Search

Multi-class sentiment classification has extensive application backgrounds, whereas studies on this issue are still relatively scarce. In this paper, a framework for multi-class sentiment classification is proposed, which includes two parts: 1) selecting important features of texts using the feature selection algorithm, and 2) training multi-class sentiment classifier using the machine learning algorithm. Then, experiments are conducted for comparing the performances of four popular feature selection algorithms (document frequency, CHI statistics, information gain and gain ratio) and five popular machine learning algorithms (decision tree, naïve Bayes, support vector machine, radial basis function neural network and K-nearest neighbor) in multi-class sentiment classification. The experiments are conducted on three public datasets which include twelve data subsets, and 10-fold cross validation is used to obtain the classification accuracy concerning each combination of feature selection algorithm, machine learning algorithm, feature set size and data subset. Based on the obtained 3600 classification accuracies (4 feature selection algorithms × 5 machine learning algorithms × 15 feature set sizes × 12 data subsets), the average classification accuracy of each algorithm is calculated, and the Wilcoxon test is used to verify the existence of significant difference between different algorithms in multi-class sentiment classification. The results show that, in terms of classification accuracy, gain ratio performs best among the four feature selection algorithms and support vector machine performs best among the five machine learning algorithms. In terms of execution time, the similar comparisons are also conducted. The obtained results would be valuable for further improving the existing multi-class sentiment classifiers and developing new multi-class sentiment classifiers. [ABSTRACT FROM AUTHOR]

Abstract: The utilization of motor-in-wheel in the electric vehicle (EV) is a very important research topic. In order to simplify the control of EV, a new system is proposed by Guilin Tao, which changes the traditional configuration and control strategy of direct-wheel-driven EV. In this system two permanent-magnet-brushless dc motors (PMBDCMs) are connected directly in serial, in order to cut down the differential algorithm thoroughly in velocity control strategy. This structure can realize the differential automatically when the EV runs on the smooth road at low speed. But this structure considers the characters of the motor only, which is an ideal system. EV is a complex electromechanical system, and its work condition is also complex. The variation of the external condition and the work condition of operation can affect the operation of motor, so the state of the EV will change. In this paper, according to the change of the work condition and road condition, combined with the dynamics of EV, this problem was well studied. The neural network PID control mode was used in this paper, and by means of the Matlab/Simulink and the experimental verification, it reveals that this method can enhance the safety and stability of this system. [Copyright &y& Elsevier]

In this paper, a novel iris feature extraction technique with intelligent classifier is proposed for high performance iris recognition. We use one dimensional circular profile to represent iris features. The reduced and significant features afterward are extracted by Sobel operator and 1-D wavelet transform. So as to improve the accuracy, this paper combines probabilistic neural network (PNN) and particle swarm optimization (PSO) for an optimized PNN classifier model. A comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The experimental results reveal the proposed algorithm provides superior performance in iris recognition. [Copyright &y& Elsevier]

Abstract: The paper deals with the problem of predicting the time to default in credit behavioural scoring. This area opens a possibility of including a dynamic component in behavioural scoring modelling which enables making decisions related to limit, collection and recovery strategies, retention and attrition, as well as providing an insight into the profitability, pricing or term structure of the loan. In this paper, we compare survival analysis and neural networks in terms of modelling and results. The neural network architecture is designed such that its output is comparable to the survival analysis output. Six neural network models were created, one for each period of default. A radial basis neural network algorithm was used to test all six models. The survival model used a Cox modelling procedure. Further, different performance measures of all models were discussed since even in highly accurate scoring models, misclassification patterns appear. A systematic comparison ‘3+2+2’ procedure is suggested to find the most effective model for a bank. Additionally, the survival analysis model is compared to neural network models according to the relative importance of different variables in predicting the time to default. Although different models can have very similar performance measures they may consist of different variables. The dataset used for the research was collected from a Croatian bank and credit customers were observed during a 12-month period. The paper emphasizes the importance of conducting a detailed comparison procedure while selecting the best model that satisfies the users’ interest. [Copyright &y& Elsevier]

• Dynamic flexible job shop scheduling problem with machine breakdown is studied. • A two-stage algorithm based on convolutional neural network is proposed. • The improved imperialist competition algorithm is proposed to generate schedules. • A predictive model is proposed to predict the robustness of scheduling. In the actual manufacturing process, the environment of the job shop is complex. There will be many kinds of uncertainties such as random job arrivals, machine breakdowns, order cancellations and other dynamic events. In this paper, an effective two-stage algorithm based on convolutional neural network is proposed to solve the flexible job shop scheduling problem (FJSP) with machine breakdown. A bi-objective dynamic flexible job shop scheduling problem (DFJSP) model with the objective of maximum completion time and robustness is established. In the two-stage algorithm, the first stage is to train the prediction model by convolutional neural network (CNN). The second stage is to predict the robustness of scheduling through the model trained in the first stage. First, an improved imperialist competition algorithm (ICA) is proposed to generate training data. Then, a predictive model constructed by CNN was proposed, and an alternative metric called RMn was developed to evaluate robustness. RMn evaluates that the float time has an effect on the robustness through the information of machine breakdown, workload and float time of the operation. The experimental results show that the proposed two-stage algorithm is effective for solving DFJSP, and RMn can evaluate the robustness of scheduling more quickly, efficiently and accurately. [ABSTRACT FROM AUTHOR]

This paper presents a new clustering algorithm that detects clusters by learning data distribution of each cluster. Different from most existing clustering techniques, the proposed method is able to generate a dynamic two-dimensional topological graph which is used to explore both partitional information and detailed data relationship in each cluster. In addition, the proposed method is also able to work incrementally and detect arbitrary-shaped clusters without requiring the number of clusters as a prerequisite. The experimental data sets including five artificial data sets with various data distributions and an original hand-gesture data set are used to evaluate the proposed method. The comparable experimental results demonstrate the superior performance of the proposed algorithm in learning robustness, efficiency, working with outliers, and visualizing data relationships. [ABSTRACT FROM AUTHOR]

One in every eight women is susceptible to breast cancer, at some point of time in her life. Early detection and effective treatment is the only rescue to reduce breast cancer mortality. Accurate classification of a breast cancer tumor is an important task in medical diagnosis. Machine learning techniques are gaining importance in medical diagnosis because of their classification capability. In this paper, we propose a new, Genetically Optimized Neural Network (GONN) algorithm, for solving classification problems. We evolve a neural network genetically to optimize its architecture (structure and weight) for classification. We introduce new crossover and mutation operators which differ from standard crossover and mutation operators to reduce the destructive nature of these operators. We use the GONN algorithm to classify breast cancer tumors as benign or malignant. To demonstrate our results, we had taken the WBCD database from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, ROC curves and AUC under ROC curves of GONN with classical model and classical back propagation model. Our algorithm gives classification accuracy of 98.24%, 99.63% and 100% for 50–50, 60–40, 70–30 training–testing partition respectively and 100% for 10 fold cross validation. The results show that our approach works well with the breast cancer database and can be a good alternative to the well-known machine learning methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-layer feedforward artificial neural network (ANN) based logistic growth curve model (LGCM) for software reliability estimation and prediction. We develop the ANN by designing different activation functions for the hidden layer neurons of the network. We explain the ANN from the mathematical viewpoint of logistic growth curve modeling for software reliability. We also propose a neuro-genetic approach for the ANN based LGCM by optimizing the weights of the network using proposed genetic algorithm (GA). We first train the ANN using back-propagation algorithm (BPA) to predict software reliability. After that, we use the proposed GA to train the ANN by globally optimizing the weights of the network. The proposed ANN based LGCM is compared with the traditional Non-homogeneous Poisson process (NHPP) based software reliability growth models (SRGMs) and ANN based software reliability models. We present the comparison between the two training algorithms when they are applied to train the proposed ANN to predict software reliability. The applicability of the different approaches is explained through three real software failure data sets. Experimental results demonstrate that the proposed ANN based LGCM has better fitting and predictive capability than the other NHPP and ANN based software reliability models. It is also noted that when the proposed GA is employed as the learning algorithm to the ANN, the proposed ANN based LGCM gives more fitting and prediction accuracy i.e. the proposed neuro-genetic approach to the LGCM provides utmost predictive validity. Proposed model can be applied during software testing time to get better software reliability estimation and prediction than the other traditional NHPP and ANN based software reliability models. [ABSTRACT FROM AUTHOR]

Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. However, manual SMA is an inexact, subjective, non-reproducible, and hard to teach process. Therefore, in this paper, we introduce a novel automatic SMA technique that is based on the neural architecture search algorithm, named Genetic Neural Architecture Search (GeNAS). For this purpose, we used a collection of images termed MHSMA dataset, which contains 1540 sperm images that have been collected from 235 patients with infertility problems. In detail, GeNAS consists of a special genetic algorithm that acts as a meta-controller which explores the constrained search space of plain convolutional neural network architectures. Every individual of this genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome). The fitness of each individual is calculated by a novel proposed method, named GeNAS Weighting Factor (GeNAS-WF). This technique is specially designed to evaluate the fitness of neural networks which, during their learning process, validation accuracy highly fluctuates. To speed up the algorithm, a hashing method is practiced to save each trained neural architecture fitness, so we could reuse them during fitness evaluation. In terms of running time and computational power, our proposed architecture search method is far more efficient than most of the other existing neural architecture search algorithms. Moreover, whereas most of the existing neural architecture search algorithms are designed to work well with well-prepared benchmark datasets, the overall paradigm of GeNAS is specially designed to address the challenges of real-world datasets, particularly shortage of data and class imbalance. In our experiments, the best neural architecture found by GeNAS has reached an accuracy of 91.66%, 77.33%, and 77.66% in the vacuole, head, and acrosome abnormality detection, respectively. In comparison to other proposed algorithms for MHSMA dataset, GeNAS achieved state-of-the-art results. • A novel neural architecture search based on Genetic Algorithm. • A novel neural architecture encoding for exploration of search space. • A specific Crossover and Mutation operations for neural architecture search. • Work on imbalanced datasets, low resolution images, and non-stained sperms. • State-of-the-art accuracy & Real-time sperm morphological analysis. [ABSTRACT FROM AUTHOR]

• Deep learning algorithm for identification of type and flight mode of detected drones. • Novel deep learning algorithm for detection of multiple drones in radio frequency domain. • Results of three indoor experiments concerning collecting and analyzing radio signals from drones. • Public available database with radio signals of drones from two different radio frequency bands. Unmanned aerial systems, especially drones have gone through remarkable improvement and expansion in recent years. Drones have been widely utilized in many applications and scenarios, due to their low price and ease of use. However, in some applications drones can pose a malicious threat. To diminish risks to public security and personal privacy, it is necessary to deploy an effective and affordable anti-drone system in sensitive areas to detect, localize, identify, and defend against intruding malicious drones. This research article presents a new publicly available radio frequency drone dataset and investigates detection and identification methodologies to detect single or multiple drones and identify a single detected drone's type. Moreover, special attention in this paper has been underlined to examine the possibility of using deep learning algorithms, particularly fully connected deep neural networks as an anti-drone solution within two different radio frequency bands. We proposed a supervised deep learning algorithm with fully-connected deep neural network models that use raw drone signals rather than features. Regarding the research results, the proposed algorithm shows a lot of potentials. The probability of detecting a single drone is 99.8%, and the probability of type identification is 96.1%. Moreover, the results of multiple drones detection demonstrate an average accuracy of 97.3%. There have not been such comprehensive publications, to this time, in the open literature that have presented and enlightened the problem of multiple drones detection in the radio frequency domain. [ABSTRACT FROM AUTHOR]

Nowadays, traffic situation prediction is a major concern. Travel time index prediction is one of the representative tasks in traffic situation prediction. Most research of the current prediction focuses on road-level speed, but the travel time index prediction of complex road network at the regional level has not been well solved. In this paper, we proposed a novel model with convolutional neural network called Seq2Img-DRNET. The sequence data with 10 min interval are converted into image data as the input of our model. The model is constructed by fusing dense connection network and residual network to solve the travel time index prediction of complex road network at the regional level. Taking two actual traffic networks of commercial district and scenic district in Chengdu as the example, we compared our model with common sequential model and convolution neural network model, and the results show that our model can effectively capture the spatial–temporal relationship characteristics of complex road network at the regional level and make accurate prediction. • Our method transforms time series data into image data for spatiotemporal information fusion. • A convolutional neural network model is constructed to accurately predict the travel time index. • Aiming at urban business districts and scenic spots prone to traffic congestion. • The prediction accuracy is significantly higher than the existing baseline model. [ABSTRACT FROM AUTHOR]

Abstract: It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: In this study, polynomial-based radial basis function neural networks are proposed as one of the functional components of the overall face recognition system. The system consists of the preprocessing and recognition module. The design methodology and resulting procedure of the proposed P-RBF NNs are presented. The structure helps construct a solution to high-dimensional pattern recognition problems. In data preprocessing part, principal component analysis (PCA) is generally used in face recognition. It is useful in reducing the dimensionality of the feature space. However, because it is concerned with the overall face image, it cannot guarantee the same classification rate when changing viewpoints. To compensate for these limitations, linear discriminant analysis (LDA) is used to enhance the separation between different classes. In this paper, we elaborate on the PCA-LDA algorithm and design an optimal P-RBF NNs for the recognition module. The proposed P-RBF NNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part realized in terms of fuzzy “if–then” rules. In the condition part of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. In the conclusion part of rules, the connection weight is realized through three types of polynomials such as constant, linear, and quadratic. The coefficients of the P-RBF NNs model are obtained by fuzzy inference method forming the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum, fuzzification coefficient, and the feature selection mechanism) of the networks are optimized by means of differential evolution (DE). The experimental results completed on benchmark face datasets – the AT&T, and Yale datasets demonstrate the effectiveness and efficiency of PCA-LDA combined algorithm compared with other algorithms such as PCA, LPP, 2D-PCA and 2D-LPP. A real time face recognition system realized in this way is also presented. [Copyright &y& Elsevier]

Abstract: In this paper we give evidence that it is possible to characterize and detect those potential users of false invoices in a given year, depending on the information in their tax payment, their historical performance and characteristics, using different types of data mining techniques. First, clustering algorithms like SOM and neural gas are used to identify groups of similar behaviour in the universe of taxpayers. Then decision trees, neural networks and Bayesian networks are used to identify those variables that are related to conduct of fraud and/or no fraud, detect patterns of associated behaviour and establishing to what extent cases of fraud and/or no fraud can be detected with the available information. This will help identify patterns of fraud and generate knowledge that can be used in the audit work performed by the Tax Administration of Chile (in Spanish Servicio de Impuestos Internos (SII)) to detect this type of tax crime. [Copyright &y& Elsevier]

Abstract: Feature selection and classification techniques have been studied independently without considering the interaction between both procedures, which leads to a degraded performance. In this paper, we present a new neural network approach, which is called an algorithm learning based neural network (ALBNN), to improve classification accuracy by integrating feature selection and classification procedures. In general, a knowledge-based artificial neural network operates on prior knowledge from domain experience, which provides it with better starting points for the target function and leads to better classification accuracy. However, prior knowledge is usually difficult to identify. Instead of using unknown background resources, the proposed method utilizes prior knowledge that is mathematically calculated from the properties of other learning algorithms such as PCA, LARS, C4.5, and SVM. We employ the extreme learning machine in this study to help obtain better initial points faster and avoid irrelevant time-consuming work, such as determining architecture and manual tuning. ALBNN correctly approximates a target hypothesis by both considering the interaction between two procedures and minimizing individual procedure errors. The approach produces new relevant features and improves the classification accuracy. Experimental results exhibit improved performance in various classification problems. ALBNN can be applied to various fields requiring high classification accuracy. [Copyright &y& Elsevier]

Abstract: This paper introduces two robust forecasting models for efficient prediction of different exchange rates for future months ahead. These models employ Wilcoxon artificial neural network (WANN) and Wilcoxon functional link artificial neural network (WFLANN). The learning algorithms required to train the weights of these models are derived by minimizing a robust norm called Wilcoxon norm. These models offer robust exchange rate predictions in the sense that the training of weight parameters of these models are not influenced by outliers present in the training samples. The Wilcoxon norm considers the rank or position of an error value rather than its amplitude. Simulation based experiments have been conducted using real life data and the results indicate that both models, unlike conventional models, demonstrate consistently superior prediction performance under different densities of outliers present in the training samples. Further, comparison of performance between the two proposed models reveals that both provide almost identical performance but the later involved low computational complexity and hence is preferable over the WANN model. [Copyright &y& Elsevier]

Abstract: The motivation for this paper is to investigate the use of two promising classes of artificial intelligence models, the Psi Sigma Neural Network (PSI) and the Gene Expression algorithm (GEP), when applied to the task of forecasting and trading the EUR/USD exchange rate. This is done by benchmarking their results with a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN), a genetic programming algorithm (GP), an autoregressive moving average model (ARMA) plus a naïve strategy. We also examine if the introduction of a time-varying leverage strategy can improve the trading performance of our models. [Copyright &y& Elsevier]

Abstract: This paper proposes delay-dependent stability conditions of fuzzy Markovian jumping Hopfield neural networks of neutral type with interval time-varying delays. By constructing a suitable Lyapunov–Krasovskii’s functional and utilizing Finsler’s lemma with no free weighting matrices, new delay-dependent stability criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Numerical examples is given to illustrate the effectiveness of the proposed methods. [Copyright &y& Elsevier]

Abstract: This paper proposes an adaptive network fuzzy inference system (ANFIS) for the prediction of entrance length in pipe for low Reynolds number flow. After using the computational fluid dynamics (CFD) technique to establish the basic database under various working conditions, an efficient rule database and optimal distribution of membership function is constructed from the hybrid-learning algorithm of ANFIS. An experimental data set is obtained with Reynolds number, diameter of the pipe, and inlet velocity as input parameters and entrance length as output parameter. The input–output data set is used for training and validation of the proposed techniques. After validation, they are forwarded for the prediction of entrance length. The entrance length estimation results obtained by the model are compared with existing predictive models and are presented. The model performed quite satisfactory results with the actual and predicted entrance length values. The model can also be used for estimating entrance length on-line but the accuracy of the model depends upon the proper training and selection of data points. [Copyright &y& Elsevier]

Abstract: Gradient Boosting (GB) is an iterative algorithm that combines simple parameterized functions with “poor” performance (high prediction error) to produce a highly accurate prediction rule. In contrast to other statistical learning methods usually providing comparable accuracy (e.g., neural networks and support vector machines), GB gives interpretable results, while requiring little data preprocessing and tuning of the parameters. The method is highly robust to less than clean data and can be applied to classification or regression problems from a variety of response distributions (Gaussian, Bernoulli, Poisson, and Laplace). Complex interactions are modeled simply, missing values in the predictors are managed almost without loss of information, and feature selection is performed as an integral part of the procedure. These properties make GB a good candidate for insurance loss cost modeling. However, to the best of our knowledge, the application of this method to insurance pricing has not been fully documented to date. This paper presents the theory of GB and its application to the problem of predicting auto “at-fault” accident loss cost using data from a major Canadian insurer. The predictive accuracy of the model is compared against the conventional Generalized Linear Model (GLM) approach. [Copyright &y& Elsevier]

Abstract: Monitoring and control of multiple process quality characteristics (responses) in grinding plays a critical role in precision parts manufacturing industries. Precise and accurate mathematical modelling of multiple response process behaviour holds the key for a better quality product with minimum variability in the process. Artificial neural network (ANN)-based nonlinear grinding process model using backpropagation weight adjustment algorithm (BPNN) is used extensively by researchers and practitioners. However, suitability and systematic approach to implement Levenberg–Marquardt (L–M) and Boyden, Fletcher, Goldfarb and Shanno (BFGS) update Quasi-Newton (Q-N) algorithm for modelling and control of grinding process is seldom explored. This paper provides L–M and BFGS algorithm-based BPNN models for grinding process, and verified their effectiveness by using a real life industrial situation. Based on the real life data, the performance of L–M and BFGS update Q-N are compared with an adaptive learning (A-L) and gradient descent algorithm-based BPNN model. The results clearly indicate that L–M and BFGS-based networks converge faster and can predict the nonlinear behaviour of multiple response grinding process with same level of accuracy as A-L based network. [Copyright &y& Elsevier]

Abstract: This paper presents the design of adaptive load-shedding strategy by executing the artificial neural network (ANN) and transient stability analysis for an Industrial cogeneration facility. ► To prepare the training data set for ANN, the transient stability analysis has been performed to solve the minimum load shedding for various operation scenarios without causing tripping problem of cogeneration units. Various training algorithms have been adopted and incorporated into the back-propagation learning algorithm for the feed-forward neural networks. ► By selecting the total power generation, total load demand and frequency decay rate as the input neurons of the ANN, the minimum amount of load shedding is determined to maintain the stability of power system. ► To demonstrate the effectiveness of the ANN minimum load-shedding scheme, the traditional method and the present load shedding schemes of the selected cogeneration system are also applied for comparison and verification of the proposed methodology. [Copyright &y& Elsevier]

Abstract: Stock prices as time series are non-stationary and highly-noisy due to the fact that stock markets are affected by a variety of factors. Predicting stock price or index with the noisy data directly is usually subject to large errors. In this paper, we propose a new approach to forecasting the stock prices via the Wavelet De-noising-based Back Propagation (WDBP) neural network. An effective algorithm for predicting the stock prices is developed. The monthly closing price data with the Shanghai Composite Index from January 1993 to December 2009 are used to illustrate the application of the WDBP neural network based algorithm in predicting the stock index. To show the advantage of this new approach for stock index forecast, the WDBP neural network is compared with the single Back Propagation (BP) neural network using the real data set. [Copyright &y& Elsevier]

Abstract: In this paper, an intelligent transportation control system (ITCS) using wavelet neural network (WNN) and proportional-integral-derivative-type (PID-type) learning algorithms is developed to increase the safety and efficiency in transportation process. The proposed control system is composed of a neural controller and an auxiliary compensation controller. The neural controller acts as the main tracking controller, which is designed via a WNN to mimic the merits of an ideal total sliding-mode control (TSMC) law. The PID-type learning algorithms are derived from the Lyapunov stability theorem, which are utilized to adjust the parameters of WNN on-line for further assuring system stability and obtaining a fast convergence. Moreover, based on H ∞ control technique, the auxiliary compensation controller is developed to attenuate the effect of the approximation error between WNN and ideal TSMC law, so that the desired attenuation level can be achieved. Finally, to investigate the effectiveness of the proposed control strategy, it is applied to control a marine transportation system and a land transportation system. The simulation results demonstrate that the proposed WNN-based ITCS with PID-type learning algorithms can achieve favorable control performance than other control methods. [Copyright &y& Elsevier]

Abstract: Nowadays, pavement distresses classification becomes more important, as the computational power increases. Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification. In this paper an expert system is proposed for pavement distress classification. A radon neural network, based on wavelet transform expert system is used for increasing the effectiveness of the scale invariant feature extraction algorithm. Wavelet modulus is calculated and Radon transform is then applied to the wavelet modulus. The features and parameters of the peaks are finally used for training and testing the neural network. Experimental results demonstrate that the proposed expert system is an effective method for pavement distress classification. The test performances of this study show the advantages of proposed expert system: it is rapid, easy to operate, and have simple structure. [Copyright &y& Elsevier]

Abstract: Understanding how the various factors in a supply chain contribute to the overall performance of its operation has become an important topic in management science research nowadays. In this paper, we propose and apply a two-stage methodology to an industrial survey data set to investigate relations among the key factors in a supply chain model. Precisely, we use the PC-algorithm to discover the connectivity relation among the factors of interest in the supply chain model. Critical factors in the model are then identified, and we then utilize the neural network to quantify the relative importance of some of the factors in predicting the critical factors. An advantage of our proposed method is that it frees up the researcher from making subjective decisions in his or her analysis, for example, from the needs of specifying plausible initial path models required in a structural equation modeling analysis (which is usually used in business and management research) and of selecting factors for the subsequent predictive modeling. We envision that the analysis results can aid a decision maker in optimizing the system performance by suggesting to the decision maker which ones of the factors are the important ones that he or she should devote more resources and efforts on. [Copyright &y& Elsevier]

Abstract: In this paper, an artificial neural network (ANN) model with the genetic algorithm (GA) is used to predict the platelet transfusion requirements for the acute myeloblastic leukemia (AML) patients. The hybrid Taguchi-genetic algorithm (HTGA) is applied in this ANN to find the optimal parameters (i.e., weights of links and biases govern the input–output relationship of an ANN) by directly maximizing the training accuracy performance criterion. Experimental results show that the HTGA-based ANN model outperforms the ANN model with backpropagation algorithm given in the Matlab toolbox in terms of prediction accuracy. Therefore, this study demonstrated the feasibility of applying the HTGA-based ANN as the mechanism of the decision support systems for the platelet transfusion requirements of the AML patients based on clinical databases. [ABSTRACT FROM AUTHOR]

Abstract: Automatic communication signal recognition plays an important role for many novel computer and communication technologies. Most of the proposed techniques can only identify a few kinds of digital signal and/or low order of them. They usually require high levels of signal to noise ratio (SNR). In this paper, we investigate twofold. First, we propose an efficient system that uses a combination set of spectral characteristics and higher order moments up to eighth and higher order cumulants up to eighth as the effective features. As the classifier we used a multi-layer perceptron (MLP) neural network. In this stage we investigate different learning algorithms of MLP neural networks that some of them, such as quick prop (QP) learning algorithm, extended delta-bar-delta (EDBD), super self adaptive back propagation (SuperSAB) and conjugate gradient (CG) are proposed for the first time in the area of communication signals recognition. Experimental results show that proposed system discriminates a lot of digital communication signals with high accuracy even at very low SNRs. But a lot of features are used for this recognition. Then at the second fold, in order to reduce the complexity of the recognizer, we have proposed a novel hybrid intelligent technique. In this technique we have optimized the classifier design by Bees Algorithm (BA) for selection of the best features that are fed to the classifier. Simulation results show that the proposed technique has very high recognition accuracy with seven features selected by BA. [ABSTRACT FROM AUTHOR]

Abstract: Biometrics recognition is one of the leading identity recognition means in the world today. Iris recognition is very effective for person identification due to the iris’ unique features and the protection of the iris from the environment and aging. This paper presents a simple methodology for pre-processing iris images and the design and training of a feedforward artificial neural network for iris recognition. Three different iris image data partitioning techniques and two data codings are proposed and explored. BrainMaker simulations reveal that recognition accuracies as high as 93.33% can be reached despite our testing of similar irises of the same color. We also experiment with various number of hidden layers, number of neurons in each hidden layer, input format (binary vs. analog), percent of data used for training vs testing, and with the addition of noise. Our recognition system achieves high accuracy despite using simple data pre-processing and a simple neural network. [ABSTRACT FROM AUTHOR]

Abstract: This paper discusses demand and supply chain management and examines how artificial intelligence techniques and RFID technology can enhance the responsiveness of the logistics workflow. This proposed system is expected to have a significant impact on the performance of logistics networks by virtue of its capabilities to adapt unexpected supply and demand changes in the volatile marketplace with the unique feature of responsiveness with the advanced technology, Radio Frequency Identification (RFID). Recent studies have found that RFID and artificial intelligence techniques drive the development of total solution in logistics industry. Apart from tracking the movement of the goods, RFID is able to play an important role to reflect the inventory level of various distribution areas. In today’s globalized industrial environment, the physical logistics operations and the associated flow of information are the essential elements for companies to realize an efficient logistics workflow scenario. Basically, a flexible logistics workflow, which is characterized by its fast responsiveness in dealing with customer requirements through the integration of various value chain activities, is fundamental to leverage business performance of enterprises. The significance of this research is the demonstration of the synergy of using a combination of advanced technologies to form an integrated system that helps achieve lean and agile logistics workflow. [ABSTRACT FROM AUTHOR]

Abstract: Kernel-based nonlinear characteristic extraction and classification algorithms are popular new research directions in machine learning. In this paper, we propose an improved photometric stereo scheme based on improved kernel-independent component analysis method to reconstruct 3D human faces. Next, we fetch the information of 3D faces for facial face recognition. For reconstruction, we obtain the correct normal vector’s sequence to form the surface, and use a method for enforcing integrability to reconstruct 3D objects. We test our algorithm on a number of real images captured from the Yale Face Database B, and use three kinds of methods to fetch characteristic values. Those methods are called contour-based, circle-based, and feature-based methods. Then, a three-layer, feed-forward neural network trained by a back-propagation algorithm is used to realize a classifier. All the experimental results were compared to those of the existing human face reconstruction and recognition approaches tested on the same images. The experimental results demonstrate that the proposed improved kernel independent component analysis (IKICA) method is efficient in reconstruction and face recognition applications. [ABSTRACT FROM AUTHOR]

Abstract: Electrical evoked potentials (EEPs) time series prediction is a novel topic concentrating on reducing the cost of the visual prostheses research. Support vector machine (SVM), a superior neural network algorithm, is a powerful tool for time series forecasting but is insensitive to multivariate analysis. Meanwhile, similarity measurement (SM), a key technology in case-based reasoning, has been applied in a wide variety of fields but is only limited to the point-to-point computation. This paper firstly attempts to take the advantages of SM and SVM to generate a high performance EEPs predictor. Four independent SM metrics, i.e. fuzzy SM, numeric SM, textual SM and interval SM are employed to calculate the similarities between input variables (including electrical stimulation parameter and spatial parameter) and corresponding experimental values. Then SVM is utilized to predict EEPs behavior in terms of the temporal input. Furthermore, we add the similarities and temporal weights into SVM to indicate that recent data from similar experimental cases could provide more information than distant data from dissimilar ones. Due to the dynamic property, the new SVM is called dynamic SVM, i.e. DSVM and the predictor is named SM-DSVM. How to implement the hybrid predictor with grid-search for parameter optimization is illustrated in detail. In the empirical comparison, the predictive performances on 30 hold-out data are used to make comparisons between SM-DSVM and other comparative predictors. Empirical results show that SM-DSVM is feasible and validated for EEPs prediction in visual prostheses research. [ABSTRACT FROM AUTHOR]

Abstract: A novel hybrid optimization algorithm combining computational intelligence techniques is presented to solve the multifactor highway passenger volume prediction problem. In this paper, we can get and discretize a reduced decision table, which implies that the number of evaluation criteria such as travel quantity, fixed-asset investment, railway mileage, and waterway passenger volume are reduced with no information loss through rough set theory (RST) method. Particle swarm optimization (PSO) algorithm based on the random global optimization is inducted into the network training. The PSO algorithm is used for glancing study in order to confirm the initial values, and then the back propagation neural network (BPNN) is used for given accuracy to found the PSO-BPNN model. And this reduced information is used to form a classification rule set, which is regarded as an appropriate input parameter to training PSO-BPNN model. The RST-PSO-BPNN model is obtained to forecast highway passenger volume. The rules developed by RST analysis show the best prediction accuracy if a case matches any one of the rules. The keystone of this hybrid optimization algorithm is using rules developed by RST for an object that matches any one of the rules and the PSO-BPNN model for one that does not match any of them. The effectiveness of our optimization algorithm was verified by experiments comparing the traditional gray model method. For the experiment, highway passenger volumes of China during the period 1995–2009 were selected, and for the validation, the novel hybrid optimization algorithm is reliable. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a comparative study of implementation of feature extraction and classification algorithms based on wavelet neural networks (WNN) for chaos-based digital modulation (CBDM) classification. Thirteen different feature extraction methods are generated by separately using Daubechies, Biorthogonal, Coiflets, and Symlets wavelet filters. WNN model is used, which consists of two layers: wavelet entropy and multi-layer perceptron (MLP) neural networks for expert CBDM classification. The chaos-based digital modulated signals used in this experimental study are Chaos Shift Keying (CSK), Chaotic On–Off Keying (COOK), Differential Chaos Shift Keying (DCSK), Correlation Delay Shift Keying (CDSK), Symmetric Chaos Shift Keying (SCSK) and Frequency-Modulated Differential Chaos Shift Keying (FM-DCSK). The performance of this comparison system is evaluated by using total 1806 CBDM signals for each of these feature extraction methods. Mean correct classification rate is about 98.76% for the sample CBDM signals. [ABSTRACT FROM AUTHOR]

Abstract: Several algorithms exist for testing linear separability. The choice of a particular testing algorithm has effects on the performance of constructive neural network algorithms that are based on the transformation of a nonlinear separability classification problem into a linearly separable one. This paper presents an empirical study of these effects in terms of the topology size, the convergence time, and generalisation level of the neural networks. Six different methods for testing linear separability were used in this study. Four out of the six methods are exact methods and the remaining two are approximative ones. A total of nine machine learning benchmarks were used for this study. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the parameter-wise optimization training process is implemented to achieve an optimal configuration of focused time lagged recurrent neural network (FTLRNN) models by embedding the gamma, laguarre, and multi-channel tapped delay line memory structure. The aim is to examine the prediction ability of the proposed models in order to predict one-day-ahead electric power load simultaneously as usual to oppose 1–24h forecast in sequel with a special emphasis on seasonal changes over a year. An improved delta-bar-delta algorithm is used to accelerate the training of neural networks and to improve the stability of the convergence. Experimental results indicate that the FTLRNN with time delay neural network (TDNN) clearly outperformed the gamma and laguarre based short-term memory structure in various performance metrics such as mean square error (MSE), normalized MSE, correlation coefficient (r) and mean absolute percentage error (MAPE) during evaluation process. Empirical results show that the proposed dynamic NN model consistently performs well on daily, weekly, and monthly average basis in terms of prediction accuracy. It is noticed from the literature review that an optimally configured FTLRNN with multi-channel tapped delay line memory structure is not currently available to solve short-term electrical power load prediction. The proposed method gives acceptable errors in all seasons, months and on daily basis. The average prediction error on three weeks is obtained as low as 1.67%. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a new initial population. At this point the main loop of an evolutionary algorithm is applied to the new population. The results show that our proposal considerably improves both the efficiency of previous methodologies and also, significantly, their efficacy in most of the data sets. We have carried out our experimentation on twelve data sets from the UCI repository and two complex real-world problems which differ in their number of instances, features and classes. [Copyright &y& Elsevier]

Abstract: In this paper, a Feature-Extraction Neuron-Fuzzy Classification Model (FENFCM) is proposed that enables the extraction of feature variables and provides the classification results. The proposed classification model synergistically integrates a standard fuzzy inference system and a neural network with supervised learning. The FENFCM automatically generates the fuzzy rules from the numerical data and triangular functions that are used as membership functions both in the feature extraction unit and in the inference unit. To adapt the proposed FENFCM, two modificatory algorithms are applied. First, we utilize Evolutionary Programming (EP) to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit. Second, the Weight Revised Algorithm (WRA) is used to regulate the weight grade of the principal output node of the inference unit. Finally, the proposed FENFCM is validated using two benchmark data sets: the Wine database and the Iris database. Computer simulation results demonstrate that the proposed classification model can provide a sufficiently high classification rate in comparison with that of other models proposed in the literature. [Copyright &y& Elsevier]

Abstract: In this paper, a new hierarchical classification method based on the use of various types of AdaBoost classification algorithms is proposed for automatic classification of marble slab images according to their quality. At first, features are extracted using the sum and difference histograms method and, at the second stage, different versions of the AdaBoost algorithms are used as classifiers together with those extracted features in a proposed hierarchical fashion. Performance of the proposed method is compared against performances of different types of neural network classifiers and a support vector machine (SVM) classifier. Computational results show that the proposed hierarchical structure employing AdaBoost algorithms performs superior to neural networks and the SVM classifier for classifying marble slab images in our large and diversified data set. [ABSTRACT FROM AUTHOR]

Abstract: Freeze drying is the best drying technology regarding quality of the end product but it is an expensive method and the high costs of process limit its application to industrial scale. At the same time, the freeze-drying process is based on different parameters, such as drying time, pressure, sample thicknesses, chamber temperature, sample temperatures and relative humidity. So, the determination of drying behaviors, such as moisture content (MC), moisture ratio (MR) and drying rate (DR), of the freeze-drying process are too complex. In this paper, to help the freeze dryer designer and simplify this complex process, the use of artificial neural networks has been proposed. An artificial neural networks (ANN) model has been developed for determination the prediction of drying behaviors, such as MC, MR and DR, of apples in the freeze-drying process. The back-propagation learning algorithm with variant which is Levenberg–Marquardt (LM) and Fermi transfer function have been used in the network. In addition, the statistical validity of the developed model has been determined by using the coefficient of determination (R 2), the root means square error (RMSE) and the mean absolute percentage error (MAPE). R 2, RMSE and MAPE have been determined for MC, MR and DR, as 0.999, 0.0078895, 0.2668459, and 0.999, 0.0001099, 0.2968427 and 0.999, 0.0000008, 0.2703797, respectively. [ABSTRACT FROM AUTHOR]

Abstract: This study presents an adaptive intelligent algorithm for forecasting gasoline demand based of artificial neural network (ANN), conventional regression and design of experiment (DOE). To show the superiority and applicability of the proposed algorithm it has been applied for monthly demand estimation of gasoline in Japan, USA, Kuwait, Canada and Iran within 1992–2005. The economic indicators used in this paper are price, GDP (Gross Domestic Production), population, number of vehicles, gasoline demand in the last periods and correlation coefficient between variables. The proposed algorithm may be used to estimate gasoline demand in the future by optimizing parameter values. The proposed algorithm uses ANOVA to select either ANN or regression for future demand estimation. Furthermore, if the null hypothesis in ANOVA and F-test is rejected, the Duncan method is used to identify which model is closer to actual data at α level of significance. Neural network is an intelligent, adaptive and optimizing method for solving the complicated problems. The results show that ANN provides far less error than regression. Moreover, the proposed algorithm provides near optimal solution for gasoline demand estimation due to its flexibility and intelligibility. The intelligent algorithm is therefore ideal for policy making with respect to gasoline consumption estimation. [ABSTRACT FROM AUTHOR]

Abstract: There are irrelevant features that are redundant or significantly degrade the learning accuracy in the real-world complex classification tasks. This paper presents a new hybrid learning algorithm based on a cooperative coevolutionary algorithm (Co-CEA) with dual populations for designing the radial basis function neural network (RBFNN) models with an explicit feature selection. This approach attempts to complete both the RBFNN construction and the feature selection simultaneously. The proposed algorithm utilizes the Co-CEA’s divide-and-cooperative mechanism, which utilizes the evolutionary algorithms executing in parallel to coevolve subpopulations, corresponding to the hidden layer structure and the dominate features respectively. The algorithm adopts the binary encoding to represent the feature subset and the matrix-form mixed encoding to represent the RBFNN hidden layer structure, and a complete solution is formed via collaborations among the two subpopulations. Experimental results illustrate that the proposed algorithm outperforms other algorithms in references in terms of the classification accuracy, and it is able to obtain both prominent features and good RBFNN structure with higher prediction capability. [Copyright &y& Elsevier]

Abstract: This paper presents a detailed study to investigate the possibility of applying the online tuning gain scheduling MIMO neural dynamic DNN-PID control architecture to a nonlinear 2-axes pneumatic artificial muscle (PAM) robot arm so as to improve its joint angle position output performance. The proposed controller was implemented as a subsystem to control the real-time 2-axes PAM robot-arm system so as to control precisely the joint angle position of the 2-axes PAM robot arm when subjected to system internal interactions and load variations. The results of the experiment have demonstrated the feasibility and benefits of the novel proposed control approach in comparison with the traditional PID control strategy. The proposed gain scheduling neural MIMO DNN-PID control scheme forced both joint angle outputs of 2-axes PAM robot arm to track those of the reference simultaneously under changes of the load and system coupled internal interactions. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID. These results can be applied to control other highly nonlinear systems. [Copyright &y& Elsevier]

Abstract: This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train and implement three neural networks to decide whether to approve or reject a credit application. Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. The neural networks are trained using real world credit application cases from the German credit approval datasets which has 1000 cases; each case with 24 numerical attributes; based on which an application is accepted or rejected. Nine learning schemes with different training-to-validation data ratios have been investigated, and a comparison between their implementation results has been provided. Experimental results will suggest which neural network model, and under which learning scheme, can the proposed credit risk evaluation system deliver optimum performance; where it may be used efficiently, and quickly in automatic processing of credit applications. [Copyright &y& Elsevier]

Abstract: This paper demonstrates an approach to predict the chaotic time series with outliers using annealing robust fuzzy neural networks (ARFNNs). A combination model that merges support vector regression (SVR), radial basis function networks (RBFNs) and simplified fuzzy inference system is used. The SVR has the good performances to determine the number of rules in the simplified fuzzy inference system and initial weights for the fuzzy neural networks (FNNs). Based on these initial structures, and then annealing robust learning algorithm (ARLA) can be used effectively to overcome outliers and adjust the parameters of structures. Simulation results show the superiority of the proposed method with different SVR for training and prediction of chaotic time series with outliers. [Copyright &y& Elsevier]

Abstract: Software defect predictors which classify the software modules into defect-prone and not-defect-prone classes are effective tools to maintain the high quality of software products. The early prediction of defect-proneness of the modules can allow software developers to allocate the limited resources on those defect-prone modules such that high quality software can be produced on time and within budget. In the process of software defect prediction, the misclassification of defect-prone modules generally incurs much higher cost than the misclassification of not-defect-prone ones. Most of the previously developed predication models do not consider this cost issue. In this paper, three cost-sensitive boosting algorithms are studied to boost neural networks for software defect prediction. The first algorithm based on threshold-moving tries to move the classification threshold towards the not-fault-prone modules such that more fault-prone modules can be classified correctly. The other two weight-updating based algorithms incorporate the misclassification costs into the weight-update rule of boosting procedure such that the algorithms boost more weights on the samples associated with misclassified defect-prone modules. The performances of the three algorithms are evaluated by using four datasets from NASA projects in terms of a singular measure, the Normalized Expected Cost of Misclassification (NECM). The experimental results suggest that threshold-moving is the best choice to build cost-sensitive software defect prediction models with boosted neural networks among the three algorithms studied, especially for the datasets from projects developed by object-oriented language. [Copyright &y& Elsevier]

Abstract: Monoblock centrifugal pumps are widely used in a variety of applications. In many applications the role of monoblock centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly artificial neural networks, fuzzy logic were employed for continuous monitoring and fault diagnosis. This paper presents the use of C4.5 decision tree algorithm for fault diagnosis through statistical feature extracted from vibration signals of good and faulty conditions. [Copyright &y& Elsevier]

Abstract: The attenuation of extremely low-frequency magnetic fields is important in reducing electromagnetic interference on electric and electronic equipment. In this paper, an innovative method is presented for shielded magnetic field level estimation at power frequencies by a neural network (NN) technique which uses experimental data. The utilized NN is applied to cylindrical shields (transformer-grade iron, copper, and aluminum) in various shield arrangements. Using the developed NN model, the mitigated magnetic field of multilayered shields is measured and evaluated to predict the magnetic field at any distance apart from the magnetic source. The NN, which is based on a feed-forward neural network (FNN), is trained with scaled conjugate gradient, gradient descent with momentum and adaptive learning back propagation, and Levenberg–Marquardt algorithms to compute the shielded magnetic field. Results have shown that the developed FNN trained with the Levenberg–Marquardt algorithm is better than the other training algorithms in predicting the shielded magnetic field value accurately even in the presence of various shield arrangements. [Copyright &y& Elsevier]

Abstract: Classification is the process of assigning an object to one of a set of classes based on its attributes. Classification problems have been examined in fields as diverse as biology, medicine, business, image recognition, and forensics. Developing more accurate and widely applicable classification methods has significant implications in these and many other fields. This paper presents a dynamic artificial neural network (DAN2) as an alternate approach for solving classification problems. We show DAN2 to be an effective approach and compare its performance with linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor algorithms, support vector machines, and traditional artificial neural networks using benchmark and real-world application data sets. These data sets vary in the number of classes (two vs. multiple) and the source of the data (synthetic vs. real-world). We found DAN2 to be a very effective classification method for two-class data sets with accuracy improvements as high as 37.2% when compared to the other methods. We also introduce a hierarchical DAN2 model for multiple class data sets that shows marked improvements (up to 89%) over all other methods, and offers better accuracy in all cases. [Copyright &y& Elsevier]

Abstract: In this paper, a novel use of Kernel–Adatron (K–A) learning algorithm to aid SVM (Support Vector Machine) for ECG arrhythmias classification is proposed. The proposed pattern classifier is compared with MLP (multi-layered perceptron) using back propagation (BP) learning algorithm. The ECG signals taken from MIT-BIH arrhythmia database are used in training to classify 6 different arrhythmia, plus normal ECG. The MLP and SVM training and testing stages were carried out twice. They were first trained only with one ECG lead signal and then a second ECG lead signal was added to the training and testing datasets. The aim was to investigate its influence on training and testing performance (generalization ability) plus time of training for both classifiers. Implementation of these three criteria for evaluation of ECG signals classification will ease the problem of structural comparisons, which has not been given attention in previous research works. The results indicate that SVM in comparison to MLP is much faster in training stage and nearly seven times higher in performance, but MLP generalization ability in terms of mean square error is more than three times less. The proposed SVM method shows considerable improvement in comparison to recently reported results obtained by . [Copyright &y& Elsevier]