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Automatic identity recognition from ear images is an active research topic in the biometric community. The ability to secretly acquire images of the ear remotely and the stability of the ear shape over time make this technology a promising alternative for surveillance, authentication, and forensic applications. In recent years, significant research has been conducted in this area. Nevertheless, challenges remain that limit the commercial use of this technology. Several phases of the ear recognition system have been studied in the literature, from ear detection, normalization, and feature extraction to classification. This paper reviews the most recent methods used to describe and classify biometric features of the ear. We propose a first taxonomy to group existing approaches to ear recognition, including 2D, 3D, and combined 2D and 3D methods, as well as an overview of historical advances in this field. It is well known that data and algorithms are the essential components in biometrics, particularly in-ear recognition. However, early ear recognition datasets were very limited and collected in laboratory with controlled environments. With the wider use of deep neural networks, a considerable amount of training data has become necessary if acceptable ear recognition performance is to be achieved. As a consequence, current ear recognition datasets have increased significantly in size. This paper gives an overview of the chronological evolution of ear recognition datasets and compares the performance of conventional vs. deep learning methods on several datasets. We proposed a second taxonomy to classify the existing databases, including 2D, 3D, and video ear datasets. Finally, some open challenges and trends are debated for future research. [ABSTRACT FROM AUTHOR]

Determining the correct mushroom species with the necessary ecological characteristics is critical to continue mushroom production, which is essential in gastronomy. The mushroom farmers and collectors technique may help identify toxic mushrooms by detecting poisonous mushrooms using images of different mushroom species with distinctive morphological features. However, it can be not easy to distinguish between species. This paper used a dataset of 6714 mushroom images obtained from nine different mushroom species to classify the mushroom species. For a more straightforward comprehension of mushroom images and feature extraction by reanalysis of data sets, data visualization was performed using Grad-CAM, LIME, and Heatmap methods. Residual block-based Convolutional Neural Network (CNN) architectures are trained to automatically classify the concatenated feature map obtained from the Grad-CAM, LIME, and Heatmap methods. After extracting the deep features of the images from each architecture, the Atom Search Optimization (ASO) algorithm has been used to select the most distinctive features. The 6714×9000 size of the concatenated feature map was reduced to 6714×600 using the ASO algorithm. Classification results were evaluated using six different classifiers based on the feature map obtained to determine the mushroom species. The nine classes of mushroom species were classified successfully with 95.45 % accuracy using the proposed model with the ASO algorithm and KNN classifier. The methodology introduces novel visualization techniques for interpreting CNN-based models' decisions in mushroom species classification tasks. Using metaheuristics-based CNN models with multi-feature fusion techniques allows the model to leverage diverse sources of information, potentially enhancing its ability to discriminate between mushroom species and achieve higher classification accuracy than existing methods. This study can advance the mushroom species classification field by introducing new methodologies, improving classification accuracy, providing insights into model interpretability, and facilitating knowledge transfer to related fields. • The proposed model automatically detects mushroom species. • Grad-CAM, LIME, and Heatmap methods based on CNN architectures are used to visualize mushroom images. • The ASO algorithm has been successfully applied to residual block-based CNN models for feature selection. • The hybrid model classification accuracy for different species of mushrooms is 95.45 %. [ABSTRACT FROM AUTHOR]

Synchronous motors play a significant role in a wide range of industrial applications. Their reliable operation is paramount. Any faults in synchronous motors can lead to costly downtime, decreased productivity, and potential safety hazards. By accurately diagnosing and classifying faults, we can proactively address issues before they escalate, ensuring the smooth operation of synchronous motors and minimizing the risk of equipment failure. The accurate diagnosis and fault detection in synchronous motors pose a significant challenge in their operation and maintenance. In the existing models, the feature data at various depths are not thoroughly extracted to maximize their feature extraction capability. Additionally, they employ a single support vector machine to make the final decision on the output. The single support vector machine may not consistently produce more accurate outcomes. Therefore, this paper proposes a novel fault diagnosis model based on a deep residual neural network and multiple support vector machines to diagnose mechanical and electrical faults of synchronous motors. The proposed model improves upon existing fault diagnosis models in two key aspects. Firstly, by employing a deep neural network, the model is able to effectively process and extract fault features from the motor fault dataset, capturing more nuanced information that may be missed by existing models. Secondly, the use of multiple support vector machines enhances the decision-making capability of the model, allowing for more accurate fault diagnosis. By combining these two aspects, the proposed model achieves superior diagnostic performance compared to single support vector machine-based models. Our proposed model has been rigorously evaluated using mechanical and electrical fault datasets, and the results of experimental tests clearly demonstrate its superior diagnostic performance when compared to existing fault diagnosis models. The synergy of deep neural networks and multiple support vector machines not only improves fault detection accuracy but also enhances the robustness and generalizability of the model, making it a valuable tool for real-world industrial applications. • A novel model is proposed for synchronous motor fault diagnostics. • A deep residual neural network effectively extracts fault features. • Multiple support vector machines enhance the detection capability. • Proposed model is compared with existing models. [ABSTRACT FROM AUTHOR]

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented. [ABSTRACT FROM AUTHOR]

• An automatic region of interest segmentation method • A set of rules as a metric to measure the region of interest segmentation results • Analysis of thermal matrices as new data source for feature extraction • Design of experiments in a more robust manner taking into account most of the images in the database • Genetic algorithm for hyperparameter tunning for the Artificial Neural Network Breast thermography images are a new type of data that has been analyzed in recent years in order to detect abnormalities, which can lead to a future breast cancer. This paper proposes a methodology for breast thermal image classification, which is useful in Computer-Aided Detection Systems. The main contribution is an automatic method to segment the region of interest (ROI) based on local operations, local analysis, interpolation and statistical operators. For our experiments, we used an image database that is widely used in this research area, obtaining accuracy results between 90.17% and 98.33%, which are competitive with respect to related works. [ABSTRACT FROM AUTHOR]

Ten years ago, linear models were applied in various domains. Before application of the algorithms, several current studies extracted features presumed to represent parochial markings from the data using engineering techniques. Recently the deep learning domain offered opportunities to directly feed data into the model without any extensive hand-crafted feature engineering techniques. In this paper, the proposed framework does the feature extraction in a non-supervised (i.e. self-supervised) manner using both Contextual Long Short-Term Memory (CLSTM) and Contextual Convolutional Neural Networks (CCNN). We can then concatenate data obtained from the CLSTM and CCNN blocks, feed it into the Attention block, pass it through the Multilayer Perceptron (MLP) block, ultimately passing it through a terminal layer for classification. The task involved here was non-trivial as there is a major challenge in implementing our model to solve the time series classification (TSC) problem: overfitting. We deal with this challenge as follows; firstly, we adjusted the number of neurons in each of the stages. Secondly we introduced dropouts after every layer in each stage of this model. Finally experiments regarding the University of California Riverside (UCR) dataset indicates the model's superiority. [ABSTRACT FROM AUTHOR]

• We focused on nonlinear methods with new aspects to extract mentioned dynamics. • This method can reduce a significant number of people at risk. • The optimal features were given for a classifier to detection. • Our results show that the presented method has great performance. • The proposed method is highly usable in a real-time monitoring system. The present study aims to simulate an alarm system for online detecting normal electrocardiogram (ECG) signals from abnormal ECG so that an individual's heart condition can be accurately and quickly monitored at any moment, and any possible serious dangers can be prevented. First, the data from Physionet database were used to analyze the ECG signal. The data were collected equally from both males and females, and the data length varied between several seconds to several minutes. The heart rate variability (HRV) signal, which reflects heart fluctuations in different time intervals, was used due to the low spatial accuracy of ECG signal and its time constraint, as well as the similarity of this signal with the normal signal in some diseases. In this study, the proposed algorithm provided a return map as well as extracted nonlinear features of the HRV signal, in addition to the application of the statistical characteristics of the signal. Then, artificial neural networks were used in the field of ECG signal processing such as multilayer perceptron (MLP) and support vector machine (SVM), as well as optimal features, to categorize normal signals from abnormal ones. In this paper, the area under the curve (AUC) of the ROC was used to determine the performance level of introduced classifiers. The results of simulation in MATLAB medium showed that AUC for MLP and SVM neural networks was 89.3% and 94.7%, respectively. Also, the results of the proposed method indicated that the more nonlinear features extracted from the ECG signal could classify normal signals from the patient. The ECG signal representing the electrical activity of the heart at different time intervals involves some important information. The signal is considered as one of the common tools used by physicians to diagnose various cardiovascular diseases, but unfortunately the proper diagnosis of disease in many cases is accompanied by an error due to limited time accuracy and hiding some important information related to this signal from the physicians' vision leading to the risks of irreparable harm for patients. Based on the results, designing the proposed alarm system can help physicians with higher speed and accuracy in the field of diagnosing normal people from patients and can be used as a complementary system in hospitals. [ABSTRACT FROM AUTHOR]

In real-world applications, factors such as head pose variation, occlusion, and poor image quality make facial expression recognition (FER) an open challenge. In this paper, a novel conditional convolutional neural network enhanced random forest (CoNERF) is proposed for FER in unconstrained environment. Our method extracts robust deep salient features from saliency-guided facial patches to reduce the influence from various distortion types, such as illumination, occlusion, low image resolution, etc. A conditional CoNERF is devised to enhance decision trees with the capability of representation learning from transferred convolutional neural networks and to model facial expression of different perspectives with conditional probabilistic learning. In the learning process, we introduce a neurally connected split function (NCSF) as the node splitting strategy in the CoNERF. Experiments were conducted using public CK+, JAFFE, multi-view BU-3DEF and LFW datasets. Compared to the state-of-the-art methods, the proposed method achieved much improved performance and great robustness with an average accuracy of 94.09% on the multi-view BU-3DEF dataset, 99.02% on CK+ and JAFFE frontal facial datasets, and 60.9% on LFW dataset. In addition, in contrast to deep neural networks which require large-scale training data, conditional CoNERF performs well even when there are only a small amount of training data. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an adequate set of input features is selected for functional expansion genetically for the purpose of solving the problem of classification in data mining using functional link neural network. The proposed method named as HFLNN aims to choose an optimal subset of input features by eliminating features with little or no predictive information and designs a more compact classifier. With an adequate set of basis functions, HFLNN overcomes the non-linearity of problems, which is a common phenomenon in single layer neural networks. The properties like simplicity of the architecture (i.e., no hidden layer) and the low computational complexity of the network (i.e., less number of weights to be learned) encourage us to use it in classification task of data mining. We present a mathematical analysis of the stability and convergence of the proposed method. Further the issue of statistical tests for comparison of algorithms on multiple datasets, which is even more essential in data mining studies, has been all but ignored. In this paper, we recommend a set of simple, yet safe, robust and non-parametric tests for statistical comparisons of the HFLNN with functional link neural network (FLNN) and radial basis function network (RBFN) classifiers over multiple datasets by an extensive set of simulation studies. [Copyright &y& Elsevier]

Abstract: An efficient predictive plan is needed for any industry because it can optimize the resources management and improve the economy plant, by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in the productive processes are caused for gear box, they began its deterioration from early stages, also called incipient level. The extracted features from the DWT are used as inputs in a neural network for classification purposes. The results show that the developed method can reliably diagnose different conditions of the gear box. The wavelet transform is used to represent all possible types of transients in vibration signals generated by faults in a gear box. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal of a spur bevel gear box in different conditions is used to demonstrate the application of various wavelets in feature extraction. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This paper investigates the use of discrete wavelets for feature extraction and artificial neural network for classification. [Copyright &y& Elsevier]

A review on the recent segmentation and tumor grade classification techniques of brain Magnetic Resonance (MR) Images is the objective of this paper. The requisite for early detection of a brain tumor and its grade is the motivation for this study. In Magnetic Resonance Imaging (MRI), the tumor might appear clear but physicians need quantification of the tumor area for further treatment. This is where the digital image processing methodologies along with machine learning aid further diagnosis, treatment, prior and post-surgical procedures, synergizing between the radiologist and computer. These hybrid techniques provide a second opinion and assistance to radiologists in understanding medical images hence improving diagnostic accuracy. This article aims to retrospect the current trends in segmentation and classification relevant to tumor infected human brain MR images with a target on gliomas which include astrocytoma. The methodologies used for extraction and grading of tumors which can be integrated into the standard clinical imaging protocols are elucidated. Lastly, a crucial assessment of the state of the art, future developments and trends are dissertated. [ABSTRACT FROM AUTHOR]

Several methods for the location and classification of faults in power transmission lines using computational intelligence and digital signal processing techniques have been described in literature. Artificial neural networks (ANNs) and wavelet transform (WT) have drawn significant attention lately, but they present some drawbacks when dealing with power systems faults where data are often contaminated by noise. This paper proposes an approach by combining independent component analysis (ICA) with travelling wave (TW) theory and support vector machine (SVM). The approach is adequate to locate and recognize faults in high-voltage (HV) transmission lines, while the acquired signals are noisy. Experiments performed for distinct types and locations of faults in a real transmission line model have shown that the proposed combined methods are able to provide excellent performance in fault location. The obtained errors are lower than 1% and accuracy is 100% for the classification of fault signals with noise. It can be stated that this method presents better performance than those regarding the main conventional techniques such as wavelets and neural networks in the presence of noise. [ABSTRACT FROM AUTHOR]

Traffic signs are one of the most important safety elements in a road network. Particularly, road markings provide information about the limits and direction of each road lane, or warn the drivers about potential danger. The optimal condition of road markings contributes to a better road safety. Mobile Laser Scanning technology can be used for infrastructure inspection and specifically for traffic sign detection and inventory. This paper presents a methodology for the detection and semantic characterization of the most common road markings, namely pedestrian crossings and arrows. The 3D point cloud data acquired by a LYNX Mobile Mapper system is filtered in order to isolate reflective points in the road, and each single element is hierarchically classified using Neural Networks. State of the art results are obtained for the extraction and classification of the markings, with F-scores of 94% and 96% respectively. Finally, data from classified markings are exported to a GIS layer and maintenance criteria based on the aforementioned data are proposed. [ABSTRACT FROM AUTHOR]

• This systematic review aims to present the emerging techniques used to improve accuracy of thermography based diagnosis systems for breast cancer. • Details of feature extraction techniques by various authors have been discussed with improvements obtained. • Methods to overcome shortcomings of breast thermography as an adjunct tool have also been mentioned. One of the most prevalent cancers among women is the breast cancer. Accurate diagnosis of breast cancer at an early stage can reduce the mortality associated with this disease. Infrared Breast Thermography, which is a screening tool used to measure the temperature distribution of breast tissue, is a suitable adjunct tool to mammography. Breast thermography has many advantages as it is non-invasive, safe and painless. Thermographic image and usage of artificial neural networks have improved the accuracy of thermography in early diagnosis of breast abnormality. This paper presents survey based on the main steps of computer aided detection systems: image acquisition protocols, segmentation techniques, feature extraction and classification methods, used in the field of breast thermography over the past few decades. The detailed survey emphasizes on the improved reliability of breast thermography.This has become possible with the utilization of machine learning techniques for correct classification of breast thermograms. Numerical Simulation can be used as a supporting method to overcome high false positive rates in thermographic diagnosis. The paper also presents future recommendations to utilize recent machine learning advances in real time. [ABSTRACT FROM AUTHOR]

Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilised, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven different artificial neural networks were then used to identify these records. The results illustrate that the combination of the Levenberg–Marquardt trained Feed-Forward Neural Network, Radial Basis Function Neural Network and the Random Neural Network classifiers performed the best, with 91% for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate. [ABSTRACT FROM AUTHOR]

This paper proposes the modified fuzzy min–max neural network (MFMMN) classification model to perform the supervised classification of data. The basic fuzzy min–max neural network (FMMN) can only be applied to the continuous attribute values and cannot handle the discrete values. Also justification of the classification results given by FMMN required to be obtained to make it more applicable to real world applications. These both issues are solved in the proposed MFMMN. In the MFMMN, each hyperbox have min–max values defined in terms of continuous attributes and a set of binary strings defined for discrete attributes. Bitwise ‘ and ’ and ‘ or ’ operators are used to update the discrete values associated with each hyperbox. The trained network is pruned to remove the less useful hyperboxes based on their confidence factor. The proposed model is applied to nine different datasets taken from the University of California, Irvine (UCI) machine learning repository. Finally the case study of a real time weather data is evaluated using MFMMN. The experimental results show that the proposed model has given very good accuracy. In addition to accuracy, the number of hyperboxes obtained after pruning are very less which lead to less number of concise rules and reduced computational complexity. [ABSTRACT FROM AUTHOR]

In this paper we explore a new model focused on integrating two classifiers; Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for offline Arabic handwriting recognition (OAHR) on which the dropout technique was applied. The suggested system altered the trainable classifier of the CNN by the SVM classifier. A convolutional network is beneficial for extracting features information and SVM functions as a recognizer. It was found that this model both automatically extracts features from the raw images and performs classification. Additionally, we protected our model against over-fitting due to the powerful performance of dropout. In this work, the recognition on the handwritten Arabic characters was evaluated; the training and test sets were taken from the HACDB and IFN/ENIT databases. Simulation results proved that the new design based-SVM of the CNN classifier architecture with dropout performs significantly more efficiently than CNN based-SVM model without dropout and the standard CNN classifier. The performance of our model is compared with character recognition accuracies gained from state-of-the-art Arabic Optical Character Recognition, producing favorable results. [ABSTRACT FROM AUTHOR]

For years, researchers in neural network (NN) area have been carried out much productive research in improving the generalization ability of NNs. In this paper, a novel neural network design algorithm is presented for solving multi-class problems, structure decomposition based on Skeletonization (SDBSkeletonization), which is to simplify NNs further. The proposed method decomposes a complex multi-class problem into a set of two-class problems, each of which can be regarded as an individual problem. After learning all these individual problems in parallel with Skeletonization algorithm, we then integrate these results to final decision. In addition, Skeletonization solves the classification problem based on automatic feature extraction. This perspective gives a broader range of application of our method. Our experimental results on Waveform and Handwritten Digits database demonstrate that SDBSkeletonization improves the overall classification performance. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a multi-instance multi-label algorithm based on neural networks is proposed for image classification. The proposed algorithm, termed multi-instance multi-label neural network (MIMLNN), consists of two stages of MultiLayer Perceptrons (MLP). For multi-instance multi-label image classification, all the regional features are fed to the first-stage MLP, with one MLP copy processing one image region. After that, the MLP in the second stage incorporates the outputs of the first-stage MLPs to produce the final labels for the input image. The first-stage MLP is expected to model the relationship between regions and labels, while the second-stage MLP aims at capturing the label correlation for classification refinement. Error Back-Propagation (BP) approach is adopted to tune the parameters of MIMLNN. In view of that traditional gradient descent algorithm suffers from long-term dependency problem, a refined BP algorithm named Rprop is extended to effectively train MIMLNN. The experiments are conducted on a synthetic dataset and the Corel dataset. Experimental results demonstrate the superior performance of MIMLNN comparing with state-of-the-art algorithms for multi-instance multi-label image classification. [Copyright &y& Elsevier]

Abstract: A cry is the first verbal communication of infants and it is described as a loud, high-pitched sound made by infants in response to certain situations. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and it has been proven to be an excellent tool to investigate the pathological status of an infant. This paper proposes short-time Fourier transform (STFT) based time-frequency analysis of infant cry signals. Few statistical features are derived from the time-frequency plot of infant cry signals and used as features to quantify infant cry signals. Two types of radial basis neural networks such as Probabilistic Neural Network (PNN) and General Regression Neural Network are employed as classifiers for discriminating infant cry signals. Two classes of infant cry signals are considered such as normal cry signals and pathological cry signals of infants with asphyxia. For comparison, the proposed features are also tested using two neural network models such as Multilayer Perceptron (MLP) and Time-Delay Neural Network (TDNN) trained by scaled conjugate gradient algorithm. The experimental results show that the PNN and GRNN give very promising classification accuracy compared to MLP and TDNN and the proposed methods can effectively classify normal and pathological infant cries of infants with asphyxia. [Copyright &y& Elsevier]

Abstract: The binary image is essential to image formats where the textual image is the best example of the binary image representation. Feature extraction is a fundamental process in pattern recognition. In this regard, pattern recognition studies involve document analysis techniques. Optical font recognition is among the pattern recognition techniques that are becoming popular today. In this paper, we propose an enhanced global feature extraction method based on the on statistical analysis of the behavior of edge pixels in binary images. A novel method in feature extraction for binary images has been proposed whereby the behavior of the edge pixels between a white background and a black pattern in a binary image captures information about the properties of the pattern. The proposed method is tested on an Arabic calligraphic script image for an optical font recognition application. To evaluate the performance of our proposed method, we compared it with a gray-level co occurrence matrix (GLCM). We classified the features using a multilayer artificial immune system, a Bayesian network, decision table rules, a decision tree, and a multilayer network to identify which approach is most suitable for our proposed method. The results of the experiments show that the proposed method with a decision tree classifier can boost the overall performance of optical font recognition. [Copyright &y& Elsevier]

Abstract: Neural network based classification of material type even with the variation in the sensor parameter is investigated in this paper. The sensor is developed by means of a lightweight plunger probe and an optical mouse sensor. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The experiment is conducted to obtain the bouncing signals for plain surface of an objects kept at different distances from the probe. During the bouncing of the probe, time varying signals are generated from optical mouse that are recorded in data files on PC. Some dominant unique features are then extracted using signal processing tools to optimize neural network based classifier. The time and features of bouncing signal are related to the material type, and each material has a unique set of such properties. It is found that the sensor system is intelligent due to its ability to classify the material type even with the variation in the sensor parameter (distance between the sensor probe and plain objects). The classifiers are developed using two neural networks configurations, namely a well-known Multi-layer Perceptron Neural Networks (MLP NN), and Radial Basis Function Neural Networks (RBF NN). MLP NN and RBF NN models are designed to maximize accuracy under the constraints of minimum network dimension. The optimal parameters of MLP NN and RBF NN models based on various performance measures that include percentage classification accuracy (PCLA) on the testing data, and area under Receiver Operating Characteristics (ROC), and are determined. For the sensor data set, the PCLA of both the classifiers are found reasonable consistently in respect of rigorous testing using different data partitions. The areas under the ROC curves are close to unity. Performances of the two classifiers have been compared. It has been found that the RBF NN is more robust to noise, and epochs required for training are very less as compared to that for MLP NN. [Copyright &y& Elsevier]

Abstract: In operation of mechanical equipment, fault diagnosis plays an important role. In this paper, a novel fault diagnosis method based on pulse coupled neural network (PCNN) and probability neural network (PNN) is presented. The shape information of shaft orbit provides an important basis for fault diagnosis. However, the feature extraction and classification of shaft orbit is difficult to realize automation. The PCNN technique has excellent performance in the feature extraction. In the present study, a PCNN combined with roundness method is used to extract the feature vector of shaft orbit, because time signature from a PCNN has the property of insensitive to rotation, scaling and translation. Meanwhile, roundness is also with the same properties. Further, the PNN is used to train the feature vectors and classify the vibration fault. By comparison with the back-propagation (BP) network and radial-basic function (RBF) network, the experimental result indicated the proposed approach achieved fast and efficient fault diagnosis. [Copyright &y& Elsevier]

Abstract: This paper proposes feature selection as an approach to deal with a bladder cancer data set with small sample size. Three feature selection methods and four classifiers were used to determine the best feature subsets that produce perfect classification accuracy. The smallest best feature subsets are used to build neural models with the small data set to achieve 100% training and testing accuracies. Therefore, the mega-trend-diffusion technique proposed by Li et al. to produce artificial samples is actually unnecessary. The similarity classifier proposed by Luukka was also applied to the small data set with the smallest best feature subsets to achieve 100% accuracy using only 4 samples (two with bladder cancer and two normal) for two selected p and m values. Given the same accuracy, using the best feature subsets selected is better than using all 13 features as done by Luukka. Furthermore, several indexes/methods commonly used in filtering feature selection methods were tested for their ability to find the best feature subsets for this particular small bladder cancer data set. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates hierarchy extraction from results of multi-label classification (MC). MC deals with instances labeled by multiple classes rather than just one, and the classes are often hierarchically organized. Usually multi-label classifiers rely on a predefined class hierarchy. A much less investigated approach is to suppose that the hierarchy is unknown and to infer it automatically. In this setting, the proposed system classifies multi-label data and extracts a class hierarchy from multi-label predictions. It is based on a combination of a novel multi-label extension of the fuzzy Adaptive Resonance Associative Map (ARAM) neural network with an association rule learner. [ABSTRACT FROM AUTHOR]

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

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

Abstract: In this paper, we propose a novel Hybrid Higher Order Neural Classifier (HHONC) which contains different high-order units. In contrast with conventional fully-connected higher order neural networks (HONN), our proposed method uses fewer learning parameters and allocates the best fitted model in dealing with different datasets by modifying the orders of different high-order units and updating the learning parameters. Structure, model selection and updating the learning parameters of HHONC is introduced and is applied in classification of the Iris data set, the breast cancer data set, the Wine recognition data set, the Glass identification data set, the Balance scale data set, and the Pima diabetes data set. Acquired results are compared with the methods presented in . It is observed that the fewer features the dataset contains, the more accurate the HHONC performs, however the accuracy of datasets with more features are acceptable. Experimental results show about 3.5% and 0.6% improvements compared to the best accuracy obtained in previously methods for classifying the Pima diabetes and Iris datasets, respectively. In addition, by using a same method for reducing the feature number, it’s shown the proposed method perform more accurate than methods presented in . In this case, improvements compared to the best acquired accuracy of mentioned methods are about 1.7%, 1.3% and 0.2% in classification of Pima, Iris and Breast cancer datasets, respectively. [Copyright &y& Elsevier]

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

Abstract: In this paper, a method for the detection and classification of defects in weld radiographs is presented. The method has been applied for detecting and discriminating discontinuities in the weld images that may correspond to false alarms or defects such as worm holes, porosity, linear slag inclusion, gas pores, lack of fusion or crack. A set of 43 descriptors corresponding to texture measurements and geometrical features is extracted for each segmented object and given as input to a classifier. The classifier is trained to classify each of the objects it into one of the defect classes or characterize it as non-defect. Three fold cross validation was utilized and experimental results are reported for three different classifiers (Support Vector Machine, Neural Network, k-NN). [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a method for electrocardiogram (ECG) beat classification based on particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six types of beats including Normal Beat, Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. For classification stage of the extracted features, a RBFNN structure which is evolved by particle swarm optimization is used. Several experiments are performed over the test set and it is observed that the proposed method classifies ECG beats with a smaller size of network without making any concessions on the classification performance. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of the paper was to extract important features of children’s mathematical gift by using neural networks and logistic regression, in order to create a model that will assist teachers in elementary schools to recognize mathematically gifted children in an early stage, therefore enabling further development and realization of that gift. The initial model was created on the basis of a theoretical background and heuristical knowledge on giftedness in mathematics, including five components: (1) mathematical competencies, (2) cognitive components of gift, (3) personal components that contribute gift development, (4) environmental factors, and (5) efficiency of active learning and exercising methods, as well as grades and out-of-school activities of pupils in the fourth year of elementary school. The three neural network classification algorithms were tested in order to extract the important variables for detecting mathematically gifted children. The best neural network model was selected on the basis of a 10-fold cross-validation procedure. The model was also investigated by the logistic regression. Important predictors detected by two methods were compared and analyzed. The results show that both methods extract similar set of variables as the most important, including grades in mathematics, mathematical competencies of a child regarding numbers and calculating, but also grades in the literature, and environmental factors. [Copyright &y& Elsevier]

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

Abstract: In this paper, a new feature selection named as multi-class f-score feature selection is proposed for sleep apnea classification having different disorder degrees (mild OSAS, moderate OSAS, serious OSAS, and non-OSAS). f-Score is used to measure the discriminating power of features in the classification of two-class pattern recognition problems. In order to apply the f-score feature selection to multi-class datasets, we have used the f-score feature selection as pairwise (in the form of two classes) in the diagnosis of obstructive sleep apnea syndrome (OSAS) with four classes. After feature selection process, MLPANN (Multi-layer perceptron artificial neural network) classifier is used to diagnose the OSAS having different disorder degrees. While MLPANN obtained 63.41% classification accuracy on the diagnosis of OSAS, the combination of MLPANN and multi-class f-score feature selection achieved 84.14% classification accuracy using 50–50% training–testing split of OSAS dataset with four classes. These results demonstrate that the proposed multi-class f-score feature selection method is effective and robust in determining the disorder degrees of OSAS. [Copyright &y& Elsevier]

Abstract: Clustering is a fundamental data analysis method. It is widely used for pattern recognition, feature extraction, vector quantization (VQ), image segmentation, function approximation, and data mining. As an unsupervised classification technique, clustering identifies some inherent structures present in a set of objects based on a similarity measure. Clustering methods can be based on statistical model identification () or competitive learning. In this paper, we give a comprehensive overview of competitive learning based clustering methods. Importance is attached to a number of competitive learning based clustering neural networks such as the self-organizing map (SOM), the learning vector quantization (LVQ), the neural gas, and the ART model, and clustering algorithms such as the -means, mountain/subtractive clustering, and fuzzy -means (FCM) algorithms. Associated topics such as the under-utilization problem, fuzzy clustering, robust clustering, clustering based on non-Euclidean distance measures, supervised clustering, hierarchical clustering as well as cluster validity are also described. Two examples are given to demonstrate the use of the clustering methods. [Copyright &y& Elsevier]

Abstract: Extracting classification rules from data is an important task of data mining and gaining considerable more attention in recent years. In this paper, a new meta-heuristic algorithm which is called as TACO-miner is proposed for rule extraction from artificial neural networks (ANN). The proposed rule extraction algorithm actually works on the trained ANNs in order to discover the hidden knowledge which is available in the form of connection weights within ANN structure. The proposed algorithm is mainly based on a meta-heuristic which is known as touring ant colony optimization (TACO) and consists of two-step hierarchical structure. The proposed algorithm is experimentally evaluated on six binary and n-ary classification benchmark data sets. Results of the comparative study show that TACO-miner is able to discover accurate and concise classification rules. [Copyright &y& Elsevier]

Abstract: Texture classification plays an important role in image analysis and understanding. The real wavelet based methods are deprived of the significant benefits of the phase information. Thus, the complex wavelet should be taken into account. This paper will combine the phase information and the magnitude information of the complex wavelet coefficient into a real measure to describe the intensity variation of a texture, and then model the measure with the real generalized Gaussian distribution (GGD). The model parameters serve as the texture feature during the classification. The experimental results on two benchmark texture databases demonstrate the superior performance of the new texture feature. [Copyright &y& Elsevier]

This study introduces the usage of multiclass least-squares support vector machines (MC-LS-SVM) for classification purposes of the analog modulated communication signals. Fulfilled study uses our previous papers where ANN and clustering methods were used as classifiers and several key features which were extracted from the instantaneous properties of the intercepted signal for characterizing the modulation types. k-fold cross-validation test, classification accuracy and confusion matrix methods are used for calculating the performance of the MC-LS-SVM classifier. Moreover, the performance of the MC-LS-SVM is compared with our previous studies where ANN and clustering efforts for modulation classification were investigated. According to the computer simulations, 100% correct classification rate was obtained when 10-fold cross-validation test method was used. [Copyright &y& Elsevier]

Abstract: In this paper, an S-transform-based neural network structure is presented for automatic classification of power quality disturbances. The S-transform (ST) technique is integrated with neural network (NN) model with multi-layer perceptron to construct the classifier. Firstly, the performance of ST is shown for detecting and localizing the disturbances by visual inspection. Then, ST technique is used to extract the significant features of distorted signal. In addition, an optimum combination of the most useful features is identified for increasing the accuracy of classification. Features extracted by using the S-transform are applied as input to NN for automatic classification of the power quality (PQ) disturbances that solves a relatively complex problem. Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the classification. Sensitivity of proposed expert system under different noise conditions is investigated. The analysis and results show that the classifier can effectively classify different PQ disturbances. [Copyright &y& Elsevier]

Abstract: A classifier based on dynamic time warping (DTW) has been developed to perform the classification of human chromosomes. DTW is used in speech recognition applications to compare two time-sequences. This paper describes a method to adapt the DTW technique in order to deal with the length and the density profile, which are common features used in classifying chromosomes. The DTW classifier is able to compare chromosomes with different elongations. Since chromosomes are non-rigid objects, the proposed classifier has the main advantage of requiring only a small training set in comparison with the conventional methods based on Bayesian classifiers or neural networks. For the same classification accuracy of 81.0%, we achieve a reduction of 88% of the size of the training set in comparison with a Bayesian classifier. [Copyright &y& Elsevier]

Dental caries, alternatively called tooth decay is considered as the most general and rapidly increasing diseases. According to the statistics, nearly 90% of the adults confront the issue of dental caries. Hence for proper dental care and health, early detection and diagnosis concerning the caries lesion plays a mandatory role. To achieve this, technique of Imaging Processing is being adopted which further helps the specialists to carry out precise diagnosis. Dental Caries can be categorized into different types depending on its position as well as seriousness. This categorization holds significant to carry out diagnosis and plan for the treatment of dental disease. The paper recommends the new HNN (Hybrid Neural Network) technique for detecting dental caries depending on, its type and therefore precisely classify the caries affected layer. HNN is a blend of ANN (Artificial Neural Network and DNN (Deep neural network) techniques. To obtain image classification, deep neural network relies upon the stacked sparse auto-encoder. Thereafter authentic information can be fetched from data via supervised fine-tuning and unsupervised pre-training. On the other hand ANN (Artificial neural network) relies upon the logistic regression for classifying the dental caries affected layer. Various processes involved in dental input image are as following: l. Pre-Processing, 2. Segmentation, 3. Feature Extraction and 4.Classification. Caries are categorized into 4 different layers by the HNN, namely: Enamel, Dentin, Pulp and root lesions. The proposed system yield in effective output against the input image provided and precisely categorizes the caries level. Hybrid neural network is the innovative technique which is giving good result in terms of accuracy and processing time. [ABSTRACT FROM AUTHOR]

• This paper imparts a road map for the researchers of biometric recognition system. • Highlights both Unimodal and multimodal biometric system. • Covering diagrammatic view and major characteristics of all the biometric traits. • Focus is given to the feature extraction methods, selection of the classifier. • Datasets used and the accuracy achieved to explore the main ideas of the authors. Biometrics is the branch of science that deals with the identification and verification of an individual based on the physiological and behavioral traits. These traits or identifiers are permanent, unique and can separate one individual from another. Biometric recognition systems integrate complex definitional, technological and operational selection under various contexts. The systems are not going to replace the authentication tools and technologies, but the combination of biometric approaches and authentication methods to help in improving the security aspects of the applications where user cooperation can be inferred. Biometric based recognition methods and tools have become popular for the development of many useful, challenging and widely accepted applications such as security issues, surveillance, forensic investigations, fraudulent technologies, identity access management and access control. These systems also help to identify an individual in group of industrial networks, home/office building and control system. For the successful implementation of the biometric systems, deep artificial neural networks are in great demand. These systems can be built up either on the single modality or multiple modalities. This article explicates the comprehensive and deep survey that compactly and systematically summarizes the literature work done on unimodal and multimodal biometric systems and analyzes the feature extraction techniques, classifiers, datasets, results, efficiency and reliability of the system with high and multi-dimensional perspectives. This article also justifies in detail the classical methods, influential methods and taxonomy based on the biometric attributes. The goal is to aware the researchers of this area regarding various dimensions for the development of biometric systems to enhance the security aspects. The article begins with the fundamentals, types, need of system, challenges, uncertainties, motivations and then to the survey work. The tabular representation prepares for each biometric trait shows the author, year, major findings and results achieved with the synthesis analysis and the evaluation. The article finally ends up with the 3D biometric, a future perspective and concluding remarks. [ABSTRACT FROM AUTHOR]

• CNNs are considered for dealing with the flood susceptibility mapping task. • Two different CNN frameworks of classification and feature extraction are presented. • Three data presentation forms are designed for the proposed CNN frameworks. • Reliable flood susceptibility maps can be obtained by using the proposed CNNs. • Prediction performance of SVM can be improved using the CNNs for feature extraction. Flood is a very destructive natural disaster in the world, which seriously threatens the safety of human life and property. In this paper, the most popular convolutional neural network (CNN) is introduced to assess flood susceptibility in Shangyou County, China. The main contributions of this study are summarized as follows. First, the CNN technique is used for flood susceptibility mapping through two different CNN classification and feature extraction frameworks. Second, three data presentation methods are designed in the CNN architecture to fit the two proposed frameworks. To construct the proposed CNN-based methods, 13 flood triggering factors related to historical flood events in the study area were prepared. The performance of these CNN-based methods was evaluated using several objective criteria in comparison to the conventional support vector machine (SVM) classifier. Experiments results demonstrate that all the CNN-based methods can produce more reliable and practical flood susceptibility maps. For example, the proposed CNN-based classifiers were 0.022–0.054 higher than SVM in terms of area under the curve (AUC). In addition, in the classification process, CNN-based feature extraction can effectively improve the prediction capability of SVM by 0.021–0.051 in terms of AUC. Therefore, the proposed CNN frameworks can help mitigate and manage floods. [ABSTRACT FROM AUTHOR]