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Active learning is focused on minimizing the effort required to obtain labeled data by iteratively choosing fresh data samples for training a machine learning model. One of the primary challenges in active learning involves the selection of the most informative instances for labeling by an annotation oracle at each iteration. A viable approach is to develop an active learning strategy that aligns with the performance of a meta-learning model. This strategy evaluates the quality of previously selected instances and subsequently trains a machine learning model to predict the quality of instances to be labeled in the current iteration. This paper introduces a novel approach to learning for active learning, wherein instances are chosen for labeling based on their potential to induce the most substantial change in the current classifier. We explore various strategies for assessing the significance of an instance, taking into account variations in the learning gradient of the classification model. Our approach can be applied to any classifier that can be trained using gradient descent optimization. Here, we present a formulation that leverages a deep neural network model, which has not been extensively explored in existing learning-to-active-learn methodologies. Through experimental validation, our approach demonstrates promising results, especially in scenarios where there are limited initially labeled instances, particularly when the number of labeled instances per class is extremely limited. • A new meta-active learning strategy for deep neural network models. • The instance selection problem is modeled as a regression problem. • Four strategies of instance importance scoring by considering gradient variations. • Experiments have shown good results when few initially labeled objects are available. • A dimensionality reduction step for the instances improves the performances. [ABSTRACT FROM AUTHOR]

The burner combustion tuning is a complex problem that has been studied through flame monitoring and characterization. It has been observed that the flame electromagnetic spectrum and flickering contain specific flame information in combustion processes. This information is helpful for combustion stoichiometry tuning on burners. This paper described a method for selecting the best flame feature subset that can be computed from the scanner signal, in order to get the flame index and induce combustion stoichiometry on burners under specific combustion conditions. We propose a method for selecting a reduced subset with only the useful flame features for flame index classification. To extract the most relevant flame features we use a feature subset selection (FSS) algorithm and to determine the combustion state in burners, five flame indices were defined that represent the most common flame states in oil fuel-fired boilers. FSS includes complete, sequential, and random searches in order to eliminate redundant and noisy flame features to decrease the flame feature set dimension. A probabilistic neural network (PNN) algorithm was implemented for flame feature clustering. Signals from the actual flame scanner system and relevant variables from the boiler data acquisition system were used by the algorithms to calculate the burner flame index. A set of parametric tests was done in a heavy oil-fired boiler under well-known flame and index conditions to train and test the flame classifier. The results showed that only the four more relevant features are enough to classify flames with a good performance (92.3% accuracy), which is useful for burner combustion monitoring and optimization. • A new approach to defining/selecting critical features in the combustion process. • A new approach to classifying flames in industrial heavy oil-fired boilers. • The proposed algorithms are tested & evaluated in a real industrial process. • The overall methodology ensures that the combustion process guarantees safety. [ABSTRACT FROM AUTHOR]

Buildings are an essential part of our social life. People spend a substantial fraction of their time and spend a high amount of energy in them. There is a grand variety of systems and services related to buildings, in order to better control and monitoring. The prompt taking of decisions may prevent costs and contamination. This paper proposes a method for energy consumption forecasting in public buildings, and thus, achieve energy savings, in order to improve the energy efficiency, without affecting the comfort and wellness. The prediction of the energy consumption is indispensable for the intelligent systems operations and planning. We propose an Elman neural network for forecasting such consumption and we use a genetic algorithm to optimize the weight of the models. This paper concludes that the proposed method optimizes the energy consumption forecasting and improves results attained in previous studies. [ABSTRACT FROM AUTHOR]

• We freshly use a sample of personal loans, provided by one of the largest Indian banks. • We uniquely use actual misclassification costs to evaluate our models. • Our scoring models can significantly reduce the default rate by 14.24%. • Redundancy and branch-closure policies could thus ultimately have been avoided. The main aim of this paper is to investigate how far applying suitably conceived and designed credit scoring models can properly account for the incidence of default and help improve the decision-making process. Four statistical modelling techniques, namely, discriminant analysis, logistic regression, multi-layer feed-forward neural network and probabilistic neural network are used in building credit scoring models for the Indian banking sector. Notably actual misclassification costs are analysed in preference to estimated misclassification costs. Our first-stage scoring models show that sophisticated credit scoring models, in particular probabilistic neural networks, can help to strengthen the decision-making processes by reducing default rates by over 14%. The second-stage of our analysis focuses upon the default cases and substantiates the significance of the timing of default. Moreover, our results reveal that State of residence, equated monthly instalment, net annual income, marital status and loan amount, are the most important predictive variables. The practical implications of this study are that our scoring models could help banks avoid high default rates, rising bad debts, shrinking cash flows and punitive cost-cutting measures. [ABSTRACT FROM AUTHOR]

Highlights • We define market shocks as the innovation of ARMA-GARCH. • We develop a methodology ARMA-GARCH-NN for market shock prediction. • A nearest-K cross-validation method is proposed and applied. • The predictability of market shocks is confirmed by experiments on the S&P 500 data. • The prediction result is used as a new signal for trading strategy development. Abstract Discovering hidden patterns under unexpected market shocks is a significant and challenging problem, which continually attracts attention from research communities of mathematics, economics, and data science. Classic financial pricing models present unsatisfactory prediction accuracy when applied to real-world data due to limited capacity in depicting complex market movements. In this paper, we develop a machine learning approach, called ARMA-GARCH-NN, to capture intra-day patterns for stock market shock forecasting. Specifically, we integrate classical financial pricing models with artificial neural networks, with explicitly designed feature selection and cross-validation methods. We conduct empirical studies on high-frequency data of the U.S. stock market for evaluation. Our results provide initial evidence of the predictability of market shocks. Additionally, we confirm the effectiveness of ARMA-GARCH-NN by recognizing patterns in massive stock data without strong assumptions on distribution. Our method can serve as a portable methodology that integrates the advantages of traditional financial models and data-driven methods to reveal hidden patterns in large-scale financial data. [ABSTRACT FROM AUTHOR]

Highlights • The hybrid intelligent model was built using artificial neural networks and fuzzy logic. • This hybrid model was tested with the MIT-BIH arrhythmia database. • A type-1 fuzzy system (type-1 FIS) and an interval type-2 fuzzy system (IT2FIS) were designed. • The best classification rate results are obtained using IT2FIS instead of type-1 FIS in the basic units. • A type-1 FIS is used to find a global classification for the 2 basic units in the hybrid model. Abstract This paper describes an approach using computational intelligence methods to form a hybrid model as a classification method for 2-lead cardiac arrhythmias. The hybridization of methods can increase the performance in a system and take advantage of the benefits offered by such techniques in solving complex problems. The interpretation of electrocardiograms is a useful task for physicians, but when it comes to reviewing more than 24 h of information, it becomes a laborious task for them. For this reason, the design a computational model that helps in such a task is very useful for the timely medical diagnosis. The hybrid model is build using artificial neural networks and fuzzy logic. Training and testing of the hybrid model was with the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) arrhythmia database. The heartbeats are preprocessed to improve results of classification. Ten different classes of normal and arrhythmia signals for building the hybrid model are considered. We used two electrode signals or leads included in the MIT-BIH arrhythmia database, MLII and V1, V2, or V3 as second electrode signal. The hybrid model is composed by two basic module units, as described below. A basic module unit to perform the classification for each signal lead is used. Each basic module unit is composed of three different classifiers based on the following models: fuzzy KNN algorithm, multilayer perceptron with gradient descent and momentum (MLP-GDM), and multilayer perceptron with scaled conjugate gradient backpropagation (MLP-SCG). The outputs from the classifiers are combined using a fuzzy system for integration of results. We designed two fuzzy systems, Mamdani type-1 fuzzy system (type-1 FIS) and an interval type-2 fuzzy system (IT2FIS). The reason is to perform a comparison between type-1 FIS and IT2FIS in the hybrid model. We have obtained best results in the classification rate using IT2FIS instead of type-1 FIS in the basic units. Finally, a type-1 FIS is used to determine the global classification for the 2 basic units in hybrid model. We obtained a good classification rate in each basic module unit, 92.90% and 92.70% of classification rate for basic modules unit 1 and unit 2 respectively. Finally, we obtained a 93.80% when used type-1 FIS and 94.20% of classification rate used IT2FIS combining both basic module units. In the results presented, we improve the global classification in proposed hybrid model combining neural networks and fuzzy logic used both signal lead included in MIT-BIH arrhythmia database. The proposed hybrid model maybe extended to use multi-lead arrhythmia classification using other databases that contain 12 leads to be able to make a complete medical diagnosis. [ABSTRACT FROM AUTHOR]

Highlights • Word2vec representation improves the summarization task compared to bag of words. • Feature learning using unsupervised neural networks improves the summarization task. • Unsupervised neural networks trained on word2vec vectors gives promising results. • Ensemble learning with word2vec representation obtains the best results. Abstract The vast amounts of data being collected and analyzed have led to invaluable source of information, which needs to be easily handled by humans. Automatic Text Summarization (ATS) systems enable users to get the gist of information and knowledge in a short time in order to make critical decisions quickly. Deep neural networks have proven their ability to achieve excellent performance in many real-world Natural Language Processing and computer vision applications. However, it still lacks attention in ATS. The key problem of traditional applications is that they involve high dimensional and sparse data, which makes it difficult to capture relevant information. One technique for overcoming these problems is learning features via dimensionality reduction. On the other hand, word embedding is another neural network technique that generates a much more compact word representation than a traditional Bag-of-Words (BOW) approach. In this paper, we are seeking to enhance the quality of ATS by integrating unsupervised deep neural network techniques with word embedding approach. First, we develop a word embedding based text summarization, and we show that Word2Vec representation gives better results than traditional BOW representation. Second, we propose other models by combining word2vec and unsupervised feature learning methods in order to merge information from different sources. We show that unsupervised neural networks models trained on Word2Vec representation give better results than those trained on BOW representation. Third, we also propose three ensemble techniques. The first ensemble combines BOW and word2vec using a majority voting technique. The second ensemble aggregates the information provided by the BOW approach and unsupervised neural networks. The third ensemble aggregates the information provided by Word2Vec and unsupervised neural networks. We show that the ensemble methods improve the quality of ATS, in particular the ensemble based on word2vec approach gives better results. Finally, we perform different experiments to evaluate the performance of the investigated models. We use two kind of datasets that are publically available for evaluating ATS task. Results of statistical studies affirm that word embedding-based models outperform the summarization task compared to those based on BOW approach. In particular, ensemble learning technique with Word2Vec representation surpass all the investigated models. [ABSTRACT FROM AUTHOR]

Domain adaptation (DA) methods facilitate cross-domain learning by minimizing the marginal or conditional distribution shift between domains. However, the conditional distribution shift is not well addressed by existing DA techniques for the cross-domain regression learning task. In this paper, we propose Multi-Domain Adaptation for Regression under Conditional shift (DARC) method. DARC constructs a shared feature space such that linear regression on top of that space generalizes to all domains. In other words, DARC aligns different domains according to the task-related information encoded in the values of the dependent variable. It is achieved using a novel Pairwise Similarity Preserver (PSP) loss function. PSP incentivizes the differences between the outcomes of any two samples, regardless of their domain(s), to match the distance between these samples in the constructed space. We perform experiments in both two-domain and multi-domain settings. The two-domain setting is helpful, especially when one domain contains few available labeled samples and can benefit from adaptation to a domain with many labeled samples. The multi-domain setting allows several domains, each with limited data, to be adapted collectively; thus, multiple domains compensate for each other's lack of data. The results from all the experiments conducted both on synthetic and real-world datasets confirm the effectiveness of DARC. • We propose a new pairwise similarity preserver loss function for regression tasks. • We propose a new domain adaptation method for regression tasks. • Our proposed method adapts domains with any arbitrary shift, concept, or covariate. • Our proposed method is capable of adapting multiple domains. • The results prove our method's efficiency in solving real-world problems. [ABSTRACT FROM AUTHOR]

This paper tackles the detection of radar targets with unknown Doppler shift in presence of clutter. A Neural Network based Constant False Alarm Rate (CFAR) technique is proposed for adapting the detection threshold in an improved architecture based on the Generalized Likelihood Ratio (GLR) detector. Detection schemes based on Doppler processors (Moving Target Indicator (MTI) and Moving Target Detector (MTD)) and conventional CFAR detectors are considered as reference. In these reference solutions, interference is assumed Gaussian and white at the output of each Doppler filter, so conventional incoherent CFAR detectors are applied. The outputs of the CFAR detectors are combined using an OR operation to decide the presence of a target if, at least, one of the CFARs declares it. As a result, the P FA is higher than the desired one, as we prove. In this paper, an improved detector is presented that combines the following features: a better approximation to the Neyman–Pearson detector based on the GLR (selecting the maximum filter bank output), and a unique CFAR detector applied to the squared modulus of the maximum filter bank output. Due to the non-linear nature of the maximum function, conventional CFAR detectors are not suitable. A Neural Network CFAR solution is proposed. A general design method is presented. Results prove that the designed CFAR allows the exploitation of the better detection capabilities of the detector based on the maximum function, providing a higher probability of detection while fulfilling the probability of false alarm requirement. The proposed method can be extended to other detection strategies and radar scenarios. [ABSTRACT FROM AUTHOR]

With the explosive growth in size of datasets, it becomes more significant to develop effective learning schemes for neural networks to deal with large scale data modelling. This paper proposes an iterative approximate Newton-type learning algorithm to build neural networks with random weights (NNRWs) for problem solving, where the whole training samples are divided into some small subsets under certain assumptions, and each subset is employed to construct a local learner model for integrating a unified classifier. The convergence of the output weights of the unified learner model is given. Experimental results on UCI datasets with comparisons demonstrate that the proposed algorithm is promising for large scale datasets. [ABSTRACT FROM AUTHOR]

A good trade-off between exploration and exploitation to find optimal values in search algorithms is very hard to achieve. On the other hand, the combination of search methods may cause computational complexity increase problems. The Gravitational Search Algorithm (GSA) is a swarm optimization algorithm based on the law of gravity, where the solution search process depends on the velocity of particles. The application of intelligent techniques can improve the search performances of GSA. This paper proposes the design of a Neuro and Fuzzy Gravitational Search Algorithm (NFGSA) to achieve better results than GSA in terms of global optimum search capability and convergence speed, without increasing the computational complexity. Both the algorithms have the same computational complexity O ( nd ), where n is the number of agents and d is the search space dimension. The main task of the designed intelligent system is to adjust a GSA parameter on a revised version of GSA. NFGSA is compared with GSA, a Plane Surface Gravitational Search Algorithm (PSGSA) and a Modified Gravitational Search Algorithm (MGSA). The results show that NFGSA improves the optimization performances of GSA and PSGSA, without adding computational costs. Moreover, the proposed algorithm is better than MGSA for a benchmark function and achieves similar results for two test functions. The analysis on the computational complexity shows that NFGSA has a better computational complexity than MGSA, because NFGSA has complexity O ( nd ), whereas MGSA has complexity O (( nd ) 2 ). [ABSTRACT FROM AUTHOR]

Recent studies into the estimation and control of an activated sludge process (ASP) at a wastewater treatment plant suggest that artificial-intelligence methods, such as neural networks, fuzzy systems and genetic algorithms, can improve the plant performance in terms of reduced operation costs and improved effluent quality. In this paper, a neural-network-based soft sensor is developed for the on-line prediction of effluent concentrations in an ASP in terms of primary hard-to-measure variables, such as chemical oxygen demand, total nitrogen content and total suspended solids, starting from secondary on-line easy-to-measure variables, such as oxygen and nitrogen compound concentrations in biological tanks, input flow rate and alkalinity, among others. An algorithm based on principal component analysis is applied to select the optimal net input vectors for the soft sensor, using an appropriated number of samples of the secondary variables set. The proposed soft sensor is tested on the ASP of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data. Satisfactory low values for mean and maximum absolute prediction errors are obtained, even when high values of sampling time of primary variables are set, as it is frequently done during monitoring operation. In this way, data-driven soft-sensors based on neural networks can become valuable tools for plant operators for the recognition of operational states in terms of low cost and efficient prediction of primary process variables such as chemical oxygen demand, total nitrogen content and total suspended solids, therefore avoiding the acquisition of expensive and sometimes unreliable instruments for measuring nutrient concentrations in plant. [ABSTRACT FROM AUTHOR]

This paper is concerned with applications of a dual Neural Network (NN) and Support Vector Machine (SVM) to prediction and analysis of beta barrel trans membrane proteins. The prediction and analysis of beta barrel proteins usually offer a host of challenges to the research community, because of their low presence in genomes. Current beta barrel prediction methodologies present intermittent misclassifications resulting in mismatch in the number of membrane spanning regions within amino-acid sequences. To address the problem, this research embarks upon a NN technique and its comparison with hybrid-two-level NN-SVM methodology to classify inter-class and intra-class transitions to predict the number and range of beta membrane spanning regions. The methodology utilizes a sliding-window-based feature extraction to train two different class transitions entitled symmetric and asymmetric models. In symmetric modelling, the NN and SVM frameworks train for sliding window over the same intra-class areas such as inner-to-inner, membrane(beta)-to-membrane and outer-to-outer. In contrast, the asymmetric transition trains a NN-SVM classifier for inter-class transition such as outer-to-membrane (beta) and membrane (beta)-to-inner, inner-to-membrane and membrane-to-outer. For the NN and NN-SVM to generate robust outcomes, the prediction methodologies are analysed by jack-knife tests and single protein tests. The computer simulation results demonstrate a significant impact and a superior performance of NN-SVM tests with a 5 residue overlap for signal protein over NN with and without redundant proteins for prediction of trans membrane beta barrel spanning regions. [ABSTRACT FROM AUTHOR]

Abstract: One of the objectives of conservation agriculture to reduce soil erosion in olive orchards is to protect the soil with cover crops between rows. Andalusian and European administrations have developed regulations to subsidise the establishment of cover crops between rows in olive orchards. Current methods to follow-up the cover crops systems by administrations consist of sampling and on ground visits of around 1% of the total olive orchards surface at any time from March to late June. This paper outlines a multi-objective neural network based method for the classification of olive trees (OT), bare soil (BS) and different cover crops (CC), using remote sensing data taken in spring and summer. The main findings of this paper are: (1) the proposed models performed well in all seasons (particularly during the summer, where only 48 pixels of CC are confused with BS and 10 of BS with CC with the best model obtained. This model obtained a 97.80% of global classification, 95.20% in the class with the worst classification rate and 0.9710 in the KAPPA statistics), and (2) the best-performing models could potentially decrease the number of complaints made to the Andalusian and European administrations. The complaints in question concern the poor performance of current on-ground methods to address the presence or absence of cover crops in olive orchards. [Copyright &y& Elsevier]

Abstract: ICT-based decision aids are currently making waves in the modern business world simultaneously with increased pressure on auditors to play a more effective role in the governance and control of corporate entities. This paper aims to review the main research efforts and current debates on auditors’ use of artificial intelligent systems, with a view to predicting future directions of research and software development in the area. The paper maps the development process of artificial intelligent systems in auditing in the light of their identified benefits and drawbacks. It also reviews previous research efforts on the use of expert systems and neural networks in auditing and the implications thereof. The synthesis of these previous studies revealed certain research vacuum which future studies in the area could fill. Such areas include matching the benefits of adopting these intelligent agents with their costs, assessing the impact of artificial intelligence on internal control systems’ design and monitoring as well as audit committees’ effectiveness, and implications of using such systems for small and medium audit firms’ operations and survival, audit education, public sector organisations’ audit, auditor independence and audit expectations-performance gap. [Copyright &y& Elsevier]

Abstract: This paper analyzes the use of feedforward multilayer perceptron neural networks in the support of the decision process during the conceptual design phase of engineering systems. A user friendly software tool is proposed in order to increase the quality of the designers’ decisions by offering intuitive insights during the early design phase. The performance gain is evaluated through controlled experiments performed with non-experienced users. This paper proposes using the prediction capabilities of Artificial Neural Networks to recommend design solutions based on earlier successful designs. It implements a feedforward multilayer perceptron architecture with sigmoid activation functions, and uses the Levenberg–Marquardt training algorithm for weight matrix update. The network complexity was abstracted to ease the utilization by non-expert users. Different stopping conditions were developed including one where the training and testing error divergence is monitored to tackle the bias/variance dilemma. The advantages of this approach for decision support were measured through a set of six different case studies such as gaseous automobile emissions prediction based on institutional data, or the choice of a launcher for a specific space mission. The decision support tool generated quality performance gains between 13% and 88% for examples ranging from simple continuous single variable to complex discrete multivariate problems. [Copyright &y& Elsevier]

Abstract: In this paper, a new machine learning solution for function approximation is presented. It combines many simple and relatively inaccurate estimators to achieve high accuracy. It creates – in incremental manner – hierarchical, tree-like structure, adapting it to the specific problem being solved. As most variants use the errors of already constructed parts to direct further construction, it may be viewed as example of boosting – as understood in general sense. The influence of particular constituent estimator on the whole solution’s output is not constant, but depends on the feature vector being evaluated. Provided in this paper are: general form of the metaalgorithm, a few specific, detailed solutions, theoretical basis and experimental results with one week power load prediction for country-wide power distribution grid and on simple test datasets. [Copyright &y& Elsevier]

Abstract: Stock price predictions have been a field of study from several points of view including, among others, artificial intelligence and expert systems. For short-term predictions, the technical indicator relative strength indicator (RSI) has been published in many papers and used worldwide. CAST is presented in this paper. CAST can be seen as a set of solutions for calculating the RSI using artificial intelligence techniques. The improvement is based on the use of feedforward neural networks to calculate the RSI in a more accurate way, which we call the iRSI. This new tool will be used in two scenarios. In the first, it will predict a market – in our case, the Spanish IBEX 35 stock market. In the second, it will predict single-company values pertaining to the IBEX 35. The results are very encouraging and reveal that the CAST can predict the given market as a whole along with individual stock pertaining to the IBEX 35 index. [Copyright &y& Elsevier]

Abstract: Artificial neural networks (ANNs) have been repeatedly and consistently applied to the domain of trading financial time series, with mixed results. Many researchers have developed their own techniques for both building and testing such ANNs, and this presents a difficulty when trying to learn lessons and compare results. In a previous paper, Vanstone and Finnie have outlined an empirical methodology for creating and testing ANNs for use within stockmarket trading systems. This paper demonstrates the use of their methodology, and creates and benchmarks a financially viable ANN-based trading system. Many researchers appear to fail at the final hurdles in their endeavour to create ANN-based trading systems, most likely due to their lack of understanding of the constraints of real-world trading. This paper also attempts to address this issue. [Copyright &y& Elsevier]

Abstract: The Nickel reduction process is a complex task where many dynamic optimization problems arises that, nowadays, requires a human operator to take decisions based on his experience and intuition. In order to help the operator to optimize the reduction process in terms of maximum amount of mineral extracted and minimum energy consumption, a control system integrated by several modules is being designed. One of the modules has the task of predicting how much petroleum will be burned in the ovens where the raw material is processed. This paper proposes an algorithm to design Radial Basis Function Neural Networks that will be able to predict accurately the amount of petroleum given a set of input parameters. The algorithm is also able of identifying the most relevant input parameters for the network so the dimensionality reduction problem is ameliorated. Hence, this paper, as it will be shown in the experiments section is able to apply the synergy of different Soft Computing techniques to the industrial process obtaining satisfactory results. [Copyright &y& Elsevier]

Abstract: In this paper, different types of classification methods are compared for effective diagnosis of Parkinson’s diseases. The reliable diagnosis of Parkinson’s disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approaches described in this paper purpose to efficiently distinguish healthy individuals. Four independent classification schemas were applied and a comparative study was carried out. These are Neural Networks, DMneural, Regression and Decision Tree respectively. Various evaluation methods were employed for calculating the performance score of the classifiers. According to the application scores, neural networks classifier yields the best results. The overall classification score for neural network is 92.9%. Moreover, we compared our results with the result that was obtained by kernel support vector machines [Singh, N., Pillay, V., & Choonara, Y. E. (2007). Advances in the treatment of Parkinson’s disease. Progress in Neurobiology, 81, 29–44]. To the best of our knowledge, our correct classification score is the highest so far. [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]

Abstract: This paper proposes and investigates a facial feature selection and fusion technique for improving the classification accuracy of face recognition systems. The proposed technique is novel in terms of feature selection and fusion processes. It incorporates neural networks and genetic algorithms for the selection and classification of facial features. The proposed technique is evaluated by using the separate facial region features and the combined features. The combined features outperform the separate facial region features in the experimental investigation. A comprehensive comparison with other existing face recognition techniques on FERET benchmark database is included in this paper. The proposed technique has produced 94% classification accuracy, which is a significant improvement and best classification accuracy among the published results in the literature. [Copyright &y& Elsevier]

A great deal of work has been published over the past decade on the application of neural networks to stockmarket trading. Individual researchers have developed their own techniques for designing and testing these neural networks, and this presents a difficulty when trying to learn lessons and compare results. This paper aims to present a methodology for designing robust mechanical trading systems using soft computing technologies, such as artificial neural networks. This paper describes the key steps involved in creating a neural network for use in stockmarket trading, and places particular emphasis on designing these steps to suit the real-world constraints the neural network will eventually operate in. Such a common methodology brings with it a transparency and clarity that should ensure that previously published results are both reliable and reusable. [Copyright &y& Elsevier]

Abstract: This paper discusses an experimental study of the Japanese candlestick method as used in hybrid stock market forecasting models. Two models are presented in this paper. Model 1 is a committee machine with simple generalized regression neural networks (GRNN) experts. This model also has a simple gating network. Model 2 has a similar committee machine along with a hybrid type gating network that contains fuzzy logic. Model 1 was developed to introduce the candlestick method and examine whether using the candlestick method improves performance. Model 2 is developed to determine whether the application of fuzzy logic could improve the former model. This model uses standard IF-THEN rules based fuzzy logic. In the experiment, a few simple Japanese candlestick patterns are integrated into the models. Both models use the same simple candlestick patterns to provide a basis for comparison. The Japanese candlestick method is implemented in the gating network. Model 1 uses features of candlestick patterns in the gating network. Model 2 uses candlestick patterns for recognizing the strength of market conditions. To investigate the performance of these models, the daily stock quotes of Hewlett-Packard, Bank of America, Ford, DuPont, and Yahoo are used as input data sets. The performance of the models was satisfactory based on the mean squared error. [Copyright &y& Elsevier]

Abstract: Product development is an important but also dynamic, lengthy and risky phase in the life of a new product. The optimisation of the product development phase through extensive knowledge of the involved procedures is believed to reduce the risks and improve the final product quality. Artificial intelligence and expert systems have been used successfully in optimising the development phase of some new products as it will be demonstrated by the first sections of this publication. This paper presents the first module of an expert system, a neural network architecture that could predict the reliability performance of a vehicle at later stages of its life by using only information from a first inspection after the vehicle’s prototype production. The paper demonstrates how a tool like neural networks can be designed and optimised for use in reliability performance predictions. Also, this paper presents an optimisation methodology that enabled the neural network to deal with the limited amount of available training data, common during new product development, and to finally achieve acceptable prediction performance with small error. A case example is presented to demonstrate the methodology. [Copyright &y& Elsevier]

In this paper, we investigate ways to apply news information on the Internet to the prediction of interest rates. We developed the Knowledge-Based News Miner (KBNMiner), which is designed to represent the knowledge of interest rate experts with cognitive maps (CMs), to search and retrieve news information on the Internet according to prior knowledge, and to apply the information, which is retrieved from news information, to a neural network model for the prediction of interest rates.This paper focuses on improving the performance of data mining by using prior knowledge. Real-world interest rate prediction data is used to illustrate the performance of the KBNMiner. Our integrated approach, which utilizes CMs and neural networks, has been shown to be effective in experiments. While the 10-fold cross validation is used to test our research model, the experimental results of the paired t-test have been found to be statistically significant. [Copyright &y& Elsevier]

This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes (a set of ten production rules of a Lindenmayer System with memory), moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System (L-System) that is used to design the neural network’s architecture. This basic neural codification confers scalability and search space reduction in relation to other methods. Furthermore, the system uses a parallel genome scan engine that increases both the implicit parallelism and convergence of the GA. The fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF). The results are statistically compared against established state-of-the-art algorithms and various forecasting methods (ADANN, ARIMA, UCM, and Forecast Pro). In most cases, our NEA outperformed the other methods, delivering the most accurate classification and time series forecasting with the least computational effort. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process. The result is an optimized neural network architecture for solving classification problems and simulating dynamical systems. [ABSTRACT FROM AUTHOR]

Learning on time series, especially on the small seasonal time series, has a wide range of practical applications. In this paper, to improve the learning effect on small seasonal time series, we extract the implicit information from the original series to enhance the data features and utilize stages to convey seasonality. Considering that many seasonal time series have implied stage characteristics, we propose Pre-SMATS, a multi-task learning based prediction model for small multi-stage seasonal time series. The model consists of three components: a feature extractor, a sub-task classifier and a main task predictor. The feature extractor is used to learn the feature vectors of the input data, the sub-task classifier is employed for stage classification and the main task predictor is applied for the final prediction. After extracting features of the input data, the extracted vectors are passed into the sub-task classifier and the main task predictor, respectively. Having learned the stage vector (i.e. the embedding of stage characteristics), the sub-task classifier outputs it to the main task, which concatenates the stage vector with the feature vector extracted by the feature extractor, to improve the prediction accuracy of the model. In order to verify our proposed model, Pre-SMATS, we conducted extensive experiments on two datasets with small time series, one is Chinese civil aviation passenger traffic (CCAPT) (2013–2019), a multi-stage seasonal time series and the other is IC board production furnace temperature curve (FTC), a general multi-stage time series. The experimental results show that our proposed model is superior to the baseline models in generalization capability as well as the accuracy of prediction on both two datasets. Specifically, compared with the best-performing baseline neural network model, the five error metrics (MSE, RMSE, MAE, MAPE and MSLE) of Pre-SMATS on CCAPT are reduced by 34%, 19%, 24%, 23% and 39% respectively, and on the FTC, they dropped by 42%, 25%, 16%, 18% and 43%. • The Pre-SMATS model is proposed to predict small multi-stage seasonal time series. • The model consists of a feature extractor, a classifier and a predictor. • We propose a new approach for processing seasonal characteristics. • Pre-SMATS can reduce the impact of insufficient data by enhancing data features. [ABSTRACT FROM AUTHOR]

In the multi-sensor activity recognition domain, the input space is often large and contains irrelevant and overlapped features. It is important to perform feature selection in order to select the smallest number of features which can describe the outputs. This paper proposes a new feature selection algorithms using the maximal relevance and maximal complementary (MRMC) based on neural networks. Unlike other feature selection algorithms that are based on relevance and redundancy measurements, the idea of how a feature complements to the already selected features is utilized. The proposed algorithm is evaluated on two well-defined problems and five real world data sets. The data sets cover different types of data i.e. real, integer and category and sizes i.e. small to large set of features. The experimental results show that the MRMC can select a smaller number of features while achieving good results. The proposed algorithm can be applied to any type of data, and demonstrate great potential for the data set with a large number of features. [ABSTRACT FROM AUTHOR]

It is essential to take into account the service quality assessment made by the passengers of a public transportation system, as well as the weight or relative importance assigned to each one of the attributes considered, in order to know its strengths and weaknesses. This paper proposes using Artificial Neural Networks (ANN) to analyze the service quality perceived by the passengers of a public transportation system. This technique is characterized by its high capability for prediction and for capturing highly non-lineal intrinsic relations between the study variables without requiring a pre-defined model. First, an ANN model was developed using the data gathered in a Customer Satisfaction Survey conducted on the Granada bus metropolitan transit system in 2007. Next, three different methods were used to determine the relative contribution of the attributes. Finally, a statistical analysis was applied to the outcomes of each method to identify groups of attributes with significant differences in their relative importance. The results show that statistical significant differences exist among several categories of attributes that have a greater or lesser impact on service quality and satisfaction. All the methods agree that Frequency is the most influential attribute in the service quality, and that other attributes such as Speed, Information and Proximity are also important. [ABSTRACT FROM AUTHOR]

Scale is highly detrimental to surface quality for tinplate products. There are a large number of process variables at a typical hot mill and principal component analysis is a well-known technique for reducing the number of process variables. This paper estimates the principal components associated with the hot mill process variables and puts these through an Adaptive Neuro Fuzzy Inference System (ANFIS) to find those hot mill running conditions that will minimise the amount of scale observed on the bottom of the rolled strip. It was found that the variation observed in all the hot mill process variables could be captured through the use of just six principal components, and that using just three of these in an ANFIS was sufficient to identify those operating conditions leading to coils being produced with a consistently low scale count. Specifically, it was found that the best operating conditions for the hot mill were when the first component was lower than -0.098 the second lower than 0.8058 and the third higher than -0.482. These ranges in turn corresponded to certain hot mill temperatures that depended to some extent on the base chemistry of the incoming slab. [ABSTRACT FROM AUTHOR]

Abstract: Computation of the stress intensity factors (SIFs) at the crack tip is the basis for pavement crack propagation analysis. Due to the three-dimensional (3-D) nature of cracked pavements and traffic loading, two-dimensional (2-D) finite element analysis (FEA) may be too simple to precisely predict SIFs, and the best choice for calculating the SIFs seems to be 3-D FEA programs. However, the 3-D FEA solutions are often computationally heavy. We had previously developed a semi-analytical FEA with multi-variable regression approach to fill this gap, but its accuracy still needs to be improved. To address this problem, a neural network approach based on semi-analytical FEA is presented in this paper: firstly, a SIFs database was generated through analyzing varieties of pavement structures using elastic semi-analytical FEA program; secondly, from the results in the database, neural network (NN) based SIF equations were developed for practical applications. The determination coefficients (R 2) of all the developed NN models were greater than 0.99 and mean square error (MSE) values were less than 1e−4. The comparisons between the prediction results from NN models and multivariable regression models also showed the advantage of NN over multivariable regression on the prediction accuracy. This proposed NN SA-FEA SIF prediction approach has been developed as a pavement crack propagation analysis tool. [Copyright &y& Elsevier]

Satellite images have become more easily accessible due to government programmes and the availability of commercial earth observation satellites. The increased volume of data and the recent advancement in deep learning-based techniques have made possible the development of fast and accurate methods for satellite image segmentation, which aims at automatically giving each pixel in the satellite image a label of the underlying object/ground type. However, despite the abundant supply of raw satellite images, the labelling of these images is a manual task and is therefore time and labour intensive. When a large labelled dataset is absent, resorting to a smaller data source significantly hampers the performance of the machine learning model. Therefore, an approach which mitigates the effects of using small labelled datasets should be investigated. This paper provides a solution to the problem of insufficient data samples for satellite image segmentation. To this end, two methods are developed and compared with the benchmark U-Net model: the use of transfer learning on an Xception architecture, and the use of steerable filters. The first model transfers knowledge from a pre-trained Xception model and predicts with a multi-resolution feature fusion module, specifically designed to recover fine details. The latter further improves the capability of our neural network model in handling data with high rotational variation. Both models are experimentally shown to significantly enhance the segmentation performance despite a small-sized training dataset. • Lower the impact of small labelled datasets on deep-learning-based methods. • Proposed the Xception-based model using transfer learning. • Proposed the novel steerable-filter-based model for handling rotation. • Verified to achieve superior performance on small labelled dataset. [ABSTRACT FROM AUTHOR]

Abstract: Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on seasonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series. [Copyright &y& Elsevier]

Abstract: This paper proposes an efficient neural network (NN) approach to tracking control of an autonomous surface vehicle (ASV) with completely unknown vehicle dynamics and subject to significant uncertainties. The proposed NN has a single-layer structure by utilising the vehicle regressor dynamics that expresses the highly nonlinear dynamics in terms of the known and unknown dynamic parameters. The learning algorithm of the NN is simple yet computationally efficient. It is derived from Lyapunov stability analysis, which guarantees that all the error signals in the control system are uniformly ultimately bounded (UUB). The proposed NN approach can force the ASV to track the desired trajectory with good control performance through the on-line learning of the NN without any off-line learning procedures. In addition, the proposed controller is capable of compensating bounded unknown disturbances. The effectiveness and efficiency are demonstrated by simulation and comparison studies. [Copyright &y& Elsevier]

Abstract: The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique. [Copyright &y& Elsevier]

Abstract: This paper investigates the performance of neural network-based techniques applied to the problem of defining the relationship between a particular type of variation in face images and the multivariate data distributions of these images. In this respect the problem of defining a mapping associating a quantified facial attribute and the overall typical facial appearance is addressed. In particular the applicability of formulating a mapping function using neural network-based methods like Multilayer Perceptrons (MLPs), Radial Basis Functions (RBFs), Mixture Density Networks (MDNs) and a latent variable method, the General Topographic Mapping (GTM) is investigated. Quantitative and visual results obtained during the experimental investigation, suggest that for one-to-many problems, where the entire variance of the distribution is not required, the RBFs are the best options when compared to MLPs, MDNs and GTM. The proposed techniques can be applied to applications involving face image synthesis and other applications that require one-to-many mapping transformations. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to develop and test a model of manufacturing cost estimating of piping elements during the early design phase through the application of artificial neural networks (ANN). The developed model can help designers to make decisions at the early phases of the design process. An ANN model would allow obtaining a fairly accurate prediction, even when enough and adequate information is not available in the early stages of the design process. The developed model is compared with traditional neural networks and conventional regression models. This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of shell and tube heat exchangers. [Copyright &y& Elsevier]

Abstract: The performance of a neural network model is affected by important constituent elements such as input variables, the number of hidden nodes, and the value of the decay constant. This paper suggests a new approach to fine-tune these factors to improve their accuracy. For the input variable selection, the generalized additive model (GAM) is applied. The grid search method and the genetic algorithm are sequentially implemented to fine-tune the number of hidden nodes and the value of the weight decay parameters. This suggested method to improve the neural network model is used to predict the probability that a firm may apply for bankruptcy, and its performance is compared with the results of existing bankruptcy forecasting models such as case-based reasoning, the decision tree, the GAM, the generalized linear model, the multi-variate discriminant analysis, and the support vector machine. Our empirical results indicate that the newly tuned neural network model significantly outperforms the other models. [Copyright &y& Elsevier]

Abstract: In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are only available once the bar has entered the mill therefore they have to be estimated. Estimation of process variables, particularly temperature, is of crucial importance for the bar front section to fulfil quality requirements and it must be performed in the shortest possible time to keep heat. Currently, temperature estimation is performed by physical modelling, however it is highly affected by measurement uncertainties, variations in the incoming bar conditions and final product changes. In order to overcome these problems, artificial intelligence techniques as artificial neural networks and fuzzy logic have been proposed. In this paper, several neural networks, neural based Grey-Box models, fuzzy inference systems, and fuzzy based Grey-Box models are designed and tested with experimental data to estimate scale breaker entry temperature given the relevance of this variable. Their performances are compared against that of the physical model used in plant. Some of the systems presented in this work were proved to have better performance indexes and hence better prediction capabilities than the current physical models used in plant. [Copyright &y& Elsevier]

Abstract: This paper presents the results of laboratory model testing of triangular labyrinth side weirs located on the straight open channel flume. The discharge capacity of triangular labyrinth side weirs is estimated by using two different artificial neural network (ANN) techniques, that is, the radial basis neural network (RBNN) and generalized regression neural network (GRNN), and gene-expression programming (GEP), which is an extension to genetic programming. 2500 laboratory test results are used for determining discharge coefficient of triangular labyrinth side weirs. The performance of the ANN and GEP models is compared with multi-linear and nonlinear regression models. Comparison results indicated that the neural computing and gene-expression programming techniques could be employed successfully in modeling discharge coefficient from the available experimental data. [Copyright &y& Elsevier]

Abstract: Takagi–Sugeno (T–S) fuzzy models are often used to represent complex nonlinear systems by means of fuzzy sets and fuzzy reasoning applied to a set of linear sub-models. In this paper, the global robust dissipativity of T–S fuzzy neural networks with interval time-varying delays are investigated. By constructing a proper Lyapunov–Krasovskii functional and using linear matrix inequality (LMI) technique, delay-dependent criteria for checking the global dissipativity and global exponential dissipativity of fuzzy neural networks have been derived in terms of LMI, which can be solved numerically using LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness of the theoretical results. [Copyright &y& Elsevier]

Abstract: This paper describes a new eye-control method for eye-based computer interaction using EOG. This work aims to resolve some of the problems encountered in current systems when used for long periods of time and users become tired. For this purpose, a new electrooculographic eye model based on wavelet transform and neural networks is proposed. The results obtained demonstrate the system’s reliability in detecting eye movements and show an error of less than 2° during long periods of use. The system proposed may be used to control any graphical interface using eye movements detected by electrooculography. [Copyright &y& Elsevier]

Abstract: This study presents the comparison of various evolutionary computation (EC) optimization techniques applied to train the noise-injected multi-layer perceptron neural networks used for estimation of longitudinal dispersion coefficient in rivers. The special attention is paid to recently developed variants of Differential Evolution (DE) algorithm. The most commonly used gradient-based optimization methods have two significant drawbacks: they cannot cope with non-differentiable problems and quickly converge to local optima. These problems can be avoided by the application of EC techniques. Although a great amount of various EC algorithms have been proposed in recent years, only some of them have been applied to neural network training – usually with no comparison to other methods. We restrict our comparison to the regression problem with limited data and noise injection technique used to avoid premature convergence and to improve robustness of the model. The optimization methods tested in the present paper are: Distributed DE with Explorative–Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization, Efficient Population Utilization Strategy Particle Swarm Optimization and Covariance Matrix Adaptation – Evolution Strategy. [Copyright &y& Elsevier]

Abstract: The knowledge of net radiation at the surface is of fundamental importance because it defines the total amount of energy available for the physical and biological processes such as evapotranspiration, air and soil warming. It is measured with net radiometers, but, the radiometers are expensive sensors, difficult to handle, that require constant care and also involve periodic calibration. This paper presents a methodology based on neural networks in order to replace the use of net radiometers (expensive tools) by modeling the relationships between the net radiation and meteorological variables measured in meteorological stations. Two different data sets (acquired at different locations) have been used in order to train and validate the developed artificial neural model. The statistical results (low root mean square errors and mean absolute error) show that the proposed methodology is suitable to estimate net radiation at surface from common meteorological variables, therefore, can be used as a substitute for net radiometers. [Copyright &y& Elsevier]

Abstract: In this paper, a dynamic over-sampling procedure is proposed to improve the classification of imbalanced datasets with more than two classes. This procedure is incorporated into a Hybrid algorithm (HA) that optimizes Multi Layer Perceptron Neural Networks (MLPs). To handle class imbalance, the training dataset is resampled in two stages. In the first stage, an over-sampling procedure is applied to the minority class to partially balance the size of the classes. In the second, the HA is run and the dataset is over-sampled in different generations of the evolution, generating new patterns in the minimum sensitivity class (the class with the worst accuracy for the best MLP of the population). To evaluate the efficiency of our technique, we pose a complex problem, the classification of 1617 real farms into three classes (efficient, intermediate and inefficient) according to the Relative Technical Efficiency (RTE) obtained by the Monte Carlo Data Envelopment Analysis (MC-DEA). The multi-classification model, named Dynamic Smote Hybrid Multi Layer Perceptron (DSHMLP) is compared to other standard classification methods with an over-sampling procedure in the preprocessing stage and to the threshold-moving method where the output threshold is moved toward inexpensive classes. The results show that our proposal is able to improve minimum sensitivity in the generalization set (35.00%) and obtains a high accuracy level (72.63%). [Copyright &y& Elsevier]

Abstract: The Social Web holds significant value for consumer intelligence. Today, tremendous valuable information behind the social Web has not yet been fully utilized by firms to achieve competitive advantages. In this paper, we propose a machine learning approach to model latent, heterogeneous consumer tastes from social Web reviews. Our approach employs the Mixture of Experts (ME) method to derive a set of distinct consumer clusters, each of which has a cluster-conditional taste template regression model. Furthermore, our approach is able to predict the cluster membership as well as the overall response solely from a consumer’s review ratings. We provide system architecture, model specification, and result analysis. We also compare the ME model with the Latent Regression Model. In practice, our ME learning approach will enable businesses to identify distinct consumer clusters directly from the social Web in order to satisfy consumers through customization and differentiation based on cluster-specific taste templates and cluster membership. [Copyright &y& Elsevier]

Abstract: In this paper, a new wrapper method for feature selection, namely IAFN-FS (Incremental ANalysis Of VAriance and Functional Networks for Feature Selection) is presented. The method uses as induction algorithm the AFN (ANOVA and Functional Networks) learning method; follows a backward non-sequential strategy from the complete set of features (thus allowing to discard several variables in one step, and so reducing computational time); and is able to consider “multivariate” relations between features. An important characteristic of the method is that it permits the user the interpretation of the results obtained, because the relevance of each feature selected or rejected is given in terms of its variance. IAFN-FS is applied to several benchmark real-world classification data sets showing adequate performance results. Also, a comparison with the results obtained by other wrapper methods is carried out, showing that the proposed method obtains better performance results in average. [Copyright &y& Elsevier]

Abstract: To improve the accurate rate of mapping multi-spectral remote sensing images, in this paper we construct a class of HyperRectangular Composite Neural Networks (HRCNNs), integrating the paradigms of neural networks with the rule-based approach. The supervised decision-directed learning (SDDL) algorithm is also adopted to construct a two-layer network in a sequential manner by adding hidden nodes as needed. Thus, the classification knowledge embedded in the numerical weights of trained HRCNNs can be extracted and represented in the form of If-Then rules. The rules facilitate justification on the responses to increase accuracy of the classification. A sample of remote sensing image containing forest land, river, dam area, and built-up land is used to examine the proposed approach. The accurate recognition rate reaching over 99% demonstrates that the proposed approach is capable of dealing with image mapping. [Copyright &y& Elsevier]