Automatic microstructural characterization and classification using dual tree complex wavelet-based features and Bees Algorithm

During the gas tungsten arc welding of nickel-based superalloys, the secondary phases such as Laves and carbides are formed in final stage of solidification. But, other phases such as γ″ and δ phases can precipitate in the microstructure, during aging at high temperatures. However, it is possible to minimize the formation of the Nb-rich Laves phases and therefore reduce the possibility of solidification cracking by adopting the appropriate welding conditions. This paper aims at the automatic microstructurally characterizing the kinetics of phase transformations on an Nb-base alloy, thermally aged at 650 and 950 °C for 10, 100 and 200 h, through backscattered ultrasound signals at frequency of 4 MHz and dual tree complex wavelet transform (DTCWT)-based feature extraction technique. The feature set comprises of statistical attributes (such as variance, skewness and kurtosis) extracted from the complex wavelet coefficients which are obtained using the DTCWT decomposition of a backscattered ultrasound signal. Also, the performance of the proposed feature extraction technique is compared with the conventional discrete wavelet transform. Finally, these features are fed to the probabilistic neural network (PNN) and radial basis function classifiers to automatic microstructural classification. The training process of these networks depends on the selection of the smoothing parameter of the networks’ activation function in a hidden layer. In this article, we introduce the application of the Bees Algorithm to the automatic adaptation of smoothing parameters. The proposed feature extraction technique coupled with the optimized PNN yielded the highest average accuracy of 96 and 83 %, respectively, for thermal aging at 650 and 950 °C. Thus, the proposed processing system provides high reliability to be used for microstructure characterization through ultrasound signals.

     

Spoken emotion recognition through optimum-path forest classification using glottal features

A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks – multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5 M and 5 F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPF generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs.     

A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients

In this study, we propose a set of new algorithms to enhance the effectiveness of classification for 5-year survivability of breast cancer patients from a massive data set with imbalanced property. The proposed classifier algorithms are a combination of synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO), while integrating some well known classifiers, such as logistic regression, C5 decision tree (C5) model, and 1-nearest neighbor search. To justify the effectiveness for this new set of classifiers, the g-mean and accuracy indices are used as performance indexes; moreover, the proposed classifiers are compared with previous literatures. Experimental results show that the hybrid algorithm of SMOTE + PSO + C5 is the best one for 5-year survivability of breast cancer patient classification among all algorithm combinations. We conclude that, implementing SMOTE in appropriate searching algorithms such as PSO and classifiers such as C5 can significantly improve the effectiveness of classification for massive imbalanced data sets.     

Using bat-modelled sonar as a navigational tool in virtual environments

Bats are able to use active sonar as a mechanism for locating object in three dimensions and for generating spatial maps of their environments. Humans use passive sound cues to detect features of the space they occupy, as well as react to the spatial location of objects which generate sound. The system described in this paper allows free-ranging humans to locate a virtual sound location using active sonar. An emitted pulse, centred on the users head, serves as an intensity and time marker. The return pulse is rendered at the virtual target location and emitted after a time delay corresponding to the two-way path from sender to target and back again. The sonar system is modelled on those of bats, using ultrasonic frequency-modulated signals reflected from simple targets. The model uses the reflectivity characteristics of ultrasound, but the frequency and temporal structure used are scaled, with the speed of sound being set to 8.5 ms⁻¹ to bring the frequency range and temporal resolution within the capabilities of the human auditory system. Orientation with respect to the ensonified target is achieved by time-of-flight time delays to give target range, and binaural location information derived from interaural timing differences, interaural intensity differences, and head-related transfer functions. Subjects performed significantly better at a localization task when given temporal data based on echo delays with an outgoing reference pulse than without a reference pulse. Frequency-modulated signals sweeping from 1.5 kHz–100 Hz over 500 ms provide the best localization cues, and users found them significantly easier to locate than continuous sounds.     

Feasible prediction in S-system models of genetic networks

To construct the model of gene expression using microarray techniques can reveal the regulation rules from the gene expression profiles. From S-system model, it is able to analyze the regulatory system dynamics. However, with 2N(N + 1) parameters (called a set), an S-system model of N-gene genetic networks takes lots of iterations to have convergent gene expression profiles. To mining the association between the gene expression profiles and 2N(N + 1) parameters may provide information about the probability of the convergent gene expression profiles instead of trying to obtain the convergent gene expression profiles in lots of iteration. Based on this novel approach, higher accuracy of the binary classifier can be used to analyze and prediction the convergence of the gene expression profiles from an initial set to reduce the search time of the inference problem. This paper applies popular data mining algorithms to the classification tasks and compares their accuracy rates with a dataset (250 cases, including 176 training cases and 74 test cases). According to decision rules of the chosen classifier, we can provide a convergence prediction of time-series gene expression profiles on the given set of parameters.     

High speed detection of potato and clod using an acoustic based intelligent system

Discriminating between potato tubers and clods is the first step in developing an automatic separation system on potato harvesters. In this study, an acoustic-based intelligent system was developed for high speed discriminating between potato tubers and soil clods. About 500 kg mixture of potato tubers and clods were loaded on a belt conveyer and were impacted against a steel plate at four different velocities. The resulting acoustic signals were recorded, processed and potential features were extracted from the analysis of sound signals in both time and frequency domains. A multilayer perceptron neural network with a back propagation algorithm was used for pattern recognition. Altogether, 17 potential discriminating features were selected and fed as input vectors to the artificial neural network models. Optimal network was selected based on mean square error, correct detection rate and correlation coefficient. At the belt velocity of 1 m s^?1, detection accuracy of the presented system was about 97.3% and 97.6% for potatoes and clods, respectively. Increasing the belt velocity resulted in the reduction of detection accuracy and increase in the number of miss classified samples. By using this system, it is expected that a potato harvester may operate at a capacity of 20 ton hr^?1 with the accuracy of about 97%.     

Pathological video game playing in Spanish and British adolescents: Towards the exploration of Internet Gaming Disorder symptomatology

Research into problematic video gaming has increased greatly over the last decade and many screening instruments have been developed to identify such behaviour. This study re-examined the Problematic Videogame Playing [PVP] Scale. The objectives of the study were to (i) examine its psychometric properties in two European countries, (ii) estimate the prevalence of potential pathological gaming among adolescents in both countries, and (iii) assess the classification accuracy of the PVP Scale based on its symptomatology as a way of exploring its relationship with both the behavioural component model of addiction and the proposed Internet Gaming Disorder. The data were collected via a survey administered to 2356 adolescents aged between 11 and 18 years from Spain (n = 1132) and Great Britain (n = 1224). Results indicated that the reliability of both versions was adequate, and the factorial and construct validity were good. Findings also showed that the prevalence of pathological gamers estimated with a rigorous cut-off point was 7.7% for Spanish and 14.6% for British adolescents. The scale showed adequate sensitivity, specificity and classification accuracy in both countries, and was able to differentiate between social and potential pathological gamers, and from their addictive symptomatology. The implications of these findings are discussed.     

Evaluating the use of ECG signal in low frequencies as a biometry

Traditional strategies, such as fingerprinting and face recognition, are becoming more and more fraud susceptible. As a consequence, new and more fraud proof biometrics modalities have been considered, one of them being the heartbeat pattern acquired by an electrocardiogram (ECG). While methods for subject identification based on ECG signal work with signals sampled in high frequencies (>100 Hz), the main goal of this work is to evaluate the use of ECG signal in low frequencies for such aim. In this work, the ECG signal is sampled in low frequencies (30 Hz and 60 Hz) and represented by four feature extraction methods available in the literature, which are then feed to a Support Vector Machines (SVM) classifier to perform the identification. In addition, a classification approach based on majority voting using multiple samples per subject is employed and compared to the traditional classification based on the presentation of single samples per subject each time. Considering a database composed of 193 subjects, results show identification accuracies higher than 95% and near to optimality (i.e., 100%) when the ECG signal is sampled in 30 Hz and 60 Hz, respectively, being the last one very close to the ones obtained when the signal is sampled in 360 Hz (the maximum frequency existing in our database). We also evaluate the impact of: (1) the number of training and testing samples for learning and identification, respectively; (2) the scalability of the biometry (i.e., increment on the number of subjects); and (3) the use of multiple samples for person identification.     

Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform

The information extraction capability of two widely used signal processing tools, Hilbert Transform (HT) and Wavelet Transform (WT), is investigated to develop a multi-class fault diagnosis scheme for induction motor using radial vibration signals. The vibration signals are associated with unique predominant frequency components and instantaneous amplitudes depending on the motor condition. Using good systematic and analytical approach this fault frequencies can be identified. However, some faults either electrical or mechanical in nature are associated with same or similar vibration frequencies leading to erroneous conclusions. Genetic Algorithm (GA) is proposed and used successfully to find the most relevant fault frequencies in radial (vertical) frame vibration signal which can be used to diagnose the induction motor faults very effectively even in the presence of noise. The information obtained by Continuous Wavelet Transform (CWT) was found to be highly redundant compared to HT and thus by selecting the most relevant features using GA, the fault classification accuracy has considerably improved especially for CWT. Almost similar fault frequencies were found using CWT + GA and HT + GA for radial vibration signal.     

Aluminum diffusion in aluminide coatings deposited by the CVD method on pure nickel

The aluminum diffusion in aluminide coatings deposited on nickel by the CVD method was investigated. The microstructure, chemical and phase compositions of coatings were examined by SEM, EDS and XRD techniques. The triple zone structure was revealed. The β-NiAl phase was on the surface of the coatings, whereas γ-(Ni) and γ′-Ni₃Al formed deeper parts of the coatings. Diffusion coefficients were calculated from the concentration profiles in coatings deposited for various times (15 min, 1 h, 4 h and 8 h) at 1000 °C and 1050 °C. The procedure was based on the classic finite difference method (FDM). Diffusion coefficients in three phases were calculated simultaneously and the influence of diffusivity in one phase on the diffusivity in the neighboring phase was taken into account. The results of the calculation agree with the literature data obtained for each of the analyzed phases separately.     

An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification

Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN = ISO ? FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN).     

Support Vector Machines for classification and locating faults on transmission lines

This paper presents a new approach to classify fault types and predict the fault location in the high-voltage power transmission lines, by using Support Vector Machines (SVM) and Wavelet Transform (WT) of the measured one-terminal voltage and current transient signals. Wavelet entropy criterion is applied to wavelet detail coefficients to reduce the size of feature vector before classification and prediction stages. The experiments performed for different kinds of faults occurred on the transmission line have proved very good accuracy of the proposed fault location algorithm. The fault classification error is below 1% for all tested fault conditions. The average error of fault location in a 380 kV–360-km transmission line is below 0.26% and the maximum error did not exceed 0.95 km.     

Lighting,font style,and polarity on visual performance and visual fatigue with electronic paper displays

Visual performance and visual fatigue of electronic paper displays (electrophoretic display and cholesteric liquid crystal display) under ambient illuminances and light sources were studied and compared with paper. Forty-eight participants participated in a character-search task in the experiment. The results showed that search speed depends on the illuminance but not light source. Search speed increased as illumination increased from 300 lx (45.6 sec), 700 lx (44.18 sec) to 1500 lx (43.24 sec). The effect of medium display and polarity on accuracy was also significant. Accuracy was greater for electrophoretic display and positive polarity. However, the effect of illuminance and light source on visual fatigue was not statistically significant. Based on the results of this study, it seems that E-paper displays may need greater illumination (700 lx or higher).     

Building compact MQDF classifier for large character set recognition by subspace distribution sharing

Quadratic classifier with modified quadratic discriminant function (MQDF) has been successfully applied to recognition of handwritten characters to achieve very good performance. However, for large category classification problem such as Chinese character recognition, the storage of the parameters for the MQDF classifier is usually too large to make it practical to be embedded in the memory limited hand-held devices. In this paper, we aim at building a compact and high accuracy MQDF classifier for these embedded systems. A method by combining linear discriminant analysis and subspace distribution sharing is proposed to greatly compress the storage of the MQDF classifier from 76.4 to 2.06 MB, while the recognition accuracy still remains above 97%, with only 0.88% accuracy loss. Furthermore, a two-level minimum distance classifier is employed to accelerate the recognition process. Fast recognition speed and compact dictionary size make the high accuracy quadratic classifier become practical for hand-held devices.     

Equation of state of solid,liquid and gaseous tantalum from first principles

We present ab initio calculations of the phase diagram and the equation of state of Ta in a wide range of volumes and temperatures, with volumes from 9 to 180 Å³/atom, temperature as high as 20 000 K, and pressure up to 7 Mbars. The calculations are based on first principles, in combination with techniques of molecular dynamics, thermodynamic integration, and statistical modeling. Multiple phases are studied, including the solid, fluid, and gas single phases, as well as two-phase coexistences. We calculate the critical point by direct molecular dynamics sampling, and extend the equation of state to very low density through virial series fitting. The accuracy of the equation of state is assessed by comparing both the predicted melting curve and the critical point with previous experimental and theoretical investigations.     

Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set

An accurate contour estimation plays a significant role in classification and estimation of shape, size, and position of thyroid nodule. This helps to reduce the number of false positives, improves the accurate detection and efficient diagnosis of thyroid nodules. This paper introduces an automated delineation method that integrates spatial information with neutrosophic clustering and level-sets for accurate and effective segmentation of thyroid nodules in ultrasound images. The proposed delineation method named as Spatial Neutrosophic Distance Regularized Level Set (SNDRLS) is based on Neutrosophic L-Means (NLM) clustering which incorporates spatial information for Level Set evolution. The SNDRLS takes rough estimation of region of interest (ROI) as input provided by Spatial NLM (SNLM) clustering for precise delineation of one or more nodules. The performance of the proposed method is compared with level set, NLM clustering, Active Contour Without Edges (ACWE), Fuzzy C-Means (FCM) clustering and Neutrosophic based Watershed segmentation methods using the same image dataset. To validate the SNDRLS method, the manual demarcations from three expert radiologists are employed as ground truth. The SNDRLS yields the closest boundaries to the ground truth compared to other methods as revealed by six assessment measures (true positive rate is 95.45 ± 3.5%, false positive rate is 7.32 ± 5.3% and overlap is 93.15 ± 5. 2%, mean absolute distance is 1.8 ± 1.4 pixels, Hausdorff distance is 0.7 ± 0.4 pixels and Dice metric is 94.25 ± 4.6%). The experimental results show that the SNDRLS is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. The proposed method achieves the automated nodule boundary even for low-contrast, blurred, and noisy thyroid ultrasound images without any human intervention. Additionally, the SNDRLS has the ability to determine the controlling parameters adaptively from SNLM clustering.     

A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions

A neural network combined to a neural classifier is used in a real time forecasting of hourly maximum ozone in the centre of France, in an urban atmosphere. This neural model is based on the MultiLayer Perceptron (MLP) structure. The inputs of the statistical network are model output statistics of the weather predictions from the French National Weather Service. These predicted meteorological parameters are very easily available through an air quality network. The lead time used in this forecasting is (t + 24) h. Efforts are related to a regularisation method which is based on a Bayesian Information Criterion-like and to the determination of a confidence interval of forecasting. We offer a statistical validation between various statistical models and a deterministic chemistry-transport model. In this experiment, with the final neural network, the ozone peaks are fairly well predicted (in terms of global fit), with an Agreement Index = 92%, the Mean Absolute Error = the Root Mean Square Error = 15 μg m⁻³ and the Mean Bias Error = 5 μg m⁻³, where the European threshold of the hourly ozone is 180 μg m⁻³.To improve the performance of this exceedance forecasting, instead of the previous model, we use a neural classifier with a sigmoid function in the output layer. The output of the network ranges from [0,1] and can be interpreted as the probability of exceedance of the threshold. This model is compared to a classical logistic regression. With this neural classifier, the Success Index of forecasting is 78% whereas it is from 65% to 72% with the classical MLPs. During the validation phase, in the Summer of 2003, six ozone peaks above the threshold were detected. They actually were seven.Finally, the model called NEUROZONE is now used in real time. New data will be introduced in the training data each year, at the end of September. The network will be re-trained and new regression parameters estimated. So, one of the main difficulties in the training phase – namely the low frequency of ozone peaks above the threshold in this region – will be solved.     

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms.     

Comparison of multilabel classification models to forecast project dispute resolutions

Early forecasting of project dispute resolutions (PDRs) provides decision-support information for resolving potential procurement problems before a dispute occurs. This study compares the performances of classification and ensemble models for predicting dispute handling methods in public–private partnership (PPP) projects. Model analyses use machine learners (i.e., Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Tree-augmented Naïve (TAN) Bayesian), classification and regression-based techniques (i.e., Classification and Regression Tree (CART), Quick, Unbiased and Efficient Statistical Tree (QUEST), Exhaustive Chi-squared Automatic Interaction Detection (Exhaustive CHAID), and C5.0), and combinations of these techniques that performed best for a set of PPP data. Analytical results exhibit that the combined technique of QUEST + CHAID + C5.0 has the best classification accuracy at 84.65% in predicting dispute resolution outcomes (i.e., mediation, arbitration, litigation, negotiation, administrative appeals or no dispute occurred). Moreover, as the dispute category and phase in which the dispute occurs are known during project execution, the best classification model is the CART model, with an accuracy of 69.05%. This study demonstrates effective classification application for early PDR prediction related to public infrastructure projects.