Pattern Recognition Methods Research Articles

Online and Cross-User Finger Movement Pattern Recognition by Decoding Neural Drive Information from Surface Electromyogram.

Cross-user variability is a well-known challenge that leads to severe performance degradation and impacts the robustness of practical myoelectric control systems. To address this issue, a novel method for myoelectric recognition of finger movement patterns is proposed by incorporating a neural decoding approach with unsupervised domain adaption (UDA) learning. In our method, the neural decoding approach is implemented by extracting microscopic features characterizing individual motor unit (MU) activities obtained from a two-stage online surface electromyogram (SEMG) decomposition. A specific deep learning model is designed and initially trained using labeled data from a set of existing users. The model can update adaptively when recognizing the movement patterns of a new user. The final movement pattern was determined by a fuzzy weighted decision strategy. SEMG signals were collected from the finger extensor muscles of 15 subjects to detect seven dexterous finger-movement patterns. The proposed method achieved a movement pattern recognition accuracy of ([Formula: see text])% over seven movements under cross-user testing scenarios, much higher than that of the conventional methods using global SEMG features. Our study presents a novel robust myoelectric pattern recognition approach at a fine-grained MU level, with wide applications in neural interface and prosthesis control.

Machine Vision Method for Crack Detection and Pattern Recognition in UHPC Prestressed Beams

Machine Vision Method for Crack Detection and Pattern Recognition in UHPC Prestressed Beams

Drainage Pattern Recognition Method Using Graph Convolutional Networks Combined With Three‐Dimensional Elevation Features

Drainage Pattern Recognition Method Using Graph Convolutional Networks Combined With Three‐Dimensional Elevation Features

A Review of Biosequences Alignment, Matching, and Mining Based on GPU

Abstract: Sequence alignment, pattern matching, and mining are important cornerstones in bioinformatics, and they include identifying genome structure, protein function, and biological metabolic regulatory network. However, because it helps speed up the dealing process, the parallel sequential pattern recognition method has gained attention as data volume has increased. This review summarizes the GPU-based sequence alignment, pattern matching, and mining with the tools and their applications in bioinformatics. After giving an overview of the background, this review first introduces the concept and database of sequence alignment, pattern matching, and mining. Then, the basic architecture and parallel computing principle of GPU are briefly described. Next, the design of GPU-based algorithms and optimization strategies in sequence alignment, pattern matching, and mining are listed in detail. By comparing and analyzing the existing research, the summarization of the advantages and challenges of GPU application in bioinformatics are given. Finally, the future research direction is prospected, including the further development of the algorithm combined with machine learning and deep learning.

Uncertainty-Based Industrial Water Supply and Demand Balance Pattern Recognition: A Case Study in the Yellow River Basin of Gansu Province, China

The balance between water supply and demand is essential for industrial growth, affecting economic, social, and environmental sustainability. Our research employs a Gaussian process regression for demand prediction. Additionally, it takes into account water limits and policy thresholds when determining the supply, thereby defining a range of uncertainty for both the industrial demand and the supply. A pattern recognition method matches this trade-off range, identifying three patterns to support water management. The study focuses on the analysis of industrial water supply and demand dynamics under uncertain conditions in nine cities (Baiyin, Dingxi, Gannan, Lanzhou, Linxia, Pingliang, Qingyang, Tianshui, and Wuwei) in Gansu Province of China’s Yellow River Basin in 2030. The results of the study show that industrial water use in Baiyin, Linxia, Dingxi, and Tianshui cities falls into Pattern I, providing water resources to support industrial development. Industrial water use in Wuwei, Pingliang, Qingyang, and Gannan cities represents Pattern II, which maintains a balance between supply and demand while allowing flexibility in water demand. Finally, the industrial water use in Lanzhou city is characterized by Pattern III, which requires optimization through structural, technological, and management improvements to mitigate the negative impacts of water scarcity on the sustainable development of the economy and society. The results of the research can be used as a reference for policy making in water resources planning and management in the basin.

Exploring The Variety of Jaran Kepang Art: Unique Explorations From Javanese

This article explores the emergence of Artificial Intelligence (AI) like ChatGPT by analyzing the form, function, and meaning of Jaran Kepang art across Java. The research investigates how ChatGPT can aid in understanding and preserving traditional cultural arts like Jaran Kepang. ChatGPT offers advanced methods for data analysis, pattern recognition, and preservation of intangible cultural heritage. This study employs cultural anthropology and traditional communication theories, enhanced by ChatGPT-driven analysis, to examine Jaran Kepang's regional differences and thematic patterns. Using a descriptive qualitative approach, the research combines literature review, direct observation, and ChatGPT technologies to analyze data. Central Java's Jaran Kepang emphasizes mystical and spiritual elements to ward off evil spirits and bring good luck. East Java's version, associated with legends like Ki Sasmita, involves trance performances and moral storytelling. In West Java, linked to the spread of Islam by Sunan Gunung Jati, the art features diverse horse forms in rituals reinforcing community identity. ChatGPT technologies facilitate the analysis of these variations, revealing how each region's Jaran Kepang reflects local adaptation and cultural evolution. The research underscores ChatGPT's potential in enhancing academic knowledge of Javanese culture and advocating for preservation efforts, ensuring traditional arts' legacy in a rapidly advancing technological era.Keywords: jaran kepang; ChatGPT; Javanese culture; mystical value; legend

Deciphering Long‐Term Economic Growth: An Exploration With Leading Machine Learning Techniques

ABSTRACTExisting studies mainly focus on short‐term economic forecasts, but research on long‐term projections, particularly for periods spanning 6–10 years, remains insufficient, despite its importance. This gap may arise from the limitations of traditional linear methods in prediction tasks and pattern recognition, whereas machine learning techniques may help overcome these challenges. To address this, we employ five widely used machine learning models—artificial neural networks (ANN), random forest regression (RF), gradient boosting regression (GBR), extreme gradient boosting (XGBoost), and support vector regression (SVR)—using cross‐country data from 109 countries between 1961 and 2019. To ensure robustness, we employ two distinct sampling methods for model validation. Our findings reveal that the ANN model outperforms others, particularly in long‐term predictions (6–10 years), with an average out‐of‐sample prediction ‐squared of 0.89. Furthermore, analyses using permutation feature importance (PFI) and SHapley Additive exPlanations (SHAP) methods indicate that while current growth rates are critical for short‐term forecasts (1–3 years), two primary variables representing a country's foundational characteristics—real GDP per capita and “country‐feature,” akin to a country dummy variable—are crucial for long‐term predictions (4–10 years). This outcome demonstrates the ANN model's capacity to capture each country's unique characteristics and, through its highly non‐linear nature, successfully execute complex, long‐range forecasts. These results unveil the remarkable potential of machine learning in the realm of long‐term economic forecasting.

Modulation pattern recognition method of wireless communication automatic system based on IABLN algorithm in intelligent system.

The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. These networks are unable to utilize temporal information effectively for feature extraction and modulation pattern recognition, resulting in inefficient modulation pattern recognition. To address this issue, a signal modulation recognition method based on a two-way interactive temporal attention network algorithm has been developed. A two-way interactive temporal network is designed on the basis of the long and short-term memory network with the objective of enhancing the contextual connection of the temporal network. The output of the temporal network is attentively weighted using the soft attention mechanism. The proposed algorithm exhibited enhanced overall, average, and maximum recognition rates at varying signal-to-noise ratios, with an increase of 10.34%, 8.33%, and 3.33%, respectively, in comparison to other algorithms within the Radio Machine Learning (RML) 2016.10b dataset. Furthermore, the modulated signal recognition accuracy was as high as 92.84%, with an average increase in the Kappa coefficient of 12.28%. The Kappa coefficient in the Communication Signal Processing Benchmark for Machine Learning (CSPB.ML2018) 2018 dataset was 0.62, representing an average increase of 10.32% over other algorithms. The results demonstrate that the proposed recognition method can enhance the network's accuracy in recognizing modulated signals. Moreover, it has potential applications in modulation pattern recognition in automatic systems for wireless communications.

A double probe-based fluorescence sensor array to detect rare earth element ions.

There is a persistent need for effective sensors to detect rare earth element ions (REEIs) due to their effects on human health and the environment. Thus, a simple and efficient fluorescence-based detection method for REEIs that offers convenience, flexibility, versatility, and efficiency is essential for ensuring environmental safety, food quality, and biomedical applications. In this study, 6-aza-2-thiothymine-gold nanoclusters (ATT-AuNCs) and bovine serum albumin/3-mercaptopropionic acid-AuNCs (BSA/MPA-AuNCs) were utilized to detect 14 REEIs (Sc3+, Gd3+, Lu3+, Y3+, Ce3+, Pr3+, Yb3+, Dy3+, Tm3+, Sm3+, Ho3+, Tb3+, La3+, and Eu3+), resulting in the creation of a simple, sensitive, and multi-target fluorescence sensor array detection platform. We observed that REEIs exert various enhancement or quenching effects on ATT-AuNCs and BSA/MPA-AuNCs. Thus, these two probes function as double signal channels, with the different effects of REEIs serving as signal inputs. Pattern recognition methods, including hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA), were used to assess the recognition performance of the constructed sensing system. Beyond the excellent ability to recognize individual REEIs, the platform is also capable of distinguishing mixed REEIs. Also, this approach was validated by applying it to detect REEIs in purified water samples. This method not only minimizes the need for synthesizing and optimizing new probes but also offers a novel approach for the determination and identification of diverse analytes, filling a gap in the detection of a large number of REEIs simultaneously.

A pattern recognition method using big data for device behaviour with linear features

A pattern recognition method using big data for device behaviour with linear features

A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand

A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand

АНАЛІЗ МЕТОДІВ РОЗПІЗНАВАННЯ ЗОБРАЖЕНЬ ТА ЇХ КЛАСИФІКАЦІЯ

In the context of the Russian-Ukrainian war, new challenges arise for electronic intelligence. The radio frequency spectrum is saturated with various types of signals, and modern communications have a high level of security. An important stage of signal processing at electronic intelligence posts is their recognition. Most weapons automatically recognize signals by measuring their input parameters, however, due to the low signal-to-noise ratio, this process can be difficult. Nevertheless, almost all modern electronic intelligence equipment displays spectrograms on which the signal can be clearly recognized by human vision. Given the widespread use of image recognition technologies in various industries, it is important to consider the methods and techniques of this process and explore the possibility of using them to solve the problem of signal recognition. The article discusses the concept of artificial intelligence with a special emphasis on the role of computer vision in this field. A classification of image recognition methods is carried out according to the scheme developed on the basis of approaches. In addition, the main stages of computer vision are analyzed, including their functional components and content. Particular attention is paid to the methods of pattern recognition, in particular to the three main types of these methods. The three main types of image recognition methods are distinguished, and their advantages and disadvantages are analyzed. The article also provides practical examples of the implementation of these methods. The final stage of the study was the selection of a method for further experimental and theoretical research.

Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics.

The bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactive components in chrysanthemums, including the total flavonoids (TFs) and chlorogenic acids (TCAs). To determine the informative wavelengths of hyperspectral images, we introduced a variable similarity regularization term into particle swarm optimization (denoted as VSPSO), which can focus on improving the combinatorial performance of key wavelengths and filtering out the features with higher collinearity simultaneously. Moreover, considering the underlying relevance of the phytochemical content and the exterior morphology characteristics, the spatial image features were also extracted. Finally, an ensemble learning model, LightGBM, was established to estimate the TF and TCA contents using the fused features. Experimental results indicated that the proposed VSPSO achieved a superior accuracy, with R2 scores of 0.9280 and 0.8882 for TF and TCA prediction. Furthermore, after the involvement of spatial image information, the fused spectral-spatial features achieved the optimal model accuracy on LightGBM. The R2 scores reached 0.9541 and 0.9137, increasing by 0.0308-0.1404 and 0.0181-0.1066 in comparison with classical wavelength-related methods and models. Overall, our research provides a novel method for estimating the bioactive components in chrysanthemum tea accurately and efficiently. These discoveries revealed the potential effectiveness for constructing feature fusion in HSI-based practical applications, such as nutritive value evaluation and heavy metal pollution detection, which will also facilitate the development of quality detection in the food industry.

Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model

Image processing and pattern recognition methods have recently been extensively implemented in histopathological images (HIs). These computer-aided techniques are aimed at detecting the attentive biological markers for assisting the final cancer grading. Mitotic count (MC) is a significant cancer detection and grading parameter. Conventionally, a pathologist examines the biopsy image physically by employing higher-power microscopy. The MC cells have been marked physically at every analysis, and total MC must be utilized as a major aspect for the cancer ranking and considered as the initiative of cancers. Numerous pattern recognition algorithms for cell-sized objects in HIs depend upon segmentation to assess features. The correct description of the segmentation has been difficult, and feature outcomes can be highly complex to the segmentation. The MC cells are an essential element in many cancer grading methods. Extraction of the MC cell from the HI is a highly challenging assignment. This manuscript proposes the Coati Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Segmentation and Classification (COADL-MNSC) technique. The major aim of the COADL-MNSC technique is to utilize the DL model to segment and classify the mitotic nuclei (MN). In the preliminary stage, the COADL-MNSC approach implements median filtering (MF) for pre-processing. Besides, the COADL-MNSC approach utilizes the Hybrid Attention Fusion U-Net (HAU-UNet) model to segment the MN. Moreover, the capsule network (CapsNet) model is employed for the feature extraction method, and its hyperparameters are adjusted by utilizing the COA model. At last, the classification procedure is performed using the bidirectional long short-term memory (BiLSTM) model. Extensive simulations are performed under the MN image dataset to exhibit the excellent performance of the COADL-MNSC methodology. The experimental validation of the COADL-MNSC methodology portrayed a superior accuracy value of 98.89% over existing techniques under diverse measures.

Enhancing the Recognition of Collinear Building Patterns by Shape Cognition Based on Graph Neural Networks

ABSTRACT Building patterns are important components of urban structures and functions, and their accurate recognition is the foundation of urban spatial analysis, cartographic generalization, and other tasks. Current building pattern recognition methods are often based on a shape index that can only characterize shape features from one aspect, resulting in significant errors. In this study, a building pattern recognition method based on a graph neural network is proposed to enhance shape cognition and focus on recognizing collinear patterns. First, a building shape classification model that integrates global shape and graph node structure features was constructed to quantitatively study shape cognition. Subsequently, a collinear pattern recognition (CPR) model was established based on a dual building graph. The shape cognition results were integrated into the model to enhance its recognition ability. The results show that the shape classification model can be used to effectively distinguish different shape categories and support building pattern recognition tasks. Based on the CPR model, false recognitions can be avoided, and recognition results similar to those of visual cognition can be obtained. Compared with the comparative methods, both models have significant advantages in terms of statistical results and implementation.

Comparison of Accuracy between Random Forest Method and K-Nearest Neighbors Method for Recognizing Solar Panel Energy Conversion Temperature

This study analyzes the classification of temperature image pattern recognition on solar panels to improve the accuracy of energy conversion performance affected by weather changes. The process begins by capturing the surface temperature image of the panel as primary data, which is then processed through the pattern recognition stage. The pattern recognition method is chosen to detect and understand patterns in temperature images that will be used as datasets. This study also compares the results of pattern recognition using the Random Forest classification method with the K-Nearest Neighbors (KNN) method, in order to build an effective model in analyzing images based on weather temperature in Medan City. The results of the study obtained that the solar panel produced a maximum output of 15.74 Wp at 12:00 pm, when the temperature tends to be higher and sunlight is optimal. Then the results of the random forest method showed good performance with an accuracy of 84% and the K-Nearest Neighbors method had an accuracy of 78%.

Application of Pattern Recognition Methods to Study Spatial Localization of Polymetallic Mineralization in the Altai–Sayan Region

The Altai–Sayan mountain-folded belt is analyzed with the purpose of (1) revealing peculiarities of localization of large-scale polymetallic mineralization in the lineament-block structure of the region and (2) determining the geophysical and geomorphic peculiarities of the locations of these deposits using the Cora-3 pattern recognition algorithm. The lineament-block structure of the region is determined using morphostructural zoning. A spatial correlation between large and superlarge polymetallic deposits and morphostructural nodes is revealed. Based on this correlation, a dichotomy problem is solved, which is to divide the entire set of nodes in the region into two classes—ore-bearing and non-ore bearing. For this purpose, we used the Cora-3 logical recognition algorithm with training, for which the input data are geomorphological and geophysical parameters of the nodes. The training set of the algorithm was composed of the nodes where large and superlarge polymetal deposits are known. At the training stage, the algorithm identified the sets of the characteristic features that are peculiar to each class. Based on these features, all the nodes in the region were divided into ore-bearing and non-ore-bearing ones. As a result of recognition, the nodes in which deposits of the considered types and sizes are known were classified as ore-bearing, and, in addition to them, another 11 nodes were identified that meet the features determined in the work and can be considered potentially ore-bearing.

An expert system for species discrimination and grade identification of fish maw

Fish maw, derived from dried swim bladders of fish, is valued for its nutritional and medicinal properties, which has led to an increased market demand. However, price variability based on species and grades has results in unethical practices such as counterfeiting and mislabeling, highlighting the need for reliable quality authentication. To address this, an expert system using MATLAB software has been developed. This system employs nuclear magnetic resonance (NMR) technology and pattern recognition methods to identify fish maw species and classify their grade. The study analyzed ten species across three grades of fish maw, identifying 43 nutritional components from NMR spectra, including sugars, amino acids, fatty acids, organic acids, and vitamins. Univariate statistical analysis was integrated with multivariate statistical analyses, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares (OPLS-DA) using SIMCA software, to develop models for species and grades identification. A four-dimensional volcano map was constructed to highlight characteristic components of various fish maws, resulting in an NMR database for common fish maws. The expert system’s accuracy was validated with new samples, achieving 92.6% for species identification and 90.0% for grade classification. This study provides a valuable tool for the quality evaluation of fish maw and a scientific basis for market regulation.

LEVERAGING EPILEPSY DETECTION ACCURACY THROUGH BURST ENERGY INTEGRATION IN CWT AND DECISION TREE CLASSIFICATION OF EEG SIGNALS

The biomedical field plays a pivotal role in advancing healthcare by leveraging technological innovations to enhance diagnostics and treatment strategies. In the context of neurological disorders, particularly epilepsy, automated EEG signal processing stands out as a critical facet of biomedical research. The ability to analyze and interpret vast amounts of electroencephalogram (EEG) data using sophisticated techniques, such as Continuous Wavelet Transform (CWT), contributes significantly to the timely and accurate detection of epileptic events. Automated EEG signal processing, which uses advanced algorithms and pattern recognition methods, enables the identification of subtle yet crucial patterns indicative of epileptic activity. This paper presents an in-depth exploration of epilepsy identification using the CWT on the CHB-MIT Scalp EEG database. The study uses nine complex mother wavelets from the Gaussian and Morlet families to look at 8920 EEG segments, including 197 seizure events. The performance of each wavelet in detecting epileptic convulsions within EEG signals is rigorously evaluated. Our study shows that the complex Gaussian wavelet of order 5 (cgau5) emerged as the optimal choice, with a sensitivity of 97.58% and a precision of 97.93%. To improve epilepsy detection, we introduce burst energy, a novel engineered feature. This method gets accurate information about brain activity from the CWT scalogram by detecting ictal and ictal-free EEGs at different energy levels. The use of burst energy has a significant impact on classification performance, highlighting its potential for improved accuracy in epilepsy identification. This comprehensive study contributes valuable insights into selecting appropriate wavelets and introduces an innovative feature for more effective EEG-based epilepsy detection.

A novel aggregation-induced emission-featured hyperbranched poly(amido amine)s stabilized copper nanoclusters‑cerium (III) sensor for detection of thiol flavor compounds in processed meat

Thiol flavor compounds are a class of flavoring ingredients that contribute significantly to food flavor. However, rapid discrimination of multiple thiol-flavor compounds remain a challenge. In this study, a ratiometric fluorescent sensor (TPE-ssHPA@Cu NCs-Ce3+) with dual-channel fluorescence features was developed using tetraphenylethene-embedded hyperbranched poly(amidoamine) as a template to stabilize the copper nanocluster‑cerium ions. The sensor was explored for the specific discrimination of six typical thiol flavor compounds, each producing diverse fluorescent fingerprints that were further identified using pattern recognition methods. The sensor achieved a rapid response in identifying thiol flavor compounds and multicomponent mixtures, with detection limits of 0.32–3.13 μM. Furthermore, it was successfully applied to differentiate between the different types and cooking times of meat broths.