Pattern Recognition Methods Research Articles

Semantic-aware automatic extraction method for bottom sediment annotations in raster nautical charts

ABSTRACT The automatic extraction of bottom sediment annotations in large-scale raster nautical charts has limitations, including an imprecise semantic information description and low efficiency. To overcome them, we propose a convolutional neural network (CNN)-based method for the automatic extraction of bottom sediment annotations in raster nautical charts, using image processing techniques to improve it. First, an adaptive chart partitioning model that considers element completeness is constructed. Second, a principle for the unique identification of elements based on spatial conflicts is designed. Finally, a model for accurately extracting semantic information for bottom sediment annotations is established. To evaluate the effectiveness of the proposed method, we implemented a model based on the PyTorch framework and used the PIL library to analyze the results. We also conducted comparative experiments on multiple CNN models to recommend the selection of such models in the proposed method by comparing their classification and recognition performance. The experimental results indicate that (1) the proposed model can achieve high-precision extraction of bottom sediment annotations in raster nautical charts. (2) Furthermore, the proposed model generally has high recognition accuracy and semantic completeness, with better recognition precision than traditional pattern recognition methods.

Research Progress of Electronic Nose and Near-Infrared Spectroscopy in Meat Adulteration Detection

China is a large consumer of meat and meat products. People’s daily diets include a variety of meat, but meat food adulteration problems are common. This paper discusses the research progress of the electronic nose and near-infrared spectroscopy in the field of meat adulteration detection. Through the study of dozens of related papers in recent years, it has been found that the use of the electronic nose and near-infrared spectroscopy for meat detection has the advantages of speed, a nondestructive nature, high sensitivity, strong quantitative analysis, high automation, a wide applicability, an improved product quality, and cost reduction over the traditional detection, but it may be limited in detecting the adulteration of a specific meat, and there are issues with the life and stability of the sensors of the electronic nose in the process of detection, along with the problems of the high requirements for the modeling of the data of near-infrared spectroscopy. This paper takes adulterated meat as the research object and briefly summarizes the detection principles of the electronic nose and near-infrared spectroscopy, as well as the types of sensors applied in the electronic nose. The research progress of the electronic nose and near-infrared detection technology in meat adulteration assessment is reviewed, the advantages and disadvantages of the two in practical application are analyzed, the classification of pattern recognition methods and their applications in meat identification are described, and the feasibility and practical significance of the joint application of the two in meat adulteration detection are envisioned. Meanwhile, the challenges faced by the two in meat detection are pointed out.

Research on structural modulus inversion method of asphalt pavement based on BP neural networ

The current inversion methods based on pattern recognition method, database search method, genetic algorithm and other inversion methods are difficult to solve the absolute convergence problem of structural modulus inversion for asphalt pavements with more than three layers, while the deep learning models and methods widely used in the scenarios of image recognition, speech recognition, etc. as well as the ways to implement them have been increasingly improved, and they can be applied to solve the problem of structural modulus inversion for asphalt pavements. This study aims to solve the modulus inversion problem of multi-layer asphalt pavement structures, obtaining enough theoretical bending basin data of asphalt pavement structures as training samples through mechanical theory and programming calculations, and using the BP neural network model to train the prediction model of structural layer modulus inversion. The test results show that the BP neural network inverse asphalt pavement structural modulus model established in this paper can not only get the prediction results quickly and effectively, but also the prediction results have high accuracy, which provides an effective way for solving the modulus inversion problem of asphalt pavement structure with more than three layers by using the BP neural network to solve the pain point and bottleneck problems in the industry.

The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns.

Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired. We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called "CGM events." We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event. The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians. Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.

An approach for traffic pattern recognition integration of ship AIS data and port geospatial features

ABSTRACT Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.

On Detecting and Measuring Exploitable JavaScript Functions in Real-world Applications

JavaScript is often rated as the most popular programming language for the development of both client-side and server-side applications. Because of its popularity, JavaScript has become a frequent target for attackers who exploit vulnerabilities in the source code to take control over the application. To address these JavaScript security issues, such vulnerabilities must be identified first. Existing studies in vulnerable code detection in JavaScript mostly consider package-level vulnerability tracking and measurements. However, such package-level analysis is largely imprecise, as real-world services that include a vulnerable package may not use the vulnerable functions in the package. Moreover, even the inclusion of a vulnerable function may not lead to a security problem if the function cannot be triggered with exploitable inputs. In this article, we develop a vulnerability detection framework that uses vulnerable pattern recognition and textual similarity methods to detect vulnerable functions in real-world JavaScript projects, combined with a static multi-file taint analysis mechanism to further assess the impact of the vulnerabilities on the whole project (i.e., whether the vulnerability can be exploited in a given project). We compose a comprehensive dataset of 1,360 verified vulnerable JavaScript functions using the Snyk vulnerability database and the VulnCode-DB project. From this ground-truth dataset, we build our vulnerable patterns for two common vulnerability types: prototype pollution and Regular Expression Denial of Service (ReDoS). With our framework, we analyze 9,205,654 functions (from 3,000 NPM packages, 1,892 websites and 557 Chrome Web extensions), and detect 117,601 prototype pollution and 7,333 ReDoS vulnerabilities. By further processing all 5,839 findings from NPM packages with our taint analyzer, we verify the exploitability of 290 zero-day cases across 134 NPM packages. In addition, we conduct an in-depth contextual analysis of the findings in 17 popular/critical projects and study the practical security exposure of 20 functions. With our semi-automated vulnerability reporting functionality, we disclosed all verified findings to project owners. We also obtained 25 published CVEs for our findings, 19 of them rated as “Critical” severity and six rated as “High” severity. Additionally, we obtained 169 CVEs that are currently “Reserved” (as of Apr. 2023). As evident from the results, our approach can shift JavaScript vulnerability detection from the coarse package/library level to the function level and thus improve the accuracy of detection and aid timely patching.

An acoustic emission based approach for damage pattern recognition in composite using linear discriminant analysis

With the increasing application of composite components in various industries, the assessment of their structural integrity, the analysis of damage processes, and the identification of damage patterns are becoming increasingly important. The accuracy of the analysis relies heavily on the selection of features. This paper presents a new idea to extract effective damage features from acoustic emission (AE) signals and accurately identify different damages in the failure process of carbon fiber reinforced polymer specimens. The method combines Hilbert–Huang transform (HHT) and Linear Discriminant Analysis (LDA) to analyze the AE signals generated during the damage process of composite specimens. Specifically, the Hilbert marginal energy spectrum of the signals was regarded as frequency domain descriptors. The frequency domain descriptors were subsequently associated with the parametric features after dimensionality reduction by LDA to construct the classification framework named HHT-LDA. The results show that the frequency domain descriptors of the AE signals associated with each damage mode characterized distinctly. The frequency band of energy distribution in the raw waveform for matrix cracking, delamination, and fiber breakage are (100–150 kHz), (150–300 kHz), and (300–350 kHz), respectively. In addition, the three damage patterns mentioned above were successfully detected and recognized from the complex AE waveforms using HHT-LDA with 85% overall classification rate. This research idea will serve as a potential method for future composite damage pattern recognition and provide supporting knowledge for practical applications of AE monitoring.

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies.

Psoriasis, a complex and recurrent chronic inflammatory skin disease involving various inflammatory cell types, requires effective cell communication to maintain the homeostatic balance of inflammation. However, patterns of communication at the single-cell level have not been systematically investigated. In this study, we employed social network analysis tools, pattern recognition, and manifold learning to compare molecular communication features between psoriasis cells and normal skin cells. Utilizing a process that facilitates the discovery of cell type-specific regulons, we analyzed internal regulatory networks among different cells in psoriasis. Advanced techniques for the quantitative detection of non-targeted proteins in pathological tissue sections were employed to demonstrate protein expression. Our findings revealed a synergistic interplay among the communication signals of immune cells in psoriasis. B-cells were activated, while Langerhans cells shifted into the primary signaling output mode to fulfill antigen presentation, mediating T-cell immunity. In contrast to normal skin cells, psoriasis cells shut down numerous signaling pathways, influencing the balance of skin cell renewal and differentiation. Additionally, we identified a significant number of active cell type-specific regulons of resident immune cells around the hair follicle. This study unveiled the molecular communication features of the hair follicle cell-psoriasis axis, showcasing its potential for therapeutic targeting at the single-cell level. By elucidating the pattern of immune cell communication in psoriasis and identifying new molecular features of the hair follicle cell-psoriasis axis, our findings present innovative strategies for drug targeting to enhance psoriasis treatment.

Implementasi Metode Convolutional Neural Network (CNN) Dalam Klasifikasi Motif Batik.

Indonesia is renowned for its diverse ethnicities, each contributing to a culturally rich mosaic. Among the distinctive regional traits, batik stands out prominently, evolving uniquely in each part of the country. However, the diversity in batik designs often confuses people trying to identify the region of origin due to similarities in patterns. Deciphering these unique batik motifs typically requires specialized knowledge, particularly from individuals well-versed in the art of batik. Reviews suggest that employing pattern recognition methods is an effective way to tackle this challenge. In today's technological landscape, various methods have emerged to aid in recognizing fabric motifs. This study utilizes the Convolutional Neural Network (CNN) method with the Efficient Net-B0 architecture. The tests conducted to identify batik motifs using this approach yielded a highest accuracy result of 79.62% for the test data and an accuracy validation result of 73.33%. These findings underscore the potential of advanced technologies, specifically the CNN with Efficient Net-B0 architecture, in accurately discerning and distinguishing batik motifs.

Investigating the changing pattern of groundwater levels and rainfall in the peninsular region of Bhagalpur and Khagaria, Bihar

Abstract Groundwater is the primary source of water for domestic and irrigation purposes in the peninsular region of the Bhagalpur and Khagaria districts of Bihar. Though this region is bounded by perennial rivers on three sides, the groundwater level is decreasing gradually because of over exploitation and misuse. In this study, spatial and temporal analyses of rainfall and groundwater levels for pre- and post-monsoon seasons from 1996 to 2020 have been carried out using GIS tools, graphical plots, and statistical methods of pattern recognition. The spatial analysis of rainfall shows less rainfall in the western region of the study area, whereas it shows heavy rainfall in the region near the Vikramshila Bridge. The temporal analysis of rainfall shows decreasing trend in the whole study area and the rate of decrease was 25.05 mm/year during 1996–2020. The results of the pre-monsoon groundwater levels analysis show decreasing trend in the majority of wells, and the rate of decrease varies from 0.005 to 0.102 m/year. By contrast, the post-monsoon groundwater levels showed an increasing trend varying from 0.005 to 0.083 m/year at wells located near the River Ganga, except at Maheshkhunt. Thus, there is a need for proper groundwater management for a sustainable future.

Retracted: Multiple Collaborative Supervision Pattern Recognition Method within Social Organizations Based on Data Clustering Algorithm

Retracted: Multiple Collaborative Supervision Pattern Recognition Method within Social Organizations Based on Data Clustering Algorithm

Facial Recognition Based Automated Door

To ensure the accuracy and efficiency of intruder identification, the proposed method is combined with Haar clssifier technology for face detection. When someone comes to the door, the Pi camera captures the image and starts the face detection process. In this research, we implement a facial recognition component to capture human images, comparing them with stored data in a database. Upon a match with an authorized individual, the system unlocks the door through an electromagnetic lock. The demand for a rapid and accurate face recognition system persists, continuously evolving to swiftly identify intruders and restrict unauthorized access to highly secure areas, thereby reducing human error. Facial recognition stands as a crucial component within secure systems, surpassing biometric pattern recognition methods, and finds widespread application across various domains.

Dynamic Successive Assessment of Water Resource Carrying Capacity Based on System Dynamics Model and Variable Fuzzy Pattern Recognition Method

The water resource carrying capacity (WRCC) system comprises multiple complex and non-linear interactions related to society, economy, water resources, and the water environment. A comprehensive comprehension of its internal mechanisms is essential for the continual enhancement of the regional WRCC. This study concentrates on the temporal and spatial variability of the WRCC to investigate a method for dynamic successive assessment. Firstly, the pressure–state–response (PSR) framework is used to develop a systematic and causal indicator system. Then, the variable fuzzy pattern recognition (VFPR) model and an analytic hierarchy process—entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resource carrying capacity in Hebei Province is mainly due to the basic attribute of the decision on the water resource shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which has made the province’s water resource carrying capacity tend to become better gradually. Simultaneously, a system dynamics model (SD) for water resource carrying capacity was established based on an analysis of the model structure. Moreover, three scenarios were designed, including existing continuation, high-efficiency water saving, and cross-regional water transfer. Subsequently, each scenario is further categorized into high- and low-speed economic development and population growth schemes. Afterward, simulations and predictions were conducted for a total of six schemes spanning from 2023 to 2030. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of the economy and population, the efficient water conservation, and the interbasin transfer scenario (scenario 2 with high speed) are the best choices for the sustainable development of water resources and social economy in Hebei.

Multiple fingerprint and pattern recognition analysis on polysaccharides of four edible mushrooms

Quality analysis of edible mushrooms based on polysaccharides is generally difficult due to their complicated structures and hard separation. Here, multiple fingerprint analysis of polysaccharides based on chromatographic and spectrometric techniques were developed, and then applied in comparative analysis of Auricularia heimuer (AH), Auricularia cornea (AC), Auricularia cornea ʻYu Muerʼ (ACY) and Tremella fuciformis (TF). Firstly, polysaccharides were obtained with the molecular weights between 1.783 × 106 and 6.774 × 106 Da. Then, complete hydrolysis by TFA and enzyme digestion by cellulase were employed and subsequently analyzed by HPLC-UV, GC–MS, HILIC-HPLC-ELSD and HILIC-HPLC-ESI−-HCD-MS/MS, and ATR-FT-IR were used to characterize the functional groups of intact polysaccharides. By chemometric analysis, differential markers of d-xyl, l-fuc, l-arb, d-glc, disaccharide and hexasaccharide were selected, and AC and ACY were proved to be same species from the viewpoint of polysaccharides firstly. Furthermore, the structures of oligomers with DPs of 2–8 and →4)-β-d-Glcp-(1→ unit with different contents were inferred by combinatory analysis of ESI−-MS/MS, glycosidic linkage, monosaccharide compositions and functional groups. In conclusion, the combinatory method of multiple fingerprint and pattern recognition is powerful not only for structural elucidation of polysaccharides, but also for quality analysis and species differentiation of edible mushrooms from the perspective of biological polysaccharides.

Prostate Cancer Detection in Colombian Patients through E-Senses Devices in Exhaled Breath and Urine Samples

This work consists of a study to detect prostate cancer using E-senses devices based on electronic tongue and electronic nose systems. Therefore, two groups of confirmed prostate cancer and control patients were invited to participate through urine and exhaled breath samples, where the control patients group was categorized as Benign Prostatic Hyperplasia, Prostatitis, and Healthy patients. Afterward, the samples were subsequently classified using Pattern Recognition and machine learning methods, where the results were compared through clinical history, obtaining a 92.9% success rate in the PCa and control samples’ classification accuracy by using eTongue and a 100% success rate of classification using eNose.

Accurate Identification of Attention-deficit/Hyperactivity Disorder Using Machine Learning Approaches

The identification of ADHD is laden with a great number of challenges and obstacles. If a patient is incorrectly diagnosed, there is a possibility that this will have adverse impact on their health. ADHD is a neurodevelopmental condition characterized by persistent patterns of inattention, hyperactivity, and impulsivity that often emerge in infancy. ADHD is a neurodevelopmental disorder characterized by difficulties in sustaining attention, concentrating, and regulating behavior. Therefore, using artificial intelligence approaches for early detection is very important for reducing the increase in disease. The goal of this research is to find out an accurate model that could differentiate between those who have ADHD and those who do not have it by making use of the method of pattern recognition. The research project was composed of a combination of event-related potential data from people who had been diagnosed with ADHD, in addition to a control group that was made up of people who did not have ADHD. This research presents novel machine learning models based on decision tree (DT), random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), using dataset collected from ADHD patients for the purpose of training. Significant performance outcomes have been seen in the context of the SVM which has achieved a high accuracy rate of 91%. MLP has demonstrated an accuracy rate of 89%. Furthermore, the RF model has shown an accuracy rate of 87%. Finally, the DT model revealed accurate results up to 78%. The aforementioned results highlight the effectiveness of the utilized methods and the ability of modern computational frameworks in attaining substantial levels of accuracy in the diagnosis and categorization of ADHD.

Unveiling critical ADHD biomarkers in limbic system and cerebellum using a binary hypothesis testing approach.

Attention deficit hyperactivity disorder (ADHD) is a common childhood developmental disorder. In recent years, pattern recognition methods have been increasingly applied to neuroimaging studies of ADHD. However, these methods often suffer from limited accuracy and interpretability, impeding their contribution to the identification of ADHD-related biomarkers. To address these limitations, we applied the amplitude of low-frequency fluctuation (ALFF) results for the limbic system and cerebellar network as input data and conducted a binary hypothesis testing framework for ADHD biomarker detection. Our study on the ADHD-200 dataset at multiple sites resulted in an average classification accuracy of 93%, indicating strong discriminative power of the input brain regions between the ADHD and control groups. Moreover, our approach identified critical brain regions, including the thalamus, hippocampal gyrus, and cerebellum Crus 2, as biomarkers. Overall, this investigation uncovered potential ADHD biomarkers in the limbic system and cerebellar network through the use of ALFF realizing highly credible results, which can provide new insights for ADHD diagnosis and treatment.

Assignment of the methanol OH-stretch overtone spectrum using the pattern recognition method.

We present the measurement and analysis of the 2OH stretching band of methanol between 7165 cm-1 and 7230 cm-1 cooled down to 26 ± 12 K in a buffer gas cooling experiment. Measurements were performed with a cavity ring-down spectrometer having a detection limit αmin = 2 × 10-10 cm-1. A total of 350 rovibrational transitions were assigned and 62 rovibrational transitions were tentatively assigned. This assignment was performed using the pattern recognition method developed by Rakvoský et al. [Phys. Chem. Chem. Phys., 2021, 23, 20193-20200]. In this work, we extended their method by using information on the relative intensities of the transitions to add one criterion to the validation of the assignments, allowing us to firmly assign 188 additional rovibrational transitions and to tentatively assign 14 more compared to the ir work.

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background.

The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.

Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning.

Electromyographic (EMG) signals have gained popularity for controlling prostheses and exoskeletons, particularly in the field of upper limbs for stroke patients. However, there is a lack of research in the lower limb area, and standardized open-source datasets of lower limb EMG signals, especially recording data of Asian race features, are scarce. Additionally, deep learning algorithms are rarely used for human motion intention recognition based on EMG, especially in the lower limb area. In response to these gaps, we present an open-source benchmark dataset of lower limb EMG with Asian race characteristics and large data volume, the JJ dataset, which includes approximately 13,350 clean EMG segments of 10 gait phases from 15 people. This is the first dataset of its kind to include the nine main muscles of human gait when walking. We used the processed time-domain signal as input and adjusted ResNet-18 as the classification tool. Our research explores and compares multiple key issues in this area, including the comparison of sliding time window method and other preprocessing methods, comparison of time-domain and frequency-domain signal processing effects, cross-subject motion recognition accuracy, and the possibility of using thigh and calf muscles in amputees. Our experiments demonstrate that the adjusted ResNet can achieve significant classification accuracy, with an average accuracy rate of 95.34% for human gait phases. Our research provides a valuable resource for future studies in this area and demonstrates the potential for ResNet as a robust and effective method for lower limb human motion intention pattern recognition.