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Abstract: The structure of data is important to the recognition of data. It is a fundamental question how measures and complements the structure of multi-features, because the fusion structure of multi-features is more complete than that of the single feature. To settle the question, we propose three methods for feature structure fusion in feature vectors or feature vector spaces. Firstly, the applicability of the different metric is analyzed. Secondly, optimization questions of various features are constructed based on manifold learning methods. Finally, multiple target optimization questions are transformed to a single target optimization question, and the principle of feature structure fusion is uncovered. In the classification of shape analysis and human action recognition, it is proven that structure fusion methods are effective. [Copyright &y& Elsevier]

Dissolved gas analysis (DGA) has been widely used for the detection of incipient faults in oil‐filled transformers. This research presents a novel approach to DGA feature prioritisation and classification, which considers not only the relations between a fault type and specific gas ratios but also their statistical characteristics based on data derived from onsite inspections. Firstly, new gas features are acquired based on the analysis of current international gas interpretation standards. Combined with conventional gas ratios, all features are then prioritised by using the Kolmogorov–Smirnov test. The rankings are obtained by using their values of maximum statistic distance. The first three features in ranking are employed as input vectors to a multi‐layer support vector machine, whose tuning parameters are acquired by particle swarm optimisation. In the experiment, a bootstrap technique is implemented to approximately equalise sample numbers of different fault cases. A common 10‐fold cross‐validation technique is employed for performance assessment. Typical artificial intelligence classifiers with gas features extracted from genetic programming are evaluated for comparison purposes. [ABSTRACT FROM AUTHOR]

Many different algorithms have been proposed for the extraction of features with a range of applications. In this work, we present Tensor-Cuts: a novel framework for feature extraction and classification from images which results in the simultaneous extraction and classification of multiple feature types (surfaces, curves and joints). The proposed framework combines the strengths of tensor encoding, feature extraction using Gabor Jets, global optimization using Graph-Cuts, and is unsupervised and requires no thresholds. We present the application of the proposed framework in the context of road extraction from satellite images, since its characteristics makes it an ideal candidate for use in remote sensing applications where the input data varies widely. We have extensively tested the proposed framework and present the results of its application to road extraction from satellite images. [ABSTRACT FROM AUTHOR]

Low-resolution face recognition (LR FR) aims to recognize faces from small size or poor quality images with varying pose, illumination, expression, etc. It has received much attention with increasing demands for long distance surveillance applications, and extensive efforts have been made on LR FR research in recent years. However, many issues in LR FR are still unsolved, such as super-resolution (SR) for face recognition, resolution-robust features, unified feature spaces, and face detection at a distance, although many methods have been developed for that. This paper provides a comprehensive survey on these methods and discusses many related issues. First, it gives an overview on LR FR, including concept description, system architecture, and method categorization. Second, many representative methods are broadly reviewed and discussed. They are classified into two different categories, super-resolution for LR FR and resolution-robust feature representation for LR FR. Their strategies and advantages/disadvantages are elaborated. Some relevant issues such as databases and evaluations for LR FR are also presented. By generalizing their performances and limitations, promising trends and crucial issues for future research are finally discussed. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents classification, representation and extraction of deformation features in sheet-metal parts. The thickness is constant for these shape features and hence these are also referred to as constant thickness features. The deformation feature is represented as a set of faces with a characteristic arrangement among the faces. Deformation of the base-sheet or forming of material creates Bends and Walls with respect to a base-sheet or a reference plane. These are referred to as Basic Deformation Features (BDFs). Compound deformation features having two or more BDFs are defined as characteristic combinations of Bends and Walls and represented as a graph called Basic Deformation Features Graph (BDFG). The graph, therefore, represents a compound deformation feature uniquely. The characteristic arrangement of the faces and type of bends belonging to the feature decide the type and nature of the deformation feature. Algorithms have been developed to extract and identify deformation features from a CAD model of sheet-metal parts. The proposed algorithm does not require folding and unfolding of the part as intermediate steps to recognize deformation features. Representations of typical features are illustrated and results of extracting these deformation features from typical sheet metal parts are presented and discussed. [Copyright &y& Elsevier]

The extraction and classification of multitype (point, curve, patch) features on manifolds are extremely challenging, due to the lack of rigorous definition for diverse feature forms. This paper seeks a novel solution of multitype features in a mathematically rigorous way and proposes an efficient method for feature classification on manifolds. We tackle this challenge by exploring a quasi-harmonic field (QHF) generated by elliptic PDEs, which is the stable state of heat diffusion governed by anisotropic diffusion tensor. Diffusion tensor locally encodes shape geometry and controls velocity and direction of the diffusion process. The global QHF weaves points into smooth regions separated by ridges and has superior performance in combating noise/holes. Our method's originality is highlighted by the integration of locally defined diffusion tensor and globally defined elliptic PDEs in an anisotropic manner. At the computational front, the heat diffusion PDE becomes a linear system with Dirichlet condition at heat sources (called seeds). Our new algorithms afford automatic seed selection, enhanced by a fast update procedure in a high-dimensional space. By employing diffusion probability, our method can handle both manufactured parts and organic objects. Various experiments demonstrate the flexibility and high performance of our method. [ABSTRACT FROM AUTHOR]

Abstract: Since cable members are the major structural components of cable bridges, they should be properly inspected for surface damage and inside defects such as corrosion and/or breakage of wires. This study introduces an efficient image-based damage detection system that can automatically identify damages to the cable surface through image processing techniques and pattern recognition. The damage detection algorithm combines image enhancement techniques with principal component analysis (PCA) algorithm. Images from three cameras attached to a cable climbing robot are wirelessly transmitted to a server computer located on a stationary cable support. To improve the overall quality of the images, this study utilizes an image enhancement method together with a noise removal technique. Next the input images are projected into PCA sub-space, the Mahalanobis square distance is used to determine the distances between the input images and sample patterns. The smallest distance is found to be a match for an input image. The proposed damage detection algorithm was verified through laboratory tests on three types of cables. Results of the tests showed that the proposed system could be used to detect damage to bridge cables. [Copyright &y& Elsevier]

Smart-phones have become an essential tool for many in daily life. Smart-phone Application development has grown rapidly to fulfill the demands of increasingly diverse usage. This paper presents an application based on a fault recognition model that can be used for recognizing different acoustic patterns. The application is made to detect various faulty conditions of an industrial air compressor based on the sound generated by the machine. The application has been tested in real time and is found to perform very well with classification accuracies above 94.75%. We propose that similar applications and recognition models with some modified specifications could be used for acoustic pattern recognition in a wide range of areas. [ABSTRACT FROM AUTHOR]

The game of cricket and the use of technology in the sport have grown rapidly over the past decade. However, technology-based systems introduced to adjudicate decisions such as run outs, stumpings, boundary infringements and close catches are still prone to human error, and thus their acceptance has not been fully embraced by cricketing administrators. In particular, technology is not employed for bat-pad decisions. Although the snickometer may assist in adjudicating such decisions it depends heavily on human interpretation. The aim of this study is to investigate the use of Wavelets in developing an edgedetection adjudication system for the game of cricket. Artificial Intelligence (AI) tools, namely Neural Networks, will be employed to automate this edge detection process. Live audio samples of ball-on-bat and ball-on-pad events from a cricket match will be recorded. DSP analysis, feature extraction and neural network classification will then be employed on these samples. Results will show the ability of the neural network to differentiate between these key events. This is crucial to developing a fully automated edge detection system. [ABSTRACT FROM AUTHOR]

Body Sensor Networks (BSNs) represent an emerging technology which has received much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, social interaction. Signal processing for BSNs usually comprises of multiple levels of data abstraction, from raw sensor data to data calculated from processing steps such as feature extraction and classification. This paper presents a multi-layer task model based on the concept of Virtual Sensors to improve architecture modularity and design reusability. Virtual Sensors are abstractions of components of BSN systems that include sensor sampling and processing tasks and provide data upon external requests. The Virtual Sensor model implementation relies on SPINE2, an open source domain-specific framework that is designed to support distributed sensing operations and signal processing for wireless sensor networks and enables code reusability, efficiency, and application interoperability. The proposed model is applied in the context of gait analysis through wearable sensors. A gait analysis system is developed according to a SPINE2-based Virtual Sensor architecture and experimentally evaluated. Obtained results confirm that great effectiveness can be achieved in designing and implementing BSN applications through the Virtual Sensor approach while maintaining high efficiency and accuracy. [ABSTRACT FROM PUBLISHER]

Representation and parameterization of freeform feature is one key issue in CAD. Based on the idea of layering freeform feature and feature global shape (FGS), a novel method for classification and parametric representation of freeform feature is proposed. Firstly, freeform features are classified in use of a multi-side method which combines FGS, approaches for surface generation, types of characteristic curves and their topological structures. Secondly, freeform feature is parameterized step by step from global to local shape, from basic to fine characteristic, and from high to low level, thus its representation can be unified. Lastly, the framework of three-level structures and two-grade mappings is put forward to define freeform feature so that parametric semantics can be clearer and it is convenient for users to edit and modify surface by intuitive parameters. It is shown in the examples that the method can effectively improve classification and parameterization of freeform feature. It can be applied to surface feature-based design and other domains as well. [ABSTRACT FROM AUTHOR]

According to the chaotic and non-linear characters of power load data, the time series matrix is established with the theory of phase-space reconstruction, and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension. Due to different features of the data, data mining algorithm is conducted to classify the data into different groups. Redundant information is eliminated by the advantage of data mining technology, and the historical loads that have highly similar features with the forecasting day are searched by the system. As a result, the training data can be decreased and the computing speed can also be improved when constructing support vector machine (SVM) model. Then, SVM algorithm is used to predict power load with parameters that get in pretreatment. In order to prove the effectiveness of the new model, the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network. It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%, 1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension, 14-dimension and BP network, respectively. This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting. [ABSTRACT FROM AUTHOR]

Accumulating evidence suggests that that non-coding RNAs (ncRNAs) play key roles in gene regulation and may form the basis of an inter-gene communication system. Many ncRNAs are synthesized similar to mRNAs and can be detected through screening of polyA-rich EST or cDNA libraries. We developed a computational pipeline to screen EST and genomic sequence data for those transcribed genes with limited protein coding potential and applied this pipeline to the model legume Medicago truncatula. This process identified a set of 503 mRNA-like transcripts that appear not to encode proteins. Further computational analysis showed that many of these ncRNA candidates share structural similarities to known ncRNAs and that they clearly differ from protein coding genes and non-transcribed regions in their base and oligonucleotide compositions, as well as in aspects of secondary structure. By using a machine learning approach, we show that the distinctive ncRNA features presented in this study can be used to discriminate most ncRNAs and may thus be useful for improving ncRNA prediction. Computational analysis of EST isolation frequencies in various plant tissues showed that the expression levels and expression profiles of the putative ncRNAs and mRNAs differ – most interestingly, the putative ncRNAs are highly expressed relative to mRNAs in the root nodule tissue and conserved only in closely related plants. The work presented here constitutes the first large-scale prediction and characterization of ncRNAs in legumes, and provides a basis for further research on elucidating ncRNA function in legume genomics. [ABSTRACT FROM AUTHOR]

Sharp edges, ridges, valleys, and prongs are critical for the appearance and an accurate representation of a 3D model. In this paper, we propose a novel approach that deals with the global shape of features in a robust way. Based on a remeshing algorithm which delivers an isotropic mesh in a feature-sensitive metric, features are recognized on multiple scales via integral invariants of local neighborhoods. Morphological and smoothing operations are then used for feature region extraction and classification into basic types such as ridges, valleys, and prongs. The resulting representation of feature regions is further used for feature-specific editing operations. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present an image retrieval technique for specific objects based on salient regions. The salient regions we select are invariant to geometric and photometric variations. Those salient regions are detected based on low level features, and need to be classified into different types before they can be applied on further vision tasks. We first classify the selected regions into four types including blobs, edges and lines, textures, and texture boundaries, by using the correlations with the neigbouring regions. Then, some specific region types are chosen for further object retrieval applications. We observe that regions selected from images of the same object are more similar to each other than regions selected from images of different objects. Correlation is used as the similarity measure between regions selected from different images. Two images are considered to contain the same object, if some regions selected from the first image are highly correlated to some regions selected from the second image. Two data sets are employed for experiment: the first data set contains human face images of a number of different people and is used for testing the retrieval algorithm on distinguishing specific objects of the same category; and the second data set contains images of different objects and is used for testing the retrieval algorithm on distinguishing objects of different categories. The results show that our method is very effective on specific object retrieval. [Copyright &y& Elsevier]

In this paper, we present a robust methodology using mathematical pattern recognition schemes to detect and classify events in action potentials for recognizing toxins in biological cells. We focus on event detection in action potential via abstraction of information content into a low dimensional feature vector within the constrained computational environment of a biosensor. We use generated families of action potentials from a classic Hodgkin–Huxley model to verify our methodology and build toxin recognition engines. We demonstrate that good recognition rates are achievable with our methodology. [ABSTRACT FROM AUTHOR]

In contrast to steganography, steganalysis is focused on detecting (the main goal of this research), tracking, extracting, and modifying secret messages transmitted through a covert channel. In this paper, a feature classification technique, based on the analysis of two statistical properties in the spatial and DCT domains, is proposed to blindly (i.e., without knowledge of the steganographic schemes) to determine the existence of hidden messages in an image. To be effective in class separation, the nonlinear neural classifier was adopted. For evaluation, a database composed of 2088 plain and stego images (generated by using six different embedding schemes) was established. Based on this database, extensive experiments were conducted to prove the feasibility and diversity of our proposed system. It was found that the proposed system consists of: 1) a 90%+ positive-detection rate; 2) not limited to the detection of a particular steganographic scheme; 3) capable of detecting stego images with an embedding rate as low as 0.01 bpp; and 4) considering the test of plain images incurred low-pass filtering, sharpening, and JPEG compression. [ABSTRACT FROM PUBLISHER]

The global drive towards a sustainable energy future is giving rise to rapidly increasing penetration of variable renewable energy into modern power grids. This creates a need for the assessment, characterization and classification of renewable energy resources in the context of the operational challenges posed by large-scale grid integration of renewable energy. This investigation explores a methodology for classifying wind energy resources, using feature vectors defined in terms of the statistical properties of the wind resource for Time-of-Use energy demand periods. Results are presented for the geographic areas associated with the South African Renewable Energy Development Zones, using a mesoscale wind atlas dataset as the resource input. The cluster formations obtained with the Time-of-Use feature vector approach are compared with results obtained by clustering the temporal power profiles using the k-means algorithm. It is shown that cluster formations obtained with the respective inputs exhibit distinct differences, especially with reference to the spatial granularity and geographical dispersion of the clusters. It is concluded that the proposed Time-of-Use feature vector approach offers advantages for application as a classification and data partitioning methodology for spatiotemporal wind profiles. • Translated wind power profiles to statistical energy Time-of-Use feature vectors. • Reducion of a high dimensionality temporal wind power dataset. • Statistically reduced dataset retaining critical underlying characteristics. • Comparison of temporospatial inputs with statistical clustering methodology. [ABSTRACT FROM AUTHOR]

Processing of image sequences is a very actual trend now. This is confirmed with a vast amount of researches in that area. The possibility of an image sequence processing and pattern recognition became available because of increased computer capabilities and better photo and video cameras. The feature extraction is one of the main steps during image processing and pattern recognition. This paper presents a novel classification of features of image sequences. The proposed classification has three groups: 1) features of a single image, 2) features of an image sequence, 3) semantic features of an observed scene. The first group includes features extracted from a single image. The second group consists of features of any kinds of image sequences. The third group contains semantic features. Reverse feature clarification method is the iterative method when on each iteration we use higher level features to extract lower level features more precisely. The proposed classification of features of image sequences solves a problem of decomposition of the source feature space into several groups. Reverse feature clarification method allows to increase the quality of image processing during iterative process. [ABSTRACT FROM AUTHOR]

• An improved version of the World Cup Optimization Algorithm is introduced. • Chaos theory is utilized for increasing the convergence and the exploration part of the world cup optimization algorithm. • A new configuration based on feature classification is introduced for skin cancer diagnosis. • The proposed optimized method is compared with 4 new methods for showing its superiority. Skin cancer has grown significantly over the past decades, and the importance of its initial treatment is increasing day by day. This study aims to use a computer-aided diagnosis automatic system for the exact diagnosis of cancerous cases. In the present study, after applying pre-processing operations including contrast enhancement, image thresholding, and mathematical morphology for highlighting the important areas, feature extraction based on 20 different features has been applied. Then, for decreasing the system complexity, a new improved version of the world cup optimization algorithm is utilized for the features pruning. After optimal selection of the features, the injected into a support vector machine as a classifier to determine the cancerous areas. Experimental results are compared by different new algorithms including the classic SVM method, hybrid optimized neural networks based on gray wolf optimizer and particle swarm optimization algorithm, and also convolutional neural network. Results obtained for skin lesions images from two different databases indicate that the proposed method has higher efficiency than other published methods, demonstrating even better performance than the well-known classification method, the convolutional neural networks. The proposed method presents the highest correct detection rate - 92.64 for ACS images and 87.5 for ISIC images with very low false acceptance rates (4.41 % and 9 % for ACS and ISIC images, respectively) and even less false rejection rates, this is, 2.94 % on ACS images and 3.5 % on ISIC images. These results prove that the proposed method may be reliably used to diagnose skin cancer through dermoscopy images. [ABSTRACT FROM AUTHOR]

Based on the kernel canonical correlation analysis (KCCA) and the ambiguity function (AF) description of radar signals, a novel hybrid fusion method for specific radar emitter identification is proposed. The near‐zero Doppler slices of the AF are firstly encoded by the corresponding kernel matrices. Then, these kernels are divided into two groups and a uniform combined kernel is calculated for each group, which contains the idea of kernel‐level fusion. Given the two integrated kernels, KCCA is employed to extract the discriminative features for classification, which is a common feature‐level fusion method. The proposed method can not only avoid searching for the representative Doppler slice of the AF (AFR), but also obtain better performance than the AFR because of the information fusion strategy. Finally, the experimental results on two real radar data demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features. [ABSTRACT FROM AUTHOR]

Commercial lightning detection networks offer detailed lightning stroke data upon subscription. Alternatively, the lightning monitoring system can be deployed at lower cost using Very Low Frequency (VLF) signal reception. With that, a dual-class high-intensity lightning recognition algorithm was developed using VLF signal features to identify the severity of lightning occurrence in the vicinity of 500 km from a single VLF signal path. The algorithm combined feature extraction, lightning detection, and lightning severity classification modules to identify two lightning severity classes based on the detected lightning peak current (by Lightning Detection System Network). For each lightning stroke sample, 20 VLF signal features (in terms of wavelet coefficient) in five signal parameters of four different levels of frequency bands were extracted as pattern representation prior to the classification. A combination of bagged and boosted trees ensemble classifiers achieved a lightning detection rate of 64.0% and correct lightning severity classification of 60.3%. These outstanding results exceeded lightning detection rate of between 20% and 40% in related past studies that applied the early VLF events technique. Good design practices such as cross-validation, feature selection, and ensemble classifiers contribute good generalisation and unbiased classification for small dataset. • Power of signal is dominant features in lightning detection. • Zero crossing rate feature useful in lightning recognition system. • Minimum 26.9% lightning detection rate improvement from Early VLF Event technique. • Features optimisation and ensemble classifiers assist small dataset classification. [ABSTRACT FROM AUTHOR]

• In this work, we presented a general feature detection and classification method with single wavelength HSRL at 532 nm, as a detailed reference of the further data analysis for other HSRLs. • To reduce false detection, the threshold profile of the feature detection is chosen to be varied with the signal-to-noise ratio of lidar signal. • Validation experiment is performed with the HSRL system at Zhejiang University. The obtained aerosol optical depth meets well with that from the sun photometer (5.9% in terms of bias error). • Measurements performed over eastern China extend the database of aerosols near the Yangtze River Delta region. The comparison of optical properties between our results and established literatures demonstrates that our measurements and method are reasonably good and satisfactory. A ground-based high-spectral-resolution lidar (HSRL), operated at 532 nm wavelength, has been developed at Zhejiang University (ZJU) for aerosols and clouds studies. This lidar provides vertical profiles of aerosol scattering ratio together with lidar ratio and particle depolarization ratio at 532 nm. These single wavelength HSRL observations can be used to automatically identify the features attributed to dust/polluted dust, urban/smoke, maritime aerosols, as well as ice and liquid water cloud during day and night. Developed algorithm was applied to extended data set obtained during field campaigns performed at coastal city Zhoushan and metropolis Hangzhou over eastern China. To evaluate the practical performance of feature detection, the obtained aerosol optical depth (AOD) from ZJU HSRL has been compared with that from the sun photometers and they agree very well (5.9% in terms of bias error). Also, aerosol classification obtained with presented algorithm agrees well with previous established literatures. Furthermore, based on our method and extensive data sets from ZJU HSRL, measurements performed over eastern China extend the database of aerosols especially with respect to urban and marine and dust near the Yangtze River Delta region. [ABSTRACT FROM AUTHOR]

• The sudden pain, which has not been so common with other pain such as the cold pain and chronic pain, is researched and a complete set of pain experiment platform is built. • Not only the traditional classic methods such as Power Spectral Density (PSD) and Support Vector Machine (SVM), but also the novel SBELM is utilized to get the higher accuracy by optimizing the features. • Every channel has been compared and the preferable channels are selected, which could provide guidance to doctors to treat the illness of patients effectively. Pain is a sensory phenomenon when the body hurts and receptors are stimulated. Although pain activates the body's protective mechanism, some excessive pain reactions will damage the nearby biological tissues, and mostly it will bring people severe mental distress. In particular, some individuals with cognitive impairment, like the infants, are unable to describe their own pain, and thereby the disease will be delayed. These types of pain require intervention and relief and sudden pain belongs to one of them. Therefore, this paper proposes on a method of sudden pain recognition based on resting state EEG signals. This method can recognize the presence of sudden pain, distinguish the location of pain and can be effectively applied to disease diagnosis. The platform of sudden pain stimulation and EEG acquisition system was designed and built, and a series of experiments were carried out for different subjects. We preprocessed the raw EEG signals and extracted features via Power Spectral Density (PSD) and Multifractal Detrended Fluctuation Analysis (MF-DFA). We also utilized the Support Vector Machine (SVM), Sparse Bayesian Extreme Learning Machine (SBELM) and D-S Evidence Theory to do classification, utilizing 10-Fold Cross-validation. The results suggested that the accuracy of judging the presence of pain was up to 89.3 % on average, accuracy of pain location discrimination was up to 81.3 % on average, and accuracy of cross validation was up to 90.1 %. [ABSTRACT FROM AUTHOR]

• An improved version of the Whale Optimization Algorithm is introduced. • Chaos theory is utilized for convergence improvement of WOA. • New configuration for feature classification is introduced. • The method is compared with 4 new methods for showing its superiority. Brain tumor is an abnormal growth of cells in the brain that its diagnosis in the early stages can help us to prevent the dangers of the next stage. In this paper, a new meta-heuristic based methodology is presented for the early diagnosis of the brain tumor to prevent this objection. The proposed method includes three main phases including background removing, feature extraction, and classification based on multilayer perceptron neural network. Here, an improved model of the whale optimization algorithm based on the chaos theory and logistic mapping technique is employed to the optimal selection of the features and the classification stages. The performance analysis of the presented method is compared with some existing methods. Final results showed that based on analyzing CDR, FAR, and FRR as testifying indices, the proposed method has better results than the other similar methods. [ABSTRACT FROM AUTHOR]

In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature extraction, (2) optimal feature selection, and (3) classification. For feature extraction, the well-known feature extraction techniques such as principle component analysis, linear discriminate analysis, and linear square regression are used. Since the length of feature vector tends to be high, the choice of the optimal features is complex task. Hence, the proposed model utilizes the optimal feature selection technology referred as Lion-based Firefly (L-FF) algorithm to select the optimal features. The main objective of this article is projected on minimizing the correlation between the selected features. It results in providing diverse information regarding the different classes of data. Once, the optimal features are selected, the classification algorithm called neural network (NN) is adopted, which effectively classify the data in an effective manner with the selected features. Furthermore, the proposed L-FF+NN model is compared with the traditional methods and proves the effectiveness over other methods. Experimental analysis shows that the proposed L-FF+NN model is 92%, 28%, 87%, 82%, and 78% superior to the state-of-art models such as GA+NN, FF+NN, PSO+NN, ABC+NN, and LA+NN, respectively.

Preterm delivery contributes to an increased risk of fetal and maternal death as well as several health deficiencies, thereby requiring special care and treatment that result in high financial costs. It is therefore of key importance to diagnose preterm delivery in advance in order to avoid or minimize its undesirable consequences. This paper proposes a novel method for non-invasive diagnosis of preterm delivery based on the classification of electrohysterography (EHG) signals. First, the EHG signal, which is related to the electrical activity of uterine muscles is recorded from the maternal fundus using surface electrodes. Then, the signal is sliced into frames for spectral analysis. Next, spectral analyses of the individual EHG signal frames are carried out and centroid frequencies of the frames are computed, establishing the elements of a feature vector that represents the time-varying spectral content of the EHG signal. Finally, this feature vector is employed for the classification of the underlying EHG signal for term-preterm diagnosis. The efficiency of the proposed approach is evaluated and compared with representative methods from the literature. Our results demonstrate that the proposed approach exhibits superior performance over other methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

High intensity lightning strike can lead to Very Low Frequency (VLF) signal reception. The lack of VLF signal feature characterisation creates the need for commercial lightning dataset subscription, so as to determine the causative lightning strike. This paper presents a signal feature analysis of VLF signal for high intensity lightning severity classification purpose. As such, the analysis was comprised of dual stages, namely feature extraction and feature classification, for two-class recognition. In the feature extraction stage, Discrete Wavelet Transform was implemented to decompose the VLF signal into four frequency bands. In each frequency band, five-signal parameter analysis was executed to yield twenty signal features. Next, the classification stage was executed to determine the best signal feature combination and the most dominant signal feature that generated the highest classification accuracy within the five classifiers. The simulation revealed that the most optimum signal feature combination was achieved via Boosted Trees classifier with classification accuracies of 70% and 50% for training and test datasets, respectively. Class prediction in the skewed test dataset appeared consistent with the test data classification accuracy. The Zero Crossing Rate between 30 and 60 kHz emerged as the most dominant signal feature due to its impact on all classifiers' best signal feature arrangement. The proposed recognition model enables the detection of high intensity lightning classes through the use of VLF signal features. • Zero Crossing Rate is the dominant signal feature to classify lightning severity. • Abrupt perturbation (Energy Entropy) has the least influence on lightning severity. • Boosted Trees is the best classifier in classification accuracy and consistency. • The largest sample to feature ratio does not yield highest classification accuracy. [ABSTRACT FROM AUTHOR]

In 2017, the New South Wales (NSW) Office of Environment and Heritage (OEH) initiated a state-wide mapping program, SeaBed NSW, which systematically acquires high-resolution (2–5 m cell size) multibeam echosounder (MBES) and marine LiDAR data along more than 2000 km of the subtropical-to-temperate southeast Australian continental shelf. This program considerably expands upon existing efforts by OEH to date, which have mapped approximately 15% of NSW waters with these technologies. The delivery of high volumes of new data, together with the vast repository of existing data, highlights the need for a standardised, automated approach to classify seabed data. Here we present a methodological approach with new procedures to semi-automate the classification of high-resolution bathymetry and intensity (backscatter and reflectivity) data into a suite of data products including classifications of seabed morphology (landforms) and composition (substrates, habitats, geomorphology). These methodologies are applied to two case study areas representing newer (Wollongong, NSW) and older (South Solitary Islands, NSW) MBES datasets to assess the transferability of classification techniques across input data of varied quality. The suite of seabed classifications produced by this study provide fundamental baseline data on seabed shape, complexity, and composition which will inform regional risk assessments and provide insights into biodiversity and geodiversity. [ABSTRACT FROM AUTHOR]