Special Issue "Applied Artificial Neural Network"

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (31 March 2016).

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A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Christian W. Dawson
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Guest Editor
Loughborough University, Department of Computer Science
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Special Issue Information

Dear Colleagues,

Since their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves—in terms of structures, training algorithms, topologies, types, etc.—an equally large proportion of work has examined the application of neural networks to a whole host of problems. Such problems are usually difficult to define and hard to solve using conventional techniques. Examples include computer vision, speech recognition, financial applications, medicine, meteorology, robotics, etc.

This Special Issue focuses on the second of these two research themes—that of the application of neural networks in diverse range of fields and problems. Papers are solicited that discuss the application of artificial neural networks. Discussion should include critical comparisons with existing techniques, modifications to neural networks, and in depth interpretation of results.

Dr. Christian Dawson
Guest Editor

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Keywords

  • Machine Learning
  • Artificial Neural Networks
  • Applications
  • MLP
  • RBF
  • SVM
  • Deep learning
  • Data Mining
  • Vision
  • Robotics

Published Papers (12 papers)

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Research

Open AccessArticle
Classifying Four Carbon Fiber Fabrics via Machine Learning: A Comparative Study Using ANNs and SVM
Appl. Sci. 2016, 6(8), 209; https://doi.org/10.3390/app6080209 - 27 Jul 2016
Cited by 11
Abstract
Carbon fiber fabrics are important engineering materials. However, it is confusing to classify different carbon fiber fabrics, leading to risks in engineering processes. Here, a classification method for four types of carbon fiber fabrics is proposed using artificial neural networks (ANNs) and support [...] Read more.
Carbon fiber fabrics are important engineering materials. However, it is confusing to classify different carbon fiber fabrics, leading to risks in engineering processes. Here, a classification method for four types of carbon fiber fabrics is proposed using artificial neural networks (ANNs) and support vector machine (SVM) based on 229 experimental data groups. Sample width, breaking strength and breaking tenacity were set as independent variables. Quantified numbers for the four carbon fiber fabrics were set as dependent variables. Results show that a multilayer feed-forward neural network with 21 hidden nodes (MLFN-21) has the best performance for classification, with the lowest root mean square error (RMSE) in the testing set. Full article
(This article belongs to the Special Issue Applied Artificial Neural Network) Printed Edition available
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Open AccessArticle
A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
Appl. Sci. 2016, 6(7), 200; https://doi.org/10.3390/app6070200 - 11 Jul 2016
Cited by 7
Abstract
In this work, we developed a Selective Dynamic Sampling Approach (SDSA) to deal with the class imbalance problem. It is based on the idea of using only the most appropriate samples during the neural network training stage. The “average samples”are the best to [...] Read more.
In this work, we developed a Selective Dynamic Sampling Approach (SDSA) to deal with the class imbalance problem. It is based on the idea of using only the most appropriate samples during the neural network training stage. The “average samples”are the best to train the neural network, they are neither hard, nor easy to learn, and they could improve the classifier performance. The experimental results show that the proposed method is a successful method to deal with the two-class imbalance problem. It is very competitive with respect to well-known over-sampling approaches and dynamic sampling approaches, even often outperforming the under-sampling and standard back-propagation methods. SDSA is a very simple method for automatically selecting the most appropriate samples (average samples) during the training of the back-propagation, and it is very efficient. In the training stage, SDSA uses significantly fewer samples than the popular over-sampling approaches and even than the standard back-propagation trained with the original dataset. Full article
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Open AccessArticle
Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble
Appl. Sci. 2016, 6(6), 175; https://doi.org/10.3390/app6060175 - 15 Jun 2016
Cited by 4
Abstract
Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors and their [...] Read more.
Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors and their cross effects in biological systems. In this paper, the Markov property and Neural Network Ensemble (NNE) are utilized to construct an estimated matrix that combines the interaction of the different local factors. With such an estimation matrix, we could obtain estimated variables that could reflect the global influence. The ensemble weights are trained by multiple population algorithms. Our prediction could fit the real trend of the two predicted measures, namely Morbidity Rate and Gross Domestic Product (GDP). It could be an effective method of reflecting the relationship between input factors and predicted measures of the health of ecosystems. The method can perform a sensitivity analysis, which could help determine the critical factors that could be adjusted to move the ecosystem in a sustainable direction. Full article
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Open AccessArticle
2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
Appl. Sci. 2016, 6(6), 174; https://doi.org/10.3390/app6060174 - 14 Jun 2016
Cited by 8
Abstract
Gaze estimation methods play an important role in a gaze tracking system. A novel 2D gaze estimation method based on the pupil-glint vector is proposed in this paper. First, the circular ring rays location (CRRL) method and Gaussian fitting are utilized for pupil [...] Read more.
Gaze estimation methods play an important role in a gaze tracking system. A novel 2D gaze estimation method based on the pupil-glint vector is proposed in this paper. First, the circular ring rays location (CRRL) method and Gaussian fitting are utilized for pupil and glint detection, respectively. Then the pupil-glint vector is calculated through subtraction of pupil and glint center fitting. Second, a mapping function is established according to the corresponding relationship between pupil-glint vectors and actual gaze calibration points. In order to solve the mapping function, an improved artificial neural network (DLSR-ANN) based on direct least squares regression is proposed. When the mapping function is determined, gaze estimation can be actualized through calculating gaze point coordinates. Finally, error compensation is implemented to further enhance accuracy of gaze estimation. The proposed method can achieve a corresponding accuracy of 1.29°, 0.89°, 0.52°, and 0.39° when a model with four, six, nine, or 16 calibration markers is utilized for calibration, respectively. Considering error compensation, gaze estimation accuracy can reach 0.36°. The experimental results show that gaze estimation accuracy of the proposed method in this paper is better than that of linear regression (direct least squares regression) and nonlinear regression (generic artificial neural network). The proposed method contributes to enhancing the total accuracy of a gaze tracking system. Full article
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Open AccessArticle
Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection
Appl. Sci. 2016, 6(6), 169; https://doi.org/10.3390/app6060169 - 03 Jun 2016
Cited by 75
Abstract
(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance [...] Read more.
(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 × 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible. Full article
(This article belongs to the Special Issue Applied Artificial Neural Network) Printed Edition available
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Open AccessArticle
Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones
Appl. Sci. 2016, 6(6), 164; https://doi.org/10.3390/app6060164 - 31 May 2016
Cited by 26
Abstract
In this study, an artificial neural network (ANN) model is developed to predict the stability number of breakwater armor stones based on the experimental data reported by Van der Meer in 1988. The harmony search (HS) algorithm is used to determine the near-global [...] Read more.
In this study, an artificial neural network (ANN) model is developed to predict the stability number of breakwater armor stones based on the experimental data reported by Van der Meer in 1988. The harmony search (HS) algorithm is used to determine the near-global optimal initial weights in the training of the model. The stratified sampling is used to sample the training data. A total of 25 HS-ANN hybrid models are tested with different combinations of HS algorithm parameters. The HS-ANN models are compared with the conventional ANN model, which uses a Monte Carlo simulation to determine the initial weights. Each model is run 50 times and the statistical analyses are conducted for the model results. The present models using stratified sampling are shown to be more accurate than those of previous studies. The statistical analyses for the model results show that the HS-ANN model with proper values of HS algorithm parameters can give much better and more stable prediction than the conventional ANN model. Full article
(This article belongs to the Special Issue Applied Artificial Neural Network) Printed Edition available
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Open AccessArticle
Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
Appl. Sci. 2016, 6(6), 160; https://doi.org/10.3390/app6060160 - 24 May 2016
Cited by 4
Abstract
Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1) the user-specific parameter for the number of clusters [...] Read more.
Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML)-extreme learning machine (ELM), to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed. Full article
(This article belongs to the Special Issue Applied Artificial Neural Network) Printed Edition available
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Open AccessArticle
Simulation of Reservoir Sediment Flushing of the Three Gorges Reservoir Using an Artificial Neural Network
Appl. Sci. 2016, 6(5), 148; https://doi.org/10.3390/app6050148 - 18 May 2016
Cited by 13
Abstract
Reservoir sedimentation and its effect on the environment are the most serious world-wide problems in water resources development and utilization today. As one of the largest water conservancy projects, the Three Gorges Reservoir (TGR) has been controversial since its demonstration period, and sedimentation [...] Read more.
Reservoir sedimentation and its effect on the environment are the most serious world-wide problems in water resources development and utilization today. As one of the largest water conservancy projects, the Three Gorges Reservoir (TGR) has been controversial since its demonstration period, and sedimentation is the major concern. Due to the complex physical mechanisms of water and sediment transport, this study adopts the Error Back Propagation Training Artificial Neural Network (BP-ANN) to analyze the relationship between the sediment flushing efficiency of the TGR and its influencing factors. The factors are determined by the analysis on 1D unsteady flow and sediment mathematical model, mainly including reservoir inflow, incoming sediment concentration, reservoir water level, and reservoir release. Considering the distinguishing features of reservoir sediment delivery in different seasons, the monthly average data from 2003, when the TGR was put into operation, to 2011 are used to train, validate, and test the BP-ANN model. The results indicate that, although the sample space is quite limited, the whole sediment delivery process can be schematized by the established BP-ANN model, which can be used to help sediment flushing and thus decrease the reservoir sedimentation. Full article
(This article belongs to the Special Issue Applied Artificial Neural Network) Printed Edition available
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Open AccessArticle
Prediction of the Hot Compressive Deformation Behavior for Superalloy Nimonic 80A by BP-ANN Model
Appl. Sci. 2016, 6(3), 66; https://doi.org/10.3390/app6030066 - 25 Feb 2016
Cited by 17
Abstract
In order to predict hot deformation behavior of superalloy nimonic 80A, a back-propagational artificial neural network (BP-ANN) and strain-dependent Arrhenius-type model were established based on the experimental data from isothermal compression tests on a Gleeble-3500 thermo-mechanical simulator at temperatures ranging of 1050–1250 °C, [...] Read more.
In order to predict hot deformation behavior of superalloy nimonic 80A, a back-propagational artificial neural network (BP-ANN) and strain-dependent Arrhenius-type model were established based on the experimental data from isothermal compression tests on a Gleeble-3500 thermo-mechanical simulator at temperatures ranging of 1050–1250 °C, strain rates ranging of 0.01–10.0 s−1. A comparison on a BP-ANN model and modified Arrhenius-type constitutive equation has been implemented in terms of statistical parameters, involving mean value of relative (μ), standard deviation (w), correlation coefficient (R) and average absolute relative error (AARE). The μ -value and w -value of the improved Arrhenius-type model are 3.0012% and 2.0533%, respectively, while their values of the BP-ANN model are 0.0714% and 0.2564%, respectively. Meanwhile, the R-value and ARRE-value for the improved Arrhenius-type model are 0.9899 and 3.06%, while their values for the BP-ANN model are 0.9998 and 1.20%. The results indicate that the BP-ANN model can accurately track the experimental data and show a good generalization capability to predict complex flow behavior. Then, a 3D continuous interaction space for temperature, strain rate, strain and stress was constructed based on the expanded data predicted by a well-trained BP-ANN model. The developed 3D continuous space for hot working parameters articulates the intrinsic relationships of superalloy nimonic 80A. Full article
(This article belongs to the Special Issue Applied Artificial Neural Network) Printed Edition available
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Open AccessArticle
Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea) via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks
Appl. Sci. 2016, 6(1), 25; https://doi.org/10.3390/app6010025 - 19 Jan 2016
Cited by 19
Abstract
1,1,1,2,3,3,3-Heptafluoropropane (R227ea) is a good refrigerant that reduces greenhouse effects and ozone depletion. In practical applications, we usually have to know the compressed liquid densities at different temperatures and pressures. However, the measurement requires a series of complex apparatus and operations, wasting too [...] Read more.
1,1,1,2,3,3,3-Heptafluoropropane (R227ea) is a good refrigerant that reduces greenhouse effects and ozone depletion. In practical applications, we usually have to know the compressed liquid densities at different temperatures and pressures. However, the measurement requires a series of complex apparatus and operations, wasting too much manpower and resources. To solve these problems, here, Song and Mason equation, support vector machine (SVM), and artificial neural networks (ANNs) were used to develop theoretical and machine learning models, respectively, in order to predict the compressed liquid densities of R227ea with only the inputs of temperatures and pressures. Results show that compared with the Song and Mason equation, appropriate machine learning models trained with precise experimental samples have better predicted results, with lower root mean square errors (RMSEs) (e.g., the RMSE of the SVM trained with data provided by Fedele et al. [1] is 0.11, while the RMSE of the Song and Mason equation is 196.26). Compared to advanced conventional measurements, knowledge-based machine learning models are proved to be more time-saving and user-friendly. Full article
(This article belongs to the Special Issue Applied Artificial Neural Network) Printed Edition available
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Open AccessArticle
A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid
Appl. Sci. 2015, 5(4), 1756-1772; https://doi.org/10.3390/app5041756 - 11 Dec 2015
Cited by 26
Abstract
In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to [...] Read more.
In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN), predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN))-based model for SGs is able to capture the non-linearity(ies) in the history load curve with 97 . 11 % accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38 . 50 % . Full article
(This article belongs to the Special Issue Applied Artificial Neural Network) Printed Edition available
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Open AccessArticle
NHL and RCGA Based Multi-Relational Fuzzy Cognitive Map Modeling for Complex Systems
Appl. Sci. 2015, 5(4), 1399-1411; https://doi.org/10.3390/app5041399 - 27 Nov 2015
Cited by 4
Abstract
In order to model multi-dimensions and multi-granularities oriented complex systems, this paper firstly proposes a kind of multi-relational Fuzzy Cognitive Map (FCM) to simulate the multi-relational system and its auto construct algorithm integrating Nonlinear Hebbian Learning (NHL) and Real Code Genetic Algorithm (RCGA). [...] Read more.
In order to model multi-dimensions and multi-granularities oriented complex systems, this paper firstly proposes a kind of multi-relational Fuzzy Cognitive Map (FCM) to simulate the multi-relational system and its auto construct algorithm integrating Nonlinear Hebbian Learning (NHL) and Real Code Genetic Algorithm (RCGA). The multi-relational FCM fits to model the complex system with multi-dimensions and multi-granularities. The auto construct algorithm can learn the multi-relational FCM from multi-relational data resources to eliminate human intervention. The Multi-Relational Data Mining (MRDM) algorithm integrates multi-instance oriented NHL and RCGA of FCM. NHL is extended to mine the causal relationships between coarse-granularity concept and its fined-granularity concepts driven by multi-instances in the multi-relational system. RCGA is used to establish high-quality high-level FCM driven by data. The multi-relational FCM and the integrating algorithm have been applied in complex system of Mutagenesis. The experiment demonstrates not only that they get better classification accuracy, but it also shows the causal relationships among the concepts of the system. Full article
(This article belongs to the Special Issue Applied Artificial Neural Network) Printed Edition available
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