Example Of Pattern Recognition Research Articles

Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation

In this paper, we develop a new alternating projection method to compute nonnegative low rank matrix approximation for nonnegative matrices. In the nonnegative low rank matrix approximation method, the projection onto the manifold of fixed rank matrices can be expensive as the singular value decomposition is required. We propose to use the tangent space of the point in the manifold to approximate the projection onto the manifold in order to reduce the computational cost. We show that the sequence generated by the alternating projections onto the tangent spaces of the fixed rank matrices manifold and the nonnegative matrix manifold, converge linearly to a point in the intersection of the two manifolds where the convergent point is sufficiently close to optimal solutions. This convergence result based inexact projection onto the manifold is new and is not studied in the literature. Numerical examples in data clustering, pattern recognition and hyperspectral data analysis are given to demonstrate that the performance of the proposed method is better than that of nonnegative matrix factorization methods in terms of computational time and accuracy.

Nonlinear distance measures under the framework of Pythagorean fuzzy sets with applications in problems of pattern recognition, medical diagnosis, and COVID-19 medicine selection

BackgroundThe concept of Pythagorean fuzzy sets (PFSs) is an utmost valuable mathematical framework, which handles the ambiguity generally arising in decision-making problems. Three parameters, namely membership degree, non-membership degree, and indeterminate (hesitancy) degree, characterize a PFS, where the sum of the square of each of the parameters equals one. PFSs have the unique ability to handle indeterminate or inconsistent information at ease, and which demonstrates its wider scope of applicability over intuitionistic fuzzy sets.ResultsIn the present article, we opt to define two nonlinear distances, namely generalized chordal distance and non-Archimedean chordal distance for PFSs. Most of the established measures possess linearity, and we cannot incorporate them to approximate the nonlinear nature of information as it might lead to counter-intuitive results. Moreover, the concept of non-Archimedean normed space theory plays a significant role in numerous research domains. The proficiency of our proposed measures to overcome the impediments of the existing measures is demonstrated utilizing twelve different sets of fuzzy numbers, supported by a diligent comparative analysis. Numerical examples of pattern recognition and medical diagnosis have been considered where we depict the validity and applicability of our newly constructed distances. In addition, we also demonstrate a problem of suitable medicine selection for COVID-19 so that the transmission rate of the prevailing viral pandemic could be minimized and more lives could be saved.ConclusionsAlthough the issues concerning the COVID-19 pandemic are very much challenging, yet it is the current need of the hour to save the human race. Furthermore, the justifiable structure of our proposed distances and also their feasible nature suggest that their applications are not only limited to some specific research domains, but decision-makers from other spheres as well shall hugely benefit from them and possibly come up with some further extensions of the ideas.

Quantum face recognition protocol with ghost imaging

Face recognition is one of the most ubiquitous examples of pattern recognition in machine learning, with numerous applications in security, access control, and law enforcement, among many others. Pattern recognition with classical algorithms requires significant computational resources, especially when dealing with high-resolution images in an extensive database. Quantum algorithms have been shown to improve the efficiency and speed of many computational tasks, and as such, they could also potentially improve the complexity of the face recognition process. Here, we propose a quantum machine learning algorithm for pattern recognition based on quantum principal component analysis, and quantum independent component analysis. A novel quantum algorithm for finding dissimilarity in the faces based on the computation of trace and determinant of a matrix (image) is also proposed. The overall complexity of our pattern recognition algorithm is O(N,log N)—N is the image dimension. As an input to these pattern recognition algorithms, we consider experimental images obtained from quantum imaging techniques with correlated photons, e.g. “interaction-free” imaging or “ghost” imaging. Interfacing these imaging techniques with our quantum pattern recognition processor provides input images that possess a better signal-to-noise ratio, lower exposures, and higher resolution, thus speeding up the machine learning process further. Our fully quantum pattern recognition system with quantum algorithm and quantum inputs promises a much-improved image acquisition and identification system with potential applications extending beyond face recognition, e.g., in medical imaging for diagnosing sensitive tissues or biology for protein identification.

Byzantine Resilient Distributed Learning in Multirobot Systems

Distributed machine learning algorithms are increasingly used in multirobot systems and are prone to Byzantine attacks. In this article, we consider a distributed implementation of the stochastic gradient descent (SGD) algorithm in a cooperative network, where networked agents optimize a global loss function using SGD on the local data and aggregation of the estimates of immediate neighbors. Byzantine agents can send arbitrary estimates to their neighbors, which may disrupt the convergence of normal agents to the optimum state. We show that if every normal agent combines its neighbors’ estimates (states) such that the aggregated state is in the convex hull of its normal neighbors’ states, then the resilient convergence is guaranteed. To assure this sufficient condition, we propose a resilient aggregation rule based on the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">centerpoint</i> , which is a generalization of the median in the higher-dimensional Euclidean space. We evaluate our results using examples of target pursuit and pattern recognition in multirobot systems. The evaluation results demonstrate that distributed learning with average, coordinate-wise median, and geometric median-based aggregation rules fail to converge to the optimum state, whereas the centerpoint-based aggregation rule is resilient in the same scenario.

Perancangan Aplikasi Absensi Karyawan Dengan Deteksi Wajah Menggunakan Metode Eigenface

Attendance is one of the most important repetitive transactions, because it is related to the productivity of employees and is one of the indicators of controlling human resources (HR) which aims to increase the potential of human resources and is used for efficiency. Current technological developments make it possible to create a system that can assist humans in recognizing a digital image. One area that is currently being developed is pattern recognition. This technology identifies a person's special physical characteristics. Examples of pattern recognition, for example, are face recognition, iris recognition, and fingerprint recognition. specific databases. Broadly speaking, the process of facial recognition is that the webcam camera captures the face. Then obtained a value of R, G, B. By using the initial processing, after that the facial processing stage is carried out using the eigenface method. In this eigenface method there are several core stages, namely: converting faces into matrices, calculating the FlatVector average, determining the eigenface values ??and carrying out the face identification process by looking for the eigenface values ??that are close to it. One of these facial recognition can be developed to become an attendance application that can be applied in the company to prevent manipulation of absenteeism by employees.

Generating optimal robust continuous piecewise linear regression with outliers through combinatorial Benders decomposition

Using piecewise linear (PWL) functions to model discrete data has applications for example in healthcare, engineering and pattern recognition. Recently, mixed-integer linear programming (MILP) approaches have been used to optimally fit continuous PWL functions. We extend these formulations to allow for outliers. The resulting MILP models rely on binary variables and big- constructs to model logical implications. The combinatorial Benders decomposition (CBD) approach removes the dependency on the big- constraints by separating the MILP model into a master problem of the complicating binary variables and a linear sub problem over the continuous variables, which feeds combinatorial solution information into the master problem. We use the CBD approach to decompose the proposed MILP model and solve for optimal PWL functions. Computational results show that vast speedups can be found using this robust approach, with problem-specific improvements including smart initialization, strong cut generation and special branching approaches leading to even faster solve times, up to more than 12,000 times faster than the standard MILP approach.

Identification of Buni Fruit Image Using Euclidean Distance Method

Identification is an important part of image analysis because in this procedure the desired image/image will be analyzed for further processing to make it easier to analyze for further purposes, for example in image identification pattern recognition which is part of image analysis used to divide an image into several parts and take some of the desired objects. This study aims to identify buni fruit with Euclidean distance and extract shape and texture features. Extraction of shape features using boundary metrics and whimsy. This boundary is considered to be able to recognize objects based on their shape and can distinguish them from other objects. For identification expositions, Euclidean distance is used which serves to represent the level of similarity of two images that take into account the distance value of the Euclidean distance. From the results of the evaluation using a disarray network by calculating precision, review, and accuracy, in order to identify the image of the buni fruit object properly.

Picture Fuzzy Soft Robust VIKOR Method and its Applications in Decision-Making

This paper introduces the Euclidean, Hamming, and the generalized distance measures for picture fuzzy soft sets and discusses their properties. The numerical examples of decision-making and pattern recognition are focused. We also develop a robust VIKOR method for PFSSs. The relative and precise ideal picture fuzzy values (PFVs), robust factors and ranking indexes are defined. Different algorithmic procedures of robust VIKOR based on the relative and precise ideal PFVs, relative and precise robust factor, precise and picture fuzzy weights and relative and precise ranking indexes are proposed. In the end, the investment problem is solved by using the proposed method.

Distance measures on intuitionistic hesitant fuzzy set and its application in decision-making

Intuitionistic hesitant fuzzy set (IHFS) provides a valid mean for dealing with the uncertainty of complex problems. Information measures on IHFS can measure the uncertain information, so that we shall introduce a method to construct a class of distance measures for IHFS in this paper. For constructing more objective distance measures to reflect the actual situation, we consider the information content and information clarity of IHFS simultaneously and utilize different functions to adjust their contribution. Its superiority is evidenced by an example of pattern recognition that the proposed distance measure improves numerically results obtained with existing distance measures. In particular, we investigate the connection between distance measure, similarity measure, and entropy measure of IHFS, and prove that they can be constructed mutually under this axiomatic framework. On this basis, we apply the proposed entropy measure to determine criteria weights in multi-criteria decision-making problems and design an extended intuitionistic hesitant fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) method, which effectiveness is presented by a practical application.

Width-based distance measures on interval-valued intuitionistic fuzzy sets

An essential topic of interval-valued intuitionistic fuzzy sets(IVIFSs) is distance measures. In this paper, we introduce a new kind of distance measures on IVIFSs. The novelty of our method lies in that we consider the width of intervals so that the uncertainty of outputs is strongly associated with the uncertainty of inputs. In addition, better than the distance measures given by predecessors, we define a new quaternary function on IVIFSs to construct the above-mentioned distance measures, which called interval-valued intuitionistic fuzzy dissimilarity function. Two specific methods for building the quaternary functions are proposed. Moreover, we also analyzed the degradation of the distance measures in this paper, and show that our measures can perfectly cover the measures on a simpler set. Finally, we provide illustrative examples in pattern recognition and medical diagnosis problems to confirm the effectiveness and advantages of the proposed distance measures.

Complex q-Rung Orthopair Fuzzy Variation Co-efficient Similarity Measures and their Approach in Medical Diagnosis and Pattern Recognition

The q-rung orthopair fuzzy set (q-ROFS) as a generalization of fuzzy set (FS), is characterized by membership and non-membership, and the sum of their q-powers is restricted to [0,1]. In this manuscript, a new Complex q-rung orthoapir fuzzy set (Cq-ROFS) is proposed by combining q-ROFS and complex fuzzy set (CFS). Cq-ROFS is a better way to process uncertain and imprecise information in decision making, which is characterized by complex-valued membership and complex-valued non-membership. First, some fundamental operational laws, score function and accuracy function, and comparison method are proposed. Further, because the vector similarity measures (VSMs) play a key role in statistics, physics and engineering sciences, some VSMs called Jaccard similarity measures (JSMs), dice similarity measures (DSMs) and cosine similarity measures (CSMs) for Cq-ROFSs and interval-valued complex q-rung orthopair fuzzy sets (IVCq-ROFSs) are investigated. Moreover, the hybrid vector similarity measures (HVSMs) called variation co-efficient similarity measures (VCSMs) for Cq-ROFSs and IVCq-ROFSs are also proposed and their properties are discussed. Finally, in order to demonstrate the feasibility of the investigated HVSMs, the existing similarity measures about complex Pythagorean fuzzy sets (CPFSs) and complex intuitionistic fuzzy sets (CIFSs) are compared with the proposed methods by numerical examples of medical diagnosis and pattern recognition.

A Dual Measure of Uncertainty: The Deng Extropy.

The extropy has recently been introduced as the dual concept of entropy. Moreover, in the context of the Dempster–Shafer evidence theory, Deng studied a new measure of discrimination, named the Deng entropy. In this paper, we define the Deng extropy and study its relation with Deng entropy, and examples are proposed in order to compare them. The behaviour of Deng extropy is studied under changes of focal elements. A characterization result is given for the maximum Deng extropy and, finally, a numerical example in pattern recognition is discussed in order to highlight the relevance of the new measure.

New distance and similarity measures for hesitant fuzzy soft sets

The hesitant fuzzy soft set (HFSS), as a combination of hesitant fuzzy and soft sets, is regarded as a useful tool for dealing with the uncertainty and ambiguity of real-world problems. In HFSSs, each element is defined in terms of several parameters with arbitrary membership degrees. In addition, distance and similarity measures are considered as the important tools in different areas such as pattern recognition, clustering, medical diagnosis, and the like. For this purpose, the present study aimed to evaluate the distance and similarity measures for HFSSs by using well-known Hamming, Euclidean, and Minkowski distance measures. Further, some examples were used to demonstrate that these measures fail to perform well in some applications. Accordingly, new distance and similarity measures were proposed by considering a hesitance index for HFSSs and the effect of considering hesitance index was shown by using an example of pattern recognition. Finally, the application of the proposed measures and hesitance index was investigated in the clustering and decision-making problem, respectively. In conclusion, the use of the proposed measures in clustering and hesitance index in decision-making can provide better and more reasonable results.

Distance measures on intuitionistic fuzzy sets based on intuitionistic fuzzy dissimilarity functions

One of the significant topics in intuitionistic fuzzy set (IFS) is the measure of the distance between IFSs. Although distance measures on IFSs have been widely studied in previous studies, there are few studies about the generation of them. In this paper, a quaternary function called intuitionistic fuzzy dissimilarity function is proposed to construct distance measures on IFSs. Two methods for building intuitionistic fuzzy dissimilarity functions are presented. The first one is obtained by combining dissimilarity functions and fuzzy equivalencies. The second one is obtained based on constructing new intuitionistic fuzzy dissimilarity functions through other existing ones. We also examine and compare some properties of intuitionistic fuzzy dissimilarity functions, through which we obtain some properties of distance measures on IFSs. Some examples of pattern recognition are applied to illustrate the effectiveness of the proposed distance measures on IFSs.

Point process models for novelty detection on spatial point patterns and their extremes

Novelty detection is a particular example of pattern recognition identifying patterns that departure from some model of “normal behaviour”. The classification of point patterns is considered that are defined as sets of N observations of a multivariate random variable X and where the value N follows a discrete stochastic distribution. The use of point process models is introduced that allow us to describe the length N as well as the geometrical configuration in data space of such patterns. It is shown that such infinite dimensional study can be translated into a one-dimensional study that is analytically tractable for a multivariate Gaussian distribution. Moreover, for other multivariate distributions, an analytic approximation is obtained, by the use of extreme value theory, to model point patterns that occur in low-density regions as defined by X. The proposed models are demonstrated on synthetic and real-world data sets.

A novel correlation coefficient of intuitionistic fuzzy sets based on the connection number of set pair analysis and its application

Set pair analysis (SPA) is an updated theory for dealing with the uncertainty, which overlaps the othertheories of uncertainty such as probability, vague, fuzzy and intuitionistic fuzzy set (IFS). Considering thefact that the correlation coecient plays an important role during the decision-making process, in this paper,after pointing out the weakness of the existing correlation coecients between the IFSs, we propose a novelcorrelation coecient and weighted correlation coecients formulation to measure the relative strength of thedi erent IFSs. For it, rstly corresponding to each intuitionistic fuzzy number, the connection number of theSPA theory has been formulated in the form of the degree of identity, discrepancy and contrary and thenbased on its, a novel correlation coecient measures have been de ned. Pairs of identity, discrepancy andcontrary of the connection number have been taken as a vector representation during the formulation. Lastly,a decision-making approach based on the proposed measures has been presented which has been illustratedby two numerical examples in pattern recognition and medical diagnosis.

Two-Dimensional Oscillatory Neural Network Based on Room-Temperature Charge-Density-Wave Devices

We propose an oscillatory neural network implemented with two-dimensional (2-D) tantalum disulfide devices operating in the change density wave regime at room temperature. An elementary cell of the network consists of two 1T-TaS 2 devices connected in series. Such a cell has a constant output and oscillatory states. All cells have the same bias voltage. There is a constant current flowing through the cell in the constant output mode. The oscillations occur at a certain bias voltage due to the electrical-field triggered metal-to-insulator transition owing to the changes in the charge-density-wave phase in the 1T-TaS 2 channel. Two 1T-TaS 2 devices oscillate out-of-phase when one of the devices is in the insulator phase while the other one is in the metallic state. The nearest-neighbor cells are coupled via graphene transistors. The cells are resistively coupled if the graphene transistor is in the on state while they are capacitively coupled if the transistor is in the off state. The operation of the oscillatory neural network is simulated numerically for the 30 × 30 node network. We present the examples of pattern recognition on a template with the fixed coupling among the near-neighbor cells. We also present the results of our numerical modeling mimicking the Game of Life in the network with the time-varying coupling. The 2-D 1T-TaS 2 devices, utilized in the network, offer a unique combination of properties such as scalability, high operational frequency, fast synchronization speed, and radiation hardness, which makes them promising for both consumer electronic and defense applications.

Measuring Similarity and Ordering Based on Interval Type-2 Fuzzy Numbers

This paper extends a preference degree to rank interval type-2 fuzzy numbers. A general method to measure the similarity between interval type-2 fuzzy numbers is also introduced. Using some theorems and lemmas, it is proven that the extended similarity measures satisfy many common and desired properties based on the common axiomatic definitions introduced for similarity measures. The main properties of the extended preference degree are studied. In addition, several possible applications of the proposed methods are illustrated by some application examples in pattern recognition and multicriteria group decision making. Moreover, the proposed methods are examined in order to compare them with other existing methods, and the feasibility and effectiveness of the proposed methods are cleared via some lemmas and numerical comparisons.

Improved linear local tangent space alignment and its application to pattern recognition

Considering the drawbacks of the linear local tangent space alignment, a Semi-Supervised Neighbourhood Adaptive Linear Local Tangent Space Alignment (SSNA-LLTSA) is proposed. The distance metric combining the Cosine similarity and the Euclidean distance is used in the algorithm instead of the Euclidean distance, and the algorithm realises the semi-supervised learning and neighbourhood adaptive adjustment by integrating some of the known category information and the method of Parzen window density estimation into the dimensionality reduction process. The simulation experiment of UCI standard data sets and the pattern recognition example of hydraulic pump show that the redefined distance metric has better performance than the Euclidean distance, and SSNA-LLTSA can overcome the defect that LLTSA is unsupervised. Meanwhile, the capability of neighbourhood adaptive adjustment makes the algorithm find the low-dimensional manifold of the data sets more effectively, which can further improve the accuracy of pattern recognition.

A Novel Correlation Coefficients between Pythagorean Fuzzy Sets and Its Applications to Decision-Making Processes

Pythagorean fuzzy set (PFS) is one of the most successful in terms of representing comprehensively uncertain and vague information. Considering that the correlation coefficient plays an important role in statistics and engineering sciences, in this paper, after pointing out the weakness of the existing correlation coefficients between intuitionistic fuzzy sets (IFSs), we propose a novel correlation coefficient and weighted correlation coefficient formulation to measure the relationship between two PFSs. Pairs of membership, nonmembership, and hesitation degree as a vector representation with the two elements have been considered during formulation. Numerical examples of pattern recognition and medical diagnosis have been taken to demonstrate the efficiency of the proposed approach. Results computed by the proposed approach are compared with the existing indices.