Advances in Artificial Intelligence The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Analysis of Changes in Market Shares of Commercial Banks Operating in Turkey Using Computational Intelligence Algorithms Tue, 15 Apr 2014 13:28:01 +0000 This paper aims to model the change in market share of 30 domestic and foreign banks, which have been operating between the years 1990 and 2009 in Turkey by taking into consideration 20 financial ratios of those banks. Due to the fragile structure of the banking sector in Turkey, this study plays an important role for determining the changes in market share of banks and taking the necessary measures promptly. For this reason, computational intelligence methods have been used in the study. According to the research results, it is seen that it was not able to properly anticipate the data for the banking sector in the periods of financial crises (2000-2001 and 2008-2009). However, it is seen that, Simple Linear Regression is distinguished as a good algorithm among the computational intelligence algorithms for all periods between the years 1990 and 2009. M. Fatih Amasyali, Ayse Demırhan, and Mert Bal Copyright © 2014 M. Fatih Amasyali et al. All rights reserved. Reinforcement Learning in an Environment Synthetically Augmented with Digital Pheromones Thu, 13 Mar 2014 17:31:21 +0000 Reinforcement learning requires information about states, actions, and outcomes as the basis for learning. For many applications, it can be difficult to construct a representative model of the environment, either due to lack of required information or because of that the model's state space may become too large to allow a solution in a reasonable amount of time, using the experience of prior actions. An environment consisting solely of the occurrence or nonoccurrence of specific events attributable to a human actor may appear to lack the necessary structure for the positioning of responding agents in time and space using reinforcement learning. Digital pheromones can be used to synthetically augment such an environment with event sequence information to create a more persistent and measurable imprint on the environment that supports reinforcement learning. We implemented this method and combined it with the ability of agents to learn from actions not taken, a concept known as fictive learning. This approach was tested against the historical sequence of Somali maritime pirate attacks from 2005 to mid-2012, enabling a set of autonomous agents representing naval vessels to successfully respond to an average of 333 of the 899 pirate attacks, outperforming the historical record of 139 successes. Salvador E. Barbosa and Mikel D. Petty Copyright © 2014 Salvador E. Barbosa and Mikel D. Petty. All rights reserved. A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation Tue, 12 Nov 2013 08:29:31 +0000 This paper presents a comparative study between optimization-based and market-based approaches used for solving the Multirobot task allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used to find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches were extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained MRS applications that include extended number of tasks and robots. Finally, a comparative study is implemented between the two approaches and the results show that the optimization-based approach outperforms the market-based approach in terms of optimal allocation and computational time. Mohamed Badreldin, Ahmed Hussein, and Alaa Khamis Copyright © 2013 Mohamed Badreldin et al. All rights reserved. Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion Sun, 03 Nov 2013 14:09:40 +0000 Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its x and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data. Waleed A. Abdulhafiz and Alaa Khamis Copyright © 2013 Waleed A. Abdulhafiz and Alaa Khamis. All rights reserved. Adaptive Group Formation in Multirobot Systems Mon, 21 Oct 2013 12:12:57 +0000 Multirobot systems (MRSs) are capable of solving task complexity, increasing performance in terms of maximizing spatial/temporal/radio coverage or minimizing mission completion time. They are also more reliable than single-robot systems as robustness is increased through redundancy. Many applications such as rescue, reconnaissance, and surveillance and communication relaying require the MRS to be able to self-organize the team members in a decentralized way. Group formation is one of the benchmark problems in MRS to study self-organization in these systems. This paper presents a hybrid approach to group formation problem in multi-robot systems. This approach combines the efficiency of the cellular automata as finite state machine, the interconnectivity of the virtual grid and its bonding technique, and last but not least the decentralization of the adaptive dynamic leadership. Ahmed Wagdy and Alaa Khamis Copyright © 2013 Ahmed Wagdy and Alaa Khamis. All rights reserved. A Novel Reinforcement Learning Architecture for Continuous State and Action Spaces Thu, 18 Apr 2013 17:00:14 +0000 We introduce a reinforcement learning architecture designed for problems with an infinite number of states, where each state can be seen as a vector of real numbers and with a finite number of actions, where each action requires a vector of real numbers as parameters. The main objective of this architecture is to distribute in two actors the work required to learn the final policy. One actor decides what action must be performed; meanwhile, a second actor determines the right parameters for the selected action. We tested our architecture and one algorithm based on it solving the robot dribbling problem, a challenging robot control problem taken from the RoboCup competitions. Our experimental work with three different function approximators provides enough evidence to prove that the proposed architecture can be used to implement fast, robust, and reliable reinforcement learning algorithms. Víctor Uc-Cetina Copyright © 2013 Víctor Uc-Cetina. All rights reserved. Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features Mon, 15 Apr 2013 11:39:23 +0000 A method for solving a classification problem when there is only partial information about some features is proposed. This partial information comprises the mean values of features for every class and the bounds of the features. In order to maximally exploit the available information, a set of probability distributions is constructed such that two distributions are selected from the set which define the minimax and minimin strategies. Random values of features are generated in accordance with the selected distributions by using the Monte Carlo technique. As a result, the classification problem is reduced to the standard model which is solved by means of the support vector machine. Numerical examples illustrate the proposed method. Lev V. Utkin and Yulia A. Zhuk Copyright © 2013 Lev V. Utkin and Yulia A. Zhuk. All rights reserved. Selection for Reinforcement-Free Learning Ability as an Organizing Factor in the Evolution of Cognition Sun, 31 Mar 2013 12:13:18 +0000 This research explores the relation between environmental structure and neurocognitive structure. We hypothesize that selection pressure on abilities for efficient learning (especially in settings with limited or no reward information) translates into selection pressure on correspondence relations between neurocognitive and environmental structure, since such correspondence allows for simple changes in the environment to be handled with simple learning updates in neurocognitive structure. We present a model in which a simple form of reinforcement-free learning is evolved in neural networks using neuromodulation and analyze the effect this selection for learning ability has on the virtual species' neural organization. We find a higher degree of organization than in a control population evolved without learning ability and discuss the relation between the observed neural structure and the environmental structure. We discuss our findings in the context of the environmental complexity thesis, the Baldwin effect, and other interactions between adaptation processes. Solvi Arnold, Reiji Suzuki, and Takaya Arita Copyright © 2013 Solvi Arnold et al. All rights reserved. Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks Wed, 27 Mar 2013 14:19:02 +0000 The long-term solution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, this may lead to better treatment opportunities and hopefully better disease outcomes in adulthood. E. Chatzimichail, E. Paraskakis, and A. Rigas Copyright © 2013 E. Chatzimichail et al. All rights reserved. A Novel Method for Training an Echo State Network with Feedback-Error Learning Wed, 27 Mar 2013 10:13:31 +0000 Echo state networks are a relatively new type of recurrent neural networks that have shown great potentials for solving non-linear, temporal problems. The basic idea is to transform the low dimensional temporal input into a higher dimensional state, and then train the output connection weights to make the system output the target information. Because only the output weights are altered, training is typically quick and computationally efficient compared to training of other recurrent neural networks. This paper investigates using an echo state network to learn the inverse kinematics model of a robot simulator with feedback-error-learning. In this scheme teacher forcing is not perfect, and joint constraints on the simulator makes the feedback error inaccurate. A novel training method which is less influenced by the noise in the training data is proposed and compared to the traditional ESN training method. Rikke Amilde Løvlid Copyright © 2013 Rikke Amilde Løvlid. All rights reserved. A Hybrid Reasoning Model for “Whole and Part” Cardinal Direction Relations Thu, 28 Feb 2013 10:01:04 +0000 We have shown how the nine tiles in the projection-based model for cardinal directions can be partitioned into sets based on horizontal and vertical constraints (called Horizontal and Vertical Constraints Model) in our previous papers (Kor and Bennett, 2003 and 2010). In order to come up with an expressive hybrid model for direction relations between two-dimensional single-piece regions (without holes), we integrate the well-known RCC-8 model with the above-mentioned model. From this expressive hybrid model, we derive 8 basic binary relations and 13 feasible as well as jointly exhaustive relations for the x- and y-directions, respectively. Based on these basic binary relations, we derive two separate composition tables for both the expressive and weak direction relations. We introduce a formula that can be used for the computation of the composition of expressive and weak direction relations between “whole or part” regions. Lastly, we also show how the expressive hybrid model can be used to make several existential inferences that are not possible for existing models. Ah-Lian Kor and Brandon Bennett Copyright © 2013 Ah-Lian Kor and Brandon Bennett. All rights reserved. Artificial Intelligence Applications in Biomedicine Wed, 27 Feb 2013 09:53:56 +0000 Panayiotis Vlamos, Konstantinos Lefkimmiatis, Catalina Cocianu, Luminita State, and Zhiyuan Luo Copyright © 2013 Panayiotis Vlamos et al. All rights reserved. Efficacious End User Measures—Part 1: Relative Class Size and End User Problem Domains Tue, 26 Feb 2013 13:21:29 +0000 Biological and medical endeavors are beginning to realize the benefits of artificial intelligence and machine learning. However, classification, prediction, and diagnostic (CPD) errors can cause significant losses, even loss of life. Hence, end users are best served when they have performance information relevant to their needs, this paper’s focus. Relative class size (rCS) is commonly recognized as a confounding factor in CPD evaluation. Unfortunately, rCS-invariant measures are not easily mapped to end user conditions. We determine a cause of rCS invariance, joint probability table (JPT) normalization. JPT normalization means that more end user efficacious measures can be used without sacrificing invariance. An important revelation is that without data normalization, the Matthews correlation coefficient (MCC) and information coefficient (IC) are not relative class size invariants; this is a potential source of confusion, as we found not all reports using MCC or IC normalize their data. We derive MCC rCS-invariant expression. JPT normalization can be extended to allow JPT rCS to be set to any desired value (JPT tuning). This makes sensitivity analysis feasible, a benefit to both applied researchers and practitioners (end users). We apply our findings to two published CPD studies to illustrate how end users benefit. E. Earl Eiland and Lorie M. Liebrock Copyright © 2013 E. Earl Eiland and Lorie M. Liebrock. All rights reserved. Conservative Intensional Extension of Tarski's Semantics Tue, 26 Feb 2013 09:00:42 +0000 We considered an extension of the first-order logic (FOL) by Bealer's intensional abstraction operator. Contemporary use of the term “intension” derives from the traditional logical Frege-Russell doctrine that an idea (logic formula) has both an extension and an intension. Although there is divergence in formulation, it is accepted that the “extension” of an idea consists of the subjects to which the idea applies, and the “intension” consists of the attributes implied by the idea. From the Montague's point of view, the meaning of an idea can be considered as particular extensions in different possible worlds. In the case of standard FOL, we obtain a commutative homomorphic diagram, which is valid in each given possible world of an intensional FOL: from a free algebra of the FOL syntax, into its intensional algebra of concepts, and, successively, into an extensional relational algebra (different from Cylindric algebras). Then we show that this composition corresponds to the Tarski's interpretation of the standard extensional FOL in this possible world. Zoran Majkić Copyright © 2013 Zoran Majkić. All rights reserved. Discrete Artificial Bee Colony for Computationally Efficient Symbol Detection in Multidevice STBC MIMO Systems Sun, 24 Feb 2013 18:22:32 +0000 A Discrete Artificial Bee Colony (DABC) is presented for joint symbol detection at the receiver in a multidevice Space-Time Block Code (STBC) Mutli-Input Multi-Output (MIMO) communication system. Exhaustive search (maximum likelihood detection) for finding an optimal detection has a computational complexity that increases exponentially with the number of mobile devices, transmit antennas per mobile device, and the number of bits per symbol. ABC is a new population-based, swarm-based Evolutionary Algorithms (EA) presented for multivariable numerical functions and has shown good performance compared to other mainstream EAs for problems in continuous domain. This algorithm simulates the intelligent foraging behavior of honeybee swarms. An enhanced discrete version of the ABC algorithm is presented and applied to the joint symbol detection problem to find a nearly optimal solution in real time. The results of multiple independent simulation runs indicate the effectiveness of DABC with other well-known algorithms previously proposed for joint symbol detection such as the near-optimal sphere decoding, minimum mean square error, zero forcing, and semidefinite relaxation, along with other EAs such as genetic algorithm, estimation of distributions algorithm, and the more novel biogeography-based optimization algorithm. Saeed Ashrafinia, Muhammad Naeem, and Daniel Lee Copyright © 2013 Saeed Ashrafinia et al. All rights reserved. Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines Thu, 07 Feb 2013 14:28:41 +0000 This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits). Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions. Anamika Jain Copyright © 2013 Anamika Jain. All rights reserved. Tree Pruning for New Search Techniques in Computer Games Tue, 22 Jan 2013 11:17:57 +0000 This paper proposes a new mechanism for pruning a search game tree in computer chess. The algorithm stores and then reuses chains or sequences of moves, built up from previous searches. These move sequences have a built-in forward-pruning mechanism that can radically reduce the search space. A typical search process might retrieve a move from a Transposition Table, where the decision of what move to retrieve would be based on the position itself. This algorithm stores move sequences based on what previous sequences were better, or caused cutoffs. The sequence is then returned based on the current move only. This is therefore position independent and could also be useful in games with imperfect information or uncertainty, where the whole situation is not known at any one time. Over a small set of tests, the algorithm was shown to clearly out perform Transposition Tables, both in terms of search reduction and game-play results. Finally, a completely new search process will be suggested for computer chess or games in general. Kieran Greer Copyright © 2013 Kieran Greer. All rights reserved. Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration Wed, 02 Jan 2013 15:52:11 +0000 This paper presents an approach to the study of switching overvoltages during power equipment energization. Switching action is one of the most important issues in the power system restoration schemes. This action may lead to overvoltages which can damage some equipment and delay power system restoration. In this work, switching overvoltages caused by power equipment energization are evaluated using artificial-neural-network- (ANN-) based approach. Both multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) algorithm and radial basis function (RBF) structure have been analyzed. In the cases of transformer and shunt reactor energization, the worst case of switching angle and remanent flux has been considered to reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. Developed ANN is tested for a partial of 39-bus New England test system, and results show the effectiveness of the proposed method to evaluate switching overvoltages. Iman Sadeghkhani, Abbas Ketabi, and Rene Feuillet Copyright © 2013 Iman Sadeghkhani et al. All rights reserved. Preference Comparison of AI Power Tracing Techniques for Deregulated Power Markets Thu, 27 Dec 2012 16:40:09 +0000 This paper compares the two preference artificial intelligent (AI) techniques, namely, artificial neural network (ANN) and genetic algorithm optimized least square support vector machine (GA-LSSVM) approach, to allocate the real power output of individual generators to system loads. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the AI techniques compared to those of the MNE method. The AI methods provide the results in a faster and convenient manner with very good accuracy. Hussain Shareef, Saifunizam Abd. Khalid, Mohd Wazir Mustafa, and Azhar Khairuddin Copyright © 2012 Hussain Shareef et al. All rights reserved. RPCA: A Novel Preprocessing Method for PCA Thu, 27 Dec 2012 13:36:52 +0000 We propose a preprocessing method to improve the performance of Principal Component Analysis (PCA) for classification problems composed of two steps; in the first step, the weight of each feature is calculated by using a feature weighting method. Then the features with weights larger than a predefined threshold are selected. The selected relevant features are then subject to the second step. In the second step, variances of features are changed until the variances of the features are corresponded to their importance. By taking the advantage of step 2 to reveal the class structure, we expect that the performance of PCA increases in classification problems. Results confirm the effectiveness of our proposed methods. Samaneh Yazdani, Jamshid Shanbehzadeh, and Mohammad Taghi Manzuri Shalmani Copyright © 2012 Samaneh Yazdani et al. All rights reserved. Contribution to Semantic Analysis of Arabic Language Tue, 25 Dec 2012 09:29:56 +0000 We propose a new approach for determining the adequate sense of Arabic words. For that, we propose an algorithm based on information retrieval measures to identify the context of use that is the closest to the sentence containing the word to be disambiguated. The contexts of use represent a set of sentences that indicates a particular sense of the ambiguous word. These contexts are generated using the words that define the senses of the ambiguous words, the exact string-matching algorithm, and the corpus. We use the measures employed in the domain of information retrieval, Harman, Croft, and Okapi combined to the Lesk algorithm, to assign the correct sense of those proposed. Anis Zouaghi, Mounir Zrigui, Georges Antoniadis, and Laroussi Merhbene Copyright © 2012 Anis Zouaghi et al. All rights reserved. Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules Tue, 04 Dec 2012 13:26:21 +0000 Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states. Brian Olson, Irina Hashmi, Kevin Molloy, and Amarda Shehu Copyright © 2012 Brian Olson et al. All rights reserved. Development of Robots with Soft Sensor Flesh for Achieving Close Interaction Behavior Tue, 13 Nov 2012 15:07:09 +0000 In order to achieve robots' working around humans, safe contacts against objects, humans, and environments with broad area of their body should be allowed. Furthermore, it is desirable to actively use those contacts for achieving tasks. Considering that, many practical applications will be realized by whole-body close interaction of many contacts with others. Therefore, robots are strongly expected to achieve whole-body interaction behavior with objects around them. Recently, it becomes possible to construct whole-body tactile sensor network by the advancement of research for tactile sensing system. Using such tactile sensors, some research groups have developed robots with whole-body tactile sensing exterior. However, their basic strategy is making a distributed 1-axis tactile sensor network covered with soft thin material. Those are not sufficient for achieving close interaction and detecting complicated contact changes. Therefore, we propose “Soft Sensor Flesh.” Basic idea of “Soft Sensor Flesh” is constructing robots' exterior with soft and thick foam with many sensor elements including multiaxis tactile sensors. In this paper, a constructing method for the robot systems with such soft sensor flesh is argued. Also, we develop some prototypes of soft sensor flesh and verify the feasibility of the proposed idea by actual behavior experiments. Tomoaki Yoshikai, Marika Hayashi, Yui Ishizaka, Hiroko Fukushima, Asuka Kadowaki, Takashi Sagisaka, Kazuya Kobayashi, Iori Kumagai, and Masayuki Inaba Copyright © 2012 Tomoaki Yoshikai et al. All rights reserved. Simulation of Land-Use Development, Using a Risk-Regarding Agent-Based Model Mon, 12 Nov 2012 07:42:16 +0000 The aim of this paper is to study the spatial consequences of applying different Attitude Utility Functions (AUFs), which reflect peoples’ simplified psychological frames, to investment plans in land-use decision making. For this purpose, we considered and implemented an agent-based model with new methods for searching landscapes, for selecting parcels to develop, and for allowing competitions among agents. Besides this, GIS (Geographic Information Systems) as a versatile and powerful medium of analyzing and representing spatial data is used. Our model is implemented on an artificial landscape in which land is being developed by agents. The agents are assumed to be mobile developers that are equipped with several land-related objectives. In this paper, agents mimic various risk-bearing attitudes and sometimes compete for developing the same parcel. The results reveal that patterns of land-use development are different in the two cases of regarding and disregarding AUFs. Therefore, it is considered here that using the attitudes of people towards risk helps the model to better simulate the decision making of land-use developers. The different attitudes toward risk used in this study can be attributed to different categories of developers based on sets of characteristics such as income, age, or education. F. Hosseinali, A. A. Alesheikh, and F. Nourian Copyright © 2012 F. Hosseinali et al. All rights reserved. Radial-Basis-Function-Network-Based Prediction of Performance and Emission Characteristics in a Bio Diesel Engine Run on WCO Ester Sun, 04 Nov 2012 09:31:15 +0000 Radial basis function neural networks (RBFNNs), which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO). The RBF networks were trained using the experimental data, where in load percentage, compression ratio, blend percentage, injection timing, and injection pressure were taken as the input parameters, and brake thermal efficiency (BTE), brake specific energy consumption (BSEC), exhaust gas temperature (), and engine emissions were used as the output parameters. The number of RBF centers was selected randomly. The network was initially trained using variable width values for the RBF units using a heuristic and then was trained by using fixed width values. Studies showed that RBFNN predicted results matched well with the experimental results over a wide range of operating conditions. Prediction accuracy for all the output parameters was above 90% in case of performance parameters and above 70% in case of emission parameters. Shiva Kumar, P. Srinivasa Pai, and B. R. Shrinivasa Rao Copyright © 2012 Shiva Kumar et al. All rights reserved. A Stochastic Hyperheuristic for Unsupervised Matching of Partial Information Wed, 31 Oct 2012 08:38:26 +0000 This paper (Revised version of a white paper “Unsupervised Problem-Solving by Optimising through Comparisons,” originally published on DCS and Scribd, October 2011.) describes the implementation and functionality of a centralised problem solving system that is included as part of the distributed “licas” system. This is an open source framework for building service-based networks, similar to what you would do on a Cloud or SOA platform. While the framework can include autonomous and distributed behaviour, the problem-solving part can perform more complex centralised optimisation operations and then feed the results back into the network. The problem-solving system is based on a novel type of evaluation mechanism that prefers comparisons between solution results, over maximisation. This paper describes the advantages of that and gives some examples of where it might perform better, including possibilities related to a more cognitive system. Kieran Greer Copyright © 2012 Kieran Greer. All rights reserved. CardioSmart365: Artificial Intelligence in the Service of Cardiologic Patients Fri, 12 Oct 2012 17:52:30 +0000 Artificial intelligence has significantly contributed in the evolution of medical informatics and biomedicine, providing a variety of tools available to be exploited, from rule-based expert systems and fuzzy logic to neural networks and genetic algorithms. Moreover, familiarizing people with smartphones and the constantly growing use of medical-related mobile applications enables complete and systematic monitoring of a series of chronic diseases both by health professionals and patients. In this work, we propose an integrated system for monitoring and early notification for patients suffering from heart diseases. CardioSmart365 consists of web applications, smartphone native applications, decision support systems, and web services that allow interaction and communication among end users: cardiologists, patients, and general doctors. The key features of the proposed solution are (a) recording and management of patients' measurements of vital signs performed at home on regular basis (blood pressure, blood glucose, oxygen saturation, weight, and height), (b) management of patients' EMRs, (c) cardiologic patient modules for the most common heart diseases, (d) decision support systems based on fuzzy logic, (e) integrated message management module for optimal communication between end users and instant notifications, and (f) interconnection to Microsoft HealthVault platform. CardioSmart365 contributes to the effort for optimal patient monitoring at home and early response in cases of emergency. Efrosini Sourla, Spyros Sioutas, Vasileios Syrimpeis, Athanasios Tsakalidis, and Giannis Tzimas Copyright © 2012 Efrosini Sourla et al. All rights reserved. Soccer Ball Detection by Comparing Different Feature Extraction Methodologies Wed, 03 Oct 2012 14:15:25 +0000 This paper presents a comparison of different feature extraction methods for automatically recognizing soccer ball patterns through a probabilistic analysis. It contributes to investigate different well-known feature extraction approaches applied in a soccer environment, in order to measure robustness accuracy and detection performances. This work, evaluating different methodologies, permits to select the one which achieves best performances in terms of detection rate and CPU processing time. The effectiveness of the different methodologies is demonstrated by a huge number of experiments on real ball examples under challenging conditions. Pier Luigi Mazzeo, Marco Leo, Paolo Spagnolo, and Massimiliano Nitti Copyright © 2012 Pier Luigi Mazzeo et al. All rights reserved. Under-Actuated Robot Manipulator Positioning Control Using Artificial Neural Network Inversion Technique Wed, 03 Oct 2012 13:45:33 +0000 This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy. Ali T. Hasan Copyright © 2012 Ali T. Hasan. All rights reserved. Ant Colony Optimisation for Backward Production Scheduling Wed, 19 Sep 2012 08:37:14 +0000 The main objective of a production scheduling system is to assign tasks (orders or jobs) to resources and sequence them as efficiently and economically (optimised) as possible. Achieving this goal is a difficult task in complex environment where capacity is usually limited. In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time. For this reason, in many cases, a good solution that is quickly found is preferred. In such situations, the use of metaheuristics is an appropriate strategy. In these last two decades, some out-of-the-shelf systems have been developed using such techniques. This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO) in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings. This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year. This work demonstrates the applicability of this artificial intelligence technique. In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster. Leandro Pereira dos Santos, Guilherme Ernani Vieira, Higor Vinicius dos R. Leite, and Maria Teresinha Arns Steiner Copyright © 2012 Leandro Pereira dos Santos et al. All rights reserved.