Pattern Recognition Problems Research Articles

Novel similarity measure between hesitant fuzzy set and their applications in pattern recognition and clustering analysis

The extension of classical fuzzy sets are hesitant fuzzy sets (HFSs), in which each element has a possible value from [0,1]. Similarity and distance measures are useful implements for solving medical, clustering and pattern-recognition problems. Most of the researchers have suggested their ideas for HFSs using distance measures and extract the similarity measure from distance measure but most of them are getting inadequate results. Therefore, we proposed a new similarity measure to resolve these problems and also satisfied the properties of proposed measure for HFSs. Additionally, numerous examples are taken in consideration using HFS and compared the performance of existing measures with proposed measure for different cases. Furthermore, we have applied proposed measure for pattern recognition problems using three different examples and also calculate performance index (i.e., Degree of Confidence) to explore the behavior of different measures. Finally, we suggested MST based clustering algorithm using HF-environment and contrast the performance of proposed measure with existing ones. All these comparison illustrate that proposed measure is getting efficient and reasonable results and it also verified that proposed measure is not restricted to particular domain, it can be effectively applied for diverse field of application.

Precision forecasting of spray-dry desulfurization using Gaussian noise data augmentation and k-fold cross-validation optimized neural computing

Perceptron models have become integral tools for pattern recognition and classification problems in engineering fields. This study envisioned implementing artificial neural networks to forecast the performance of a mini-spray dryer for desulfurization activities. This work adopted k-fold cross-validation, a rigorous technique that evaluates model performance across multiple data segments. Several ANN models (21) were trained on data obtained from sulfation conditions, including sulfation temperature (120 °C–200 °C), slurry pH (4–12), stoichiometric ratio (0.5–2.5), slurry solid concentration (6%–14%) as the feed input and sulfur capture as the response. Three hundred synthetic datasets generated using the Gaussian noise data augmentation underwent a 10-fold cross-validation process before simulation on neural networks triggered by the logsig and tansig activation functions. The computation accuracy was further evaluated by altering the number of hidden cells from 2 to 10. The ANN architectures were assessed using statistical metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R 2) techniques. Overall, error estimation suggests cross-validation and data augmentation are critical in efficient neural network generalization. The logsig function trained with 10 hidden cells presented closer data articulation when mapped onto actual values.

АВТОМАТИЗАЦИЯ ПРОЦЕДУР ОБНАРУЖЕНИЯ ЛЕСНЫХ ПОЖАРОВ ПО РЕЗУЛЬТАТАМ ОБРАБОТКИ ВИДЕО

The results of developing a proposal for automating forest fire detection procedures based on pattern recognition methods are presented. The mathematical problem of constructing a feature space and the problem of pattern recognition based on the results of comparing feature vectors have been formulated and posed. As part of the solution to the problem under consideration, it is proposed to form a feature space from two vectors characterizing the image of the current and previous frames. The choice of histograms characterizing the distribution of image brightness intensity as feature vectors is justified. Experimental results are presented that confirm the validity of the theoretical conclusions and recommendations for automating detection procedures 
 are formulated.

Distance and similarity measures of Hesitant bi-fuzzy set and its applications in renewable energy systems

The concept of Hesitant bi-fuzzy set (HBFS) is an extension of dual hesitant fuzzy set, which play an important role in minimizing uncertainty in an efficient way. Distance and Similarity measures are an important tool for illustrating and integrating the differences between elements and its class and have a great importance in decision making, medical diagnosis and pattern recognition. To enhance and expand the applicability of HBFS, in this study, we have defined a series of distance and similarity measures of HBFS in both continuous and discrete environments. Since the information in day-to-day life may be biased, therefore, to show the importance of each factor/criterion weighted measures are also defined. Due to global warming and climate change, the utilization of renewable energy systems not only plays a significant role but also decreases greenhouse gas emissions. Choosing the right energy system affects each nation's economic, social, and environmental development. To show the efficiency of proposed measures, these measures are implemented in renewable energy systems. An algorithm for hesitant bi-fuzzy TOPSIS has also been developed to demonstrate the application of these measures in decision- making. Finally, a comparative study is done in pattern recognition problem, to show the supremacy and efficiency of the proposed measures.

Interpreting and processing side-scan sonar data with the objective of further automation

One of the most effective tools of remote sensing and visualization of underwater surfaces and objects are acoustic devices, in particular side-scan sonars (SSS). Recently, largely due to the emergence of affordable devices, the geography and scope of application of this device has been significantly expanded. Meanwhile, despite certain progress achieved in terms of improving and minimizing the SSS hardware, the software used remains, in general, at a basic level, providing the operator mainly with a simple tool for visualizing benthic environments and data recording for further post-processing. Existing experience in SSS exploitation reveals that the key issue of interpreting acoustic images lies in the physical essence of their acquisition. Arguably, attempts to implement methods of automated interpretation of optical images have no perspective. Hence, the objective of this paper is to provide a theoretical and practical background of SSS data interpretation and processing. A layout for the potential automation of this process is provided with the objective of eliminating human operation in the process of conducting survey and search operations. The authors consider the conditions of SSS exploitation including special attention to such issues as the vastness of search areas, which, as argued is the key problem of data and data pattern recognition. SSS image recognition is an issue relevant for a wide range of academic topics such as geometric distortion, image recognition, and navigation satellite system target localization.

Spectral Methods in Data Analysis and Pattern Recognition Problems: Works of the Pushchino School

Spectral Methods in Data Analysis and Pattern Recognition Problems: Works of the Pushchino School

Machine learning-based fault diagnosis for high-voltage transmission lines

In order to comply with the speed and diversification process of human economic and technological development, the power system gradually develops in the direction of intelligence and complexity, and it is urgent to combine transmission line fault diagnosis methods with machine learning pattern recognition problems in order to reflect the representative characteristics of transmission lines under different operating conditions and different operation modes. In this paper, a convolutional neural network (CNN) model is established for transmission line fault diagnosis, and a series of parameters such as weights, bias, learning rate, batch size and loss function of the proposed CNN model are debugged through experimental comparison, and the best CNN model structure is determined through experimental comparison and debugging, and the recognition accuracy is higher than other traditional machine learning algorithms, which can be as high as 98.38%, with validity, practicality and advancedness.

Lightweight Deep Learning for Object Detection on Mobile Device

Computer vision is a research in the development of technology to obtain information from images and replicate or imitate human visual processes, so that computers can know the objects around them. Deep learning is now the key word as a new era in machine learning that trains computers in finding patterns from large amounts of data. The Convolution Neural Networks (CNN) algorithm has proven impressive in terms of performance for detecting objects, image classification and semantic segmentation. Object detection is a technique used to identify the type of object in an image and also the exact location of the object in the image. Face detection is one of the most challenging problems of pattern recognition. Effective training needs to be done to be able to detect faces effectively. The accuracy in face detection using machine learning does not give good results. This research focuses on the level of accuracy of detecting faces using deep learning methods. This study compares the level of accuracy of deep learning and machine learning in detecting faces effective and efficient. This study uses the Convolution Neural Networks (CNN) model in the deep learning method to detect faces in real time on Android. According to the test results, the accuracy obtained in this study reached 97.97% in several normal facial conditions and face masks.

Distance and similarity measures for (p, q)-fuzzy sets and their application in assessing common lung diseases

This paper aims to develop distance and similarity measures for (p, q)-fuzzy sets from t-norms and t-conorms. It showcases a way to generate these measures from a given t-norm or a t-conorm operator. These newly developed distance and similarity measures have applications in various domains, including problems in pattern recognition. The article provides a comparison between the proposed measures and the existing measures for Fermatean fuzzy sets. Additionally, it illustrates a numerical example to showcase the practical application of the proposed distance measures in diagnosing common lung diseases.

Trade-offs in the latent representation of microstructure evolution

Characterizing and quantifying microstructure evolution is critical to forming quantitative relationships between material processing conditions, resulting microstructure, and observed properties. Machine-learning methods are increasingly accelerating the development of these relationships by treating microstructure evolution as a pattern recognition problem, discovering relationships explicitly or implicitly. These methods often rely on identifying low-dimensional microstructural fingerprints as latent variables. However, using inappropriate latent variables can lead to challenges in learning meaningful relationships. In this work, we survey and discuss the ability of various linear and nonlinear dimensionality reduction methods including principal component analysis, autoencoders, and diffusion maps to quantify and characterize the learned latent space microstructural representations and their time evolution. We characterize latent spaces by their ability to represent high-dimensional microstructural data in terms of compression achieved as a function of the number of latent dimensions required to represent the data accurately, their accuracy based on their reconstruction performance, and the smoothness of the microstructural trajectories in latent dimension. We quantify these metrics for common microstructure evolution problems in material science including spinodal decomposition of a binary metallic alloy, thin film deposition of a binary metallic alloy, dendritic growth, and grain growth in a polycrystal. This study provides considerations and guidelines for choosing dimensionality reduction methods when considering materials problems that involve high dimensional data and a variety of features over a range of lengths and time scales.

Picture fuzzy reasoning full implication method and its application

In this paper, we focus on studying picture fuzzy reasoning full implication methods. First, picture associated t-norm and its residuated implication operator are given. Then, picture fuzzy reasoning full implication principles and the solutions of picture fuzzy reasoning full implication methods based on picture associated t-norm are given. Moreover, the reducibility and robustness of picture fuzzy reasoning full implication methods based on picture associated t-norm are proved. Finally, pattern recognition problems can be successfully solved by the proposed method.

Quantum Neural Networks in the Problem of Pattern Recognition

Three computational models are considered: classical, hybrid (NISQ) and quantum computational models, their pros, cons, possibilities of implementation in modern realities and the problem of image classification and its solution using neural networks in these computational models. Three computational experiments were carried out using the described image recognition approaches with visualization of the learning process and a comparison of the final metrics was carried out aimed at clarifying the prospects of the applied approach based on quantum computing.

Switching perspectives: Investigating the relationships and applications of q-fractional fuzzy sets with other fuzzy set classes in decision making

We explore switching techniques between q-fractional fuzzy sets (qFr sets) and various other classes of fuzzy sets to establish connections and provide a comprehensive framework. In particular, we examine the relationships between qFr sets and interval-valued fuzzy sets (IVFS), type 2 fuzzy sets(T2FS), intuitionistic fuzzy sets(IFS), Pythagorean fuzzy sets(PFS), q-rung orthopair fuzzy sets (q-ROFS), and linear diophantine fuzzy sets(LDFS). By examining these interconnections, we aim to understand better qFr sets and their applications in a wide range of fuzzy systems. It is possible to convert qFr sets into other fuzzy set models using the derived switching techniques, facilitating the utilization of existing methods and algorithms. The versatility of qFr sets, combined with the bridging techniques presented, holds promise for addressing complex problems in decision-making, pattern recognition, and other applications where uncertainty and imprecision play significant roles. Through case studies and practical applications, we illustrate the effectiveness and usefulness of the proposed switching techniques, showcasing their potential impact on real-world scenarios.

Distance measures for geometric graphs

A geometric graph is a combinatorial graph, endowed with a geometry that is inherited from its embedding in a Euclidean space. Formulation of a meaningful measure of (dis-)similarity in both the combinatorial and geometric structures of two such geometric graphs is a challenging problem in pattern recognition. We study two notions of distance measures for geometric graphs, called the geometric edit distance (GED) and geometric graph distance (GGD). While the former is based on the idea of editing one graph to transform it into the other graph, the latter is inspired by inexact matching of the graphs. For decades, both notions have been lending themselves well as measures of similarity between attributed graphs. If used without any modification, however, they fail to provide a meaningful distance measure for geometric graphs—even cease to be a metric. We have curated their associated cost functions for the context of geometric graphs. Alongside studying the metric properties of GED and GGD, we investigate how the two notions compare. We further our understanding of the computational aspects of GGD by showing that the distance is NP-hard to compute, even if the graphs are planar and arbitrary cost coefficients are allowed.As a computationally tractable alternative, we propose in this paper the Graph Mover's Distance (GMD), which has been formulated as an instance of the earth mover's distance. The computation of the GMD between two geometric graphs with at most n vertices takes only O(n3)-time. The GMD demonstrates extremely promising empirical evidence at recognizing letter drawings.

Semi-supervised medical image segmentation via hard positives oriented contrastive learning

Semi-supervised learning (SSL) has been a popular technique to resolve the annotation scarcity problem in pattern recognition and medical image segmentation, which usually focuses on two critical issues: 1) learning a well-structured categorizable embedding space, and 2) establishing a robust mapping from the embedding space to the pixel space. In this paper, to resolve the first issue, we propose a hard positives oriented contrastive (HPC) learning strategy to pre-train an encoder-decoder-based segmentation model. Different from vanilla contrastive learning tending to focus only on hard negatives, our HPC learning strategy additionally concentrates on hard positives (i.e., samples with the same category but dissimilar feature representations to the anchor), which are considered to play an even more crucial role in delivering discriminative knowledge for semi-supervised medical image segmentation. Specifically, the HPC is constructed from two levels, including an unsupervised image-level HPC (IHPC) and a supervised pixel-level HPC (PHPC), empowering the embedding space learned by the encoder with both local and global senses. Particularly, the PHPC learning strategy is implemented in a region-based manner, saving memory usage while delivering more multi-granularity information. In response to the second issue, we insert several feature swap (FS) modules into the pre-trained decoder. These FS modules aim to perturb the mapping from the intermediate embedding space towards the pixel space, trying to encourage more robust segmentation predictions. Experiments on two public clinical datasets demonstrate that our proposed framework surpasses the state-of-the-art methods by a large margin. Source codes are available at https://github.com/PerPerZXY/BHPC.

Performance Evaluation of Block-Sized Algorithms for Majority Vote in Facial Recognition

Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of pixels.There are manychallenges that affect the performance of face recognitionincluding illumination variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification methods, experiments involving various block sizes are conducted to assess the computation performance and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2 block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function performs extremely well on theAT&Tdataset for both holistic and block-based approaches.

Multi-day activity pattern recognition based on semantic embeddings of activity chains

Understanding residents' multi-day activity patterns is crucial for urban transportation planning. However, traditional travel surveys have limitations in observing the recurring regularity of residents' activities over multiple days, resulting in limited insights into long-term activity patterns. This study aims to provide a general and practical analytic framework for multi-day activity pattern recognition by fusing mobile phone data, travel survey data, and built environment data. First, a rule-based model is proposed to infer home/work activities, while a machine learning model is proposed to infer non-home/work activities of mobile phone users. Then, their multi-day activity chains are reconstructed with sequences of activity units which are labeled by inferred activity types. To measure the differences and similarities among these activity chains, this study develops a word embedding model to represent activity units and a sentence embedding model to represent activity chains in numerical vector space. Finally, regular multi-day activity patterns of mobile phone users can be recognized based on the clustering of vectorized activity chains. A case study in Shanghai is conducted to validate the proposed analytic framework for understanding the multi-day activity patterns of residents. The results indicate the effective recognition of 39 representative patterns of 7-day activity chains in Shanghai, reflecting the various lifestyles of residents. In summary, the main contribution of this study is to transform the problem of multi-day activity pattern recognition into similarity measurement of sentence vectors of activity chains by employing techniques from the field of natural language processing.

Research on Land Use Classification Method of High Resolution Remote Sensing Image Based on SVM

Considering the high spatial resolution remote sensing image has huge amounts of data and complex spectral distribution and the characteristics of the space characteristics of the rich, in combination with support vector machine (SVM) in tackling small sample, nonlinear and high dimensional pattern recognition problems show unique advantages, in this chapter will experiment data by using support vector machine (SVM) for high spatial resolution remote sensing image classification.

A Novel Event-Driven Spiking Convolutional Neural Network for Electromyography Pattern Recognition.

Electromyography (EMG) pattern recognition is an important technology for prosthesis control and human-computer interaction etc. However, the practical application of EMG pattern recognition is hampered by poor accuracy and robustness due to electrode shift caused by repeated wearing of the signal acquisition device. Moreover, the user's acceptability is low due to the heavy training burden, which is caused by the need for a large amount of training data by traditional methods. In order to explore the advantage of spiking neural network (SNN) in solving the poor robustness and heavy training burden problems in EMG pattern recognition, a spiking convolutional neural network (SCNN) composed of cyclic convolutional neural network (CNN) and fully connected modules is proposed and implemented in this study. High density surface electromyography (HD-sEMG) signals collected from 6 gestures of 10 subjects at 6 electrode positions are taken as the research object. Compared to CNN with the same structure, CNN-Long Short Term Memory (CNN-LSTM), linear kernel linear discriminant analysis classifier (LDA) and spiking multilayer perceptron (Spiking MLP), the accuracy of SCNN is 50.69%, 33.92%, 32.94% and 9.41% higher in the small sample training experiment, 6.50%, 4.23%, 28.73%, and 2.57% higher in the electrode shifts experiment respectively. In addition, the power consumption of SCNN is about 1/93 of CNN. The advantages of the proposed framework in alleviating user training burden, mitigating the adverse effect of electrode shifts and reducing power consumption make it very meaningful for promoting the development of user-friendly real-time myoelectric control system.

SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments

In recent years, deep learning models have become ubiquitous in industry and academia alike. Deep neural networks can solve some of the most complex pattern-recognition problems today, but come with the price of massive compute and memory requirements. This makes the problem of deploying such large-scale neural networks challenging in resource-constrained mobile edge computing platforms. To solve this, a promising solution is to split resource-hungry neural networks into lightweight disjoint smaller components for pipelined distributed processing. At present, there are two main approaches to do this: semantic and layer-wise splitting. The former partitions a neural network into parallel disjoint models that produce a part of the result, whereas the latter partitions into sequential models that produce intermediate results. However, there is no intelligent algorithm that decides which splitting strategy to use and places such modular splits to edge nodes for optimal performance. To combat this, this work proposes a novel AI-driven online policy, SplitPlace, that uses Multi-Armed-Bandits to intelligently decide between layer and semantic splitting strategies based on the input task's service deadline demands. SplitPlace places such neural network split fragments on mobile edge devices using decision-aware reinforcement learning for efficient and scalable computing. Moreover, SplitPlace fine-tunes its placement engine to adapt to volatile environments. Our experiments on physical mobile-edge environments with real-world workloads show that SplitPlace can significantly improve the state-of-the-art in terms of average response time, deadline violation rate, inference accuracy, and total reward by up to 46, 69, 3 and 12 percent respectively.