Pattern Recognition Methods Research Articles

Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor

Gas detection and monitoring are critical to protect human health and safeguard the environment and ecosystems. Chemiresistive sensors are widely used in gas monitoring due to their ease of fabrication, high customizability, mechanical flexibility, and fast response time. However, with the rapid development of industrialization and technology, the main challenges faced by chemiresistive gas sensors are poor selectivity and insufficient anti-interference stability in complex application environments. In order to overcome these shortcomings of chemiresistive gas sensors, the pattern recognition method is emerging and is having a great impact in the field of sensing. In this review, we focus systematically on the advancements in the field of data processing methods for feature extraction, such as the methods of determining the characteristics of the original response curve, the curve fitting parameters, and the transform domain. Additionally, we emphasized the developments of traditional recognition algorithms and neural network algorithm in gas discrimination and analyzed the advantages through an extensive literature review. Lastly, we summarized the research on chemiresistive gas sensors and provided prospects for future development.

Literature Review on Multi-Criteria Analysis and Application in Education Environment

Due to the increasing complexity of educational data, the use of decision analysis techniques such as Multi-Criteria Decision Making (MCDM) models has become more popular in the education system in recent years. Multi criteria decision making methods provide data analysis methods faster and more efficient for revealing unhidden patterns and other meaningful information from vast educational data those conventional analytics are unable to discover in a reasonable amount of time. Particularly, MCDM techniques have been demonstrated to be effective methods for pattern recognition in educational systems. Motivated by this consideration, the purpose of this paper is to investigate the MCDM approaches applied to education systems through a review of new architectures, applications, and educational trends. The primary objective of this paper is to provide extensive insight into the application of MCDM models to education solutions in order to bridge the gap between MCDM techniques and human-based education interpretability. Then, the application of MCDM to various aspects of education are categorized. Finally, we present the current open challenges and future directions.

Multivariate Modelling Based on Isotopic, Elemental, and Fatty Acid Profiles to Distinguish the Backyard and Barn Eggs.

The ability to trace the origin of eggs from backyard-raised hens is important due to their higher market value compared to barn-raised eggs. This study aimed to differentiate eggs from these two rearing systems using isotopic, elemental, and fatty acid profiles of egg yolks. A total of 90 egg yolk samples were analyzed, analytical results being followed by statistical tests (Student's t-test) showing significant differences in δ18O, several elements (Mg, K, Sc, Mn, Fe, Ni, Cu, Zn, As, Cd, Ba, Pb), and fatty acids compositions (C23:0, C17:0, C18:0, C16:1n7, C18:1n9, C18:2n6, C20:1n7, C20:4n6, C20:5n3, C22:6n3), as well as in the ratios of SFA, PUFA, and UFA. The results indicated a nutritional advantage in backyard eggs due to their lower n-6 polyunsaturated fatty acid content and a more favorable n-6 to n-3 ratio, linked to differences in the hens' diet and rearing systems. To classify the production system (backyard vs. barn), three pattern recognition methods were applied: linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and multilayer perceptron artificial neural networks (MLP-ANN). LDA provided perfect initial separation, achieving 98.9% accuracy in cross-validation. k-NN yielded classification rates of 98.4% for the training set and 85.7% for the test set, while MLP-ANN achieved 100% accuracy in training and 92.3% in testing, with minor misclassification. These results demonstrate the effectiveness of fusion among isotopic, elemental, and fatty acid profiles in distinguishing backyard eggs from barn eggs and highlight the nutritional benefits of the backyard-rearing system.

Identifying High-Risk Patterns in Single-Vehicle, Single-Occupant Road Traffic Accidents: A Novel Pattern Recognition Approach

Despite various interventions in road safety work, fatal and severe road traffic accidents (RTAs) remain a significant challenge, leading to human suffering and economic costs. Understanding the multicausal nature of RTAs, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in RTAs, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns (BPs), using the Austrian RTA database. This study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe RTAs (severe casualties). In terms of pattern recognition, this study develops the maximum combination value (MCV) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX method, created to accurately detect and analyse variable-specific BPs in RTA data. Findings indicate significant BPs contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity.

Fast identification of Baijius based on organic acid response colorimetric sensor array

Herein, a simple colorimetric sensor array sensitive to organic acids was designed by gold (Au) and silver (Ag) nanoparticles modified with four different compounds for the identification of 16 famous Baijius containing different organic acids. In detail, the sensing mechanism of the colorimetric sensor array was based on the charge transfer between the nanoparticle surface protectant and H+, which reduced the electrostatic force between the nanoparticles, leading to the aggregation and color change of the nanoparticles. The unique color change of the colorimetric array before and after the reaction was used as a unique fingerprint profile for each specific analyte, which can be identified by the naked eye. The generated digital database was analyzed using pattern recognition methods, including principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). All Baijius can be easily identified by fingerprint mapping or PCA scoring maps. 32 blind samples were tested by LDA and 30 samples were correctly classified. The HCA results showed that all samples were correctly classified and even very similar 1 % dilutions of the Baijiu can be easily distinguished, demonstrating the potential of the constructed colorimetric sensor array technology for quality control applications of Baijius and other beverages.

Luminescent Metal-Organic Framework-Based Fluorescent Sensor Array for Screening and Discrimination of Bisphenols.

Extensive applications of bisphenols in industrial products have led to their release into aquatic environments, causing a great threat to human health due to their endocrine-disrupting effects, whereas existing methods are difficult to implement the rapid and high-throughput detection of multiple bisphenols. To circumvent this issue, we constructed a sensor array using two luminescent metal-organic frameworks (LMOFs) (Zr-BUT-12 and Ga-MIL-61) for the rapid discrimination of six bisphenol contaminants (BPA, BPS, BPB, BPF, BPAF, and TBBPA). Wherein, Zr-BUT-12 and Ga-MIL-61 exhibited different fluorescence-emission properties and good luminescent stability. Interestingly, bisphenols with different structures had diverse quenching effects on the fluorescence intensity of Zr-BUT-12 and Ga-MIL-61 via the adsorptive interaction, resulting in unique fluorescent fingerprints. Based on pattern recognition methods, different bisphenols were successfully identified, with the limit of detection in the range of 1.59-16.7 ng/mL for six bisphenols. More importantly, the developed sensor array could be effectively utilized for distinguishing different ratios of mixed bisphenols, which was further applied for bisphenol discrimination in real water samples. Consequently, our finding provides a promising strategy for the simultaneous recognition of multiple bisphenols, which encourages the development of a sensor array for the detection of multiple contaminants in environmental monitoring and food safety.

Production monitoring and quality characterization of black garlic using Vis-NIR hyperspectral imaging integrated with chemometrics strategies

As a new deep-processing garlic product with notable health benefits, the accurate discrimination of processing stages and prediction of key physicochemical constituents in black garlic are vital for maintaining product quality. This study proposed a novel method utilizing hyperspectral imaging technology to both rapidly monitor the processing stages and quantitatively predict changes in the key physicochemical constituents during black garlic processing. Multiple methods of noise reduction and feature screening were used to process the acquired hyperspectral information. To differentiate processing stages, pattern recognition methods including linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine classification (SVC) analysis were utilized, achieving a discriminant accuracy of up to 98.46 %. Furthermore, partial least squares regression (PLSR) and support vector machine regression (SVR) analysis were performed to achieve quantitative prediction of the key physicochemical constituents including moisture and 5-HMF. PLSR models outperformed SVR models, with correlation coefficient of prediction of 0.9762 and 0.9744 for moisture and 5-HMF content, respectively. The current study can not only offer an effective approach for quality detection and assessment during black garlic processing, but also have a positive significance for the advancement of black garlic related industries.

Optimal weighted multi-scale entropy-energy ratio feature for machine fault diagnosis

Identifying the sensitive characteristics of mechanical equipment components is crucial for effective fault diagnosis. However, focusing solely on a specific feature at a single time scale fails to comprehensively capture the device’s operational state. Inspired by the concept of multi-scale analysis and recognizing the complementary strengths of permutation entropy (PE) and root mean square (RMS) in fault characterization, we propose a novel feature called the Optimal Weighted Multi-Scale Entropy-Energy Ratio (OWMEER). This feature aims to enhance fault characterization by optimally combining the strengths of PE and RMS, thereby providing a more comprehensive assessment of the device’s condition. The effectiveness and superiority of OWMEER in fault characterization have been validated through experimental data, including both public and self-test datasets, when combined with the commonly used pattern recognition methods such as random forest (RF) and support vector machine (SVM). The results demonstrate that using OWMEER as a fault feature not only yields better results than using the original features RMS and PE, but also maintains strong diagnostic performance across different classifiers and datasets.

A one-dimensional higher-order dynamic modeling method for thin-walled beams with circular cross-sections

This paper addresses the construction of a dynamical model for a thin-walled beam with circular cross-section in the framework of one-dimensional higher-order beam theory. And a method for pattern recognition of circular thin-walled structures is proposed based on principal component analysis. Initially, a set of equal length linear segments are defined to discretize the mid-line of a circular section. Preliminary deformation modes of thin-walled structures, defined over the cross-section through kinematic concept, are parametrically derived through changing the discretization degree of the section. Next, the generalized eigenvectors are derived from the governing equations, and the characteristic deformation modes of circular sections with different discretization degrees are solved based on principal component analysis. In addition, a reduced higher-order model can be obtained by updating the initial governing equations with a selective set of cross-section deformation modes. The features include further reducing the number of degree of freedoms (DOFs) and significantly improving computational efficiency while ensuring accuracy. For illustrative purposes, the versatility of the procedure is validated through both numerical examples and comparisons with other theories.

Huangqi Guizhi Wuwu Decoction Improves Inflammatory Factor Levels in Chemotherapy-induced Peripheral Neuropathy by Regulating the Arachidonic Acid Metabolic Pathway.

Chemotherapy-induced Peripheral Neuropathy (CIPN) is a common complication that arises from the use of anticancer drugs. Huangqi Guizhi Wuwu Decoction (HGWWD) is an effective classic prescription for treating CIPN; however, the mechanism of the activity is not entirely understood. This study aimed to investigate the remedial effects and mechanisms of HGWWD on CIPN. Changes in behavioral, biochemical, histopathological, and biomarker indices were used to evaluate the efficacy of HGWWD treatment. Ultra-high-performance liquid chromatography/mass spectrometry combined with the pattern recognition method was used to screen biomarkers and metabolic pathways related to CIPN. The results of pathway analyses were verified by protein blotting experiments. A total of 29 potential biomarkers were identified and 13 metabolic pathways were found to be involved in CIPN. In addition HGWWD reversed the levels of 19 biomarkers. Prostaglandin H2 and 17α,21-dihydroxypregnenolone were targeted as core biomarkers. This study provides scientific evidence to support the finding that HGWWD mainly inhibits the inflammatory response during CIPN by regulating arachidonic acid metabolism.

Heavy Metal Contamination of Surface Sediments-Soil Adjoining the Largest Copper Mine Waste Dump in Central India Using Multivariate Pattern Recognition Techniques and Geo-Statistical Mapping

This detailed study assessed heavy metal contamination of sediments/soil near central India’s largest copper mining area using 38 sampling sites within 10 km of the mine using atomic absorption spectroscopy. This study utilized multivariate pattern recognition methods, namely hierarchical clustering analysis (HCA) and principal component analysis (PCA), for source identification. Twelve parameters, i.e., copper (Cu), manganese (Mn), cobalt (Co), zinc (Zn), nickel (Ni), lead (Pb), organic matter (OM), cation exchange capacity (CEC), soil pH, distance (D), and elevation (E) were analyzed. The hierarchical cluster analysis (HCA) was used to analyze the sample sites with similar metal contamination and principal component analysis (PCA) was used to analyze the relationship between the parameters as well as to identify sources of heavy metal pollution. Three major pollution hotspots were detected by AHC and were classified as unpolluted/low pollution sites (UPS: mean concentration factor of 1.35 for Cu), highly polluted sites (HPS: mean concentration factor of 22 for Cu), and extremely polluted sites (EPS: mean concentration factor of 74 for Cu). PCA revealed three hidden factors/components, namely PC1 (explaining 38% of the variability), PC2 (18% of the variability), and PC3 (14% of the variability). Metals showed strong positive loading in PC1, explaining the highest variability. The mean content of Cu in soil/sediment samples was 502.526 mg/kg. The mean copper content was 10 times higher than the natural crustal value of 45mg/kg, indicating severe pollution in several sites around the study area. Mapping of copper contamination was conducted to reveal the spatial distribution of copper contamination using QGIS. This study exposes the heavy metal contamination level in surface sediments/soil and the effectiveness of pattern recognition techniques for the assessment of multivariate datasets in discerning spatial disparities and identifying the contamination causes.

3D-CNN Method for Drowsy Driving Detection Based on Driving Pattern Recognition

Drowsiness impairs drivers’ concentration and reaction time, doubling the risk of car accidents. Various methods for detecting drowsy driving have been proposed that rely on facial changes. However, they have poor detection for drivers wearing a mask or sunglasses, and they do not reflect the driver’s drowsiness habits. Therefore, this paper proposes a novel method to detect drowsy driving even with facial detection obstructions, such as masks or sunglasses, and regardless of the driver’s different drowsiness habits, by recognizing behavioral patterns. We achieve this by constructing both normal driving and drowsy driving datasets and developing a 3D-CNN (3D Convolutional Neural Network) model reflecting the Inception structure of GoogleNet. This binary classification model classifies normal driving and drowsy driving videos. Using actual videos captured inside real vehicles, this model achieved a classification accuracy of 85% for detecting drowsy driving without facial obstructions and 75% for detecting drowsy driving when masks and sunglasses are worn. Our results demonstrate that the behavioral pattern recognition method is effective in detecting drowsy driving.

A rapid and accurate fluorescent sensor array based on lanthanide metal-organic framework for identification and determination of perfluorinated compounds

Perfluorinated compounds (PFCs), as an important class of environmental pollutants, have chemical and structural similarities that make their detection a great technical challenge. This study synthesized three species of metal-organic frameworks (MOFs) using different lanthanide metal ions or organic ligands, which were integrated into a fluorescent sensor array. This innovative approach offers a straightforward, rapid, and precise detection strategy for PFCs. Different ionization properties and fluorinated hydrophobic tails of PFCs lead to different electrostatic attraction and hydrophobic effects between PFCs and sensing elements, which become the basis for differential sensing. Furthermore, the fluorescence signal is more convenient to collect, making the sensor array simple to complete the identification. Combined with pattern recognition methods, the array successfully identified seven kinds of PFCs and mixtures with a classification accuracy of 100 % and a detection limit as low as 51 nM. Finally, the utility of the sensor array in river water sample analysis was verified. The strategy provides an effective method for identifying and determining PFCs and offers new opportunities for developing sensor arrays based on lanthanide MOFs.

Metabolomic profiling of Greyia radlkoferi on water stress, phenological growth stages, and leaf age using 1H NMR

Greyia radlkoferi Szyszyl (Melianthaceae) is a native plant to southern Africa identified as a viable commercial crop. The leaf extract is rich in flavonoid compounds with the ethanolic leaf extract found to treat skin hyper-pigmentation of humans without adverse effects (anti-tyrosinase activity value IC50 17.86 μg/ml). Anti-tyrosinase activity is found to be variable between plants, even when planted at the same site, which raises concerns regarding commercial production, necessitating the need for cultivation interventions that will not compromise the activity of the extract. Interventions to limit the variation were thus explored by imposing targeted water stress (different durations) before harvesting and determining the effect on the different phenological growth stages and activity of different leaf ages. The inhibitory tyrosinase activity was evaluated using mushroom tyrosinase and kojic acid as a control and data was presented as IC50 values. The metabolomics investigation was conducted through the use of nuclear magnetic resonance (1H NMR) analysis coupled with the multivariate statistical data set. Pattern recognition methods such as principal component analysis (PCA) and orthogonal projection on latent structure-discriminant analysis (OPLS-DA) were employed to obtain the metabolic discrimination between the treatments. No clear pattern was observed in the grouping of samples exposed to targeted water stress on both plant age and leaf position, except for younger leaves of one month and four months old which had a strong clustering of high activity samples as compared to older leaves of six months old. However, plant materials harvested during the flowering growth stage had a strong grouping of samples producing high anti-tyrosinase activity as compared to the budding and vegetative growth stages. The study determined the changes in the concentration of various compounds, although quantification of compounds should be determined in the future.

Application of Pattern Recognition and Mixture Methods in Exposome Profiling of Seminal Plasma and Associations with Semen Parameters

Application of Pattern Recognition and Mixture Methods in Exposome Profiling of Seminal Plasma and Associations with Semen Parameters

Why consider quantum instead classical pattern recognition techniques?

This article delves into the evolving landscape of pattern recognition, transitioning from classical methodologies to quantum-based techniques. It underscores how quantum algorithms offer a new paradigm with the potential to overcome the limitations of classical techniques. Unlike conventional methods, which, while effective, often struggle with complex and high-dimensional datasets, quantum algorithms are poised to surpass these limitations. This study explores the applications, benefits, drawbacks, and open issues surrounding quantum pattern recognition methods and provides a comprehensive overview of the current state of quantum technology and outlines potential future directions, highlighting the intersection of quantum computing and pattern recognition for breakthroughs.

Feature extraction and pattern recognition in time-lapse pressure transient responses

Monitoring of modern wells equipped with permanent downhole gauges and flowmeters provides large datasets of pressure, temperature and flowrate measurements. On a limited scale, selected events from these datasets are traditionally used in well performance analysis and monitoring, reservoir simulation, and many other tasks employing physics-based models. New methods that combine model- and data-driven approaches for full-scale analysis of these large datasets have recently attracted interest, suggesting new metrics for knowledge extraction. Most recent studies have used a combination of Pressure Transient Analysis (PTA), which is a key reservoir engineering tool, with hybrid methods, such as PTA-metrics and AI-powered models. The emergence of hybrid methodologies combining model- and data-driven approaches has opened new perspectives for comprehensive analysis and knowledge extraction from big well monitoring data sets, accumulated in the industry. Despite their potential, these methodologies often face challenges in reliability, effective data processing, interpretation and feature engineering.This paper introduces a novel PTA-feature extraction and pattern recognition methods for analysis of time-lapse pressure transient responses and their Bourdet derivatives. The pattern recognition method builds on an unsupervised classification method to extract PTA-features, associated with different flow regimes commonly used in PTA. Each pressure transient in a time-lapse series is first processed by an autonomously fine-tuned algorithm that measures the signal's distance to an ensemble of physically meaningful responses defined by PTA-feature library, thus breaking the transient into a series of likely PTA-features. A set of hyperparameters is used in the unsupervised classification, where an optimization procedure is employed for automated tuning of the hyperparameters to a particular transient. Subsequently, recognition of underlying patterns governed by sequences of these PTA-features in the time-lapse transient responses is performed. Testing of the combination of these new methods through synthetic and field cases is further carried out with verification via comparison with expert’ interpretation results. Added value to the previously introduced PTA metrics, which provide on-the-fly well and reservoir performance analysis, is finally demonstrated. The proposed pattern recognition method improves reliability and automates the calculation of the PTA metrics.The developed methodology serves as a new tool for knowledge extraction from big well monitoring datasets, available in the companies operating in the oil and gas industry, as well as the emerging industries such as carbon capture and storage and geothermal energy production. The article concludes with a discussion of the main advantages and limitations of the suggested feature extraction and pattern recognition methods. Besides the combined use of the methods described in this article, these methods may also be integrated with conventional physics-based approaches widely used in the industry for well data interpretation and reservoir simulations, improving their performance and efficiency for big data sets.

Confinement and heating promoted RTP of flumequine, oxolinic acid and levofloxacin on papers for their detection and discrimination

Developing simple assays to identify and discriminate quinolones are highly desired for food safety, which remain a great challenge due to the interferences from food matrices and the minor variation on the molecular structure of massive quinolones. We proposed a room-temperature phosphorescence (RTP) assay for the quantitative detection and discrimination of quinolones, aided by a confinement and heating strategy. Quinolones were loaded into the porous framework of paper, which provided confinement effects on the molecular motions of quinolones. The heating process further removed the solvents, eliminating their quenching effects on excitons. These synergistic effects improved the RTP intensity and emission lifetime by 1144-fold and from nanoseconds to seconds, respectively. Three types of quinolones were quantitatively detected and discriminated through pattern recognition methods. The proposed assay showed excellent detection performance in complicated meat samples, aided by the delayed signal collecting of RTP. The reported results provided a clue to modulate the RTP properties of organic molecules, showing great potential for application prospect in food safety analysis, environmental and healthy monitoring.

Simulation Analysis and Image Processing of the Exhaust Silencing Chamber and Coil

The compressor is the most core component of refrigeration products, and its performance directly affects the overall performance of the product. The exhaust silencing chamber and coil are key components of a refrigeration compressor, and their working stability and lifespan determine the reliability and economy of the refrigeration compressor. This paper obtains reference data for the fluid domain of the exhaust silencing chamber through simulation analysis and image processing of the existing exhaust silencing chamber structure and inner diameter size of the coil. Then, the optimal model of the prioritized exhaust chamber is obtained through pattern recognition method. Based on the image processing method, the internal flow field of the exhaust chamber are obtained, providing a strong reference for the optimal structural design of the exhaust silencing chamber and coil of the refrigeration compression body.

Sorted sample- wavelet feature clustering based on the range for classification of multiple nutrient deficiencies in tomato plants

The phenomenon of physiological change in plants hyperpolarizes membrane potentials showing a significant change in electrophysiology. For plants to survive, develop, and reproduce, the proper ratio of nutrients is required. Malnourished plants exhibit unfavorable changes in their physiology. Nutrient deficiencies are one of the primary abiotic stresses occurred in plants which are commonly identified and classified through various hyperspectral imaging, and pattern recognition methods. In contrast to image-based methods, in this paper, the electrophysiological signal data of tomato plants in multiple nutrient deficiency states are classified. These electrical signals of tomato plants are acquired in healthy and in deficient states of calcium (Ca), manganese (Mn), nitrogen (N), and iron (Fe). The features are extracted by 7-level orthogonal wavelet decomposition using four different wavelets Daubechies (Db), Coiflet (Cf), Symlet (Sym), and Discrete Meyer (Dm). A novel range-based sorted sample clustering method is proposed to boost classification performance. Class assignment is performed in two modes homogeneous and non-homogeneous, where multi-class classification in non-homogeneous class assignment attained 99.4 % accuracy and in homogeneous class assignment a highest accuracy of 96.8 % is attained using the KNN classifier. Identifying nutrient deficiencies through electrophysiological signal analysis dominated the detection through imaging techniques, impacting a drastic change in crop protection practices.