Pattern Recognition Applications Research Articles

An incremental clustering using bat-spotted hyena optimiser with spark framework

Recently, clustering techniques gained more importance due to huge range of applications in the field of data mining, pattern recognition, data clustering, bio informatics and many other applications. In this paper, a new approach called spotted hyena bat algorithm (SHBA)-based incremental clustering with spark framework is proposed. The SHBA algorithm is derived by integrating the spotted hyena optimiser (SHO) and bat algorithm (BA), that is highly desirable for handling high dimensional data and provides a unique solution with high satisfactory results. The process of incremental clustering is performed in a spark framework by considering the master and the slave nodes. The proposed approach effectively clusters the data, especially high dimensional data and is more robust against various attacks and provides more unified solution. Moreover, the proposed SHBA achieves higher performance by considering the evaluation metrics, such as Jaccard coefficient, rand coefficient, and clustering accuracy of 0.950, 0.943, and 0.962.

Analysis of Hamming and Hausdorff 3D distance measures for complex pythagorean fuzzy sets and their applications in pattern recognition and medical diagnosis

AbstractSimilarity measures are very effective and meaningful tool used for evaluating the closeness between any two attributes which are very important and valuable to manage awkward and complex information in real-life problems. Therefore, for better handing of fuzzy information in real life, Ullah et al. (Complex Intell Syst 6(1): 15–27, 2020) recently introduced the concept of complex Pythagorean fuzzy set (CPyFS) and also described valuable and dominant measures, called various types of distance measures (DisMs) based on CPyFSs. The theory of CPyFS is the essential modification of Pythagorean fuzzy set to handle awkward and complicated in real-life problems. Keeping the advantages of the CPyFS, in this paper, we first construct an example to illustrate that a DisM proposed by Ullah et al. does not satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Then, combining the 3D Hamming distance with the Hausdorff distance, we propose a new DisM for CPyFSs, which is proved to satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Moreover, similarly to some DisMs for intuitionistic fuzzy sets, we present some other new complex Pythagorean fuzzy DisMs. Finally, we apply our proposed DisMs to a building material recognition problem and a medical diagnosis problem to illustrate the effectiveness of our DisMs. Finally, we aim to compare the proposed work with some existing measures is to enhance the worth of the derived measures.

Correlation coefficients for T-spherical fuzzy sets and their applications in pattern analysis and multi-attribute decision-making.

T-spherical fuzzy set is an effective tool to deal with vagueness and uncertainty in real-life problems, especially in a situation when there are more than two circumstances, like in casting a ballot. The correlation coefficient of T-spherical fuzzy sets is a tool to calculate the association of two T-spherical fuzzy sets. It has several applications in various disciplines like science, management, and engineering. The noticeable applications incorporate pattern analysis, decision-making, medical diagnosis, and clustering. The aim of this article is to introduce some correlation coefficients for T-spherical fuzzy sets with their applications in pattern recognition and decision-making. The under discussion correlation coefficients are far more advantageous than the existing and many other tools used for T-spherical fuzzy sets, because they are used completely and demonstrate the nature as well as the limit of association between two T-spherical fuzzy sets. Further, an application of proposed correlation coefficients in pattern analysis is discussed here. In addition to it, the proposed correlation coefficients are applied to a multi-attribute decision-making problem, in which the selection of a suitable COVID-19 mask is presented. A comparative analysis has also been made to check the effectiveness of the proposed work with the existing correlation coefficients.

A Hybrid Picture Fuzzy Similarity Measure and Improved VIKOR Method

Picture fuzzy set (PFS) can intuitively express the answers of “yes”, “neutral”, “no” and “reject”, which have strong advantages in solving uncertain information. The similarity measure is an effective tool to determine the relationship between two picture fuzzy sets (PFSs). In this paper, we propose a hybrid picture fuzzy (PF) similarity measure which combines the Hamming distance and the transformed tetrahedral centroid distance and verifies that it satisfies the four properties of the similarity measure. The proposed and existing picture fuzzy similarity measures are compared and investigated through numerical examples and some applications of pattern recognition. The results show that the proposed similarity measure not only produces no unreasonable results, but also overcomes the shortcomings of the existing similarity measures. Furthermore, we investigate an improved VIKOR method based on the proposed similarity measure of PFS. Finally, through an example, several multi-attribute decision-making (MADM) methods are compared and analyzed to illustrate the effectiveness and practicability of the improved VIKOR method.

Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures

Handling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to be taken into consideration during preprocessing tasks as a critical problem. To this end, the authors proposed a new algorithm for manipulating MVs using FS. Bayesian ridge regression (BRR) is the most beneficial type of Bayesian regression. BRR estimates a probabilistic model of the regression problem. The proposed algorithm is dubbed as cumulative Bayesian ridge with similarity and Luca’s fuzzy entropy measure (CBRSL). CBRSL reveals how the fuzzy entropy FS used for selecting the candidate feature holding MVs aids in the prediction of the MVs within the selected feature using the Bayesian Ridge technique. CBRSL can be utilized to manipulate MVs within other features in a cumulative order; the filled features are incorporated within the BRR equation in order to predict the MVs for the next selected incomplete feature. An experimental analysis was conducted on four datasets holding MVs generated from three missingness mechanisms to compare CBRSL with state-of-the-art practical imputation methods. The performance was measured in terms of R2 score (determination coefficient), RMSE (root mean square error), and MAE (mean absolute error). Experimental results indicate that the accuracy and execution times differ depending on the amount of MVs, the dataset’s size, and the mechanism type of missingness. In addition, the results show that CBRSL can manipulate MVs generated from any missingness mechanism with a competitive accuracy against the compared methods.

Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey

Historically, geoscience has been a prominent domain for applications of computer vision and pattern recognition. The numerous challenges associated with geoscience-related imaging data, which include poor imaging quality, noise, missing values, lack of precise boundaries defining various geoscience objects and processes, as well as non-stationarity in space and/or time, provide an ideal test bed for advanced computer vision techniques. On the other hand, the developments in pattern recognition, especially with the rapid evolution of powerful graphical processing units (GPUs) and the subsequent deep learning breakthrough, enable valuable computational tools, which can aid geoscientists in important problems, such as land cover mapping, target detection, pattern mining in imaging data, boundary extraction and change detection. In this landscape, classical computer vision approaches, such as active contours, superpixels, or descriptor-guided classification, provide alternatives that remain relevant when domain expert labelling of large sample collections is often not feasible. This issue persists, despite efforts for the standardization of geoscience datasets, such as Microsoft’s effort for AI on Earth, or Google Earth. This work covers developments in applications of computer vision and pattern recognition on geoscience-related imaging data, following both pre-deep learning and post-deep learning paradigms. Various imaging modalities are addressed, including: multispectral images, hyperspectral images (HSIs), synthetic aperture radar (SAR) images, point clouds obtained from light detection and ranging (LiDAR) sensors or digital elevation models (DEMs).

An Image Edge Detection Method Based on Fractional-Order Grey System Model

The detection of edges in images is a pressing issue in the field of image processing. This technique has found widespread application in image pattern recognition, machine vision, and a variety of other areas. The feasibility and effectiveness of grey theory in image engineering applications have prompted researchers to continuously explore it. The grey model (GM (1,1)) with the first-order differentiation of one variable is the grey prediction model that is most frequently used. It is a typical trend analysis model and can be used for image edge detection. The traditional integer-order differential image edge detection operator has problems such as blurred and discontinuous edges, incomplete image details, and high influence by noise. We present a novel grey model for detecting image edges based on a fractional-order discrete operator in this paper. To improve the features of the original image, our model first preprocesses it before calculating the prediction of the original image using our fractional-order cumulative greyscale model. We obtain the edge information of the image by first subtracting a preprocessed image from the predicted image and then eliminating isolated noise points using the median filtering method. Based on the discrete wavelet transform, image edges are finally extracted. The comparison experiments with a traditional edge detection operator show that our algorithm can accurately locate the image edges, the image edges are clear and complete, and this model has better anti-noise performance.

Handwritten Geez Digit Recognition Using Deep Learning

Amharic language is the second most spoken language in the Semitic family after Arabic. In Ethiopia and neighboring countries more than 100 million people speak the Amharic language. There are many historical documents that are written using the Geez script. Digitizing historical handwritten documents and recognizing handwritten characters is essential to preserving valuable documents. Handwritten digit recognition is one of the tasks of digitizing handwritten documents from different sources. Currently, handwritten Geez digit recognition researches are very few, and there is no available organized dataset for the public researchers. Convolutional neural network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. In this work, the proposed model is to recognize Geez digits using CNN. Deep neural networks, which have recently shown exceptional performance in numerous pattern recognition and machine learning applications, are used to recognize handwritten Geez digits, but this has not been attempted for Ethiopic scripts. Our dataset, which contains 51,952 images of handwritten Geez digits collected from 524 individuals, is used to train and evaluate the CNN model. The application of the CNN improves the performance of several machine-learning classification methods significantly. Our proposed CNN model has an accuracy of 96.21% and a loss of 0.2013. In comparison to earlier research works on Geez handwritten digit recognition, the study was able to attain higher recognition accuracy using the developed CNN model.

A New Correlation Coefficient Based on T-Spherical Fuzzy Information with Its Applications in Medical Diagnosis and Pattern Recognition

The T-Spherical fuzzy set (TSFS) is the most generalized form among the introduced fuzzy frameworks. It obtains maximum information from real-life phenomena due to its maximum range. Consequently, TSFS is a very useful structure for dealing with information uncertainties, especially when human opinion is involved. The correlation coefficient (CC) is a valuable tool, possessing symmetry, to determine the similarity degree between objects under uncertainties. This research aims to develop a new CC for TSFS to overcome the drawbacks of existing methods. The proposed CCs are generalized, flexible, and can handle uncertain situations where information has more than one aspect. In addition, the proposed CCs provide decision-makers independence in establishing their opinion. Based on some remarks, the usefulness of the new CC is reviewed, and its generalizability is evaluated. Moreover, the developed new CC is applied to pattern recognition for investment decisions and medical diagnosis of real-life problems to observe their effectiveness and applicability. Finally, the validity of the presented CC is tested by comparing it with the results of the previously developed CC.

Simultaneous Robust Matching Pursuit for Multi-view Learning

Joint sparse representation (JSR) has attracted massive attention with many successful applications in pattern recognition recently. In this paper, we propose a novel robust multi-view JSR method referred to as Simultaneous Robust Matching Pursuit (SRMP) based on the outlier-resistant M-estimator originating from robust statistics. Because of the complexity of the objective function, we design an efficient optimization algorithm to implement SRMP based on the half-quadratic theory. In addition, we have also extended the proposed method for the problems of multi-view subspace clustering and multi-view pattern classification, respectively. The experimental results corroborate the efficacy and robustness of SRMP for multi-view data recovery, subspace clustering and classification.

An innovative decisive framework for optimized agri-automobile evaluation and HRM pattern recognition via possibility fuzzy hypersoft setting

In multi-attribute decision-making system, decision-makers have to face two kinds of situations: (i) the opted parameters are likely to be classified into their respective parametric-valued sub-collections and (ii) the acceptance degree for approximate opinions of decision-makers is required to be assessed by possibility setting. The literature related to fuzzy soft sets is unable to provide any model which can tackle such situations collectively. Therefore, this study aims to address this scarcity through the development of a novel structure, that is, possibility fuzzy hypersoft set ([Formula: see text]-set). Firstly, the algebraic properties and set-theoretic operations of [Formula: see text]-set are characterized by mathematical illustration. Secondly, two algorithms based on AND and OR-operations of [Formula: see text]-set are proposed and authenticated through application in multi-attribute decision-making real-world problems for the evaluation of agri-automobile and then their suitability is judged through vivid comparison. Thirdly, similarity measures between [Formula: see text]-sets are formulated and validated with the help of application in recruitment-based pattern recognition and its significance is assessed through comparison with most relevant models. Lastly, the advantageous aspects of the proposed structure are analyzed by its comparison with possibility fuzzy soft set-like models by observing some important evaluating features.

Novel similarity measures for T-spherical fuzzy sets and their applications in pattern recognition and clustering

T-spherical fuzzy sets, the direct extension of fuzzy sets, intuitionistic fuzzy sets and picture fuzzy sets are examined in this composition, and a mathematical examination among them is set up. A T-spherical fuzzy set can demonstrate phenomenon like choice utilizing four trademark capacities indicating the level of choice of inclusion, restraint, resistance, and exclusion, another example of such situation is that human opinion cannot be restricted to yes or no but it can be yes, abstain, no and refusal. T-spherical fuzzy set can deal the said situation with a boundless space. With the assistance of some mathematical outcomes, it is talked about that current similarity measures have a few drawbacks and could not be implemented where the data is in T-spherical fuzzy mode. Thus, some new similarity measures in T-spherical fuzzy environment are proposed, with the assistance of certain outcomes, it is demonstrated that the suggested similarity measures are generalization of current ones. Further the proposed similarity measures are applied in pattern recognition with numerical supportive examples. The maximum spanning tree clustering algorithm has been extended into T-spherical fuzzy context and supports our theory with numerical examples. A parallel investigation of fresh and existing similarity measures have been made and some of the benefits of designated work have been discussed.

Resolving-power domination number of probabilistic neural networks

Power utilities must track their power networks to respond to changing demand and availability conditions to ensure effective and efficient operation. As a result, several power companies continuously employ phase measuring units (PMUs) to continuously check their power networks. Supervising an electric power system with the fewest possible measurement equipment is precisely the vertex covering graph-theoretic problems otherwise a variation of the dominating set problem, in which a set D is defined as a power dominating set (PDS) of a graph if it supervises every vertex and edge in the system with a couple of rules. If the distance vector eccentrically characterizes each node in G with respect to the nodes in R, then the subset R of V (G) is a resolving set of G. The problem of finding power dominating set and resolving set problems are proven to be NP-complete in general. The finite subset R of V (G) is said to be resolving-power dominating set (RPDS) if it is both resolving and power dominating set, which is another NP-complete problem. The ηp (G) is the minimal cardinality of an RPDS of a graph G. A neural network is a collection of algorithms that tries to figure out the underlying correlations in a set of data by employing a method that replicates how the human brain functions. Various neural networks have seen rapid progress in multiple fields of study during the last few decades, including neurochemistry, artificial intelligence, automatic control, and informational sciences. Probabilistic neural networks (PNNs) offer a scalable alternative to traditional back-propagation neural networks in classification and pattern recognition applications. They do not necessitate the massive forward and backward calculations that ordinary neural networks entail. This paper investigates the resolving-power domination number of probabilistic neural networks.

Complex interval number‐based uncertainty modeling method with its application in decision fusion

Complex evidence theory, a generalization of Dempster–Shafer evidence theory, is an effective uncertainty reasoning for decision fusion in complex-valued domain. In particular, the generation of complex basic belief assignment (CBBA) is a key issue for uncertainty modeling in complex evidence theory. In this paper, we first construct complex interval number (CIN) model. In this context, we propose a novel CBBA generation method to model uncertainty in the framework of complex planes. Furthermore, we propose a novel decision-making algorithm on the basis of the CIN-based CBBA generation method. Through an application in pattern recognition on several real-world data sets, the efficiency of the proposed decision-making algorithm is verified.

A novel distance between single valued neutrosophic sets and its application in pattern recognition

A novel distance between single valued neutrosophic sets and its application in pattern recognition

Emotion Recognition Based on Brain Connectivity Reservoir and Valence Lateralization for Cyber-Physical-Social Systems

• An EEG emotion recognition method is proposed for Cyber-Physical-Social Systems. • An emotion recognition model based on brain connectivity reservoir is established. • A training algorithm based on valence lateralization is proposed. • The proposed method improves the average recognition accuracy to 85.55 As an important application of pattern recognition, emotion recognition can make Cyber-Physical-Social Systems (CPSS) provide more efficient services for humans. In order to improve the recognition accuracy, this paper proposes an electroencephalogram (EEG) emotion recognition method based on brain connectivity reservoir (BCR) and valence lateralization (VL) for CPSS. First, for the purpose of comprehensively considering the temporality, nonlinearity, and correlation of EEG signals, an emotion recognition model based on BCR is established. Specifically, according to the connectivity index, the correlation between EEG channels is calculated to determine the brain connectivity structure of BCR, and the features of EEG signals are represented through BCR, then the classification result is obtained by the fully connected neural network according to the feature representation. Second, for the purpose of enhancing the feature representation capability of BCR, a training algorithm of BCR based on VL is proposed. Specifically, BCR is divided into two parts, i.e., the left hemi-BCR and the right hemi-BCR. These two parts are trained separately, so that the lateralization characteristic of the brain is better reflected. Finally, the experimental results on DEAP demonstrate that the proposed method achieves a recognition accuracy of 85.55% which is higher than the state-of-the-art methods.

A PNU-Based Methodology to Improve the Reliability of Biometric Systems.

Face recognition is an important application of pattern recognition and image analysis in biometric security systems. The COVID-19 outbreak has introduced several issues that can negatively affect the reliability of the facial recognition systems currently available: on the one hand, wearing a face mask/covering has led to growth in failure cases, while on the other, the restrictions on direct contact between people can prevent any biometric data being acquired in controlled environments. To effectively address these issues, we designed a hybrid methodology that improves the reliability of facial recognition systems. A well-known Source Camera Identification (SCI) technique, based on Pixel Non-Uniformity (PNU), was applied to analyze the integrity of the input video stream as well as to detect any tampered/fake frames. To examine the behavior of this methodology in real-life use cases, we implemented a prototype that showed two novel properties compared to the current state-of-the-art of biometric systems: (a) high accuracy even when subjects are wearing a face mask; (b) whenever the input video is produced by deep fake techniques (replacing the face of the main subject) the system can recognize that it has been altered providing more than one alert message. This methodology proved not only to be simultaneously more robust to mask induced occlusions but also even more reliable in preventing forgery attacks on the input video stream.

An improved belief $$\chi ^2$$ divergence for Dempster–Shafer theory and its applications in pattern recognition

An improved belief $$\chi ^2$$ divergence for Dempster–Shafer theory and its applications in pattern recognition

A belief Rényi divergence for multi-source information fusion and its application in pattern recognition

A belief Rényi divergence for multi-source information fusion and its application in pattern recognition

Similarity Measures based on the Novel Interval-valued Picture Hesitant Fuzzy Sets and their Applications in Pattern Recognition

Similarity Measures based on the Novel Interval-valued Picture Hesitant Fuzzy Sets and their Applications in Pattern Recognition