Pattern Recognition Problems Research Articles

Justification of Image Format Structure for Solving Pattern Recognition Problems in Environmental Monitoring

Introduction. Environmental monitoring of marine areas and timely detection of the results of man-made disasters on the water surface, including identification of oil contaminated areas, represents an urgent task. To facilitate its solution, marine areas are controlled using space and airborne means. However, the volume of data subject to control is constantly growing. Therefore, the problem can be solved by selecting only those photo and video materials with detected traces of oil spills and other man-made disasters.Aim. To develop an approach to automatic selection of images obtained from visual control systems, providing only relevant images.Materials and methods. The theoretical part of the study was carried out using a classification method based on pattern recognition theory. A test image with an oil spill on the surface of the Black Sea was compared with the same image presented in BMP format with different color depth encodings, as well as with the same image presented in JPEG format. Raster images were processed using a specialized software application. Simulation was carried out in the MathCAD environment.Results. The developed approach was tested experimentally by processing 200 images. The conducted visual analysis confirmed that the image, where the given boundary allows the area of oil spill to be clearly distinguishes, is the closest to the test image.Conclusion. According to the results of the experiment with different formats of raster image files, the conclusion is made about the feasibility of using images obtained by visual control systems presented in JPEG format as initial data. Further research directions are outlined.

A Distance Measure for Intuitionistic Fuzzy Multicriteria Decision Making in Pattern Recognition and Medical Diagnosis

ABSTRACT In artificial intelligence, decision-making is a widely studied field, and distance measure plays a crucial role in decision science. Several distance measures for Intuitionistic Fuzzy sets (IFSs) have been developed to handle uncertain situations. However, some of these measures lack properties of distance measures or produce unreasonable results. This study introduces a new distance measure between IFSs that satisfies the necessary properties and resolves counter-intuitive cases. The proposed distance measure is verified to be more reasonable through numerical examples, and it is applied to pattern recognition and medical diagnosis problems.

Optimal segmentation of non-linear and non-stationary time series based on fractal dimension and Poincare section and its application in solving EEG-signal

Non-linearity and non-stationarity characteristics in physiological and biological time series limit the reliability of conventional analysis. The main challenge in pattern recognition problems is determining the Optimal-Window-Duration (OWD) for segmenting these time series. Our study presents an innovative algorithm, based on Individual-Optimal-Segmentation (IOS), which integrates chaos theory using fractal dimension, and Poincare section into the estimation of the OWD for segmenting time series with non-linear and non-stationary characteristics. In the logistic map case study, the IOS algorithm proved to be effective in detecting cycles in a range of numerical examples, from period-2 to chaotic behavior. So that its performance was superior to previous approaches, especially when it comes to detecting chaotic behaviors. Furthermore, the combination of the IOS algorithm with the Welch signal processing method shows promise in identifying abnormal Power-Spectral-Density (PSD) curves related to Beta and Gamma rhythms in depression diagnosis scenarios. The generated depression diagnosis system attained an accuracy (%) of 99.07±0.35 (with 2-channel) using the Monte Carlo simulation method. These findings suggest a potentially valuable tool for addressing segmentation challenges in time series analysis, particularly in contexts involving complex, dynamic data. While, it is suggested that future research investigate the computational efficiency and robustness of the IOS algorithm when handling large-scale or real-time data and increase its application in various fields in data science and signal processing. Furthermore, expanding the application range of the IOS algorithm for other pattern recognition problems with physiological or biological data can strengthen the generalizability and practical application of the algorithm.

Parametric picture fuzzy cross-entropy measures based on [formula omitted]-Choquet integral for building material recognition

This research paper introduces a novel picture fuzzy cross-entropy measure that utilizes Lin’s divergence as a basis for comparing two picture fuzzy sets. To improve the flexibility and applicability of this new cross-entropy measure, a family of parametric cross-entropy measures is defined. By adjusting the parameters in applications, the influence of the degree of positive membership, negative membership, and neutral membership on the picture fuzzy cross-entropy can be observed, allowing for a more tailored analysis. Additionally, we investigate the relationship between power weighted means and the d-Choquet integral, which serves as an extension of the ordinary Choquet integral. Using this knowledge, picture fuzzy cross-entropy measures based on the d-Choquet integral are introduced to yield more sensitive results, particularly when interactions between criteria exist in specific problem domains. This consideration of criterion interactions is often absent in existing cross-entropy measures. Moreover, an algorithm is presented for solving pattern recognition problems and applied to a building material recognition problem sourced from existing literature. Then, we proposed another algorithm and use it to investigate a novel material classification problem. Through these applications, the effectiveness of the proposed cross-entropy measures in pattern recognition is demonstrated. The paper conducts a comparative analysis between existing methods and the proposed approaches, followed by a sensitivity analysis. This analysis involves manipulating the parameters derived from both the parametric cross-entropy measures and the d-Choquet integrals to assess their respective impacts and sensitivities. Finally, the results regarding the classification problem are examined with performance metrics such as accuracy, precision, recall, and F1 score.

Quantum support vector data description for anomaly detection

Abstract Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly detection. QSVDD utilizes a shallow-depth quantum circuit to learn a minimum-volume hypersphere that tightly encloses normal data, tailored for the constraints of noisy intermediate-scale quantum (NISQ) computing. Simulation results on the MNIST and Fashion MNIST image datasets, as well as credit card fraud detection, demonstrate that QSVDD outperforms both quantum autoencoder and deep learning-based approaches under similar training conditions. Notably, QSVDD requires an extremely small number of model parameters, which increases logarithmically with the number of input qubits. This enables efficient learning with a simple training landscape, presenting a compact quantum machine learning model with strong performance for anomaly detection.

Maximum-correntropy-based sequential method for fast neural population activity reconstruction in the cortex from incomplete abnormally-disturbed noisy measurements

This paper continues to explore the membrane potential reconstruction and pattern recognition problem in a neural tissue modeled by Stochastic Dynamic Neural Field (SDNF) equation. Although recent research has suggested an efficient solution based on the state-space approach through nonlinear Bayesian filtering framework, it is becoming extremely difficult to ignore the existence of non-Gaussian uncertainties in the SDNFs as well as the stability problem of neuronal population dynamics to outliers. Motivated by recent events in signal processing and mathematical neuroscience, this paper explores the SDNFs in a presence of non-Gaussian uncertainties, which is the shot noise case, where the corrupted data might appear due to broken sensors. We derive the “distributionally robust” state estimator for the membrane potential reconstruction process that is the Maximum Correntropy Criterion Extended Kalman Filter (MCC-EKF) as well as its fast and numerically robust (to roundoff) implementation method by using the sequential principle of processing the measurement vectors. The numerical experiments are provided to illustrate the performance of the novel estimation methods.

Negative cumulative extropy of uncertain variables with applications

Recently, a measure of uncertainty termed by negative cumulative extropy (NCEX) is proposed by Tahmasebi and Toomaj (2022). It is a flexible extension of extropy measure which was proposed by Lad, Sanfilippo, and Agro (2015). To measure the uncertainty of uncertain variables, in this article, we propose the concept of NCEX through uncertainty distributions. Some properties of NCEX are derived, and some practical examples of uncertain variables are given. Several basic theorems are proposed. A formula of cross-extropy is defined via inverse uncertainty distributions. We also delve into a pattern recognition problem to highlight the importance of cross-extropy. Finally, applications to portfolio selection and signal processing are presented.

F-norm based low-power motion recognition on wearable devices in the presence of outlier motions

Motion recognition refers to the intelligent recognition of human motion using data collected from wearable sensors, which exceedingly has gained significant interest from both academic and industrial fields. However, temporary-sudden activities caused by accidental behavior pose a major challenge to motion recognition and have been largely overlooked in existing works. To address this problem, the multi-dimensional time series of motion data is modeled as a Time-Frequency (TF) tensor, and the original challenge is transformed into a problem of outlier-corrupted tensor pattern recognition, where transient sudden activity data are considered as outliers. Since the TF tensor can capture the latent spatio-temporal correlations of the motion data, the tensor MPCA is used to derive the principle spatio-temporal pattern of the motion data. However, traditional MPCA uses the squared F-norm as the projection distance measure, which makes it sensitive to the presence of outlier motion data. Therefore, in the proposed outlier-robust MPCA scheme, the F-norm with the desirable geometric properties is used as the distance measure to simultaneously mitigate the interference of outlier motion data while preserving rotational invariance. Moreover, to reduce the complexity of outlier-robust motion recognition, we impose the proposed outlier-robust MPCA scheme on the traditional MPCANet which is a low-complexity deep learning network. The experimental results show that our proposed outlier-robust MPCANet can simultaneously improve motion recognition performance and reduce the complexity, especially in practical scenarios where the real-time data is corrupted by temporary-sudden activities.

Novel strict intuitionistic fuzzy similarity measures-based on fuzzy negation and their applications

The concept of similarity measure is fundamental for evaluating the consistency between intuitionistic fuzzy sets (IFSs). Several researchers have developed various intuitionistic fuzzy similarity measures (IFSimMs) utilizing intuitionistic fuzzy distance measures (IFDisMs) or combinations of membership and non-membership degrees of IFSs. Nevertheless, there is still ample room for the improvement, as some of these measures do not satisfy the axiomatic properties of IFSimMs and produce unreasonable results. In this paper, we first establish a dual relationship between IFDisMs and IFSimMs based on fuzzy negations, enabling the construction of an infinite number of IFSimMs from a given IFDisM. We then propose two novel IFSimMs by directly manipulating the membership and non-membership degrees of IFSs and prove that they satisfy the axiomatic properties of strict IFSimMs (SIFSimMs). Furthermore, a comparative analysis reveals that the proposed SIFSimMs do not yield any unreasonable results in various scenarios. Finally, we apply these SIFSimMs to pattern recognition, medical diagnosis, and clustering analysis problems, demonstrating their superior performance compared to some existing measures.

An Ensemble Approach To Face Recognition In Access Control Systems

An Ensemble Approach To Face Recognition In Access Control Systems

A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures

Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.

Aggregation operators-based divergence measures for single-valued neutrosophic sets with their applications to pattern recognition

A Single-valued neutrosophic set (SVNS) has recently been explored as a comprehensive tool to assess uncertain information due to varied human cognition. This notion stretches the domain of application of the classical fuzzy set and its extended versions. Various comparison measures based on SVNSs like distance measure, similarity measure, and, divergence measure have practical significance in the study of clustering analysis, pattern recognition, machine learning, and computer vision-related problems. Existing measures have some drawbacks in terms of precision and exclusion of information and produce unreasonable results in categorization problems. In this paper, we propose a generic method to define new divergence measures based on common aggregation operators and discuss some algebraic properties of the proposed divergence measures. To further appreciate the proposed divergence measures, their application to pattern recognition has been investigated in conjunction with the prominent existing comparison measures based on SVNSs. The comparative assessment sensitivity analysis of the proposed measures establishes their edge over the existing ones because of appropriate classification results.

Deep Learning-Based Dzongkha Handwritten Digit Classification

In computer vision applications, pattern recognition is one of the important fields in artificial intelligence. With the advancement in deep learning technology, many machine learning algorithms were developed to tackle the problem of pattern recognition. The purpose of conducting the research is to create the first-ever Dzongkha handwritten digit dataset and develop a model to classify the digit. In the study, the 3 layer set of CONV → ReLU → POOL, followed by a fully connected layer, dropout layer, and softmax function were used to train the digit. In the dataset, each class (0-9) contains 1500 images which are split into train, validation, and test sets: 70:20:10. The model was trained on three different image dimensions: 28 by 28, 32 by 32, and 64 by 64. Compared to image dimensions 28 by 28 and 32 by 32, 64 by 64 gave the highest train, validation, and test accuracy of 98.66%, 98.9%, and 99.13% respectively. In the future, the sample of digits needs to be increased and use the transfer learning concept to train the model.

Operations and Similarity Measures Between (m,n)-Fuzzy Sets

Recently Jun and Hur proposed (m, n)-fuzzy sets which can handle vagueness and uncertainty in information very efficiently in the process of solving complex problems. They defined basic operations over (m, n)-fuzzy sets. The present paper created some new operations over this super class of fuzzy sets and established many theorems related to the their properties. Further some distance and similarity measures of (m, n)-fuzzy sets are proposed and their properties are examined. Moreover, the proposed similarity measures are applied to the problem of pattern recognition.

Forecasting Maximum Water Level Data for Post Sangkuliman using An Artificial Neural Network Backpropagation Algorithm

Neural Network (NN) is an information processing system that has characteristics similar to biological neural networks. One of the algorithms in NN is Backpropagation Neural Network (BPNN). BPNN is an excellent method for dealing with complex pattern recognition problems. In this research, maximum water level forecasting was carried out at Sangkuliman Post using a Backpropagation Neural Network. This research aims to obtain modeling for forecasting maximum water level, as well as forecasting results using the best model. The research results show that the best model is five neurons in hidden layer 1 and 3 neurons in hidden layer 2 with the backpropagation algorithm, the activation function used is binary sigmoid, the learning rate is 0.001, and the maximum iteration is 10,000,000 with the smallest RMSE result being 1.816. The forecast results for the following 12 periods are 1.672, 1.779, 1.523, 1.271, 1.752, 1.692, 1.335, 1.479, 1.750, 1.779, 1.340, 1.269, and 1.754. Forecasting results can be used by various parties in decision-making and planning in multiple fields, as an example to see the patterns of biological and vegetable life around Sangkuliman Post. Based of forecasting results, certain months show an increase in maximum water levels.

Detecting and locating chemical intrusion in water distribution systems using 911 calls

ABSTRACT Intentional or unintentional chemical contamination of water distribution systems (WDSs) could have severe health and socio-economic consequences. High potency chemicals constituting, in essence, “super poisons” have the potential to be used in such intrusion scenarios. Some of these contaminants are capable of killing the victim in less than 1 h. Due to their high toxicity levels and short time from exposure to onset of symptoms, 911 call centers are likely the first point of contact for victims or their families with the authorities. Information such as 911 calls could be used to identify the ongoing event and potential intrusion locations. In this way, such emergency calls could function as an intrusion warning system. This study employs network hydraulic modelling to synthesize the 911 call patterns in the aftermath of such events. It then defines the scenarios as a multi-label pattern recognition problem. The synthesized data then was used to train a Convolutional Neural Network (CNN). The trained AI was applied to a real-world WDS with approximately 4000 km of pipe and 26,000 demand nodes. The results indicated that CNN is capable of accurately recognizing the pattern and pinpointing the originating location of the intrusion with an accuracy greater than 93%.

SAT-Based Algorithms for Regular Graph Pattern Matching

Graph matching is a fundamental problem in pattern recognition, with many applications such as software analysis and computational biology. One well-known type of graph matching problem is graph isomorphism, which consists of deciding if two graphs are identical. Despite its usefulness, the properties that one may check using graph isomorphism are rather limited, since it only allows strict equality checks between two graphs. For example, it does not allow one to check complex structural properties such as if the target graph is an arbitrary length sequence followed by an arbitrary size loop. We propose a generalization of graph isomorphism that allows one to check such properties through a declarative specification. This specification is given in the form of a Regular Graph Pattern (ReGaP), a special type of graph, inspired by regular expressions, that may contain wildcard nodes that represent arbitrary structures such as variable-sized sequences or subgraphs. We propose a SAT-based algorithm for checking if a target graph matches a given ReGaP. We also propose a preprocessing technique for improving the performance of the algorithm and evaluate it through an extensive experimental evaluation on benchmarks from the CodeSearchNet dataset.

Distance measure and its application to decision making, medical diagnosis, and pattern recognition problems under complex picture fuzzy sets

Distance measure and its application to decision making, medical diagnosis, and pattern recognition problems under complex picture fuzzy sets

Multi-back-propagation Algorithm for Signal Neural Network Decomposition

In this paper, a novel back-propagation error technique is presented. This neural network structure allows for two fundamental basic modes: (1) To decompose the neurones by transforming their variables, weights, and scalar functions into vectors. This conveys for the decomposition of the transfer function of every neurone (where the output variables are the components of the decomposition) and, consequently, to be written as the invariant sum of orthogonal functions, with the safeguard of preserving information This orthogonality is proven using Fourier theory. (2) In a second mode, a tuned neural network that occupies one of the channels of the neural network can see the weights of its supplementary channels adjusted to retain additional information. Only the decomposition algorithm of the network is presented here—Multi-back-propagation algorithm. The adopted methodology is validated step-by-step with some representative examples. Namely, to assess the performance of the splitting method, two different examples have been constructed from scratch: (1) a 2D classification problem and (2) a 3D surface. In both problems, the signal and transfer functions of the neural network are successfully decomposed without information losses. Therefore, since the main contribution of this work is to allow for the organisation of the information stored in neural network structure, through a split process, this promising method shows potential use in various areas—e.g. classification and/or pattern recognition problems, data analysis, modelling and so on. In the future, we expect to work further in the method computational aspects to render it more efficient, versatile and robust.

Method for Fault Diagnosis of Track Circuits Based on a Time–Frequency Intelligent Network

In response to the limitations posed by noise interference in complex environments and the narrow focus of existing diagnosis methods for jointless track circuit faults, an innovative approach is put forward in this study. It involves the application of the continuous wavelet transform (CWT) for signal preprocessing, along with the integration of a deep belief network (DBN) and a genetic algorithm (GA) to improve the least-squares support vector machine (LSSVM) model for intelligent time–frequency fault diagnosis. Initially, the raw induced voltage signals are transformed using continuous wavelet transformation resulting in wavelet time–frequency representations that combine temporal and spectral information. Subsequently, these time–frequency representations are fed into the deep belief networks, which perform semi-supervised dimensionality reduction and feature extraction, thereby uncovering distinct fault characteristics in the track circuit. Finally, the genetic algorithms are employed to improve the kernel function and penalty factor parameters of the least-squares support vector machine, thus establishing an optimal DBN-GA-LSSVM diagnostic model. Experimental validation demonstrates the effectiveness of the proposed time–frequency intelligent network model by leveraging the advantages of deep belief networks in hierarchical feature extraction and the superior performance of the least-squares support vector machine in addressing high-dimensional pattern recognition problems with limited samples. The achieved accuracy rate on the testing dataset reaches an impressive 99.6%. Consequently, this comprehensive approach provides a viable solution for data-driven track circuit fault diagnosis.