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Printed fabrics often have rich colors and variable patterns in different sizes and shapes, which make it difficult to achieve accurate pattern recognition and color measurement using traditional spectrophotometers and digital cameras. This paper develops a grid-based density peaks clustering (GDPC) algorithm to automatically recognize patterns and extract colors of multispectral images of printed fabrics. The multispectral images captured by a self-developed multispectral imaging system is firstly converted into color images in CIELAB color space and three principal components are calculated by applying principal component analysis to reduce the dimensions of the multispectral images. During the multispectral image processing, the noise pixels are removed by calculating the local stability of each pixel, and then the remaining stable pixels are clustered using proposed GDPC algorithm based on three CIELAB color channels and three principal components. Compared with widely-used color clustering algorithms, the proposed GDPC algorithm can recognize the color patterns from more intricate multispectral printed fabric images with higher accuracy and less computational time. [ABSTRACT FROM AUTHOR]

In this paper, two new distance measures are introduced, which are used in various application problems in decision-making, pattern recognition, and clustering. Intuitionistic fuzzy sets are sources of information that contain both the membership and non-membership degrees of the elements in the set. As such, distance measures based on geometric concepts are sometimes misleading. Hence, six parameters are identified to construct the distance measures. These parameters are membership information dissimilarity, non-membership information dissimilarity, hesitancy information dissimilarity, product cross-information dissimilarity, maximum cross-information dissimilarity, and minimum cross-information dissimilarity. Among all the parameters, the product cross-information dissimilarity is newly introduced in this work. Compensations for the proposed distance measures are established by various counter-intuitive problems in decision-making and pattern recognition. Finally, validation of the proposed distance measures is established by diverse problems of applications in decision-making, pattern recognition, and clustering problems. [ABSTRACT FROM AUTHOR]

Image pattern recognition in the field of big data has gained increasing importance and attention from researchers and practitioners in many domains of science and technology. This paper focuses on the usage of image pattern recognition for big data applications. In this context, the taxonomy of image pattern recognition and big data is revealed. The applications of image pattern recognition for big data, including multimedia, biometrics, and biology/biomedical, are also highlighted. Moreover, the significance of using pattern-based feature reduction in big data is discussed, and machine-learning techniques in pattern recognition applications are presented. A comparison based on the objectives of the approaches is presented to underline the taxonomy. This paper provides a novel review in exploring image recognition approaches for big data, which can be used in future research. [ABSTRACT FROM AUTHOR]

The automatic creation of a repository of the building's floor plan helps a lot to the architects to reuse them. The basic approach is to extract and recognize texts, symbols or graphics to retrieve the information of the floor plan from the images. This paper proposes a floor plan information retrieval algorithm. The proposed algorithm is based on shape extraction and room identification. α -shape is used for finding an accurate shape. From the detected shapes, actual areas of rooms are calculated. Later, a regression model-based binary room classification model is proposed to classify them into room-type, i.e., bedroom, drawing room, kitchen, and non-room-type, i.e., parking porch, bathroom, study room and prayer room. The proposed model is tested on the CVC-FP dataset with an average room detection accuracy of 85.71% and room recognition accuracy of 88%. [ABSTRACT FROM AUTHOR]

The debate about direct perception encompasses different topics, one of which concerns the richness of the contents of perceptual experiences. Can we directly perceive only low-level properties, like edges, colors etc. (the sparse-content view), or can we perceive high-level properties and entities as well (the liberal-content view)? The aim of the paper is to defend the claim that the content of our perceptual experience can include emotions and also person impressions. Using these examples, an argument is developed to defend a liberal-content view for core examples of social cognition. This view is developed and contrasted with accounts which claim that in the case of registering another person's emotion while seeing them, we have to describe the relevant content not as the content of a perceptual experience, but of a perceptual belief. The paper defends the view that perceptual experiences can have a rich content yet remain separable from beliefs formed on the basis of the experience. How liberal and enriched the content of a perceptual experience is will depend upon the expertise a person has developed in the field. This is supported by the argument that perceptual experiences can be systematically enriched by perceiving affordances of objects, by pattern recognition or by top-down processes, as analyzed by processes of cognitive penetration or predictive coding. [ABSTRACT FROM AUTHOR]

The Euler number of a binary image is an important topological feature for many image processing, image analysis, pattern recognition, and computer vision applications. This paper proposes a new run-based Euler number computation algorithm. The conventional run-based algorithm processes rows of the given image one-by-one from top to bottom in a single phase. For each row, it finds the runs in the row and records the start and end locations of each run to compute neighbor runs. In contrast, our algorithm calculates the Euler number of an image in two phases. In the first phase, we process odd rows alternately to find runs and only record its end location. In the second phase, we process each of the remaining even rows to find runs and calculate neighboring runs between the current row and the rows immediately above and below using the recorded run data. Using this method, the number of accesses required to compute the Euler number decreases in almost all cases. Analysis of the time complexity and experimental results demonstrate that our algorithm outperforms conventional Euler number computation algorithms. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) is an important composite material. It has many applications in aerospace and automotive fields. The little information available about the machining process of this material, specifically when routing process is considered, makes the process control quite difficult. In this paper, we propose a new process control technique and we apply it to the routing process for that important material. The measured machining conditions are used to evaluate the quality and the geometric profile of the machined part. The machining conditions, whether controllable or uncontrollable are used to control part accuracy and its quality. We present a pattern-based machine learning approach in order to detect the characteristic patterns, and use them to control the quality of a machined part at specific range. The approach is called logical analysis of data (LAD). LAD finds the characteristic patterns which lead to conforming products and those that lead to nonconforming products. As an example, LAD is used for online control of a simulated routing process of CFRP. We introduce the LAD technique, we apply it to the high speed routing of woven carbon fiber reinforced epoxy, and we compare the accuracy of LAD to that of an artificial neural network, since the latter is the most known machine learning technique. By using experimental results, we show how LAD is used to control the routing process by tuning autonomously the routing conditions. We conclude with a discussion of the potential use of LAD in manufacturing. [ABSTRACT FROM AUTHOR]

Since rapid growth of IT technologies, the use of multimedia data such as image and videos are explosively increasing. It is an important aspect of not only for users but also researchers. Duplicate images and videos are rapidly increasing and it causes difficulties in retrieval and management as well. It also causes copyright problems. In this paper, we discus prior duplicate video detection techniques and overcome previous research problems using block histogram and dynamic matching approach duplicate video detection method. We improved excessive abstract of previous block mean-value based feature extraction method to be robust in various video transformations. Also, we created feature vector of timely histogram by unit of blocks to reflect video features. We proposed dynamic matching algorithm to match videos which is suitable for large-scale video data. To evaluate our proposal, we used VIREO video datasets which is provided by Hong Kong City University and Carnegie Mellon University and MUSCLE-VCD-2007 dataset which is provided by INRIA. Our method showed 90 % of accuracy on duplicate video detection. Our proposed method showed robustness especially in various video transformations. Also, we tested video clustering test to prove our method and dynamic matching method showed 5 times fast compare to existing method which is suitable for real-time and large-scale video detection process. [ABSTRACT FROM AUTHOR]

In this paper, biometric methods for contactless and unrestricted access control for mobile devices are proposed. The major contribution of this paper are palmprint and knuckles feature extraction methods dedicated for the mobile contactless biometrics. We use texture mask-based features for the palmprint. For the knuckles, we use Probabilistic Hough Transform and Speeded Up Robust Features as well as the 3-step classification methodology. We prove the efficiency of the presented methods by reporting promising results. [ABSTRACT FROM AUTHOR]

In recent past, there has been a steep increase in the use of online platforms for the search of desired products. Real estate industry is no exception and has started initiating rent/sale of houses through online platforms. In this paper, we propose a deep neural network framework to facilitate automatic search of homes based on their floor plans. The salient features of this framework are that the query can be either an image (existing floor plan) or a sketch through a sketch pad interface. Our proposed framework automatically determines the type of query (image or sketch) and retrieves similar floor plan images from the database. The critical contributions of our proposed approach are: (1) a novel unified floor plan retrieval framework using multimodal query, i.e., an intuitive and convenient sketch query mode as well as a query by example mode ; (2) a conjunction of autoencoder, Cyclic GAN and CNN for the task of domain mapping and floor plan image retrieval. We have reported results of extensive experimentation and comparison with baseline results to establish the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper aims at comparing robustness of local features for camera-based document image retrieval and spotting system. We present a literature review of the state of the art of local features extraction that includes keypoint detectors and keypoint descriptors. We also present a dataset and evaluation protocol for camera-based document image retrieval and spotting systems. This dataset is composed of three subparts: The first dataset represents the images with textual content only; the second dataset represents images with graphical content mainly; the third dataset contains text plus graphical elements. Along with the datasets, we present the protocol that describes measurements to evaluate the accuracy and processing time of camera-based document image retrieval and spotting systems. The latter is employed for presenting a detailed evaluation of local features from the literature. [ABSTRACT FROM AUTHOR]

A Pythagorean fuzzy set is one of the successful extensions of the intuitionistic fuzzy set to handle the uncertain and fuzzy information in a more wider way. In this paper, some new exponential similarity measures (SMs) for measuring the similarities between objects are proposed. For it, we used the exponential function for the membership and the non-membership degrees and hence defined some series of the SMs for PFSs. The various desirable properties and their relations are examined. Several counter-intuitive cases are given to show the effectiveness of the proposed measures with the existing SMs. Furthermore, examples to classify the pattern recognition and the decision-making problems are presented and compared with the existing approaches. [ABSTRACT FROM AUTHOR]

Mammographic pattern recognition is one of the most essential tasks in breast cancer diagnosis, and has been studied for several years now to make it suitable and faster. In this paper, we developed a novel deep Convolutional Neural Network (CNN) approach to discriminate normal from abnormal breast tissues using Gaussian pyramid representation for multi-scale analysis (Pyramid-CNN). In order to improve image processing time, we extracted representative region proposals from each mammogram using determinant of the Hessian operator. To improve performance of our model and avoid overfitting, data augmentation techniques based on geometric transformation and sub-histogram equalization were applied on all regions to increase the variance of significant mammographic samples. We evaluated our methodology on the publicly available mammography dataset such as Breast Cancer Digital Repository (BCDR) database. In comparison with the current state-of-the-art methods, the experiments show that our proposed system provides efficient results, achieving the average accuracy of 96.84%, sensitivity of 92.12%, specificity of 98.02%, precision of 92.15%, F1-score of 92.12%, and area under the receiver operating characteristic curve (AUC) of 96.76%. Hence, the study demonstrates that our proposed approach has the potential to significantly improve the conventional recognition and classification strategies for use in advanced clinical application and practice or in general, biomedical imaging field. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy sets, introduced by Atanassov, offer a new possibility to describe in a more adequate way many real problems. An important tool for determining the degree of similarity between two objects is represented by similarity measures. Different types of similarity measures for intuitionistic fuzzy sets were proposed and used in various applications. More existing similarity measures involve little or no the hesitancy degree in intuitionistic fuzzy sets. The capacity of a similarity measure is determined by the form of expression and the information contained therein; more information used implies a greater power to distinguish. In this paper, we propose a family of intuitionistic fuzzy similarity measures that contain the hesitancy degree in their expression. This family represents a generalization of some known measures. For particular values of parameters, various similarity measures can be obtained: some of them are known but others are new, as will be shown in sections "The proposed intuitionistic fuzzy similarity measures" and "Conclusion". In order to evaluate the performance of these measures, our experimental study is oriented in two basic directions: one shows how much are reasonable the proposed measures, and other describes the possibilities of application in solving some real-world problems. The experiments prove that our measures give the same results as the measures used as comparison. [ABSTRACT FROM AUTHOR]

Uncertainty is the most usual problem in decision making, for which intuitionistic fuzzy set (IFS) considered as appropriate means allowing numerous feasible degree of an element to a set. In this paper, we mention first dilemma of the existing divergence measures for IFSs. Next, we propose new divergence and entropy measures for IFSs and compare with the existing measures. To facilitate the interactive or interdependent features among elements in a set, new Shapley-weighted divergence measures for IFSs are developed via the eminent Shapley function, which can be perceived as generalization of the allied weighted divergence measures. Furthermore, we develop Shapley-weighted divergence measures based VIKOR method, which is stimulated by conventional VIKOR method. The proposed method is more adaptable and sinuous for correlative decision-making problems, which is used to determine the ranking order of alternatives and find the optimal one(s), so that it is surmounted the difficulty of the decision makers. To demonstrate the validity of the proposed method, numerical examples related to pattern recognition and multi-criteria decision making are presented, which displays the advantages and feasibility. [ABSTRACT FROM AUTHOR]

This paper aims to predict the landing state during the landing phase to ensure landing safety and reduce the accidents loss. Some past researches have demonstrated the landing phase is the most dangerous phase in flight cycle and fatal accident. The landing safety problem has become a hot research problem in safety field. This study concentrates more on the prediction and advanced warning for landing safety. Firstly, four landing states are divided by three flight parameter variables including touchdown, vertical acceleration and distance to go; subsequently, pattern recognition based on BP neural network is used to establish the landing state prediction model; the genetic algorithm is used to initialize the model parameter; the Markov chain is proposed to revise and improve the model for higher prediction precision. Finally, in comparison with pattern recognition and the Markov chain revision results, the Markov chain revision method is demonstrated to be practical and effective. [ABSTRACT FROM AUTHOR]

Recognition of environmental sound is usually based on two main architectures, depending on whether the model is trained with frame-level features or with aggregated descriptions of acoustic scenes or events. The former architecture is appropriate for applications where target categories are known in advance, while the later affords a less supervised approach. In this paper, we propose a framework for environmental sound recognition based on blind segmentation and feature aggregation. We describe a new set of descriptors, based on Recurrence Quantification Analysis (RQA), which can be extracted from the similarity matrix of a time series of audio descriptors. We analyze their usefulness for recognition of acoustic scenes and events in addition to standard feature aggregation. Our results show the potential of non-linear time series analysis techniques for dealing with environmental sounds. [ABSTRACT FROM AUTHOR]

Distance is an important fundamental concept of the set theory. Since the intuitionistic fuzzy sets (IFSs) was put forward, distance between IFSs has been widely concerned by some researchers and many types of measures have been proposed. However, although intuitionistic fuzzy sets have the advantage of being able to consider waver (lack of knowledge), existing distance measures have not yet considered waver and most of them have counter-intuitive cases. To deal with this problem, this paper introduces the concept of intuitive distance for IFSs, which embodies the property requirements of classical distance measure and highlights the characteristics of intuitionistic fuzzy information. Then, a new intuitive distance measure for IFSs is proposed along with its proofs. After that, a comparative analysis between the intuitive distance measure and the existing distance measures is conducted based on an extended artificial benchmark test set, in which eleven pairs of single-element IFSs are used as an illustration of six typical counter-intuitive cases. Finally, the proposed distance measure is applied to deal with pattern recognition. Results show that the proposed distance does not provide any counter-intuitive cases and the waver that brought from hesitance index can be well reflected. [ABSTRACT FROM AUTHOR]

For almost the past four decades, image classification has gained a lot of attention in the field of pattern recognition due to its application in various fields. Given its importance, several approaches have been proposed up to now. In this paper, we will present a dyadic multi-resolution deep convolutional neural wavelets’ network approach for image classification. This approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a Deep Convolutional Neural Wavelet Network (DCNWN). This network is based on the Neural Network (NN) architecture, the Fast Wavelet Transform (FWT) and the Adaboost algorithm. It consists, first, of extracting features using the FWT based on the Multi-Resolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Second, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. Third, we create an AutoEncoder (AE) using wavelet networks of all images. Finally, we apply a pooling for each hidden layer of the wavelet network to obtain a DCNWN that permits the classification of one class and rejects all other classes of the dataset. Classification rates given by our approach show a clear improvement compared to those cited in this article. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing image classification is important aspect of current research. Extreme learning machine (ELM) has been widely used in the field of pattern recognition for its efficient and good generalization performance. With the study of hyperspectral remote sensing image classification, this paper proposes an information discriminative extreme learning machine (IELM). IELM inherits the advantages of ELM, can solve the problems that ELM learning is insufficient for hyperspectral remote sensing image with limited scale of sample data. The proposed algorithm is tested by experiments for hyperspectral remote sensing image classification. The experiment results show that the proposed algorithm has better classification effect. [ABSTRACT FROM AUTHOR]

Accurate control chart patterns recognition (CCPR) plays an essential role in the implementation of control charts. However, it is a challenging problem since nonrandom control chart patterns (CCPs) are normally distorted by 'common process variations'. In this paper, a novel method of CCPR by integrating fuzzy support vector machine (SVM) with hybrid kernel function and genetic algorithm (GA) is proposed. Firstly, two shape features and two statistical features that do not depend on the distribution parameters and number of samples are presented to explicitly describe the characteristics of CCPs. Then, a novel multiclass method based on fuzzy SVM with a hybrid kernel function is proposed. In this method, the influence of outliers on classification accuracy of SVM-based classifiers is weakened by assigning a degree of membership for every training sample. Meanwhile, a hybrid kernel function combining Gaussian kernel and polynomial kernel is adopted to further enhance the generalization ability of the classifiers. To solve the issue of features selection and parameters optimization, GA is used to simultaneously optimize the input features subsets and parameters of fuzzy SVM-based classifier. Finally, several simulation experiments and a real example are addressed to validate the feasibility and effectiveness of the proposed methodology. And the results of simulation experiments demonstrate that it can achieve excellent performance for CCPR and outperforms other approaches, such as learning vector quantization network, multi-layer perceptron network, probability neural network, fuzzy clustering and SVM, in term of recognition accuracy. The results of the practical cases manifest that the proposed method has application potential for solving the problem of control chart interpretation in real-world. [ABSTRACT FROM AUTHOR]

Facial Expression Recognition is an already well-developed research area, mainly due to its applicability in the construction of different system types. Facial expressions are especially important in the area which relates to the construction of discourses through sign language. Sign languages are visual-spatial languages that are not assisted by voice intonation. Therefore, they use facial expressions to support the manifestation of prosody aspects and some grammatical constructions. Such expressions are called Grammatical Facial Expressions (GFEs) and they are present at sign language morphological and syntactic levels. GFEs stand out in automated recognition processes for sign languages, as they help removing ambiguity among signals, and they also contribute to compose the semantic meaning of discourse. This paper aims to present a study which applies inductive reasoning to recognize patterns, as a way to study the problem involving the automated recognition of GFEs at the discourse syntactic level in the Libras Sign Language (Brazilian Sign Language). In this study, sensor Microsoft Kinect was used to capture three-dimensional points in the faces of subjects who were fluent in sign language, generating a corpus of Libras phrases, which comprised different syntactic constructions. This corpus was analyzed through classifiers that were implemented through neural network Multilayer Perceptron, and then a series of experiments was conducted. The experiments allowed investigating: the recognition complexity that is inherent to each of the GFEs that are present in the corpus; the use suitability of different vector representations, considering descriptive characteristics that are based on coordinates of points in three dimensions, distances and angles therefrom; the need for using time data regarding the execution of expressions during speech; and particularities that are connected to data labeling and the evaluation of classifying models in the context of a sign language. [ABSTRACT FROM AUTHOR]

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered. [ABSTRACT FROM AUTHOR]

Organizing data into sensible groups is called as 'data clustering.' It is an open research problem in various scientific fields. Neither a universal solution nor an absolute strategy for its evaluation exists in the literature. In this context, through this paper, we make following three contributions: (1) A new method for finding 'natural groupings' or clusters in the data set is presented. For this, a new term 'vicinity' is coined. Vicinity captures the idea of density together with spatial distribution of data points in feature space. This new notion has a potential to separate various type of clusters. In summary, the approach presented here is non-convex admissive (i.e., convex hulls of the clusters found can intersect which is desirable for non-convex clusters), cluster proportion and omission admissive (i.e., duplicating a cluster arbitrary number of times or deleting a cluster does not alter other cluster's boundaries), scale covariant, consistent (shrinking within cluster distances and enlarging inter-cluster distances does not affect the clustering results) but not rich (does not generates exhaustive partitions of the data) and density invariant. (2) Strategy for automatic detection of various tunable parameters in the proposed 'Vicinity Based Cluster Detection' (VBCD) algorithm is presented. (3) New internal evaluation index called 'Space-Density Index' (SDI) for the clustered results (by any method) is also presented. Experimental results reveal that VBCD captures the idea of 'natural groupings' better than the existing approaches. Also, SDI evaluation scheme provides a better judgment as compared to earlier internal cluster validity indices. [ABSTRACT FROM AUTHOR]

Nowadays, image processing is an interesting research area due to the growth of the communication technologies. Matching problem, which consists of localizing one texture in an image, that contains several textures is one of the fundamental problem of image processing and pattern recognition. In this paper, a new feature extraction method and texture segmentation system are proposed. The proposed method (RINBP) is robust against rotation and improves the ability of extracting the local information. The segmentation architecture follows several steps. First, fixing a converging point α. After that, a Main analysis Window (MW) starting from α to the bottom left corner of the image is determined. Then, several possible windows are extracted and the feature extraction method is applied on each window. Finally, a similarity measure is calculated in order to decide if this window is pertinent or not. This process is stopped until the size of the MW reaches a minimum size. Each pertinent window increases the relevance of the desired texture in the output image. Finally, an image of relevance is obtained by considering the most relevant area. For the experiments, textured images generated from Brodatz album database are used. The experiments have shown the superiority of our method compared to other existing methods. The obtained results have illustrated the robustness and the efficiency of the proposed segmentation method based on the relevance of the analysis windows. [ABSTRACT FROM AUTHOR]

The problems in computer vision of finding the global correspondences across a set of images can be formulated as a multiple graph matching problem consisting of pairwise matching problems. In the multiple graph matching problem, matching consistency is as important as matching accuracy for preventing the contrariety among matched results. Unfortunately, since the majority of conventional pairwise matching methods only approximate the original graph matching problem owing to its computational complexity, a framework that separately matches each graph pair could generate inconsistent results in practical environments. In this paper, we propose a novel multiple graph matching method based on the second-order consistency concept, which simultaneously considers the matching information of all possible graph pairs. We reformulate the multiple graph matching problem to encourage second-order consistency and design an iterative optimization framework. In our experiments, the proposed method outperforms the state-of-the-art methods in terms of both consistency and accuracy. [ABSTRACT FROM AUTHOR]

The number of Wi-Fi devices and their requirements for bandwidth keep on increasing, along with their hunger for spectrum. This fact is mostly noticeable in dense urban scenarios where neighbors fight for bandwidth and their networks struggle to deliver the requested information. While the inherent limitations of Wi-Fi technologies cannot be overcome, they can be mitigated through configuration. Optimally selecting a wireless channel is a critical aspect of access point (AP) configuration, and a challenging task due to a broad range of factors affecting the wireless connection performance. This paper addresses the channel selection problem by relying on a time-varying dynamic approach capable of modeling its surrounding wireless networks with respect to their usage patterns, channel utilization and adjacent channel interference. This contextual data is used in a channel selection model, which combines utilization patterns and statistics with a probabilistic mathematical model to accurately estimate the impact of adjacent channel interference on the signal to interference plus noise ratio, hence effectively selecting a wireless channel whose optimal performance is exhibited when the users' need it. The experimental results demonstrate that the proposed approach outperforms competing methods while closely tracking the simulation models, thus paving the way for smarter APs. [ABSTRACT FROM AUTHOR]

Multiple empirical kernel learning (MEKL) is demonstrated to be flexible and effective due to introducing multiple kernels. But MEKL also brings a large computational complexity in practice. Therefore, in this paper we adopt the random projection (RP) technique to efficiently construct the low-dimensional feature space, and then develop an efficient and effective MEKL named MEKLRP so as to decrease the computational complexity. The proposed MEKLRP randomly selects a subset $$S'$$ of $$p$$ samples from the whole training set $$S$$ of $$N$$ samples, and then utilizes $$S'$$ to generate $$M$$ different EKMs $$\{\Phi ^{rpe}_l(x)\}_{l=1}^M$$ . Following that, MEKLRP maps each sample $$x$$ into $$\Phi _l^{rpe}(x), l=1...M$$ . Finally, MEKLRP applies the transformed samples into our previous MEKL framework. We highlight the contributions of the MEKLRP as follows. Firstly, the MEKLRP adopts the random characteristic of RP and efficiently decreases the computational cost of the matrix eigen-decomposition from $$O(N^3)$$ to $$O(p^3)$$ . Secondly, the MEKLRP maintains an approximate separability at one certain margin and preserves most of the discriminant information in a low-dimensional space since the characteristic of RP in kernel-based learning. Thirdly, the MEKLRP behaves a lower generalization risk bound than its corresponding original learning machine according to the Rademacher complexity. [ABSTRACT FROM AUTHOR]

The dangerous, overgrown vegetation/trees under high voltage transmission lines right-of-ways (ROWs) have caused severe blackouts/flashovers due to interference with power lines which leads to short circuiting among the conductors. Therefore, these dangerous encroachments are monitored periodically along the electrical distribution networks ROWs through visual inspection, or by airborne system. Each of these methods has its own attributes and limitations and have proved to be costly, time consuming and not much accurate. In these circumstances, it is necessary for the electrical utilities to review their vegetation management practices so as to avoid incidents of unintended encroachments. In tropical countries, overgrown vegetation is a common cause for power line failure. This paper proposes an innovative concept of utilizing a single camera for monitoring dangerous vegetation (trees, shrubs and plants, etc.) under transmission lines ROWs. The main focus is on using an imaging device (camera) integrated on each transmission pole to automate inspection for the vegetation encroachments endangering the transmission lines. These cameras are envisioned to be connected wirelessly to each other, forming a series of wireless camera networks that can be monitored remotely. A single camera mounted on power poles acquires images and sends them wirelessly to the base station. At base station, algorithm (software) trained by image processing and pattern recognition techniques is used to identify (height, depth, and width of encroached vegetation, etc.) excess vegetation encroachments within and outside ROWs. The performance evaluation of a real time developed test-bed scenario proves the feasibilities of integrating the method for transmission line maintenance. [ABSTRACT FROM AUTHOR]

The recently proposed twin support vector machine (TWSVM) obtains much faster training speed and comparable performance than classical support vector machine. However, it only considers the empirical risk minimization principle, which leads to poor generalization for real-world applications. In this paper, we formulate a robust minimum class variance twin support vector machine (RMCV-TWSVM). RMCV-TWSVM effectively overcomes the shortcoming in TWSVM by introducing a pair of uncertain class variance matrices in its objective functions. As a special case, we present a special type of the uncertain class variance matrices by combining the empirical positive and negative class variance matrices. Computational results on several synthetic as well as benchmark datasets indicate the significant advantages of proposed classifier in both computational time and test accuracy. [ABSTRACT FROM AUTHOR]

Elite rugby union teams currently employ the latest technology to monitor and evaluate the physical demands of training and games on their players. Tackling has been shown to be the most common cause of injury in rugby union, yet current player monitoring technology does not effectively evaluate player tackling measurements. Currently, to evaluate measurements specific to player tackles, a time-consuming manual analysis of player sensor data and video footage is required. The purpose of this work is to investigate tackle modeling techniques which can be utilised to automatically detect player tackles and collisions using sensing technology already being used by elite international and club level rugby union teams. This paper discusses issues relevant to automatic tackle analysis, describes a technique to detect tackles using sensing data and validates the technique by comparing automatically detected collisions to manually labeled collisions using data from elite club and international level players. The results of the validation show that the system is able to consistently identify collisions with very few false positives and false negatives, achieving a recall and precision rating of 0.933 and 0.958, respectively. The aim is that the automatically detected tackles can provide coaching, medical and strength and conditioning staff with objective tackle-specific measurements, in real time, which can be used in injury prevention and rehabilitation strategies. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive and low-cost robotic coating platform for small production series is presented. This new platform presents a flexible architecture that enables fast/automatic system adaptive behaviour without human intervention. The concept is based on contactless technology, using artificial vision and laser scanning to identify and characterize different workpieces travelling on a conveyor. Using laser triangulation, the workpieces are virtually reconstructed through a simplified cloud of three-dimensional (3D) points. From those reconstructed models, several algorithms are implemented to extract information about workpieces profile (pattern recognition), size, boundary and pose. Such information is then used to on-line adjust the 'base' robot programmes. These robot programmes are off-line generated from a 3D computer-aided design model of each different workpiece profile. Finally, the robotic manipulator executes the coating process after its 'base' programmes have been adjusted. This is a low-cost and fully autonomous system that allows adapting the robot's behaviour to different manufacturing situations. It means that the robot is ready to work over any piece at any time, and thus, small production series can be reduced to as much as a one-object series. No skilled workers and large setup times are needed to operate it. Experimental results showed that this solution proved to be efficient and can be applied not only for spray coating purposes but also for many other industrial processes (automatic manipulation, pick-and-place, inspection, etc.). [ABSTRACT FROM AUTHOR]

Hierarchical clustering based on semantic feature thresholds is well applied in data mining. However, this method uses systematic sampling to select the edge points of the region during the region merging phase, resulting in the problems of under-consolidation and computational complexity when region merging based on the Euclidean distance criterion. To solve this problem, an improved hierarchical clustering method is proposed, which adopts the weighted Euclidean distance and optimizes the components of region combination. According to the orientation of the centers of two region clustering and the geometric relation among the edge points, two novel type of edge point selection strategies are established to reduce the number of edge points involved in the operation and ensure that the selected ones possess the minimum Euclidean distance in all edge point combinations. Experimental results show that the main advantages of this research project lie in its high clustering accuracy and less average clustering time. [ABSTRACT FROM AUTHOR]

Classical classification methods usually assume that pattern recognition models do not depend on the timing of the data. However, this assumption is not valid in cases where new data frequently become available. Such situations are common in practice, for example, spam filtering or fraud detection, where dependencies between feature values and class numbers are continually changing. Unfortunately, most classical machine learning methods (such as decision trees) do not take into consideration the possibility of the model changing, as a result of so-called concept drift and they cannot adapt to a new classification model. This paper focuses on the problem of concept drift, which is a very important issue, especially in data mining methods that use complex structures (such as decision trees) for making decisions. We propose an algorithm that is able to co-train decision trees using a modified NGE ( Nested Generalized Exemplar) algorithm. The potential for adaptation of the proposed algorithm and the quality thereof are evaluated through computer experiments, carried out on benchmark datasets from the UCI Machine Learning Repository. [ABSTRACT FROM AUTHOR]

In this paper a PSIAC-based multi-parameter fuzzy pattern recognition (MPFPR) model is proposed and applied for classifying and ranking the potential soil erosion (PSE). In this approach, standard value matrix is used to define the membership degrees of each catchment to each class and the feature values are used for alternative ranking. The characteristic of PSE for each class is expressed by linguistic variables. The proposed method is straightforward, easy to understand, very practical, and its results may easily be interpreted. To assess the performance of the model, the results of PSIAC MPFPR and original PSIAC method are interpreted and compared with the observed data. It is shown that the proposed approach reflects the fuzzy nature of the soil erosion more efficiently and is quite robust for application in real world cases. [ABSTRACT FROM AUTHOR]

In this paper, a novel type of radial basis function network is proposed for multitask pattern recognition. We assume that recognition tasks are switched sequentially without notice to a learner and they have relatedness to some extent. We further assume that training data are given to learn one by one and they are discarded after learning. To learn a recognition system incrementally in such a multitask environment, we propose Resource Allocating Network for Multi-Task Pattern Recognition (RAN-MTPR). There are five distinguished functions in RAN-MTPR: one-pass incremental learning, task change detection, task categorization, knowledge consolidation, and knowledge transfer. The first three functions enable RAN-MTPR not only to acquire and accumulate knowledge of tasks stably but also to allocate classes to appropriate tasks unless task labels are not explicitly given. The fourth function enables RAN-MTPR to recover the failure in task categorization by minimizing the conflict in class allocation to tasks. The fifth function, knowledge transfer from one task to another, is realized by sharing the internal representation of a hidden layer with different tasks and by transferring class information of the most related task to a new task. The experimental results show that the recognition performance of RAN-MTPR is enhanced by introducing the two types of knowledge transfer and the consolidation works well to reduce the failure in task change detection and task categorization if the RBF width is properly set. [ABSTRACT FROM AUTHOR]

The paper continues the analysis of clustering and signal classification problems. For problems of point-set clustering in a feature space, an optimal algorithm to synthesize hyperplane clusters and an algorithm to solve a problem on linear separability of a finite point set are obtained, criteria for linear strip separability of points in a feature space into two classes are formulated, and methods of finding optimal nonlinear transform of a coordinate of the feature vector in given classes of functions and its index are developed. [ABSTRACT FROM AUTHOR]

This paper describes an application of emotion recognition in human gait by means of kinetic and kinematic data using artificial neural nets. Two experiments were undertaken, one attempting to identify participants’ emotional states from gait patterns, and the second analyzing effects on gait patterns of listening to music while walking. In the first experiment gait was analyzed as participants attempted to simulate four distinct emotional states (normal, happy, sad, angry). In the second experiment, participants were asked to listen to different types of music (excitatory, calming, no music) before and during gait analysis. Derived data were fed into different types of artificial neural nets. Results showed not only a clear distinction between individuals, but also revealed clear indications of emotion recognition in nets. [ABSTRACT FROM AUTHOR]

The identification of various unnatural patterns that are usually exhibited in quality control charts leads to more focussed diagnosis and, thereby, significantly minimises the effort towards effective troubleshooting. Feature-based control chart pattern (CCP) recognition systems have the advantage that the users can easily understand how a particular pattern is identified. Pham and Wani have presented a feature-based heuristic approach for CCP recognition which can differentiate six types of CCPs, based on the extraction of nine shape features. The extraction of some of these features requires users’ inputs and, thus, this CCP recognition system is not truly automated. Moreover, many real-life situations require detection of all of the eight basic CCPs, including stratification and systematic patterns. The extraction of the features after the scaling of pattern data into an (0, 1) interval can ensure that the magnitudes of the features are independent of the mean and standard deviation of the underlying process. But the distinction between normal and stratification patterns is lost when the pattern data are scaled. A CCP recogniser that will identify a stratification pattern, therefore, needs to be developed using unscaled pattern data. In this paper, potentially useful 32 features, the extraction of which do not require users’ inputs of any form, are proposed. The magnitudes of these features are independent of the process mean and are considerably insensitive to changes in the process standard deviation. An easy mechanism for the determination of the optimal set of features and a heuristic is also presented. Sensitivity studies on the performance of the heuristic show that it is robust against the estimation error of the process mean and is reasonably robust against the estimation error of the process standard deviation. Thus, it has enough potential for use in real-time process control applications. [ABSTRACT FROM AUTHOR]

Agile manufacturing is the capability to prosper in a competitive environment of continuous and unpredictable changes by reacting quickly and effectively to the changing markets and other exogenous factors. Agility of petroleum refineries is determined by two factors – ability to control the process and ability to efficiently manage the supply chain. In this paper, we outline some challenges faced by refineries that seek to be lean, nimble, and proactive. These problems, which arise in supply chain management and operations management are seldom amenable to traditional, monolithic solutions. As discussed here using several examples, methodologies drawn from artificial intelligence – software agents, pattern recognition, expert systems – have a role to play in this path toward agility. [ABSTRACT FROM AUTHOR]

In this paper a new, abstract method for analysis and visualization of multidimensional data sets in pattern recognition problems is introduced. It can be used to determine the properties of an unknown, complex data set and to assist in finding the most appropriate recognition algorithm Additionally, it can be employed to design layers of a feedforward artificial neural network or to visualize the higher-dimensional problems in 2-D and 3-D without losing relevant data set information. The method is derived from the convex set theory and works by considering convex subsets within the data and analyzing their respective positions in the original dimension. Its ability to describe certain set features that cannot be explicitly projected into lower dimensions sets it apart from many other visualization techniques. Two classical multidimensional problems are analyzed and the results show the usefulness of the presented method and underline its strengths and weaknesses. [ABSTRACT FROM AUTHOR]

The paper describes a methodology for constructing transfer functions for the hidden layer of a back-propagation network, which is based on evolutionary programming. The method allows the construction of almost any mathematical form. It is tested using four benchmark classification problems from the well-known machine intelligence problems repository maintained by the University of California, Irvine. It was found that functions other than the commonly used sigmoidal function could perform well when used as hidden layer transfer functions. Three of the four problems showed improved test results when these evolved functions were used. [ABSTRACT FROM AUTHOR]

Pattern recognition is an important skill of Computational Thinking and is one of the most important competences for solving a problem that involves finding similarities or patterns in small problems to solve more complex ones. In this work, we present the mobile application software Patrony. The main contribution of this work is to promote the learning of Computational Thinking, especially pattern recognition, in specific sectors of education in Mexico through the simple use of a software application. To evaluate the effectiveness of the mobile application, tests were carried out in two elementary schools with a total of 43 students, which were divided into 2 groups: a control group and an experimental group. The results of the tests showed that the learning gain (M = 6.50 in postest compared to M = 4.94 on pretest) of the students who used our mobile application produces a significant difference with respect to students who learned using a traditional method of classroom teaching. The results also infer that computational thinking applications can be used as effective learning tools within some important Mathematics topics in public and private schools in Mexico. [ABSTRACT FROM AUTHOR]

Copy toner samples were analyzed using reflection-absorption infrared microscopy (R-A IR). The grouping of copy toners into distinguishable classes achieved by visual comparison and computer-assisted spectral matching was compared to that achieved by multivariate discriminant analysis. For a data set containing spectra of 430 copy toners, 90% (388/430) of the spectra were initially correctly grouped into the classifications previously established by spectral matching. Three groups of samples that did not classify well contained too few samples to allow reliable classification. Samples from two other pairs of groups were similar and often misclassified. Closer examination of spectra from these groups revealed discriminating features that could be used in separate discriminant analyses to improve classification. For one pair of groups, the classification accuracy improved to 91% (81/89) and 97% (28/29), for the two groups, respectively. The other pair of groups were completely distinguishable from one another. With these additional tests, multivariate discriminant analysis correctly classified 96% of the 430 R-A IR toner spectra into the toner groups found previously by spectral matching. [ABSTRACT FROM AUTHOR]

The application of Poincaré plot analysis to characterize inter-beat interval dynamics has been successfully proposed in the scientific literature for the assessment of humans' physiological states and related aberrations. In this study, we proposed novel descriptors to trace the evolution of Poincaré plot shape over the lags. Their reliability in ultra-short cardiovascular series analysis was validated on synthetic inter-beat series generated through a physiologically plausible integral pulse frequency modulation model. Furthermore, we used the proposed approach for the investigation of the direct relationship between autonomic nervous system (ANS) dynamics and hedonic olfactory elicitation, in a group of 30 healthy subjects. Participants with a similar olfactory threshold were selected, and were asked to score 5-s stimuli in terms of arousal and valence levels according to the Russell's circumflex model of affect. Their ANS response was investigated in 35-s windows after the elicitation. Experimental results showed a gender-specific, high discriminant power of the proposed approach, discerning between pleasant and unpleasant odorants with an accuracy of 83.33% and 73.33% for men and for women, respectively. Graphical Abstract Olfaction plays a crucial role in our life and is strictly related to the Autonomic Nervous System (ANS) activity, which can be monitored studying Heart Rate Variability. We used the Lagged Poincare Plot approach to recognize gender-specific ANS response in 35-second windows after the elicitation through pleasant/unpleasant odorants. [ABSTRACT FROM AUTHOR]

Although Artificial Intelligence (AI) has become a buzzword for self-organizing IT applications, its relevance to software engineering has hardly been analyzed systematically. This study combines a systematic review of previous research in the field and five qualitative interviews with software developers who use or want to use AI tools in their daily work routines, to assess the status of development, future development potentials and equally the risks of AI application to software engineering. The study classifies the insights in the software development life cycle. The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e.g. for debugging and documentation, b) the structured analysis of big data pools to discover patterns and novel information clusters and c) the systematic evaluation of these data in neural networks. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains. AI to date depends on man-made structures and is mainly reproductive, but the automation of software engineering routines entails a major advantage: Human developers multiply their creative potential when using AI tools effectively. [ABSTRACT FROM AUTHOR]

It is well known that high impedance contrasts between salt bodies and their surroundings yield blurriness in seismic images which does not allow adequate interpretation; some of the artifacts generated by this process are multiple reflections and diffractions. In this work we propose a novel method of detection and attenuation of multiples from seismic gathers which is based on pattern recognition and fuzzy logic. In order to corroborate the effectiveness of this new approach, we use synthetic models which mimic a region intensely affected by salt tectonics in deep-water Gulf of Mexico. We obtained synthetic seismic sections by simulating wave propagation with an FDTD–CPML formulation. Detection of seismic events is performed through the mapping of parabolic shapes in shot gathers that approximate the recorded wavefronts. Undesired multiple events are then surgically attenuated by using fuzzy threshold criteria. Results show an optimal attenuation of multiple events, even when they are not directly related to salt structures. [ABSTRACT FROM AUTHOR]

Research in cognitive psychology has focused mainly on the visual modality as the input interface for mental processes. We suggest that integrating studies from different modalities can aid in resolving theoretical controversies. We demonstrate this in the case of subitizing. Subitizing, the quick and accurate enumeration of small quantities, has been studied since the 19th century. Nevertheless, to date, the underlying mechanism is still debated. Two mechanisms have been suggested: a domain-general mechanism—attention, and a domain-specific mechanism—pattern recognition. Here, we review pivotal studies in the visual, tactile, and auditory modalities. The accumulative findings shed light on the theoretical debate. Accordingly, we suggest that subitizing is a subprocess of counting that occurs in the presence of facilitating factors, such as attentional resources and familiar patterns. [ABSTRACT FROM AUTHOR]

Arabic Text document clustering is an important aspect for providing conjectural navigation and browsing techniques by organizing massive amounts of data into a small number of defined clusters. However, Words in form of vector are used for clustering methods is often unsatisfactory as it ignores relationships between important terms. Cluster analysis separates data into groups on clusters for the purposes of improved understanding or summarization. Clustering has a long history and many techniques developed in statistics, data mining, pattern recognition and other fields. This research proposes three approaches; Unsupervised, Semi Supervised techniques and Semi Supervised with dimensionality reduction to construct a clustering based classifier for Arabic text documents. Using k-means, incremental k-means, Threshold + k-means and k-means with dimensionality reduction, after document preprocessing removing stop words and gets the root for each term in each document. Then apply a term weighting method to get the weight of each term with respect to its document. Then apply a similarity measure method to each document and its similarity with other documents. And using F-measure, entropy and support vector machine (SVM) for calculate accuracy. The datasets are online dynamic datasets that are characterized by its availability and credibility on the internet. Arabic language is a challenging language when applied in an inference based algorithm. So, selecting the appropriate dataset is a principal factor in such research. The accuracy of those methods compared with other approaches and the proposed methods shows better accuracy and fewer errors for new classification test cases. Considering that the dimension reduction process is very sensitive because increasing the ratio of reduction can destroy important terms. [ABSTRACT FROM AUTHOR]

Seismogenic nodes capable of generating shallow earthquakes with M ≥ 5.0 have been identified in the northeast part of Egypt using the pattern recognition approach. The study region includes the Sinai Peninsula and the northern part of the Eastern Desert between the Nile River and the Red Sea, which comprises the new capital of Egypt and Suez Canal projects and represents a place for much future development. We apply a phenomenological approach based on pattern recognition for identifying seismogenic nodes. The methodology is based on the idea that earthquakes nucleate at nodes: specific structures forming around fault intersections. Morphostructural zoning (MZ) is applied to map nodes over the entire study region without using a priori knowledge of regional seismicity. The pattern recognition is employed to pinpoint seismogenic nodes where earthquakes with M ≥ 5.0 may occur in the future. As a result, seismogenic nodes capable of generating earthquakes with M ≥ 5.0 have been defined in the Sinai Peninsula and the northern part of the Eastern Desert. We also found characteristic geological-geophysical features that discriminate seismogenic nodes (D) from non-seismogenic ones (N). Information on the potential seismic sources represents the main issue in seismic hazard evaluation. Seismogenic nodes prone to earthquakes with M ≥ 5.0, as described in this work, may contribute to the definition of earthquake source zones in the northeast of Egypt because some of the D nodes are potential sources of seismic risk for critical facilities (e.g., Ismailia Tunnels) and large metropolitan areas (e.g., Nile Valley). This information can contribute to the revision of seismic zoning or microzoning and improve the performance of seismic hazard studies of Egypt by adding a new source of information about seismogenic nodes to the seismogenic zones. [ABSTRACT FROM AUTHOR]