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A calendar of events related to artificial intelligence is presented, which includes the AISec 2009--The 2nd Workshop on Security and Artificial Intelligence Search Site on November 9, 2009 in Chicago, Illinois, and the 4th Conference on Computational Cognitive Neuroscience on November 18-19, 2009 in Boston, Massachusetts, and the ANNPR 2010--4th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition April 11-13, 2010 in Cairo, Egypt.

The article discusses various reports published within the issue, including one by Steve Green on the detection and classification of persons in real-world beach scenes for the automated monitoring of public recreation area and another by Hosur and Carrasco on the development of conventional frame-based coding.

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The article discusses the importance of computational intelligence. Computational intelligence has become a key technology and is playing today an important role in many academic and industrial areas. The aim of computational intelligence is to apply the mechanisms of human brain for solving many real-world problems and to achieve a robust and low cost implementation. The area of interest of the selected papers covers a broad spectrum of computational intelligence techniques with application to image and signal processing.

Surface electromyogram (SEMG) has numerous applications, but the presence of artifacts and cross talk especially at low level of muscle activity makes the recordings unreliable. Spectral and temporal overlap can make the removal of artifacts and noise, or separation of relevant signals from other bioelectric signals extremely difficult. Identification of hand gestures using low level of SEMG is one application that has a number of applications but the presence of high level of cross talk makes such an application highly unreliable. Individual muscles may be considered as independent at the local level and this makes an argument for separating the signals using independent component analysis (ICA). In the recent past, due to the easy availability of ICA tools, a number of researchers have attempted to use ICA for this application. This paper reports research conducted to evaluate the use of ICA for the separation of muscle activity and removal of the artifacts from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and a number of sources. This paper also identifies the lack of suitable measure of quality of separation for bioelectric signals and it recommends and tests a more robust measure of separation. This paper also proposes semi-blind ICA approach with the combination of prior knowledge of SEMG sources with ICA to identify hand gestures using low level of SEMG recordings. The theoretical analysis and experimental results demonstrate that ICA is suitable for SEMG signals. The results demonstrate the limitations of such applications due to the inability of the system to identify the correct order and magnitude of the signals. This paper determines the suitability of the use of error between estimated and actual mixing matrix as a mean for identifying the quality of separation of the output. This work also demonstrates that semi-blind ICA can accurately identify complex hand gestures from the low-level SEMG recordings. [ABSTRACT FROM AUTHOR]

The paper presents an intelligent hybrid approach for content-based image retrieval based on texture feature. The proposed approach employs an Auto-Associative Neural Network (AANN) for feature extraction and a Multi-Lawyer Perceptron (MLP) with a single hidden lawyer for the classification. Two intelligent approaches such as AANN-MLP and statistical-MLP were investigated. The performance of the proposed approaches was evaluated on a large benchmark database of texture patterns. The results are very promising compared to other existing traditional and intelligent techniques. Some of the experimental results conducted during the investigation, comparative analysis of the results and suggestions to select the appropriate techniques for texture feature extraction and classification are presented in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a new idea is developed for improving the agent intelligence. In fact with the presented convolutional neural network (CNN) approach for knowledge classification, the agent will be able to manage its knowledge. This new concept allows the agent to select only the actionable rule class, instead of trying to infer its whole rule base exhaustively. In addition, through this research, we developed a comparative study between the proposed CNN approach and the classical classification approaches. As foreseeable the deep learning method outperforms the others in term of classification accuracy. [ABSTRACT FROM AUTHOR]

In order to solve the defects of consequent part expression in ANFIS model and several shortcomings in FIS, this paper presents a Choquet Integral-OWA based Fuzzy Inference System, known as AggFIS. This model has advantages in consequent part of fuzzy rule, universal expression of fuzzy inference operator and importance factor of each criteria and each rule, which is trying to establish fuzzy inference system that can fully reflect the essence of fuzzy logic and human thinking pattern. If we combine AggFIS with a feed forward-type neural network according to the basic principles of fuzzy neural network, we can obtain Choquet Integral-OWA based Adaptive Neural Fuzzy Inference System, which is named Agg-ANFIS. We apply this Agg-ANFIS model into the evaluation of traffic level of service. The experimental results show that Choquet Integral-OWA based Adaptive Neural Fuzzy Inference System (Agg-ANFIS) is a universal approximator because of its infinite approximating capability by training and can be used in complex systems modeling, analysis and prediction. [ABSTRACT FROM AUTHOR]

The availability of inexpensive, miniaturized sensors and the development of efficient and accurate filtering algorithms are important elements in order that pedestrian navigation system (PNS) will become an enabling technology for monitoring unconstrained daily living activities of human subjects. In this paper, we develop and test a computational method that estimates the path traveled by a walker from in-shoe mounted tri-axis inertial and magnetic sensors. The interest for inertial and magnetic sensors stems from their low cost, suitable form factors, small power consumption, which may give rise to self-contained, portable PNS whose hindrance to walking is limited. An important element in our approach is represented by an extended Kalman filter (EKF), whose aim is to estimate the body part orientation and to perform the in-line calibration of the tri-axis magnetometer. Two validation tests are applied to either acceleration or magnetic vector measurements, in order to adapt the measurement noise covariance matrix against the effects of body motion and external magnetic fields on the sensed gravity and earth's magnetic fields involved in orientation determination. Additionally, some biomechanical facts about how humans normally move are exploited to enhance the positioning performance of the developed method. [ABSTRACT FROM AUTHOR]

This paper presents a Finite State Machine (FSM) implementation method based on symbolic functional decomposition. This novel approach to multilevel logic synthesis of FSMs targets Field Programmable Gate Array (FPGA) architectures. Traditional methods consist of two steps: internal state encoding and then mapping the encoded state transition table into target architecture. In the case of FPGAs, functional decomposition is recognized as the most efficient method of implementing digital circuits. However, none of the known state encoding algorithms can be considered as a good method to be used with functional decomposition. In this paper, the concept of symbolic functional decomposition is applied to obtain a multilevel structure that is suitable for implementation in FPGA architectures. The symbolic functional decomposition does not require a separate encoding step. It accepts FSM description with symbolic states and performs decomposition, producing such a state encoding that guarantees the optimal or near-optimal solution. [ABSTRACT FROM AUTHOR]

This paper proposes an effective lecture allocation method based on users' profiles and utilities in elective subjects. In many universities and colleges, elective subject systems are employed as a curriculum in which students make their own learning experiences. Students select multiple subjects based on their interests and preferences. Generally, each university determines members of elective subjects based on simple rules, such as the order of grade, random selection, and first arrival. Current determination systems have strong limitations in terms of reflecting users' multiple preferences. We propose a new determination algorithm and a user support system based on users' profiles and multi-attribute preferences. When users' preferences are not reflected in algorithm-based determination, a coordination agent decides lecture allocation cooperatively with each user's agent. Main advantage of our system is mainly that lectures are allocated effectively that reflected users' multiple preferences. [ABSTRACT FROM AUTHOR]

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired. [ABSTRACT FROM AUTHOR]

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams-Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. Multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste byproducts. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions. [ABSTRACT FROM AUTHOR]

IP address lookup engine is the beating heart of a router. For meeting the requirements of a desirable high-speed router, speed, memory consumption, scalability, and reconfigurability of its IP lookup engine are critical. This paper uses a genetic-algorithm approach to optimise the structure of fixed-stride multibit-trie IP lookup methods. In this work, the genetic algorithm is used first in the design phase as an offline optimisation mechanism. This nature-inspired simulation finds the most memory-efficient configuration of IP address segmentation for a fixed number of address segments. Then, for adapting to network variations, the proposed method dynamically changes the number of address segments to compromise between speed and memory consumption. Each time the number of segments changes, an online genetic program is run to optimise the segmentation. Therefore, the lookup engine reconfigures itself to cover more prefixes during the time. The reconfigurability in response to the network variations, and scalability to the number of prefixes improves the life time of a router that uses this method. [ABSTRACT FROM AUTHOR]

This paper presents the use of a co-operative co-evolutionary genetic algorithm (CCGA) for solving optimal control problems in a hysteresis system. The hysteresis system is a hybrid control system which can be described by a continuous multivalued state-space representation that can switch between two possible discrete modes. The problems investigated cover the optimal control of the hysteresis system with fixed and free final state/time requirements. With the use of the Pontryagin maximum principle, the optimal control problems can be formulated as optimisation problems. In this case, the decision variables consist of the value of control signal when a switch between discrete modes occurs while the objective value is calculated from an energy cost function. The simulation results indicate that the use of the CCGA is proven to be highly efficient in terms of the minimal energy cost obtained in comparison to the results given by the searches using a standard genetic algorithm and a dynamic programming technique. This helps to confirm that the CCGA can handle complex optimal control problems by exploiting a co-evolutionary effect in an efficient manner. [ABSTRACT FROM AUTHOR]

In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios. [ABSTRACT FROM AUTHOR]

Parallel genetic algorithms techniques have been used in a variety of computer engineering and science areas. This paper presents a parallel genetic algorithm to solve the site layout problem with unequal-size and constrained facilities. The problem involves coordinating the use of limited space to accommodate temporary facilities subject to geometric constraints. The problem is characterised by affinity weights used to model transportation costs between facilities, and by geometric constraints between relative positions of facilities on site. The algorithm is parallelised based on a message passing SPMD architecture using parallel search and chromosomes migration. The algorithm is tested on a variety of layout problems to illustrate its performance. In specific, in the case of: (1) loosely versus tightly constrained layouts with equal levels of interaction between facilities, (2) loosely versus tightly packed layouts with variable levels of interactions between facilities, and (3) loosely versus tightly constrained layouts. Favorable results are reported. [ABSTRACT FROM AUTHOR]

This paper presents the use of neural networks and a genetic algorithm in time-optimal control of a closed-loop 3-dof robotic system. Extended Kohonen networks which contain an aclditional lattice of output neurons are used in conjunction with PID controllers in position control to minimize command tracking errors. The extended Kohonen networks are trained using reinforcement learning where the overall learning algorithm is derived from a self-organizing feature-mapping algorithm and a delta learning rule. The results indicate that the extended Kohonen network controller is more efficient than other techniques reported in early literature when the robot is operated under normal conditions. Subsequently, a multi-objective genetic algorithm (MOGA) is used to solve an optimization problem related to time-optimal control. This problem involves the selection of actuator torque limits and an end-effector path subject to time-optimality and tracking error constraints. Two chromosome coding schemes are explored in the investigation: Gray and integer-based coding schemes. The results suggest that the integer-based chromosome is more suitable at representing the decision variables. As a result of using both neural networks and a genetic algorithm in this application, an idea of a hybridization between a neural network and a genetic algorithm at the task level for use in a control system is also effectively demonstrated. [ABSTRACT FROM AUTHOR]

There is a widespread perception that in conflict situations, more intermediate choices between full peace and total war makes full peace less likely. This view is a motivation for opposing the proposed National Missile Defense. This perception is partly clue to research in the abstract game of Iterated Prisoner's Dilemma. This paper critically evaluates this perception. [ABSTRACT FROM AUTHOR]

Wireless sensor networking is a promising technology that can lead to automatic, intelligent, easier and more secure systems. A wireless sensor network (WSN) consists of small battery powered devices with limited energy resources. One of the major challenges in WSN lies in the energy constraint and computation resources available at the sensor nodes. One way to achieve energy efficiency would be through the use of a clustering technique. In this paper, we propose computational intelligence (CI) approaches to deal with the problem of sensor nodes clustering in a WSN with the ultimate goal to reduce energy expenditures and thus to extend the lifetime of the network. The main motivation is that CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures, and scenario changes. The main features of the proposed work span over two aspects. First, four metaheuristics have been adapted to deal with the tackled problem namely genetic algorithms, evolution strategies, particle swarm optimization and artificial bees colony. In this context, a suitable solution representation scheme has been developed and accordingly the algorithms operators, and overall dynamics have been defined. Second, the four developed algorithms are combined with an exhaustive search that is triggered whenever the number of alive sensor nodes drops below a given threshold. The performance of the proposed algorithms has been assessed and compared to the most known state of the art clustering based method namely low energy adaptive clustering hierarchy algorithm (LEACH). The obtained results show that the proposed approaches optimize in an efficient manner the lifetime of WSNs. They also show that the proposed algorithms compete and even outperform LEACH algorithm. [ABSTRACT FROM AUTHOR]

In this paper, two intelligent strategies for energy management unit for a home integrated with smart grid are proposed. The strategies are based on classical Boolean and genetic algorithm (GA). The objective is to optimize the cost saving for the end consumer. The price of energy varies by the hour depending on the load on the grid. The two strategies predict when and by how much the storage unit installed in the house should charge and release for 24 h of the day, satisfying the constraint that the load demand of the house at any particular hour should always be met. The strategies were tested by real time data collected by the Department of Energy for a typical house in the Chicago, Illinois region for the year 2013. Both the strategies achieve cost savings; however, it has been found that GA-based strategy results in higher cost saving. The impact of the capacity of the energy storage unit (ESU) on the cost saving has been analyzed for a GA strategy and cost saving obtained when the capacity of ESU is 1.5 times and 2 times the house hold load at any given hour is presented. [ABSTRACT FROM AUTHOR]

Although lots of ways can be used to estimate geo-stress state, estimation of geo-stress state without knowing geomechanical parameters such as pore pressure, tensile strength and Poisson's ratio, etc., still remains one of the most challenging tasks in geotechnical engineering. The main contribution of this paper is to present a back-propagation neural network (BPNN) with genetic algorithm (GA) optimization to predict the geo-stresses based on wellbore pressures of hydraulic fracturing tests during drilling. In the suggested hybrid model, the BPNN is used establish a mapping between the recording pressures and the geo-stress state. Also the GA is used to carry out the optimization of the weights and thresholds of BPNN model for improving accuracy of prediction. Finally, based on the record pressures in hydraulic fracturing (HF) tests, the BPNN model with genetic algorithm optimization successfully predicts the geo-stresses at the corresponding formation in the event that these parameters such as pore pressure, tensile strength and Poisson's ratio are unavailable. In the meantime, the geo-stress state has been calculated using the theoretical formula by assuming pore pressure and tensile strength of rock mass are known. Then results from theoretical equation, BPNN and BPNN with GA optimization are compared, which shows that the degree accuracy of geo-stresses predicted by using GA-BPNN model is more obviously improvement than the predicted results by the basic BPNN model. [ABSTRACT FROM AUTHOR]

Petroleum geomechanics characterization refers to the process of quantitatively assigning geomechanical parameters using all available field data. In this paper, an attempt is made to develop a computational intelligence method that integrates genetic algorithm (GA) and artificial neural network (ANN). Through this method, these geomechanical parameters such as horizontal in situ stresses, fracture aperture and joint spacing are determined based on the borehole displacements during drilling well. In the hybrid ANN-GA model, GA can automatically identify geomechanical parameters as neural inputs from monitored borehole displacements, and the ANN is trained to learn the complex relationship between geomechanical parameters and target borehole displacements. Data from numerical experiments on petroleum wells are used in verification of the proposed computational intelligence method for geomechanics characterization. The study of numerical experiment illustrates that the proposed computational intelligence method has the ability to generate reliable results. [ABSTRACT FROM AUTHOR]

Abstract-Cardiac arrhythmia is one of the major causes of human death, and most of the time it cannot be predicted well in advance at the right time. Computational intelligence algorithms can help in extracting the hidden patterns of biological datasets. This paper explores the use of advanced and intelligent computational algorithms for automated detection, classification and clustering of cardiac arrhythmia (CA). Application of Fuzzy C-Mean and Extended Fuzzy C-Mean method to the arrhythmia dataset (165 normal healthy and 138 with CA) demonstrated their good CA classification capabilities. Fuzzy C Mean algorithm was able to classify the two group of data set with an overall accuracy of 97.2% [sensitivity 96.4%, specificity 98.12% and area under the receiver operating curve (AUC-ROC = 0.963)]. The classification accuracy improved significantly when GK-based extended Fuzzy was employed, and an overall accuracy of 99.14% was achieved (sensitivity 97.11%, specificity 99.18% and AUC-ROC = 0.995). These accuracy results were respectively, 19.02%, 7%, 9.14% and 11.06% higher when compared to multi-input single layer perceptron (SLP), feed forward back propagation (FFBP), self organizing maps (SOM) and support vector machine (SVM). The performance measures of fuzzy techniques were found to be better if a Principal Component Analysis (PCA) technique was used to preprocess the arrhythmia datasets. [ABSTRACT FROM AUTHOR]

The design of intelligent sensor systems for pattern recognition applications requires sophisticated methods from conventional signal processing and computational intelligence. A significant part of the overall system architecture still has to be manually elaborated in a tedious and time consuming process by an experienced designer for each new application or modification. Clearly, an automated method for auto-configuration of sensor systems would be attractive. This paper expands our previous works on automatic design of multisensor systems applying optimized Gaussian kernel for feature computation. The purpose of optimizing feature computation in the design of sensor systems is to increase the classification accuracy and the computation efficiency as well as to reduce storage and computational requirements in embedded sensor systems. Here, we investigate the feature computation of Gaussian kernel assessed by support vector machine (SVM) using genetic algorithms (GA). The experiment results are compared with the previous work using k-nearest neighbor (k-NN) and LDA/k-NN. Our experiments are tested with gas-sensor benchmark data. From the experimental results, we verify that SVM can achieve better results than k-NN and LDA/k-NN methods. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into the detection and quantification of persons in real-world beach scenes for the automated monitoring of public recreation areas. Aside from the obvious use of video and digital imagery for surveillance applications, this research focuses on the analysis of images for the purpose of predicting trends in the intensity of public usage at beach sites in Australia. The proposed system uses image enhancement and segmentation techniques to detect objects in cluttered scenes. Following these steps, a newly proposed feature extraction technique is used to represent salient information in the extracted objects for training of a neural network. The neural classifier is used to distinguish the extracted objects between "person" and "non-person" categories to facilitate analysis of tourist activity. Encouraging results are presented for person classification on a database of real-word beach scene images. [ABSTRACT FROM AUTHOR]

In this paper we present a novel technique for determining term importance by exploiting concept-based information found in ontologies. Calculating term importance is a significant and fundamental aspect of most information retrieval approaches, and it is traditionally determined through inverse document frequency (IDF). We propose concept-based term weighting (CBW), a technique that is fundamentally different to IDF in that it calculates term importance by intuitively interpreting the conceptual information in ontologies. We show that when CBW is used in an approach for web information retrieval on benchmark data, it performs comparatively to IDF, with only a 3.5% degradation in retrieval accuracy. While this small degradation has been observed, the significance of this technique is that (1) unlike IDF, CBW is independent of document collection statistics, (2) it presents a new way of interpreting ontologies for retrieval, and (3) it introduces an additional source of term importance information that can be used for term weighting. [ABSTRACT FROM AUTHOR]

Medical image segmentation plays an instrumental role in clinical diagnosis. An ideal medical image segmentation scheme should possess some preferred properties such as minimum user interaction, fast computation, and accurate and robust segmentation results. In this paper, an automated algorithm is proposed to enable the doctors to detect the presence of abnormal tissues in brain magnetic resonance images (MRIs). The merged image of different weighted images of each slice is obtained by averaging the intensities of pixels and is enhanced based on their local information by variance mapping. The abnormal regions are segmented by using minimum error thresholding method by formulating a criterion function. The segmentation is performed on the real data of MRI images for different abnormalities and the results are compared with radiologist labeled ground truth. Quantitative analysis between ground truth and segmented abnormal region is presented in terms of Percent Match and Correspondence Ratio. A maximum average percent match of 98.56% and correspondence ratio of 0.8892 of an MRI data is obtained. [ABSTRACT FROM AUTHOR]

This paper presents the enhanced frame-based video coding scheme. The input source video to the enhanced frame-based video encoder consists of a rectangular-sized video and shapes of arbitrarily shaped objects on video frames. The rectangular frame texture is encoded by the conventional frame-based coding technique and the video object's shape is encoded using the contour-based vertex coding. It is possible to achieve several useful content-based functionalities by utilizing the shape information in the bitstream at the cost of a very small overhead to the bit-rate. [ABSTRACT FROM AUTHOR]

Data availability is not a major issue at present times in view of the widespread use of Internet; however, information and knowledge availability are the issues. Due to data overload and time-critical nature of information need, automatic summarization of documents plays a significant role in information retrieval and text data mining. This paper discusses the design of a multi-document summarizer that uses Katz's K-mixture model for term distribution. The model helps in ranking the sentences by a modified term weight assignment. Highly ranked sentences are selected for the final summary. The sentences that are repetitive in nature are eliminated, and a tiled summary is produced. Our method avoids redundancy and produces a readable (even browsable) summary, which we refer to as an event-specific tiled summary. The system has been evaluated against the frequently occurring sentences in the summaries generated by a set of human subjects. Our system outperforms other auto-summarizers at different extraction levels of summarization with respect to the ideal summary, and is close to the ideal summary at 40% extraction level. [ABSTRACT FROM AUTHOR]

This paper proposes a system applying a pyramid neural network for classifying the hepatic parenchymal diseases in ultrasonic B-scan texture. The conventional multilayer neural network emphasizing on the data carried by the last hidden layer has the drawback of not fully utilizing the information carried by the input data. A pyramid network can solve the problem successfully. To solve the common problem of neural network, which is time-consuming in computation, FDWT (Fast Discrete Wavelet Transform) is a key technique used during preprocessing to cut down the size of patterns feed to the network. The B-scan patterns are wavelet transformed, and then the compressed data is fed into a pyramid neural network to diagnose the type of cirrhotic diseases. The performance of the proposed system and that of a system based on the conventional multilayer network architecture is compared. The result shows that compared to the conventional 3-layer neural network, the performance of the proposed pyramid neural network is improved by effectively utilizing the lower layer of the neural network. [ABSTRACT FROM AUTHOR]

Constantly improving gene expression technology offer the ability to measure the expression levels of thousand of genes in parallel. Gene expression data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Key issue that needs to be addressed is the selection of small number of genes that contribute to a disease from the thousands of genes measured on microarrays that are inherently noisy. This work deals with finding a small subset of informative genes from gene expression microarray data which maximise the classification accuracy. This paper introduces a new algorithm of hybrid Genetic Algorithm and Support Vector Machine for genes selection and classification task. We show that the classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods of two widely used benchmark datasets. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biologist and computer scientist researches in order to explore the biological plausibility. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive moving sliding mode fuzzy control for robotic manipulators. The consequence parameters of the fuzzy control rules are adjusted online by applying the Lyapunov stability condition. Furthermore, parameters tuning is carried out in order to guarantee the sliding condition. Comparison between zero order and first order Sugeno model are presented. Moreover, a time-varying sliding surface is used with the aim of enhancing tracking-performance. Indeed, moving sliding surface improves the robustness during the reaching phase against uncertainties, parameter variations and extraneous disturbances. As an illustration, the trajectory control of a two degrees-of-freedom robotic manipulator is considered. [ABSTRACT FROM AUTHOR]

Presents schedule of events related to Computational Intelligence and Applications compiled as of March 2005. 13th European Symposium on Artificial Neural Networks; Eighteenth Canadian Conference on Artificial Intelligence; First International Workshop on Adversarial Information Retrieval on the Web at the 14th International World Wide Web Conference.

In this paper, a system that can automatically detect and recognise frontal faces is proposed. Three methods are used for face recognition; neural network, template matching and distance measure. One of the main problems encountered when using neural networks for face recognition is insufficient training data. This problem arises because, in most cases, only one image per subject is available. Therefore, amongst the objectives is to solve this problem by "increasing" the data available from the original image using several preprocesses, for example, image mirroring, colour and edges information, etc. Moreover, template matching is not trivial because of differences in the template shapes and sizes. In this work, template matching is aided by a genetic algorithm to automatically test several positions around the target and automatically adjust the size of the template as the matching process progresses. Distance measure method depends heavily on good facial feature extraction results. The image segmentation method applied matches such demand. The face colour information is represented using YIQ and the XYZ colour spaces. The effectiveness of the proposed method is verified by performing computer simulations. Two sets of databases were used. Database1 consists of 267 faces from the Oulu university database and database2 (for comparision purposes) consists of 250 faces from the ORL database. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of obtaining the optimal rotation to match two functions on the sphere by minimizing the squared error norm and the Kullback–Leibler information criteria. In addition, the accuracy in terms of the band-limited approximations in both cases are also discussed. Algorithms for fast and accurate rotational matching play a significant role in many fields ranging from computational biology to spacecraft attitude estimation. In electron microscopy, peaks in the so-called "rotation function" determine correlations in orientation between density maps of macromolecular structures when the correspondence between the coordinates of the structures is not known. In X-ray crystallography, the rotational matching of Patterson functions in Fourier space is an important step in the determination of protein structures. In spacecraft attitude estimation, a star tracker compares observed patterns of stars with rotated versions of a template that is stored in its memory. Many algorithms for computing and sampling the rotation function have been proposed over the years. These methods usually expand the rotation function in a bandlimited Fourier series on the rotation group. In some contexts the highest peak of this function is interpreted as the optimal rotation of one structure into the other, and in other contexts multiple peaks describe symmetries in the functions being compared. Prior works on rotational matching seek to maximize the correlation between two functions on the sphere. We also consider the use of the Kullback–Leibler information criteria. A gradient descent algorithm is proposed for obtaining the optimal rotation, and a measure is defined to compare the convergence of this procedure applied to the maximal correlation and Kullback–Leibler information criteria. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid evolutionary algorithm for constrained multiple destinations routing problem. The problem can be formulated as minimising tree cost under several constraints or QoS metrics. Computing such constrained multicast tree has been proven to be NP-complete. The proposed hybrid algorithm is based on a population based incremental learning algorithm and a constrained distance network heuristic (or CKMB) algorithm. In the proposed algorithm, CKMB is utilised as a decoding scheme. Experimental results show that, in most cases, the proposed algorithm yields better solutions than other heuristic algorithms proposed in the literature including the best known one BSMA. [ABSTRACT FROM AUTHOR]

Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques. [ABSTRACT FROM AUTHOR]

In general, in competitive learning the requirement for the initial number of prototypes is a difficult task, as we do not usually know the number of clusters in the input data a priori. The behavior and performance of the competitive algorithms are very sensitive to the initial locations and number of the prototypes. In this paper after investigating several important competitive learning paradigms, we present compensation techniques for overcoming the problems in competitive learning. Our experimental results show that competition with compensation can improve the performance of the learning algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to the concurrent solution of three High-Level Synthesis (HLS) problems that are modeled as a Constraint-Satisfaction Problem (CSP) and solved using an Enhanced Genetic Algorithm (EGA). We focus on the core problems of high-level synthesis: Scheduling, Allocation, and Binding. Scheduling consists of assigning of operations in a Data-Flow Graph (DFG) to control steps or clock cycles. Allocation selects specific numbers and types of functional units from a hardware library to perform the operations specified in the DFG. Binding assigns constituent operations of the DFG to specific unit instances. A very general version of this problem is considered where functional units may perform different operations in different numbers of control steps. The EGA is designed to solve CSPs quickly and does not require a user to specify appropriate mutation and crossover rates a priori; these are determined automatically during the course of the genetic search. The enhancements include a directed mutation operator and a new type of elitism that avoids premature convergence. The HLS problems are solved by applying two EGAs in a hierarchical manner. The first performs allocation, while the second performs scheduling and binding and serves as the fitness function for the second. When compared to other, well-known techniques, our results show a reduction in time to obtain optimal solutions for standard benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, a feedback self-organizing map (FSOM), which is an extension of the self-organizing map (SOM) by employing feedback loops, is proposed. The SOM consists of an input layer and a competitive layer, and the input vectors applied to the input layer are mapped to the competitive layer keeping their spatial features. In order to embed the temporal information to the SOM, feedback loops from the competitive layer to the input layer are employed. The winner unit in the competitive layer is not assigned by only current input vector but also past winner units, thus the temporal information can be embedded. The effectiveness and validity of the proposed FSOM are verified by applying it to a spatio-temporal pattern classification. [ABSTRACT FROM AUTHOR]

Restoration work of archaeological artifacts broken into pieces is similar to putting together a jigsaw puzzle. The purpose of this study is to construct an intelligent computer assistance system to conveniently restore archaeological discoveries from some fragments. A Real-Coded Genetic Algorithm (RCGA) was applicable for solving the positioning problem of a three-dimensional (3D) restoration. The fitness function value for RCGA was calculated from image similarity between the target and correct patterns in plane images at multiple camera angles. Image features of a 3D object were obtained by the ORB ( Oriented FAST and Rotated BRIEF), BRISK ( Binary Robust Invariant Scalable Keypoints), and Accelerated KAZE (AKAZE) techniques; they were considered as a part of the fitness function value. Simulation study revealed that the RCGA approach was capable of automatically and efficiently adjusting the positions of 3D fragments, especially in the AKAZE technique. A user interface with the functions of design drawing was also created to assist in repair work. The interactive assistance interface for 3D restoration based on RCGA and followed by the hill-climbing algorithm would be applied to practical applications for digital archives of artifacts. [ABSTRACT FROM AUTHOR]

A common step in most of water treatment plants is the chemical coagulation. The chemical coagulation is the process of destabilizing the colloidal particles suspended in raw water by the addition of coagulants. Generally, the determination of the quantity of coagulant to be added to water is made manually by jar tests. However, the manual control has slow response to changes of raw water and it requires intensive laboratory analysis. To reduce the manual effort and to improve the response to change in raw water quality, this work proposes the determination of the coagulant dosage using dynamic neural network modeling using the available sensors as input of the model. The case of study is a large scale water treatment plant in Ceará, Brazil, where the kinds of coagulants added to water are the aluminum sulphate (AS) and poly aluminum chloride (PAC). Several dynamic neural network models with different combinations of the input variables have been evaluated. The best solution found is composed by a nonlinear autoregressive with exogenous input (NARX) model having three input variables, the pH in raw and coagulated water, and the turbidity in the coagulated water, with coefficient of determination of R2 = 0.95 and R2 = 0.91 for the AS and PAC dosage prediction, respectively. [ABSTRACT FROM AUTHOR]

Patients with transhumeral amputations and C5/C6 quadriplegia may be able to use voluntary shoulder motion as command signals for powered prostheses and functional electrical stimulation, respectively. Spatiotemporal synergies exist between the shoulder and elbow joints for goal-oriented reaching movements as performed by able-bodied subjects. We are using a multi-layer perceptron neural network to discover and embody these synergies. Such a network could be used as a high-level controller that could predict the desired distal arm joint kinematics from the voluntary movements of the shoulder joint of an able-bodied subject. We evaluated this for a task that involved reaching to 16 targets in a horizontal plane. After reaching reasonable offline prediction accuracy for our neural networks, we then deployed the best network to make real-time predictions of the elbow angles and examined its performance on both inter- and intra-subject trials. Finally, we extended the model to utilize the five degrees-of-freedom at the shoulder to control the five degrees-of-freedom required for a prosthetic arm and hand to reach and grasp variously oriented objects in the extrapersonal workspace. Such a system, although very simple, was readily controllable for a reach and grasp task presented to the subject in a virtual reality environment. [ABSTRACT FROM AUTHOR]

An automated method is presented for segmentation of two-dimensional HRCT images of the lung into regions of four lung patterns: normal, emphysema, honeycombing, and ground-glass opacity (GGO). Segmentation was implemented in two stages. At the first stage, pixel-wise classification of the lung area was performed using local textural features extracted by the wavelet transform. At the second stage, classification results were refined by application of knowledge-based rules. Performance of the method was compared on two sets of HRCT images: one included HRCT images with characteristic examples of lung patterns and the other consisted of unselected HRCT images that represented a model of routine operations at a general radiology practice. On the first set of images, sensitivity of the method ranged from 0.92 to 0.99, and specificity ranged from 0.96 to 0.99. On the second set of images, sensitivity and specificity were, respectively, 0.49 and 0.95 for emphysema, 0.87 and 0.55 for normal, 0.34 and 0.99 for honeycombing, and 0.57 and 0.94 for GGO. The two-stage approach allowed for simple and effective application of high-level knowledge about appearance of lung patterns on HRCT images and did not require feature and region of interest size selection for the first stage of pixel-wise lung pattern classification. [ABSTRACT FROM AUTHOR]

In this study, we have investigated the electrophysiological differences between alcoholic and control subjects using two different approaches namely complexity and energy (power) analyses. The electroencephalogram data used in this study were recorded from 77 alcoholic and 44 control subjects while the subjects were performing delayed matching to sample object recognition task for three types of stimuli. These were a single stimulus and a second matching or nonmatching stimulus that followed the single stimulus after a delay. The experimental paradigm evokes object recognition, visual short-term memory, and decision-making abilities. The results indicated that all regions (i.e. frontal, central, temporal, parietal, and occipital) in the brain exhibit more complexity and less energy for alcoholic subjects as compared to controls. When different visual stimuli pairs were compared among alcoholic and control subjects, the results from energy analysis showed groupwise differences in occipital and parietal regions. These results provide a strong indication on the impairment in brain's electrophysiological activity for alcoholic subjects due to a history of long-term alcohol abuse. [ABSTRACT FROM AUTHOR]

Body surface potential maps were investigated to identify a set of optimal recording sites required to discriminate between several diseases. Specifically, recordings captured from subjects exhibiting myocardial infarction or left ventricular hypertrophy, as well as a control group consisting of healthy subjects, were investigated. Owing to the fact that multi-class problems are inherently difficult to solve we divided the problem into several two-class scenarios. Six data sets were generated from the available 744 records, each viewing the available data differently, to form several two-class problems. A data-driven selection algorithm was applied to each of the generated data sets to produce six classification models, each utilizing as features those recording sites offering most to the discrimination task being investigated. Subsequently, a framework was introduced to facilitate the combination of outputs from each classifier. Essentially, the framework used the outputs from half of the classification models to determine which of the remaining models would be employed to form a final decision. A benchmark, in the form of a multi-group classifier, was introduced to evaluate the perceived benefits of the proposed approach. An improvement of approximately 10% upon the benchmark was observed resulting in an overall accuracy of 79.19%. [ABSTRACT FROM AUTHOR]

The artificial mouth is a robotic device that simulates a human mouth. It consists of moveable lips and an adjustable air supply. In this study the use of a genetic algorithm to compute air pressure and lip pressure values so that the artificial mouth can correctly play five notes on a brass instrument is investigated. In order to properly play a brass instrument, a player must apply proper tension between the lips and apply proper airflow so that the lips vibrate at the proper frequency. A fast search algorithm was needed because it takes about 4 seconds to measure the frequency produced by each combination of pressure parameters. This measurement is slow because of the slow moving mechanics of the system and a delay produced while the notes are measured for pitch. The efficiency of this algorithm was compared to an exhaustive search. Experimentation was performed using various combinations of genetic parameters, including population size, crossover rate, and mutation rate. The genetic search was shown to be about 10 times faster than the exhaustive search because the genetic algorithm searches only very small portions of the search space. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new function visualization method and demonstrate that numerical integration and visualization of multi-dimensional functions are closely related. Adaptive numerical integration is utilized to reduce the number of function evaluations, and generate time series data. The integration region is partitioned into a uniform grid. A grid cell can be sampled many times, or is not sampled at all, depending on the function properties and the integration rule. Function properties are extracted during the process of function evaluation. An aging technique helps visualize functions by retaining the most recently sampled areas and making the older ones transparent. This also results in giving the non-smooth areas more attention than the smooth areas. The new function visualization method gives a view of the whole function while elaborating on important areas such as ridges and troughs, which are critical in many fields, including numerical integration. A Grid service, called Integration Service, is used to solve computationally intensive integration problems. Remote visualization based on the adaptive method helps monitor the progress of a computation, and can be utilized for computational steering. The data are filtered by the server and transferred to the client, which is responsible for visualization mapping and rendering. [ABSTRACT FROM AUTHOR]