid approach to train MLPs which combines the advantages of a powerful artificial immune system, called GAIS, with the advantages of Extreme Learning Machine (ELM). In that proposal, the GAIS algorithm is responsible for finding a proper set of input weights whereas the output weights are determined by the Moore-Penrose generalized inverse. The methodology was evaluated only in classification problems and its performance compares favorably with that presented by state-of-the-art-algorithms. Motivated by this scenario, this paper better formalizes the proposal and performs a deeper investigation of its usefulness for synthesizing MLP and RBF neural networks on several real-world classification and regression problems. The computational experiments have shown that the proposed methodology outperforms other approaches in both quantitative and qualitative aspects. Content Type Journal Article Pages 1-12 DOI 10.3233/HIS-140201 Authors Pablo A.D. Castro, Federal Institute of Education, Science and Technology of São Paulo (IFSP), São Carlos, São Paulo, Brazil. E-mail: dalbem@ifsp.edu.br Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2015

Description: We have developed an approach to identification and tracking of currently unfolding news stories extracted from the news articles published on the Web. Our approach employs a set of agents to retrieve those articles from the Web that might refer to some developing news story. The set of agents is inspired by social insects, in particular by a bee colony. Bees identify popular terms, referred to as story words, relevant to the ongoing news stories and use them in foraging articles. This allows for a dynamic approach that reflects the changes in article space as new stories unfold and new articles are added. Subsequently a graph representation of the article space is constructed that contains retrieved articles and identified story words interconnected by edges according to relationships of relevance identified between elements of the graph. Stories are then extracted from the constructed graph by using Louvain method, commonly used to identify communities within modular graphs. Using this approach we have been able to identify news stories in a stream of articles retrieved from the Web with precision of 75.56%, with best precision generally achieved for recent news stories described by popular story words. Further we developed ways of visualization of multiple stories represented by sets of articles ordered in time. We propose two new metaphors both employing an exponential timeline. Both galactic streams and concurrent streams are highly suitable for visualizing multiple developing stories. Content Type Journal Article Pages 27-39 DOI 10.3233/HIS-140203 Authors Stefan Sabo, Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia Alena Kovarova, Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia Pavol Navrat, Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2015

Description: Sleep apnea (SA) is one of the common sleep disorders. It has several consequences that can affect daily life activities. The common diagnose procedure is carried out through an overnight sleep test. The test usually includes of several bio-signals recordings that are used to detect this syndrome. The conventional approach of detecting the sleep apnea uses a manual analysis of most bio-signals to achieve reasonable accuracy. The manual process of this test, is highly cost and time consuming. This paper presents a novel automatic system for detecting and classifying apnea events by using just a few of bio-signals that are related to breathe defect. This method uses only the air flow, thoracic and abdominal respiratory movement as inputs for the system. The proposed technique consists of four main parts which are; signal segmentation, feature generation, feature selection and data reduction based on genetic SVM, and classification. Statistical analyzes on the attained results show efficiency of this system and its superiority versus previous methods even with more bio-signals as input. Content Type Journal Article Pages 203-210 DOI 10.3233/HIS-2012-00157 Authors Yashar Maali, Faculty of Engineering and IT, University of Technology Sydney (UTS), Sydney, Australia Adel Al-Jumaily, Faculty of Engineering and IT, University of Technology Sydney (UTS), Sydney, Australia Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 4 / 2012

Description: Parameter tuning of metaheuristic is the process of finding and controlling correct combination and values of an algorithm's parameters for each individual problem. Since the performance of Ant Colony Optimization (ACO) is influenced by its parameter values, many techniques were proposed in the literature to tune the parameters in ACO. This is because parameters can implicitly determine the amplification and diversification of the search process. ACO is applied to a variety of optimization problems and, unfortunately, there are no universal parameter values which can be used in ACO to solve all kinds of real-world optimization problems efficiently and effectively due to the differences in size and type of these real-world applications. In this paper, we present a mechanism using Particle Swarm Optimization (PSO) to adaptively tune the parameters of ACO using different ranges for each parameter. The parameter-tuned ACO is applied to provide Quality of Service routing in mobile ad-hoc network (MANET). The performance of the parameter-tuned ACO is compared with a non-adaptive ACO version. Content Type Journal Article Pages 171-183 DOI 10.3233/HIS-2012-00155 Authors P. Deepalakshmi, Department of Computer Science and Engineering, Kalasalingam University, Tamil Nadu, India S. Radhakrishnan, Department of Computer Science and Engineering, Kalasalingam University, Tamil Nadu, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 4 / 2012

Description: Swarm Intelligence is a relatively new approach for solving complex computational problems. An ant colony optimization (ACO) is based on the natural behavior of ants which deposit pheromone on the ground during their trails for foraging. ACO exploits the sensing capabilities of the group and avoids the premature convergence by way of distributed computing. This paper presents a new approach for edge detection using a combination of binarization, ACO and universal law of gravity. In the proposed approach, edge detection problem is handled by first converting the input gray scale image into binary image so that the image matrix consists of only two gray levels, i.e. 0 and 255. Ants are then placed randomly all over the image which would then undergo foraging in search for food (edge pixels). The proposed approach uses the law of universal gravity to calculate the heuristic function which acts as the way to food source for the artificial ants to detect the edge pixels. Content Type Journal Article Pages 229-239 DOI 10.3233/HIS-2012-00159 Authors Om Prakash Verma, Department of Information Technology, Delhi Technological University, Delhi, India Madasu Hanmandlu, Department of Electrical Engineering, IIT Delhi, Delhi, India Rishabh Sharma, Ericsson India Global Services Pvt. Ltd, Noida UP, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 4 / 2012

Description: The problem of path planning in stochastic environments where the shortest path is not always the best one is a challenging issue required in many real-world applications such as autonomous vehicles, robotics, logistics, etc. … In this paper, we consider the problem of path planning in stochastic environments where the length of the path is not the unique criterion to consider. We formalize this problem as a multi-objective decision-theoretic path planning and we transform this latter into 2VMDP (Vector-Valued Markov Decision Process). We show, then, how we can compute a policy balancing between different considered criteria. We describe different techniques that allow us to derive an optimal policy where it is hard to express the expected utilities, rewards and values with a unique numerical measure. Firstly, we examine different existing approaches based on preferences and we define notions of optimality with preferred solutions and secondly we present approaches based on egalitarian social welfare techniques. Finally, some experimental results have been developed to show the feasibility of the approach and the benefit of this approach on the single-objective techniques. Content Type Journal Article Pages 45-60 DOI 10.3233/HIS-2012-0146 Authors Abdel-Illah Mouaddib, GREYC – Université de Caen, Bd Maréchal Juin, Caen Cedex, France Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 1 / 2012

Description: Most of in music information retrieval (MIR) has been focused on the symbolic representations of music. However, most of the digitally available music is in the form of raw audio signals. Although various attempts for monophonic and polyphonic transcription have been developed, none has been successful and general enough in the case of real world signals. So far, most of the research has been based on developing efficient music retrieval systems. In this paper, we introduce a music retrieval system based on Dynamic Neural Networks (DNN), which are trained with the signal melody, and not with traditional descriptors. The proposal was tested with a database composed of 1000 melodies. The results are very encouraging. Content Type Journal Article Pages 1-11 DOI 10.3233/HIS-2011-0143 Authors Laura E. Gomez, Centro de Investigación en Computación-IPN. Av. Juan de Dios Batiz S/N. Col. Nueva Industrial Vallejo, C.P. 07738, México, D.F. Humberto Sossa, Centro de Investigación en Computación-IPN. Av. Juan de Dios Batiz S/N. Col. Nueva Industrial Vallejo, C.P. 07738, México, D.F. Ricardo Barron, Centro de Investigación en Computación-IPN. Av. Juan de Dios Batiz S/N. Col. Nueva Industrial Vallejo, C.P. 07738, México, D.F. Julio F. Jimenez, Centro de Investigación en Computación-IPN. Av. Juan de Dios Batiz S/N. Col. Nueva Industrial Vallejo, C.P. 07738, México, D.F. Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 1 / 2012

Description: Most email users have experienced spam problems, which have been addressed as text classification problem. In this paper, we propose a novel spam detection method which uses an ensemble of classifiers based on subsampling and dynamic weighted voting techniques. Since there is diversity in genre of emails' contents, the proposed method finds different topics in emails by using a clustering algorithm. The proposed algorithm first extracts disjoint clusters of emails, and then a classifier is trained on each cluster, and finally decisions of classifiers are combined using dynamic weighted majority techniques. In order to classify a new input sample, first it is compared with all cluster centers and its similarity to each cluster is identified; then the classifiers in the vicinity of the input sample obtain greater weights for the final decision of the ensemble. Finally, the outputs of the classifiers are combined using weighted voting with weights calculated from the similarity of the input sample with cluster centers. The experimental results show that the proposed algorithm outperforms pure SVM and the related ensemble based classifiers. Content Type Journal Article Pages 27-43 DOI 10.3233/HIS-2011-0145 Authors Mehrnoush Famil Saeedian, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. E-mail: {saeedian,beigy}@ce.sharif.edu Hamid Beigy, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. E-mail: {saeedian,beigy}@ce.sharif.edu Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 1 / 2012

Description: Automatic recognition of handwritten numerals is difficult because of the huge variety of ways in which people write. Attempts in the literature employ complicated features and recognition engines in trying to cope with the variety of symbols. But this makes the process slow. In this work, a hybrid technique is proposed to achieve the objective of recognition of handwritten Devanagari numerals with less time consumption and without sacrificing recognition accuracy. A database of 11,000 samples is created while ensuring that the samples include a variety of handwritings which are written with different writing instruments and in different colors. The features employed are density features and spline-based edge direction histogram features and combination thereof. The database size is reduced by using clustering to identify similar samples and putting only one representative sample in lieu of the whole cluster as well as reducing the number of features using PCA. This two-fold reduction provides a smaller database. A hybrid technique utilizing artificial Neural Networks (A-NN), K-nearest neighbour (K-NN) and other learning methods is implemented to ensure higher recognition accuracy and speed. These ideas are put together to provide a fast and robust scheme for recognition of handwritten Devanagari numerals with high recognition accuracy, i.e., 99.40% at a reasonable speed. Content Type Journal Article Pages 13-25 DOI 10.3233/HIS-2011-0144 Authors C. Vasantha Lakshmi, Department of Physics & Computer Science, Dayalbagh Educational Institute, Agra, Uttar Pradesh, India Ritu Jain, Department of Physics & Computer Science, Dayalbagh Educational Institute, Agra, Uttar Pradesh, India C. Patvardhan, Department of Electrical Engineering, Dayalbagh Educational Institute, Agra, Uttar Pradesh, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 1 / 2012

Description: Content Type Journal Article Category Guest editorial Pages 1-2 DOI 10.3233/HIS-2011-0125 Authors Aboul Ella Hassanien, Cairo University, Egypt Hiroshi Sakai, Kyushu Institute of Technology, Japan Dominik Śl&ecedil;zak, University of Warsaw & Infobright Inc., Poland Michir K. Chakraborty, University of Calcutta, India William Zhu, UESTC, Chengdu, China Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 1 / 2011

Description: The clusters tend to have vague or imprecise boundaries in some fields such as web mining, since clustering has been widely used. Fuzzy clustering is sensitive to noises and possibilistic clustering is sensitive to the initialization of cluster centers and generates coincident clusters. Based on combination of fuzzy clustering and possibilistic clustering, a novel possibilistic fuzzy leader (PFL) clustering algorithm is proposed in this paper to overcome these shortcomings. Considering the advantages of the leader algorithm in time efficiency and the initialization of cluster, the framework of the leader algorithm is used. In addition, a λ-cut set is defined to produce the overlapping clusters autonomously. The comparative experiments with synthetic and standard data sets show that the proposed algorithm is valid, efficient, and has better accuracy. The experiments with the web users access paths data set show that the proposed algorithm is capable of clustering access paths at an acceptable computational expense. Content Type Journal Article Pages 31-40 DOI 10.3233/HIS-2011-0129 Authors Hong Yu, Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China Hu Luo, Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 1 / 2011

Description: The integration of most of the network components such as the Internet, cellular, IEEE 802.11, IEEE 802.15, IEEE 802.16, sensor networks, is mandatory for present engineering application. Heterogeneous Network Architecture will suit such major services like warfare and healthcare, which provides integration of wired, wireless, wireless mesh, and sensor devices with internet connectivity. Data transmission in such heterogeneity networks renders ineffective communication, which is also complicated due to higher percentage of pocket loss, lesser response time, and traffic. In this paper, we proposed an optimal congestion free routing, called priority and compound rule based routing protocol. This proposed routing will suit for both wired as well as wireless environment, and this also will provide congestion free path(s) in single path routing and multipath routing. Content Type Journal Article Pages 93-97 DOI 10.3233/HIS-2011-0131 Authors B. Chandra Mohan, Department of CSE, Anna University, Chennai, India R. Baskaran, Department of CSE, Anna University, Chennai, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 2 / 2011

Description: Short term electricity load forecasting is nowadays of paramount importance in order to estimate next day load, resulting in energy save and environment protection. Electricity demand is influenced (among other things) by the day of the week, the time of year and special periods and/or days, such as religious and national events all of which must be identified prior to modeling. This identification, known as day-type identification, must be included in the modeling stage either by segmenting the data and modeling each day-type separately or by including the day-type as an input. In this study, a two stage clustering approach, based on unsupervised clustering methods is examined to identify regional Algerian electricity load day types. For instance, SOM (Self Organizing Maps) and fuzzy C-means will be presented and applied in detail to load day-type identification with the investigation of the fuzzy C-means clustering algorithm. The optimal number of clusters is obtained using fuzzy cluster validation measures, such as Xie and Beni's index (XB), among others. This application shows that the two-level clustering approach, can effectively identify load day types, for subsequent prediction modeling stages given a possible weighted classification among clusters for particular days. Content Type Journal Article Pages 81-92 DOI 10.3233/HIS-2011-0124 Authors Farouk Benabbas, Laboratoire de Gestion Electronique de Document (LabGED), Department of Computer Science, University Badji Mokhtar, Annaba, Algeria Mohamed Tarek Khadir, Laboratoire de Gestion Electronique de Document (LabGED), Department of Computer Science, University Badji Mokhtar, Annaba, Algeria Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 2 / 2011

Description: This paper compares two pattern classifiers with applications in medicine: the first is an artificial neural network with weight-elimination (ANN-we); the second is a hybrid classifier consisting of a decision-tree (DT) to eliminate variables which have little impact on predicting the outcome of interest, then processing the remaining variables through an artificial neural network with weight elimination (ANN-we). A small database of adult intensive care unit patients was used to compare the performance of the two pattern classifiers. The hybrid classifier performed better than the ANN-we alone as measured by the resulting sensitivity, specificity, and area under the receiver operating characteristic curve (ROC). The second part of the paper describes the application of the better classifier to the problem of predicting pre-term birth, using a very large and complex medical database of mothers and newborns. The hybrid classifier was able to estimate pre-term birth with an accuracy as high as the invasive and expensive fibronectin test, using only 19 variables available in North America before 23 weeks of gestation for parous women (who had a previous child). Additionally, the classifier was able to predict pre-term birth in nulliparous women (who had no previous children) with slightly less accuracy, but higher than any found in the literature today for this population. Content Type Journal Article Pages 71-79 DOI 10.3233/HIS-2011-0123 Authors Monique Frize, Systems and Computer Engineering, Carleton University Nicole Yu, Systems and Computer Engineering, Carleton University Sabine Weyand, School of Information Technology and Engineering, University of Ottawa, Canada Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 2 / 2011

Description: This paper presents a comprehensive simulation study which aims to profile the performance capabilities of the global-local hybrid ensemble in comparison with leading ensemble classifiers as reported in recent studies in the literature. The global-local hybrid ensemble is implemented with decision tree (global) and nearest-neighbor (local) base learners and its accuracy performance is compared, on 46 benchmark datasets from the UCI machine learning repository, to those of other ensembles from six prominent studies in the literature. Through statistical significance testing, it is shown that the global-local hybrid ensemble is a robust classifier design: over a larger spectrum of data domains, it performs competitively with other leading ensembles. Content Type Journal Article Pages 59-70 DOI 10.3233/HIS-2011-0122 Authors Dustin Baumgartner, Electrical Engineering and Computer Science, University of Toledo, Toledo OH, 43606, USA Gursel Serpen, Electrical Engineering and Computer Science, University of Toledo, Toledo OH, 43606, USA Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 2 / 2011

Description: A vast data repository such as the web contains many broad domains of data which are quite distinct from each other e.g. medicine, education, sports and politics. Each of these domains constitutes a subspace of the data within which the documents are similar to each other but quite distinct from the documents in another subspace. The data within these domains is frequently further divided into many subcategories. In this paper we present a novel hybrid parallel architecture using different types of classifiers trained on different subspaces to improve text classification within these subspaces. The classifier to be used on a particular input and the relevant feature subset to be extracted is determined dynamically by using maximum significance values. We use the conditional significance vector representation which enhances the distinction between classes within the subspace. We further compare the performance of our hybrid architecture with that of a single classifier – full data space learning system and show that it outperforms the single classifier system by a large margin when tested with a variety of hybrid combinations on two different corpora. Our results show that subspace classification accuracy is boosted and learning time reduced significantly with this new hybrid architecture. Content Type Journal Article Pages 99-114 DOI 10.3233/HIS-2011-0137 Authors Nandita Tripathi, University of Sunderland, UK Michael Oakes, University of Sunderland, UK Stefan Wermter, University of Hamburg, Germany Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 2 / 2011

Description: This paper presents an application study of exploiting fuzzy-rough feature selection (FRFS) techniques in aid of efficient and accurate Mars terrain image classification. The employment of FRFS allows the induction of low-dimensionality feature sets from sample descriptions of feature vectors of a much higher dimensionality. Supported with comparative studies, the work demonstrates that FRFS helps to enhance both the effectiveness and the efficiency of conventional classification systems such as multi-layer perceptrons and K-nearest neighbors, by minimizing redundant and noisy features. This is of particular significance for on-board image classification in future Mars rover missions. Content Type Journal Article Pages 3-13 DOI 10.3233/HIS-2011-0126 Authors Changjing Shang, Department of Computer Science, Aberystwyth University, Wales, UK Dave Barnes, Department of Computer Science, Aberystwyth University, Wales, UK Qiang Shen, Department of Computer Science, Aberystwyth University, Wales, UK Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 1 / 2011

Description: Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best represent the colours in an image. In this paper, we use a rough c-means clustering algorithm for colour quantisation of images. Experimental results on a standard set of images show that this rough colour quantisation approach performs significantly better than other, purpose built colour reduction algorithms. Content Type Journal Article Pages 25-30 DOI 10.3233/HIS-2011-0128 Authors Gerald Schaefer, Department of Computer Science, Loughborough University, Loughborough, UK Huiyu Zhou, Institute of Electronics, Communications and Information Technology, Queen's University Belfast, Belfast, UK M. Emre Celebi, Department of Computer Science, Louisiana State University in Shreveport, Shreveport, USA Aboul Ella Hassanien, Information Technology Department, Cairo University, Giza, Egypt Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 1 / 2011

Description: Rough Non-deterministic Information Analysis (RNIA) is a rough set-based data analysis framework for Non-deterministic Information Systems (NISs). RNIA-related algorithms and software tools developed so far for rule generation provide good characteristics of NISs and can be successfully applied to decision making based on non-deterministic data. In this paper, we extend RNIA by introducing stability factor that enables to evaluate rules in a more flexible way and by developing a question-answering functionality that enables decision makers to analyze data gathered in NISs in case there are no pre-extracted rules that may address specified conditions. Content Type Journal Article Pages 41-57 DOI 10.3233/HIS-2011-0130 Authors Hiroshi Sakai, Mathematical Sciences Section, Department of Basic Sciences, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu 804, Japan Hitomi Okuma, Faculty of Education and Welfare Science, Oita University, Dannoharu, Oita 870, Japan Michinori Nakata, Faculty of Management and Information Science, Josai International University, Gumyo, Togane, Chiba 283, Japan Dominik Śl&ecedil;zak, Institute of Mathematics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 1 / 2011

Description: There are several papers in the literature in which generalized fuzzy numbers are used for solving real life problems but to the best of our knowledge, till now no one has used generalized fuzzy numbers for solving the maximal flow problems. In this paper, the existing algorithm is modified to find fuzzy maximal flow between source and sink by representing all the parameters as generalized triangular fuzzy numbers. To illustrate the modified algorithm a numerical example is solved and the obtained results are compared with the existing results. If there is no uncertainty about the flow between source and sink then the proposed algorithm gives the same result as in crisp maximal flow problems. Content Type Journal Article Pages 15-24 DOI 10.3233/HIS-2011-0127 Authors Amit Kumar, School of Mathematics and Computer Applications, Thapar University, Patiala-147 004, India Manjot Kaur, School of Mathematics and Computer Applications, Thapar University, Patiala-147 004, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 1 / 2011

Description: Q-Learning is a widely used method for dealing with reinforcement learning problems. To speed up learning and to exploit gained experience more efficiently it is highly beneficial to add generalization to Q-Learning and thus enabling the transfer of experience to unseen but similar states. In this paper, we report on improvements for GNG-Q, a combination of Q-Learning and growing neural gas (GNG). It solves reinforcement learning problems with continuous state spaces and simultaneously learns a proper approximation of the state space by starting with a coarse resolution that is gradually refined based on information achieved during learning. We introduce the Interpolating GNG-Q (IGNG-Q) that uses distance-based interpolation between learned Q-vectors, adjust the update rule, suggest a new refinement strategy and propose a new criterion to decide when a refinement is necessary. Furthermore, we argue that this criterion offers an implicit local stopping condition for changes made to the approximation. Additionally, we employ eligibility traces to speed up learning. The improved method is evaluated in continuous state spaces and the results are compared with several approaches from literature. Our experiments confirm that the modifications highly improve the efficiency of the approximation and that IGNG-Q is well competitive with existing methods. Content Type Journal Article Pages 55-69 DOI 10.3233/HIS-130183 Authors Michael Baumann, Department of Computer Science, University of Paderborn, Paderborn, Germany Hans Kleine Büning, Department of Computer Science, University of Paderborn, Paderborn, Germany Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2014

Description: In this paper, we propose a novel evolutionary clustering algorithm for mixed type data (numerical and categorical). It is doing clustering and feature selection simultaneously. Feature subset selection improves quality of clustering. It also improves understandability and scalability. It unfastens attraction on numerical or categorical dataset only. K-prototype (KP) is a well-known partitional clustering algorithm for mixed type data. However, this type of algorithm is sensitive to initialization and may converge to local optima. It is optimizing a single measure only i.e. minimizations of intra cluster distance. We have considered clustering as a multi objective optimization problem (MOOP). Minimization of intra cluster distance and maximization of inter cluster distance are two objectives of optimization. Multi objective genetic algorithm (MOGA) is a well-known algorithm which can be applicable for MOOP to find out near global optimal solution. So in this paper we have developed a hybridized genetic clustering algorithm by combining the global search ability of MOGA and local search ability of KP. Experiments on real-life benchmark datasets from UCI machine learning repository prove the superiority of the proposed algorithm. Content Type Journal Article Pages 41-54 DOI 10.3233/HIS-130182 Authors Dipankar Dutta, Department of Computer Science and Information Technology, University Institute of Technology, The University of Burdwan, Golapbug (North), Burdwan, West Bengal, India Paramartha Dutta, Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, West Bengal, India Jaya Sil, Department of Computer Science and Technology, Bengal Engineering and Science University, Shibpur, Howrah, West Bengal, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2014

Description: Several sensor measurements collected from drilling rig during oil well drilling process. These measurements carry information not only about operational states of drilling rig but also about all high-level operations and activities performed by drilling crew. The work presented in this paper shed the light on analysis of hidden lost time in drilling process through automatic detection and classification of drilling operations. This paper develops a novel algorithm for detecting drilling events and operations in sensor data of drilling rig. Expectation Maximization EM and Piecewise Linear Approximation PLA algorithms applied for detecting drilling events. The Expectation Maximization algorithm performs high-level segmentation on hook-load sensor data. In addition, Piecewise Linear Approximation algorithm slices standpipe pressure; pump flow rate; rotational speed and torque of top drive motor into labeled segments (low-level segmentation). Merging results from both Expectation Maximization and Piecewise Linear Approximation gives the suggested algorithm ability to detect all drilling events and activities performed by drilling rig and crew. Moreover, this paper shows the usage of discrete orthonormal basis functions (Gram basis) as a tool to classify drilling operations from detected segments in drilling time series. The classification process performed in cooperation with the concept of Patterns Templates Base. The optimal polynomial degree to represent drilling operations has been concluded through analysis of polynomial spectrum of each drilling operation. Content Type Journal Article Pages 25-39 DOI 10.3233/HIS-130181 Authors Arghad Arnaout, Institute for Automation, University of Loeben, Leoben, Austria Paul O'Leary, Institute for Automation, University of Loeben, Leoben, Austria Bilal Esmael, Chair of Drilling, University of Leoben, Leoben, Austria Gerhard Thonhauser, Institute for Automation, University of Loeben, Leoben, Austria Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2014

Description: This paper introduces the Hybrid Extreme Rotation Forest (HERF) classifier describing two succesful applications in the image segmentation domain. The HERF is an ensemble of classifiers composed of Extreme Learning Machines (ELM) and Decision Trees. Training of the HERF includes optimal rotation of random partitions of the feature set aimed to increase diversity. The first application is the segmentation of 3D Computed Tomography Angiography (CTA) following an Active Learning (AL) strategy for the optimal sample selection to minimize the number of data samples needed to obtain a required accuracy degree. AL is pertinent for interactive learning processes where a human operator is required to select training samples to enhance the classifier in an iterative process, therefore labeling samples for training may be a time consuming and expensive process. CTA image segmentation is one of such processes, due to the variability in CTA images which hinders the generalization of classifiers trained on one dataset to new datasets. Following an AL strategy, the human operator is presented with a visual selection of pixels whose labeling would be most informative for the classifier. After adding those labeled pixels to the training data, the classifier is retrained. This iteration is repeated until image segmentation quality meets the required level. The approach is applied to the segmentation of the thrombus in CTA imaging of Abdominal Aortic Aneurysm (AAA) patients, showing that the structures of interest can be accurately segmented after a few iterations using a small data sample. The second application is a new semisupervised segmentation algorithm for hyperspectral images. The algorithm steps are: 1) supervised training an initial classifier from a small balanced training set, 2) clustering of the image pixels, by a k-means algorithm 3) adding unlabeled pixels to the original trainning data set according to the spatial neighborhood and the cluster membership, 4) supervised training of the classifier with the enriched training data set, 6) classification of the hyperspectral image 4) spatial regularization of classification results consisting in selecting the most frequent class in each pixel neighborhood. Results on two well known benchmarking hyperspectral images improve over state of the art algorithms. Content Type Journal Article Pages 13-24 DOI 10.3233/HIS-130180 Authors Borja Ayerdi, Computational Intelligence Group, University of the Basque Country, UPV/EHU, Bilbao, Spain Josu Maiora, Computational Intelligence Group, University of the Basque Country, UPV/EHU, Bilbao, Spain Alicia d'Anjou, Computational Intelligence Group, University of the Basque Country, UPV/EHU, Bilbao, Spain Manuel Graña, Computational Intelligence Group, University of the Basque Country, UPV/EHU, Bilbao, Spain Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2014

Description: Outlier detection being an important data mining problem has attracted a lot of research interest in the recent past. As a result, various methods for outlier detection have been developed particularly for dealing with numerical data, whereas categorical data needs some attention. Addressing this requirement, we propose a two-phase algorithm for detecting outliers in categorical data based on a novel definition of outliers. In the first phase, this algorithm explores a clustering of the given data, followed by the ranking phase for determining the set of most likely outliers. The proposed algorithm is expected to perform better as it can identify different types of outliers, employing two independent ranking schemes based on the attribute value frequencies and the inherent clustering structure in the given data. Unlike some existing methods, the computational complexity of this algorithm is not affected by the number of outliers to be detected. The efficacy of this algorithm is demonstrated through experiments on various public domain categorical data sets. Content Type Journal Article Pages 1-11 DOI 10.3233/HIS-130179 Authors N.N.R. Ranga Suri, Centre for Artificial Intelligence and Robotics (CAIR), C V Raman Nagar, Bangalore, India M. Narasimha Murty, Department of CSA, Indian Institute of Science (IISc), Bangalore, India G. Athithan, Centre for Artificial Intelligence and Robotics (CAIR), C V Raman Nagar, Bangalore, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2014

Description: A novel learning methodology based on a hybrid mechanism for training interval singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems uses recursive orthogonal least-squares to tune the type-1 consequent parameters and the steepest descent method to tune the interval type-2 antecedent parameters. The proposed hybrid-learning algorithm changes the interval type-2 model parameters adaptively to minimize some criteria function as new information becomes available and to match desired input-output data pairs. Its antecedent sets are type-2 fuzzy sets, its consequent sets are type-1 fuzzy sets, and its inputs are singleton fuzzy numbers without uncertain standard deviations. As reported in the literature, the performance indices of hybrid models have proved to be better than those of the individual training mechanisms used alone. Experiments were carried out involving the application of hybrid interval type-2 Takagi-Sugeno-Kang fuzzy logic systems for modeling and prediction of the scale-breaker entry temperature in a hot strip mill for three different types of coils. The results demonstrate how the interval type-2 fuzzy system learns from selected input-output data pairs and improves its performance as hybrid training progresses. Content Type Journal Article Pages 125-135 DOI 10.3233/HIS-130188 Authors Gerardo M. Méndez, Centro de Manufactura Avanzada, Corporación Mexicana de Investigación en Materiales SA de CV – COMIMSA, Saltillo, Coah, México J. Cruz Martinez, Departamento de Economía y Administración, Instituto Tecnológico de Nuevo León, Cd. Guadalupe, N.L., México David S. González, Centro de Manufactura Avanzada, Corporación Mexicana de Investigación en Materiales SA de CV – COMIMSA, Saltillo, Coah, México F. Javier Rendón-Espinoza, Departamento de Economía y Administración, Instituto Tecnológico de Nuevo León, Cd. Guadalupe, N.L., México Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2014

Description: Obstacle detection is a fundamental issue of robot navigation and there have been several proposed methods for this problem. In this paper, we propose a new approach to find out obstacles on Depth Camera streams. The proposed approach consists of three stages. First, preprocessing stage is for noise removal. Second, different depths in a frame are clustered based on the Interval Type-2 Fuzzy Subtractive Clustering algorithm. Third, the objects of interest are detected from the obtained clusters. Beside that, it gives an improvement in the Interval Type-2 Fuzzy Subtractive Clustering algorithm to reduce the time consuming. In theory, it is at least 3700 times better than the original one, and approximate 980100 in practice on our depth frames. The results conducted on frames demonstrate that the distance from the camera to objects retrieved is exact enough for indoor robot navigation problems. Content Type Journal Article Pages 97-107 DOI 10.3233/HIS-130186 Authors Mau Uyen Nguyen, Department of Information Systems, Le Quy Don Technical University, Hanoi, Vietnam Long Thanh Ngo, Department of Information Systems, Le Quy Don Technical University, Hanoi, Vietnam Thanh Tinh Dao, Department of Information Systems, Le Quy Don Technical University, Hanoi, Vietnam Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2014

Description: In this paper we study L-fuzzy proximity spaces, where L represents a completely distributive lattice.We shall investigate the level decomposition of L-fuzzy proximity on X and the corresponding L-fuzzy proximity continuous maps. In addition, we shall establish the representation theorems of L-fuzzy proximity on X. Content Type Journal Article Pages 137-144 DOI 10.3233/HIS-130189 Authors M. El-Dardery, Department of Mathematics, Faculty of Science, Fayoum University, Fayoum, Egypt J. Zhang, College of Science, North China University of Technology, Beijing, China Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2014

Description: This paper revisits a class of recently proposed so-called invariant manifold methods for zero finding of ill-posed problems, showing that they can be profitably viewed as homotopy methods, in which the homotopy parameter is interpreted as a learning parameter. Moreover, it is shown that the choice of this learning parameter can be made in a natural manner from a control Liapunov function approach (CLF). From this viewpoint, maintaining manifold invariance is equivalent to ensuring that the CLF satisfies a certain ordinary differential equation, involving the learning parameter, that allows an estimate of rate of convergence. In order to illustrate this approach, algorithms recently proposed using the invariant manifold approach, are rederived, via CLFs, in a unified manner. Adaptive regularization parameters for solving linear algebraic ill-posed problems were also proposed. This paper also shows that the discretizations of the ODEs to solve the zero finding problem, as well as the different adaptive choices of the regularization parameter, yield iterative methods for linear systems, which are also derived using the Liapunov optimizing control (LOC) method. Content Type Journal Article Pages 109-123 DOI 10.3233/HIS-130187 Authors Fernando Pazos, Department of Electrical Engineering, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil Amit Bhaya, Department of Electrical Engineering, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2014

Description: In this paper, Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach is hybridized with Wavelet Mutation (PSOCFIWA-WM) strategy for the optimal design of linear phase FIR filters. Real coded genetic algorithm (RGA), particle swarm optimization (PSO) and particle swarm optimization with constriction factor and inertia weight (PSOCFIWA) have also been adopted for the sake of comparison. PSOCFIWA-WM incorporates a new definition of swarm updating in PSOCFIWA with the help of wavelet based mutation. Wavelet mutation enhances the effectiveness of PSOCFIWA to explore the multidimensional solution space more effectively. In this design approach, filter length, pass band and stop band edge frequencies, feasible pass band and stop band ripple sizes are specified. A comparison of simulation results reveals the optimization superiority of the proposed technique over the other optimization techniques for the solution of FIR low pass (LP), high pass (HP), band pass (BP) and band stop (BS) filter designs. Content Type Journal Article Pages 81-96 DOI 10.3233/HIS-130185 Authors S.K. Saha, Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, West Bengal, India R. Kar, Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, West Bengal, India D. Mandal, Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, West Bengal, India S.P. Ghoshal, Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2014

Description: This paper investigates the automatic generation of a Map-Reduce program, which implements a heuristic for an NP-complete problem with machine learning. The objective is to automatically design a new concurrent algorithm that finds solutions of comparable quality to the original heuristic. Our approach, called Savant, is inspired from the savant syndrome. Its concurrency model is based on Map-Reduce. The approach is evaluated with the well-known Min-Min heuristic. Experimental results on two problem sizes are promising, the produced algorithm is able to find solutions of comparable quality. Content Type Journal Article Pages 287-302 DOI 10.3233/HIS-140200 Authors Frédéric Pinel, University of Luxembourg, Luxembourg, Luxembourg Bernabé Dorronsoro, University of Lille, Lille, France Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2014

Description: Recentering-Restarting Genetic Algorithms have been used successfully to evolve multiple epidemic networks and perform DNA error correction. This work studies variations of the Recentering-Restarting Genetic Algorithm for the purpose of evaluating its effectiveness for ordered gene problems. These variations use multiple seeds and two adaptive representations which use generating sets to produce local search. These algorithm variations are applied to what many considered the quintessential ordered gene problem, the Travelling Salesman Problem. Two distinct sets of experimental analysis was performed: first, using large problem instances to determine the effectiveness of the Recentering-Restarting Genetic Algorithm in comparison to benchmarks and second, studying many small problem instances ranging from 12 to 20 cities to determine if any one of the algorithm variations always outperforms the others. These algorithm variations were comparable to highly competitive optimization algorithms submitted to the DIMACS TSP implementation challenge. In studying the small problem instances, it was observed that no one algorithm always dominates on all problem instances within a domain. This study demonstrates how the Recentering-Restarting Genetic Algorithm is a useful tool for improving upon results generated by other powerful heuristics. Content Type Journal Article Pages 257-271 DOI 10.3233/HIS-140198 Authors James Alexander Hughes, Computer Science, Brock University, St. Catharines, ON, Canada Sheridan Houghten, Computer Science, Brock University, St. Catharines, ON, Canada Daniel Ashlock, Mathematics and Statistics, University of Guelph, Guelph, ON, Canada Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2014

Description: Computational grids have been used to solve large scale problems in science, engineering and commerce. The task scheduling in computational grid is a complex optimization problem. Task scheduling is the fundamental issue in grid scheduling. The heuristic algorithms play a vital role in solving complex optimization problems. The distributive nature of Ant Colony Optimization (ACO) helps to find optimal or near optimal solution in efficient manner. Artificial Bee Colony Algorithm is one of the latest heuristics, which out performs classical heuristics, such as Tabu Search (TS), Simulated Annealing (SA), or even ACO. Its exploration capacity can be improved by modifying in the fitness value computation. The hybridization and the modifications in ACO and ABC improve the exploration and exploitation capability of the algorithms and enhance the convergence ability of the algorithm. The proposed hybridization of ABC algorithm with ACO algorithm reduces the waiting time, communication delay and makespan time of the schedule with good load balancing. It improves the convergence to the optimal solution. The proposed Pareto based hybrid ABC-ACO algorithm finds a population of solutions, then uses Pareto ranking to sort these solutions, and then derives the Pareto front for optimal task scheduling. The group of non-dominated solutions assists to schedule the tasks to the best available resources with a tradeoff between makespan and cost in the computational grid. Content Type Journal Article Pages 241-255 DOI 10.3233/HIS-140197 Authors K.R. Remesh Babu, Department of Information Technology, Government Engineering College, Idukki, Kerala, India P. Mathiyalagan, Department of CSE, Sri Ramakrishna Engineering College Coimbatore, Tamil Nadu, India S.N. Sivanandam, Karpagam College of Engineering, Tamil Nadu, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2014

Description: Computer-aided diagnosis (CAD) of mammograms has received great attention because of its speed and consistency and could provide a promising solution. However, poor visibility of mammographic features addresses the necessity of accurate contrast enhancement technique. In the present study, we have examined the problem of fuzziness/impreciseness of mammograms such as inhomogeneous background, indistinct borders, ill-defined shapes, varying intensities of the suspicious region and low distinguishability from surroundings. Though fuzzy logic based contrast enhancement technique has good potential to handle the problem of impreciseness in mammograms, more generalized and flexible Vague Set approach is proposed here to capture the vagueness of mammograms. Performance of the proposed contrast enhancement technique is compared and evaluated with other two techniques using CAD based classification approach. Content Type Journal Article Pages 227-240 DOI 10.3233/HIS-140191 Authors Arpita Das, Department of Radio Physics and Electronics, University of Calcutta, Kolkata, India Mahua Bhattacharya, Indian Institute of Information Technology and Management, Gwalior, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2014

Description: In this article we explore and develop a holistic scheme of self adaptive, individualized genetic operators combined with an adaptive tournament size together with a novel implementation of an inversion genetic operator which is suitable for tree based Genetic Programming. We test this scheme on several benchmark Binary Classification problems and find that the proposed techniques deliver superior performance when compared with both a tuned GP configuration and a feedback adaptive Genetic Programming implementation. Our results also demonstrate that an inversion operator may have a useful role to play in exploitation, particularly when used towards the end of evolution to facilitate gradual convergence of the learning system towards a good solution. Content Type Journal Article Pages 273-285 DOI 10.3233/HIS-140199 Authors Jeannie Fitzgerald, Bio-Computing and Developmental, Systems Group, University of Limerick, Limerick, Ireland Conor Ryan, Bio-Computing and Developmental, Systems Group, University of Limerick, Limerick, Ireland Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2014

Description: Extracting frequent and reliable rules has been the main interest of the association task of data mining. However, the discovery or infrequent or rare rules is attracting a lot of interest in many domains, such as banking frauds, biomedical data and network intrusion. Most of existent solutions for discovering reliable rules that rarely appear are based on exhaustive classical approaches, which have the drawback of becoming infeasible when dealing with high complex data sets, and which do not take into account any measure of the interestingness of the rules mined. This paper explores the application of ant programming, a bio-inspired technique for finding computer programs, to the discovery of rare association rules. To this end, it proposes two algorithms: a first one which evaluates individuals generated from a single-objective point of view, and a second one which considers simultaneously several objectives to evaluate individuals' fitness. Both of them show their ability to find a high reliable and interesting set of rare rules for the data miner in a short period of time, lacking the drawbacks of exhaustive algorithms. Content Type Journal Article Pages 197-209 DOI 10.3233/HIS-140195 Authors Juan Luis Olmo, Department of Computer Science, University of Córdoba, Spain José Raúl Romero, Department of Computer Science, University of Córdoba, Spain Sebastián Ventura, Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2014

Description: This paper describes an optimization of interval type-2 and type-1 fuzzy integrators in ensembles of ANFIS models with genetic algorithms (GAs), this with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The Mackey-Glass time series was considered to validate the proposed ensemble approach. The methods used for the integration of the ensembles of ANFIS are: type-1 and interval type-2 Mamdani fuzzy inference systems (FIS). Genetic Algorithms are used for optimization of the membership function parameters of the FIS in each integrator. In the experiments we changed the type of the membership functions for each type-1 and interval type-2 FIS, thereby increasing the complexity of the training, The output (Forecast) generated by each integrator is calculated with the RMSE (root mean square error) to minimize the prediction error, therefore we compared the performance obtained by each FIS. Content Type Journal Article Pages 211-226 DOI 10.3233/HIS-140196 Authors Jesus Soto, Division of Graduates Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico Patricia Melin, Division of Graduates Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico Oscar Castillo, Division of Graduates Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2014

Description: Cancellable biometrics adopt an approach in which biometric templates can be cancelled and replaced if compromised, for example if they are lost or stolen, and can therefore overcome some of the security concerns about biometric-based authentication systems. This paper proposes a simple and effective template protection method (cancellable transformation) for providing cancellable data, called the double sum (DS) method, which performs a sum procedure over the attributes that makes the definition of the original data a hard (and almost impossible) process. In order to investigate the feasibility of the proposed method, an empirical analysis is conducted and we use as examples two different biometric modalities (face and voice) separately and in a multi-modal context (multi-biometric). The main aim of this paper is to provide greater security and improved performance in the biometric authentication process. The datasets used in this analysis were TIMIT for voice and the AR Face dataset for face. As a result of this analysis, we will observe that the proposed transformation offered higher performance to two existing cancellable transformations. Content Type Journal Article Pages 157-166 DOI 10.3233/HIS-140192 Authors Anne M.P. Canuto, Department of Informatics and Applied Mathematics (DIMAp), Federal University of RN, Natal, RN, Brazil Fernando Pintro, Department of Informatics and Applied Mathematics (DIMAp), Federal University of RN, Natal, RN, Brazil Michael C. Fairhurst, School of Engineering and Digital Arts University of Kent, Canterbury, UK Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2014

Description: The need to deduce interesting and valuable information from large, complex, information-rich data sets is common to many research fields. Rule discovery or rule mining uses a set of IF-THEN rules to classify a class or category in a comprehensible way. Besides the classical approaches, many rule mining approaches use biologically-inspired algorithms such as evolutionary algorithms and swarm intelligence approaches. In this paper, a Particle Swarm Optimization based discrete classification implementation with a local search strategy (DPSO-LS) was devised and applied to discrete data. In addition, a fuzzy DPSO-LS (FDPSO-LS) classifier is proposed for both discrete and continuous data in order to manage imprecision and uncertainty. Experimental results reveal that DPSO-LS and FDPSO-LS outperform other classification methods in most cases based on rule size, True Positive Rate (TPR), False Positive Rate (FPR), and precision, showing slightly improved results for FDPSO-LS. Content Type Journal Article Pages 145-156 DOI 10.3233/HIS-140190 Authors Min Chen, Department of Computer Science, North Dakota State University, Fargo, ND, USA Simone A. Ludwig, Department of Computer Science, North Dakota State University, Fargo, ND, USA Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2014

Description: This paper introduces a new ensemble based on different artificial immune algorithms and it is optimized by using a Particle Swarm algorithm. The new proposed architecture of the ensemble introduces a major enhancement to the data classification. The main focus of this paper is devoted for building an ensemble model that integrates three different AIS techniques towards achieving better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome the limitations of the individual algorithms and to achieve synergistic effects through the combination of these techniques. Furthermore, a new method for measuring confidence level of AIS based classifier is introduced in this work as well. On the other hand and in order to enhance the overall performance of the classification process, an optimizer using particle swarm optimization algorithm is going to be adopted. The performance of the proposed ensemble is tested by running several experiments using different medical datasets. Content Type Journal Article Pages 167-181 DOI 10.3233/HIS-140193 Authors Jamal Al-Enzi, Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London, UK Salah Al-Sharhan, Computer Science Department, Gulf University for Science and Technology, Mishref, Kuwait Maysam Abbod, Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London, UK Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2014

Description: Variable Ordering (VO) plays an important role when inducing Bayesian Networks (BNs) and Bayesian Classifiers (BCs). Previous works in the literature suggest that it is worth pursuing the use of genetic/evolutionary algorithms for identifying a suitable VO, when learning a BN structure from data. This paper proposes a collaborative Evolutionary-Bayes algorithm named VOEA (Variable Ordering Evolutionary Algorithm) aimed at inducing BCs from data. The two VOEA versions presented in the paper refine a previously proposed algorithm named VOGA by employing only a single evolutionary operator (either crossover or mutation) as well as by using information about the class variable when defining the most suitable variable ordering for learning a BC. Experiments performed in a number of datasets revealed that the VOEA approach is promising and tends to generate suitable and representative BCs, particularly in its version VOEA_M, which only implements the mutation operator. Content Type Journal Article Pages 183-195 DOI 10.3233/HIS-140194 Authors Edimilson B. dos Santos, Federal University of S. Joao del-Rei – DCOMP/UFSJ, S. Joao del-Rei, MG, Brazil Estevam R. Hruschka Jr., Federal University of S. Carlos – DC/UFSCar, S. Carlos, SP, Brazil M. do Carmo Nicoletti, Federal University of S. Carlos – DC/UFSCar, S. Carlos, SP, Brazil Nelson F. F. Ebecken, Federal University of Rio de Janeiro – COPPE/UFRJ, Rio de Janeiro, RJ, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2014

Description: Reliability centered maintenance provides failure free functionality of heavy plant equipment. Tedious preventive maintenance tasks, inapplicable solutions and significant start up costs are the major limitations of this strategy. The aim of this paper is to improve the analysis efficiency of reliability centered maintenance without trading off quality by its integration with artificial intelligence. In order to achieve key performance indicators for the plant management, we incorporated rule based reasoning along with the case based reasoning. While only case based technique was introduced previously, our approach reduces repetitive tasks and also provides support in predicting equipment needs, by applying artificial neural networks. Content Type Journal Article Pages 213-224 DOI 10.3233/HIS-2011-0141 Authors Maliha Saleem Bakhshi, Department of MCT, U.E.T., Lahore, Pakistan Aslam Muhammad, Department of CS & E, U.E.T., Lahore, Pakistan A.M. Martinez-Enriquez, Department of CS, CINVESTAV-IPN, D.F., Mexico G. Escalada-Imaz, Artificial Intelligence Research Institute, IIIA-CSIC, Barcelona, Spain Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 4 / 2011

Description: Many times in classification problems, particularly in critical real world applications, one of the classes has much less samples than the others (usually known as the class imbalance problem). In this work we discuss and evaluate the use of the REPMAC algorithm to solve imbalanced problems. Using a clustering method, REPMAC recursively splits the majority class in several subsets, creating a decision tree, until the resulting sub-problems are balanced or easy to solve. We use two diverse clustering methods and three different classifiers coupled with REPMAC to evaluate the new method on several benchmark datasets spanning a wide range of number of features, samples and imbalance degree. We also apply our method to a real world problem, the identification of weed seeds. We find that the good performance of REPMAC is almost independent of the classifier or the clustering method coupled to it, which suggests that its success is mostly related to the use of an appropriate strategy to cope with imbalanced problems. Content Type Journal Article Pages 199-211 DOI 10.3233/HIS-2011-0140 Authors Hernán Ahumada, CIFASIS, French Argentine International Center for Information and Systems, Sciences, UPCAM (France) / UNR--CONICET (Argentina), Bv 27 de Febrero 210 Bis, 2000 Rosario, Argentina Guillermo L. Grinblat, CIFASIS, French Argentine International Center for Information and Systems, Sciences, UPCAM (France) / UNR--CONICET (Argentina), Bv 27 de Febrero 210 Bis, 2000 Rosario, Argentina Lucas C. Uzal, CIFASIS, French Argentine International Center for Information and Systems, Sciences, UPCAM (France) / UNR--CONICET (Argentina), Bv 27 de Febrero 210 Bis, 2000 Rosario, Argentina Alejandro Ceccatto, CIFASIS, French Argentine International Center for Information and Systems, Sciences, UPCAM (France) / UNR--CONICET (Argentina), Bv 27 de Febrero 210 Bis, 2000 Rosario, Argentina Pablo M. Granitto, CIFASIS, French Argentine International Center for Information and Systems, Sciences, UPCAM (France) / UNR--CONICET (Argentina), Bv 27 de Febrero 210 Bis, 2000 Rosario, Argentina Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 4 / 2011

Description: In this paper we are analyzing the performance of the Hopfield neural network as an associative memory feature for pattern storage and recalling purposes. A genetic algorithm is employed for recalling of the stored patterns corresponding to the presented input overlapped patterns. The training pattern set considered is the English characters and the Hebbian learning rule is used for encoding the pattern information in the Hopfield network. The recalling of patterns is accomplished with a genetic algorithm by settling the network in appropriate stable states for the presented overlapped input pattern. This is achieved by one set of weights used for recalling of stored patterns. If an overlapped pattern is presented, the network goes into a stable state which represents one of the characters for the presented pattern, and then this recalled character is eliminated from the overlapped pattern and a new input pattern is formed. The network will then be assigned this new input pattern as an initial state and the network goes into a state representing the other character of the pattern. The simulated results demonstrate the performance of the network for the pattern recalling corresponding to the presented overlapped input patterns. Content Type Journal Article Pages 169-184 DOI 10.3233/HIS-2011-0138 Authors Somesh Kumar, Research Scholar, Dr. B. R. Ambedkar University, Agra, Uttar Pradesh, India Manu Pratap Singh, Department of Computer Science, Institute of Computer and Information Science, Dr. B. R. Ambedkar University, Agra, Uttar Pradesh, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 4 / 2011

Description: In this paper, we present a bio-inspired learning methodology based on swarm particle optimization to learn both weights and topology of a multilayer feedforward neural network. The training algorithm represents a novel adaptive version of the particle swarm algorithm where the inertia weight is improved to increase the accuracy of the neural network. In addition to the updated exploration parameter, the proposed algorithm encloses a new acceleration parameter to deal with the convergence rate. In fact, the adopted optimization strategy aims to simulate a mutation rate with higher values in the favor of a global search. The swarm-based feedforward neural network was tested with benchmarking problems which includes both classification and regression problems. Some results are also presented to evaluate the algorithm performances. Content Type Journal Article Pages 185-198 DOI 10.3233/HIS-2011-0139 Authors Yamina Mohamed Ben Ali, Computer Sciences Department, Badji Mokhtar University BP 12 Annaba 23000, Algeria. E-mail: Benaliyam2@yahoo.fr Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 4 / 2011

Description: Writings by the same author usually share specific traits, the so-called stylome, which is defined as an abstraction of the constraints and specific sequences of words and phrases used in the texts. Although identifying a stylome has been elusive, some advancements in this area have been made. Here, we present a system trained with texts from a given author that then unveiled some of its features and, in turn, detected texts not written by that author, or written within a different style. The system is based on time series processing capabilities of an unsupervised neural network model known as the self-organizing map. The core idea is that a system trained with texts by one author should detect an anomaly when presented with texts from other authors. We present results of authorship identification in several contexts including known benchmarks as well as some examples from literature, journalism, and popular science. Content Type Journal Article Pages 225-235 DOI 10.3233/HIS-2011-0142 Authors Antonio Neme, Complex Systems Group, Universidad Autónoma de la Ciudad de México, Mexico Blanca Lugo, Coahuila Universidad Autónoma del Estado de Coahuila, Saltillo, Coah, Mexico Alejandra Cervera, Department of Computer Science University of Helsinki, Finland Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 8 Journal Issue Volume 8, Number 4 / 2011

Description: Learning with imbalanced data causes high error-rates. Several approaches have been developed for addressing this problem. In this paper, a new learning model, integrating the C4.5 classifier and evolutionary algorithms, is introduced. To strengthen the model, two separate partitioning data sets are chosen for each original data set, by applying two distinct partitioning schemes proposed in this investigation, and these are used in sequence by the learning model. More specifically, the hybrid system first applies the base method (C4.5) to produce a set of rules (R) from a training set (say T_1), as constructed by the first data partitioning scheme. The R is then passed to the Genetic Algorithm to discover another set of rules (say R_{GA}) from another disjoint training set (say T_2). T_2 is decided by the proposed second partitioning method. Finally, some informative rules of R_{GA} are included into R. The presented system is tested on several real data sets collected from the UCI machine learning repository and compared with standard C4.5. Experimental results show the good suitability of the system on imbalanced data sets. However, the model does not show negative performance on balanced data sets too. Content Type Journal Article Pages 185-202 DOI 10.3233/HIS-2012-00156 Authors B.K. Sarkar, Department of Information Technology, Birla Institute of Technology, Mesra, Ranchi, India S.S. Sana, Department of Mathematics, Bhangar Mahavidyalaya (C.U.), Bhangar, India K.S. Chaudhuri, Department of Mathematics, Jadavpur University, Kolkata, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 4 / 2012

Description: In this paper, various swarm based algorithms like conventional Particle Swarm Optimization (PSO), Improved Particle Swarm Optimization (IPSO) and another novel Improved Particle Swarm Optimization with Wavelet Mutation (IPSOWM) have been applied for the optimal design of linear phase FIR filters. Real coded genetic algorithm (RGA) has also been adopted for the sake of comparison. IPSO uses new definition for the velocity vector. Whereas in addition to the above-mentioned new definition added in IPSO, IPSOWM incorporates a new definition of swarm updating with the help of wavelet mutation based on wavelet theory. Wavelet mutation enhances the PSO to explore the solution space more effectively compared to the other optimization methods. IPSOWM is apparently free from getting trapped at local optima and premature convergence. Low pass (LP), high pass (HP), band pass (BP) and band stop (BS) FIR filters are designed with the proposed IPSOWM and other afore-mentioned algorithms individually for comparative optimization performance. A comparison of simulation results reveals the optimization efficacy of the IPSOWM over the other optimization techniques for the solution of the multimodal, non-differentiable, highly non-linear, and constrained FIR filter design problems. Content Type Journal Article Pages 211-227 DOI 10.3233/HIS-2012-00158 Authors Suman Saha, Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, India Rajib Kar, Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, India Durbadal Mandal, Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, India S.P. Ghoshal, Department of Electrical Engineering, National Institute of Technology, Durgapur, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 4 / 2012

Description: Watermarking is one of the most known techniques for authentication, tampering detection, privacy control, etc. Concerning privacy protection of patients' medical records, efforts have been devoted to guarantee the confidentiality of data and medical images during storage and transmission via an untrustworthy channel. Our developed watermarking system, aims at fulfilling such demands through the utilization of a biometric authentication code (sender physician's iris code), encrypted patient data and a fuzzy-based Region-of-Interest (ROI) segmentation algorithm. In this paper, a new hybrid DCT fuzzy biometric-based watermarking scheme has been introduced for privacy protection and source verification of medical and non-medical images. The proposed scheme integrates forward DCT transform with enhanced fuzzy-based ROI regions segmentation. To comply with the imperceptibility and high image quality requirements, the coefficients selection decision depends on perceptual visibility threshold estimation, based upon characteristics of the human visual system (HVS). The inclusion of a just-noticeable distortion (JND) profile, computed for DCT coefficients, is proved to outperform the traditional DCT model, with the major contributions of a new formula for luminance adaptation adjustment and the incorporation of block classification for contrast masking. A block classification has been utilized to differentiate edge regions and thus effectively avoid over-estimation of JND in the selected regions. Moreover, several enhanced fuzzy-based clustering models have been suggested for extraction of ROI regions in medical patterns, which aim at increasing the robustness to noise and yield more homogeneous regions with less spurious blobs. The proposed hybrid fuzzy-based ROI extraction scheme integrates the effect of the local neighborhood and allow it to influence the membership value of each pixel. A new Hybrid FCM (H-FCM) algorithm is proposed, which integrates spatial information with a 2D adaptive noise removal SS-FCM model. Experiments have been conducted to verify the proposed model. Several attacks have been applied to the proposed scheme and the experiments revealed promising results in terms of visual quality and extracted watermark distortion. Content Type Journal Article Pages 105-121 DOI 10.3233/HIS-2012-0150 Authors Sherin M. Youssef, Department of Computer Engineering, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt Yasser El-Sonbaty, College of Computing and IT, Arab Academy for Science and Technology, Alexandria, Egypt Karma M. Fathalla, Department of Computer Engineering, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 2 / 2012

Description: In this paper, Hybrid Particle Swarm Optimization and Tabu Search (HPSOTS) is proposed to automatically generate Fuzzy Controller for MIMO systems. The algorithm is used to simultaneously optimize the premise and consequent parameters of the fuzzy rules for the appropriate design of fuzzy controller for Takagi-Sugeno zero-order. Solution found after each step of PSO algorithm was introduced in Tabu search algorithm as initial solution to seek its best neighboring solution. This hybridization of global and local optimization algorithms minimizes the number of iterations and computation time while maintaining accuracy and minimum response time. The algorithm was tested to control two nonlinear dynamical MIMO systems. Results proved the effectiveness of the proposed algorithm. Content Type Journal Article Pages 1-9 DOI 10.3233/HIS-120160 Authors N. Talbi, Faculty of Science and Technology, Jijel University, Jijel, Algeria K. Belarbi, Faculty of Engineering, Mentoury University, Constantine, Algeria Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 1 / 2013

Description: Genetic Algorithm (GA) is one of the most popular heuristic search algorithms inspired by nature's evolutionary behavior. Among the various genetic operators, mutation is one important operator that helps to accelerate the searching ability of GA. As GA finds numerous applications, it undergoes various enhancements and modifications, especially with respect to mutation operator. Numerous mutation techniques have been reported in the literature that can be broadly categorized into static and adaptive mutation techniques. This work selectively analyzes six mutation techniques in a common bench of experiments. Among the six mutation techniques, two are the popular variants of static mutation techniques called as Uniform mutation and Gaussian Mutation. The remaining four were recently introduced: two individual adaptive mutation techniques, a self adaptive mutation technique and a deterministic mutation technique. Totally, 28 benchmark functions, which fall under the benchmark categories of unimodal, multimodal, extended multimodal, diagonal and quadratic functions, are used in the work. The analysis mainly intends to determine a best mutation technique for every benchmark problem and to understand the dependency behavior of mutation techniques with other GA parameters such as crossover probabilities, population sizes and number of generations. It leads to interesting findings which would help to improve the GA performance on other practical and benchmark problems. Content Type Journal Article Pages 11-22 DOI 10.3233/HIS-120161 Authors B.R. Rajakumar, Aloy Labs, Bengaluru, India. E-mail: rajakumar_be08@acm.org Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 1 / 2013

Description: Lately, many academic and industrial fields have shifted their research focus from data acquisition to data analysis. This transition has been facilitated by the usage of Machine Learning (ML) techniques to automatically identify patterns and extract non-trivial knowledge from data. The experimental procedures associated with that are usually complex and computationally demanding. To deal with such scenario, Distributed Heterogeneous Computing (DHC) systems can be employed. In order to fully benefit from DHT facilities, a suitabble scheduling policy should be applied to decide how to allocate tasks into the available resources. An important step for such is to guess how long an application would take to execute. In this paper, we present an approach for predicting execution time specifically of ML tasks. It employs a metalearning framework to relate characteristics of datasets and current machine state to actual execution time. An empirical study was conducted using 78 publicly available datasets, 6 ML algorithms and 4 meta-regressors. Experimental results show that our approach outperforms a commonly used baseline method. After establishing SVM as the most promising meta-regressor, we employed its predictions to actually build schedule plans. In a simulation considering a small scale DHC enviroment, a simple Genetic Algorithm based scheduler was employed for task allocation, leading to minimized overall completion time. These achievements indicate the potential of meta-learning to tackle the problem and encourage further developments. Content Type Journal Article Pages 23-32 DOI 10.3233/HIS-130162 Authors Rattan Priya, Computer Science Engineering, Indira Gandhi Institute of Technology, GGSIPU, New Delhi, India Bruno Feres de Souza, Computer Science Department, Insitutute of Mathematics and Computer Sciences, University of São Paulo, São Carlos-SP, Brazil André L.D. Rossi, Computer Science Department, Insitutute of Mathematics and Computer Sciences, University of São Paulo, São Carlos-SP, Brazil André C.P.L.F. de Carvalho, Computer Science Department, Insitutute of Mathematics and Computer Sciences, University of São Paulo, São Carlos-SP, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 1 / 2013

Description: Many organizations are nowadays keeping their data in the form of multi-level categories for easier manageability. An example of this is the Reuters Corpus which has news items categorized in a hierarchy of up to five levels. The volume and diversity of documents available in such category hierarchies is also increasing daily. As such, it becomes difficult for a traditional classifier to efficiently handle multi-level categorization of such a varied document space. In this paper, we present hybrid classifiers involving various two-classifier and four-classifier combinations for two-level text categorization. We show that the classification accuracy of the hybrid combination is better than the classification accuracies of all the corresponding single classifiers. The constituent classifiers of the hybrid combination operate on different subspaces obtained by semantic separation of data. Our experiments show that dividing a document space into different semantic subspaces increases the efficiency of such hybrid classifier combinations. We further show that hierarchies with a larger number of categories at the first level benefit more from this general hybrid architecture. Content Type Journal Article Pages 33-41 DOI 10.3233/HIS-130163 Authors Nandita Tripathi, University of Sunderland, Sunderland, UK Michael Oakes, University of Sunderland, Sunderland, UK Stefan Wermter, University of Hamburg, Hamburg, Germany Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 1 / 2013

Description: A method of progressive transmission of Magnetic Resonance Image with lesions over long distances is proposed. The Magnetic Resonance Images at the transmitter end are segregated on the basis of presence of lesions. Entropy Maximization using Hybrid Particle Swarm Optimization algorithm that incorporates a Wavelet theory based mutation operation is used for segmentation of Magnetic Resonance Images. It applies the Multi-resolution Wavelet theory to overcome the stagnation phenomena of the Particle Swarm Optimization. Thus the segmentation algorithm using Hybrid Particle Swarm Algorithm explores the solution space more effectively for a better solution. Varying percentages of Discrete Cosine Transform coefficients of segmented Magnetic Resonance Images are used for progressive image transmission. For a particular image data, the progressively received images are of different resolutions. At the receiver's end the progressively received images of different resolutions are fused using Multi-resolution wavelet analysis to get a visually suitable image for diagnosis. The doctor or a radiologist identifies a particular class of image with lesions and may ask for the entire un-segmented Magnetic Resonance Image dataset of a particular patient for further diagnosis. The proposed system helps to reduce the load on the system by choosing not to transmit the Magnetic Resonance Images without lesions. Content Type Journal Article Pages 57-69 DOI 10.3233/HIS-130165 Authors Arunava De, Department of Electronics and Communication, National Institute of Technology, Durgapur, India Anup Kumar Bhattacharjee, Department of Electronics and Communication, National Institute of Technology, Durgapur, India Chandan Kumar Chanda, Department of Electrical Engineering, Bengal Engineering and Science University, Shibpur, India Bansibadan Maji, Department of Electronics and Communication, National Institute of Technology, Durgapur, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 2 / 2013

Description: Many efforts have been done to tackle the problem of information abundance in the World Wide Web. Growth in the number of web users and the necessity of making the information available on the web, make web recommender systems very critical and popular. Recommender systems use the knowledge obtained through the analysis of users' navigational behavior, to customize a web site to the needs of each particular user or set of users. Most of the existing recommender systems use either content-based or collaborative filtering approach. It is difficult to decide which one of these approaches is the most effective one to be used, as each of them has both strengths and weaknesses. Therefore, a combination of these methods as a hybrid system can overcome the limitations and increase the effectiveness of the system. This paper introduces a new hybrid recommender system by exploiting a combination of collaborative filtering and content-based approaches in a way that resolves the drawbacks of each approach and makes a great improvement over a variety of recommendations in comparison to each individual approach. We introduce a new fuzzy clustering approach based on genetic algorithm and create a two-layer graph. After applying this clustering algorithm to both layers of the graph, we compute the similarity between web pages and users, and propose recommendations using the content-based, collaborative and hybrid approaches. A detailed comparison on all the mentioned approaches shows that the hybrid approach recommends the web pages which haven't been yet viewed by any user, more accurately and precisely than other approaches. Therefore, the evaluation of the results reveals that the novel proposed hybrid approach achieves more accurate predictions and more appropriate recommendations than each individual approach. Content Type Journal Article Pages 71-81 DOI 10.3233/HIS-130166 Authors Rana Forsati, NLP Research Lab, Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Tehran, Iran Hanieh Mohammadi Doustdar, Department of Computer Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran Mehrnoush Shamsfard, NLP Research Lab, Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Tehran, Iran Andisheh Keikha, NLP Research Lab, Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Tehran, Iran Mohammad Reza Meybodi, Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 2 / 2013

Description: The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. This paper is meant to propose a novel feature selection framework for Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS), by exploring existing feature selection frameworks, and compare the performance of proposed feature selection framework against various feature selection methods in Writer Identification in order to find the most significant features. The promising applicability of the proposed framework has been demonstrated in the result and worth to receive further exploration in identifying the handwritten authorship. Content Type Journal Article Pages 83-91 DOI 10.3233/HIS-130167 Authors Satrya Fajri Pratama, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Azah Kamilah Muda, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Yun-Huoy Choo, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Noor Azilah Muda, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 2 / 2013

Description: In this paper we perform a deep investigation about the usefulness of an immune-inspired algorithm to design accurate and compact fuzzy rule bases for classification problems. The algorithm, called Bayesian Artificial Immune System (BAIS), incorporates a mechanism to learn a probability graphical model from the promising solutions found so far. Thus, BAIS utilizes this model to sample new candidate solutions. The probabilistic model utilized here is a Bayesian network due to its capability of expressing the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions (building blocks). Besides the capability to identify and manipulate building blocks, the algorithm maintains diversity in the population, performs multimodal optimization and adjusts the size of the population automatically according to the problem. These attributes are generally absent from alternative algorithms, and can be considered useful attributes when generating fuzzy rule bases, thus guiding to high-performance classifiers. BAIS was evaluated in thirteen well-known classification problems and its performance compares favorably with that produced by contenders. Content Type Journal Article Pages 43-55 DOI 10.3233/HIS-130164 Authors Pablo A.D. Castro, São Paulo Federal Institute of Education, Science and Technology (IFSP), São Carlos, São Paulo, Brazil Heloisa A. Camargo, University of São Carlos (UFSCar), São Carlos, São Paulo, Brazil Fernando J. Von Zuben, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 2 / 2013

Description: This paper presents a comparative performance evaluation of a random subsample classifier ensemble with leading machine learning classifiers on high dimensional datasets. Classification performance of the hybrid random subsample ensemble is compared to those of a comprehensive set of machine learning classification algorithms through both in-house simulations and the results published by others in the literature. Performance comparison is based on prediction accuracies on six datasets from the UCI Machine Learning repository, namely Dexter, Madelon, Isolet, Multiple Features, Internet Ads, and Citeseer, with feature counts of up to 105,000. Simulation results establish the competitive performance aspect of the hybrid random subsample ensemble for high dimensional datasets. Specifically, the study findings indicate that hybrid random subsample ensembles with a subsample rate of 15% and base classifier count of 25 or more can achieve a very competitive performance on high dimensional data sets when compared to leading machine learning classifier algorithms. Content Type Journal Article Pages 91-103 DOI 10.3233/HIS-2012-0149 Authors Santhosh Pathical, Electrical Engineering and Computer Science Department, University of Toledo, Toledo, OH, USA Gursel Serpen, Electrical Engineering and Computer Science Department, University of Toledo, Toledo, OH, USA Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 2 / 2012

Description: Multi-objective random one-bit climbers (moRBCs) are one class of stochastic local search-based algorithms that maintain a reference population of solutions to guide their search. They have been shown to perform well in solving multi-objective optimization problems. In this work, we analyze the effects of introducing a list of tabu moves in the performance of moRBCs and investigate how moRBCs behave varying the size of this list. We also study their behavior when the selection to update the reference population and archive is replaced with a procedure that provides an alternative mechanism for preserving better distribution among the solutions. We use several MNK-landscape models as test instances and apply statistical testings to analyze the results. Our study shows that the two modifications complement each other in significantly improving moRBCs' performance especially in many-objective problems. They can play specific roles in enhancing the convergence and diversity of moRBCs. Moreover, they help improve the rate by which moRBCs can find solutions that have desired qualities. Content Type Journal Article Pages 75-90 DOI 10.3233/HIS-2012-0148 Authors Joseph M. Pasia, Institute of Mathematics, University of the Philippines-Diliman, Quezon City, Philippines Hernán Aguirre, Faculty of Engineering, Shinshu University, Nagano, Japan Kiyoshi Tanaka, Faculty of Engineering, Shinshu University, Nagano, Japan Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 2 / 2012

Description: Wireless sensor networks (WSNs) consist of a set of sensor nodes in order to detect and transmit environmental characteristics, such as, temperature, humidity or lightness. These sensor nodes, after capturing an event, should communicate with a special node, named sink node. However, the use of a single sink node implies a bottleneck in the sensor network, specially for real time applications. To overcome this problem, researches are focusing on studies for the selection of routes in sensor networks with multiple sink nodes. The approach proposed by this paper presents the application of Genetic Fuzzy Systems (GFSs) to estimate the quality of routes in WSNs, in order to ensure communication between multiple sensor nodes and multiple sink nodes. A Mamdani Fuzzy Inference System is used to select the best sink node for communication at a given moment, based on network features such as the available energy of the route and the number of hops to reaches the sink node. Genetic Algorithms (GAs) are used to obtain the optimal setting of design parameters of the Mamdani fuzzy inference system. The proposed route classification was implemented by computer simulations to demonstrate its feasibility and the results showed a sensor network with longer lifetime, based on the appropriate selection of the sink and the route used to send packets through the network. Content Type Journal Article Pages 61-74 DOI 10.3233/HIS-2012-0147 Authors Ricardo A.L. Rabelo, Computer Science Center of Technology and Urban Planning, University of State of Piaui, Teresina, PI, Brazil Marcus Vinícius de S. Lemos, Computer Science Center of Technology and Urban Planning, University of State of Piaui, Teresina, PI, Brazil Líliam B. Leal, Applied Computer Sciences Department, University of Fortaleza, Fortaleza, Brazil Raimir Holanda Filho, Applied Computer Sciences Department, University of Fortaleza, Fortaleza, Brazil Fabbio A.S. Borges, Unified Teaching Center of Teresina, Teresina, PI, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 2 / 2012

Description: This paper deals with the problem of optimally regulating planned vehicle timetables for public transport when unforeseen events occur in real-time in the network. A multicriteria problem is solved by using an integrated intelligent approach that combines agent-based techniques, Tabu search algorithm and fuzzy preference model. The agents of our model act cooperatively in order to generate efficient solutions that optimize simultaneously and separately the different regulation objectives. This optimization is performed by means of a distributed tabu search algorithm. Efficient solutions are then, classified according to a fuzzy preference model by using an interactive solutions evaluation among agents. In order to assess the distributed approach, an experimental study was carried out on the base of some scenarios of disturbances occurred in a public transportation network. Content Type Journal Article Pages 159-169 DOI 10.3233/HIS-2012-0154 Authors Imen Boudali, B.P.14 Menzah, Ariana, Tunisia Khaled Ghedira, Institut Supérieur de Gestion de Tunis, Cité Bouchoucha, Tunis, Tunisia Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 3 / 2012

Description: A modified version of Boosted Mixture of Experts (BME) for low-resolution face recognition is presented in this paper. Most of the methods developed for low-resolution face recognition focused on improving the resolution of face images and/or special feature extraction methods that can deal effectively with low-resolution problem. However, we focused on the classification step of face recognition process in this paper. Using Neural Networks (NN) combinations is an efficient approach to deal with complex classification problems, such as the low-resolution face recognition which involves high-dimensional feature sets and highly overlapped classes. Mixture of Experts (ME) and boosting methods are two of the most popular and interesting NN combining methods, which have great potential for improving performance in classification. A modified combining approach based on both features of ME and boosting is presented in order to deal with this complex classification problem efficiently. Previous works [1,2] made attempts to incorporate the complementary features of boosting method in ME training algorithm to boost the performance. These approaches called Boosted Mixture of Experts (BME) have some drawbacks. Based on the analysis of the problems of previous approaches, some modifications are suggested in this paper. A modification in the pre-loading (initialization) procedure of ME is proposed to address the limitations of previous approaches and overcome them using a two stages pre-loading procedure. In our suggested approach, both the error and confidence measures are used as the difficulty criteria in boosting-based partitioning of the problem space. Regarding the nature of this approach, we call the proposed method Boosted Pre-loaded Mixture of Experts (BPME). The proposed method is tested in a low-resolution face recognition problem and compared to the other variations of ME and boosting method. The experiments are conducted using low-resolution variations of two common face databases including the ORL and Yale databases. The experimental results show that BPME method has significant better recognition rates against the other compared combining methods in various tested conditions including different quality grades of face images and different sizes of the training set. Content Type Journal Article Pages 145-158 DOI 10.3233/HIS-2012-0153 Authors Reza Ebrahimpour, School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran Naser Sadeghnejad, Brain and Intelligent Systems Research Laboratory, Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran Saeed Masoudnia, Central Tehran Branch and Young Researchers Club, Islamic Azad University, Tehran, Iran Seyed Ali Asghar Abbaszadeh Arani, Brain and Intelligent Systems Research Laboratory, Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 3 / 2012

Description: This paper introduces an efficient algorithm for unsupervised clustering that is based on barebones Particle Swarm (BB). The proposed algorithm introduces significant enhancement to the Particle Swarm Optimization (PSO) by eliminating the parameters tuning. The Algorithm aims at finding the centroids of predefined number of clusters where each centroid attracts similar patterns. This research tests and investigates the application of the proposed algorithm to the problem of unsupervised pattern classification by applying the algorithm to segmentations of different images. Experimental results show that the the proposed BB-based algorithm outperforms other state-of-the-art clustering algorithms on all the different levels of comparison. The impact of eliminating the parameters tuning is evident on the performance of the algorithm. In addition, the influence of different values for the swarm size of BB on performance is also illustrated. Content Type Journal Article Pages 135-143 DOI 10.3233/HIS-2012-0152 Authors Salah Al-Sharhan, Computer Science Department, Gulf University for Science and Technology, Hawally, Kuwait M.G.H. Omran, Computer Science Department, Gulf University for Science and Technology, Hawally, Kuwait Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 9 Journal Issue Volume 9, Number 3 / 2012

Description: This article describes a modeling of knowledge for a Case Based Reasoning system (CBR) applied to the diagnosis of the hepatic pathologies, where the cases and the knowledge of the domain are expressed by a Bayesian network (BN). In fact, we are interested in the retrieval and adaptation phases. The retrieval phase consists in selecting the most similar case of log linear model by considering the Bayesian Network as a log-linear model based on the simplification of the probabilities. The adaptation phase means modifying solutions of retrieved cases to fit the current problem. The dependence between these two phases is defined by two measures: a similarity measure and an adaptation measure. The objective of this dependence is to guarantee the retrieved case, which is the easiest way to adapt and improve the performance of CBR. An example of the diagnosis of the hepatic pathologies will illustrate the proposed approach. Content Type Journal Article Pages - DOI 10.3233/HIS-2012-0151 Authors Akila Djebbar, Computer Science Department, LRI Laboratory, Badji Mokhtar University, Annaba, Algeria Hayet Farida Merouani, Computer Science Department, LRI Laboratory, Badji Mokhtar University, Annaba, Algeria Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869

Description: This study presents an adaptive-type controller based on a quantum neural network and investigates its characteristics for control systems. A multi-layer quantum neural network which uses qubit neurons as an information processing unit is utilized to design three types of the adaptive-type quantum-neural-network-based controllers which conduct the learning of the quantum neural network as an online process: a direct controller, a parallel controller and an indirect controller. Computational experiments to control the single-input single-output non-linear discrete-time plant are conducted to evaluate the learning performance and capability of quantum neural controllers. The results of the computational experiments confirm the feasibility and effectiveness of adaptive-type quantum neural controllers. Content Type Journal Article Pages 151-164 DOI 10.3233/HIS-130174 Authors Kazuhiko Takahashi, Information Systems Design, Faculty of Science and Engineering, Doshisha University, Kyoto, Japan. E-mail: katakaha@mail.doshisha.ac.jp Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 3 / 2013

Description: This paper presents the development and comparative application of recently developed evolutionary-based Memetic Algorithm (MA) along with its variants/hybrid methods for solving single objective Optimal Power Flow (OPF) problem. Two different variants based on single and double local search MA methods are implemented to solve single objective OPF problem. The MA algorithm is also hybridized with Differential Evolution (DE) to solve the OPF problem. The simulation studies are carried out on a standard IEEE 30-bus test system to demonstrate the effectiveness of the developed methods. The simulation results are compared with the existing methods available in the literature. The performance of the best variant/hybrid method is also tested for solution of single objective OPF problem by considering load uncertainty. Content Type Journal Article Pages 117-128 DOI 10.3233/HIS-130170 Authors B. Srinivasa Rao, Department of Electrical Engineering, VRS Engineering College, Vijayawada, India K. Vaisakh, AU College of Engineering, Andhra University, Visakhapatnam, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 3 / 2013

Description: This paper introduces a novel bio inspired clustering algorithm called Cuckoo Search Clustering Algorithm (CSCA). This algorithm is based on the recently proposed Cuckoo Search Optimization technique which mimics the breeding strategy of the parasitic bird-cuckoo. The algorithm is further extended to a classification method, Biogeography Based Cuckoo Search Classification Algorithm (BCSCA), which is a hybrid approach of the two nature inspired metaheuristic techniques. The proposed algorithms are validated with real time remote sensing satellite image datasets. The CSCA was first tested with benchmark dataset, which yields good results. Inspired by the results, it was applied on two real time remote sensing satellite image datasets for extraction of the water body, which itself is a quite complex problem. A new method for the generation of new cuckoos has been proposed, which is used in the algorithms. The resulting algorithm is conceptually simpler, takes less parameter than other nature inspired algorithms, and, after some parameter tuning, yields very good results. The extended algorithm BCSCA is also tested on the same satellite image for identifying different land covers by classifying the image in various classes. The algorithm was successful in classifying other land cover regions like rocky, barren, urban and vegetation. We strongly feel that results can be further improved by finer tuning of the parameters. Both the algorithms use Davies-Bouldin index (DBI) as fitness function. Further exploration of suggested algorithms CSCA and BCSCA may prove them to be strong entrants in the pool of nature inspired techniques. Content Type Journal Article Pages 107-116 DOI 10.3233/HIS-130169 Authors Samiksha Goel, Department of Computer Science, Delhi University, Delhi, India Arpita Sharma, Department of Computer Science, Delhi University, Delhi, India Punam Bedi, Department of Computer Science, Delhi University, Delhi, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 3 / 2013

Description: Complex organisms exhibit both evolved instincts and experiential learning as adaptive mechanisms. In isolation, neither mechanism is sufficient to successfully navigate the environments of such organisms. Instincts provide behaviors that are generally adaptive but fail in specific cases. Learning must rely on some internal or external guidance to succeed on challenging tasks. This paper explores how instincts and experiential learning can work in tandem to solve a maze environment. Specifically, instincts comprise general knowledge of a set of related mazes representing worlds that an organism might be born into, and experiential learning discriminates specific situations in the particular maze world that an organism is born into. Synergy is accomplished by a hybrid neural network, one part instinctive and the other part capable of learning. After sufficient discriminating experiences, learning can override instinct to navigate a maze when instinct would otherwise fail. Results show a marked improvement in performance when this synergistic approach is employed relative to using either instincts or learning in isolation. Content Type Journal Article Pages 129-136 DOI 10.3233/HIS-130171 Authors Thomas E. Portegys, Dialectek, Duvall, WA, USA. E-mail: portegys@gmail.com Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 3 / 2013

Description: For solving classification and regression problems, we propose a hybrid system consisting of two phases which work in tandem. In the first phase, particle swarm optimization is employed to train a 3-layered auto associative neural network (henceforth called PSOAANN). In this phase, dimensionality reduction takes place in hidden layer, where the hidden nodes should be less than the input nodes. The outputs from the hidden nodes are then treated as nonlinear principal components (NLPC). They are fed to the second phase where several classifiers and regression methods are invoked. The second phase includes techniques viz., threshold accepting logistic regression (TALR), probabilistic neural network (PNN), group method of data handling (GMDH), support vector machine (SVM) and genetic programming (GP) for classification problems. For regression problems, general regression neural network (GRNN) is used in place of PNN. In addition, support vector machine (SVM), Genetic Programming (GP), GMDH are also employed, as they are versatile. The efficiency of the hybrid is analyzed on five banking datasets namely Spanish banks, Turkish banks, US banks and UK banks and UK credit dataset and five regression datasets viz., Bodyfat, Forestfires, AutoMPG, Boston Housing and Pollution. All the datasets are analyzed using 10 fold cross validation (10 FCV). It turns out that the proposed hybrid yielded higher accuracies across classification and regression problems. Content Type Journal Article Pages 137-149 DOI 10.3233/HIS-130173 Authors Vadlamani Ravi, Institute for Development and Research in Banking Technology, Hyderabad, India Nekuri Naveen, Institute for Development and Research in Banking Technology, Hyderabad, India Mayank Pandey, Institute for Development and Research in Banking Technology, Hyderabad, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 3 / 2013

Description: Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. It is used in rough reducts calculation for identifying optimally significant attributes set. This paper proposes a hybrid ant swarm optimization algorithm by using immunity to discover better fitness value in optimizing rough reducts set. By integrating PSO with ACO, it will enhance the ability of PSO when updating its local search upon quality solution as the number of generations is increased. Unlike the conventional PSO/ACO algorithm, proposed Immune ant swarm algorithm aims to preserve global search convergence of PSO when reaching the optimum especially under the high dimension situation of optimization with small population size. By combining PSO with ACO algorithms and embedding immune approach, the approach is expected to be able to generate better optimal rough reducts, where PSO algorithm performs the global exploration which can effectively reach the optimal or near optimal solution to increase fitness value as compared to the past research in optimization of attribute reduction. This research is also to enhance the optimization ability by defining a suitable fitness function with immunity process to increase the competency in attribute reduction and has shown improvement of the classification accuracy with its generated reducts in solving NP-Hard problem. The proposed algorithm has shown promising experimental results in obtaining optimal reducts when tested on 12 common benchmark datasets. Result for rough reducts and fitness value performance has been discussed and briefly explored in order to identify the best solution. The experimental analysis on the initial results of IASORR has been proven to offer a better quality algorithm and to maintain PSO's performance, which are also encouraging in t-test analysis, for most of the tested datasets. Content Type Journal Article Pages 93-105 DOI 10.3233/HIS-130168 Authors Lustiana Pratiwi, Computational Intelligence and Technologies Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Yun-Huoy Choo, Computational Intelligence and Technologies Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Azah Kamilah Muda, Computational Intelligence and Technologies Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Noor Azilah Muda, Computational Intelligence and Technologies Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 3 / 2013

Description: This paper presents a technique of path planning of a mobile robot using artificial neural network, fuzzy logic and genetic algorithm. The artificial neural network (ANN) is trained to choose a path from a set of n paths for the mobile robot to move ahead towards the destination. Fuzzy logic (FL) is used to avoid collisions when all the n paths are blocked by obstacles. Genetic Algorithm (GA) is used as optimizer to find optimal locations along the obstacle-free directions and positions by selecting a set of fuzzy rules for the fuzzy logic system from a large rule base. Results show that the combination of these features is computationally efficient by helping each other to eliminate their individual limitations. Content Type Journal Article Pages 71-80 DOI 10.3233/HIS-130184 Authors Thongam Khelchandra, Information Systems Department, The University of Aizu, Aizu-Wakamatsu, Japan Jie Huang, Information Systems Department, The University of Aizu, Aizu-Wakamatsu, Japan Somen Debnath, Department of Information Technology, Mizoram University, Tanhril, Aizawl, India Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2014