Pattern Recognition Algorithms Research Articles

Evaluation of rural tourism development level using BERT-enhanced deep learning model and BP algorithm

To address the insufficient expressive capabilities of traditional methods in assessing the development level of rural tourism, this study explores the fusion application of the Bidirectional Encoder Representations from Transformers (BERT) and the Back Propagation (BP) algorithm to enhance the accuracy and comprehensiveness of rural tourism development assessment. Firstly, this study introduces the BERT deep learning model and its applications in natural language processing, alongside the role of the BP algorithm in pattern recognition and predictive analysis. Subsequently, a framework for assessing rural tourism development levels, integrating BERT and the BP algorithm, is proposed. This framework collects multidimensional rural tourism-related data and utilizes the BERT model for sentiment analysis and topic extraction from textual data. Empirical analysis of rural tourism development in a specific region validates the effectiveness of the proposed approach. Experimental results demonstrate: (1) The model achieves an accuracy of 84.33% and an F1 score of 85.33% on the publicly available Laptop dataset, with a processing time of 20 s, significantly outperforming other methods. Compared to traditional approaches, the proposed method accurately captures correlations between textual information and numerical data, thereby enhancing the credibility and accuracy of assessment results. (2) From the ablation study results, it is evident that removing any component from the model leads to performance degradation. Specifically, removing the Bidirectional Gated Recurrent Unit (BiGRU) reduces accuracy and F1 scores to 78.21% and 76.33% on the Laptop dataset, and to 85.10% and 70.45% on the Tourist_F dataset. Removing Text Convolutional Neural Network (CNN) reduces accuracy and F1 scores to 79.34% and 77.56% on the Laptop dataset, and to 86.25% and 72.11% on the Tourist_F dataset. The most significant performance decline occurs upon removing BERT, with accuracy and F1 scores decreasing to 70.14% and 66.43% on the Laptop dataset, and to 78.45% and 60.33% on the Tourist_F dataset. These results indicate that BiGcRU, TextCNN, and BERT each contribute significantly to the model’s performance. This study provides substantial support for advancing sustainable development in rural tourism, offering significant practical and innovative value.

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.

Exploration of chemical changes in bituminous mastics induced by aging: insights from FTIR spectroscopy, DSR measurements, and machine learning

ABSTRACT The aging phenomenon in bituminous mastics leads to distinct chemical changes that affect their mechanical and rheological properties. Experimental testing like Fourier Transform Infrared (FTIR) spectroscopy and Dynamic Shear Rheometer (DSR) often require advanced post-processing to reveal underlying patterns. This research employs advanced pattern recognition algorithms and chemometric analyses to investigate aging-induced chemical changes in bituminous mastics. The study seeks to establish a rational relationship between their chemical and rheological properties. FTIR data were analyzed using Partial Least Squares Regression-Linear Discriminant Analysis (PLSR-LDA) to derive normalised Variable Importance in Projection (VIP) scores. Post-processing of PLSR-LDA results, including Pearson correlation testing and classification through data mining algorithms, identified carbonyl groups, alcohols, and carboxylic acids as key indicators of aging. Significant mineral vibrations in the Si-O band and carbonate-related vibrations were also important for differentiating various mineral compositions in the mastics. The study demonstrated that combining PLSR with CatBoost regression analysis effectively predicts rheological behaviour based on chemical composition. These findings underscore the non-linear relationship between their chemical and rheological properties and provide a framework for predicting material performance based on chemical characteristics.

Medical diagnosis based on artificial intelligence and decision support system in the management of health development.

Medical diagnosis plays a critical role in our daily lives. Every day, over 10 billion cases of both mental and physical health disorders are diagnosed and reported worldwide. To diagnose these disorders, medical practitioners and health professionals employ various assessment tools. However, these tools often face scrutiny due to their complexity, prompting researchers to increase their experimental parameters to provide accurate justifications. Additionally, it is essential for professionals to properly justify, interpret, and analyse the results from these prediction tools. This research paper explores the use of artificial intelligence and advanced analytics in developing Clinical Decision Support Systems (CDSS). These systems are capable of diagnosing and detecting patterns of various medical disorders. Various machine learning algorithms contribute to building these assessment tools, with the Network Pattern Recognition (NEPAR) algorithm being the first to aid in developing CDSS. Over time, researchers have recognised the value of machine learning-based prediction models in successfully justifying medical diagnoses. The proposed CDSS models have demonstrated the ability to diagnose mental disorders with an accuracy of up to 89% using only 28 questions, without requiring human input. For physical health issues, additional parameters are used to enhance the accuracy of CDSS models. Consequently, medical professionals are increasingly relying on these machine learning-based CDSS models, utilising these tools to improve medical diagnosis and assist in decision-making. The different cross-validation values are considered to remove the data biasness.

Infrared spectroscopy as a new approach for early fabry disease screening: a pilot study

BackgroundFabry disease (FD) is a rare X-linked lysosomal storage disorder marked by alpha-galactosidase-A (α-Gal A) deficiency, caused by pathogenic mutations in the GLA gene, resulting in the accumulation of glycosphingolipids within lysosomes. The current screening test relies on measuring α-Gal A activity. However, this approach is limited to males. Infrared (IR) spectroscopy is a technique that can generate fingerprint spectra of a biofluid’s molecular composition and has been successfully applied to screen numerous diseases. Herein, we investigate the discriminating vibration profile of plasma chemical bonds in patients with FD using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy.ResultsThe Fabry disease group (n = 47) and the healthy control group (n = 52) recruited were age-matched (39.2 ± 16.9 and 36.7 ± 10.9 years, respectively), and females were predominant in both groups (59.6% and 65.4%, respectively). All patients had the classic phenotype (100%), and no late-onset phenotype was detected. A generated partial least squares discriminant analysis (PLS-DA) classification model, independent of gender, allowed differentiation of samples from FD vs. control groups, reaching 100% sensitivity, specificity and accuracy.ConclusionATR-FTIR spectroscopy harnessed to pattern recognition algorithms can distinguish between FD patients and healthy control participants, offering the potential of a fast and inexpensive screening test.

An Easily Customizable Approach for Automated Species-Specific Detection of Anuran Calls Using the European Green Toad as an Example

Machine learning approaches for pattern recognition are increasingly popular. However, the underlying algorithms are often not open source, may require substantial data for model training, and are not geared toward specific tasks. We used open-source software to build a green toad breeding call detection algorithm that will aid in field data analysis. We provide instructions on how to reproduce our approach for other animal sounds and research questions. Our approach using 34 green toad call sequences and 166 audio files without green toad sounds had an accuracy of 0.99 when split into training (70%) and testing (30%) datasets. The final algorithm was applied to amphibian sounds newly collected by citizen scientists. Our function used three categories: “Green toad(s) detected”, “No green toad(s) detected”, and “Double check”. Ninety percent of files containing green toad calls were classified as “Green toad(s) detected”, and the remaining 10% as “Double check”. Eighty-nine percent of files not containing green toad calls were classified as “No green toad(s) detected”, and the remaining 11% as “Double check”. Hence, none of the files were classified in the wrong category. We conclude that it is feasible for researchers to build their own efficient pattern recognition algorithm.

Designing User Experience Improvement and User Behavior Pattern Recognition Algorithms in Design Operation

Enhancing user experience (UX) is a key component in customer retention and sales promotion in e-commerce platforms. To build an effective UX model it is necessary to predict the user behavior more accurately and develop UX model that is tailored based on those behavior patterns. Existing models lack the ability to integrate advanced Machine Learning (ML) models to address the challenges. This study is an attempt to tackle these limitations that employs advanced AI tools to predict user behavior so that to construct an more effective UX model. The study involved 80 users from China who were aged 26 to 52, with diverse backgrounds in education, occupation, and tech proficiency. The work have employed Google Analytics, Hotjar, and FullStory to collect the user interactions and by using Generalized Sequential Pattern (GSP) algorithm, Decision Trees (DT), and Logistic Regression (LR) the work attempts to accurately predict the user behavior patterns. The results show that the model achieved better accuracy of 0.8795 and an F1 Score of 0.8610 on the test dataset. It also excelled in conversion rate (12.34%) and bounce rate (28.65%) which show effectiveness in retaining users and converting visits into actions.

Some novel Dice similarity measures for picture fuzzy sets and their applications

The similarity measures, as an important tool for assessing the similarity between picture fuzzy sets, have attracted the attention of numerous scholars and achieved rich achievements. Although existing similarity measures have exhibited their unique research contributions, they still fail to distinguish between highly similar but different picture fuzzy sets. This phenomenon poses a major challenge in applications, limiting our ability to make accurate judgments in scenarios that necessitate meticulous differentiation of picture fuzzy sets. Furthermore, the current research focuses on proposing individual and specific calculation formulas, particularly in the realm of Dice similarity measures. However, the systematic, uniform construction of Dice similarity measures remains mostly untapped. The main objective of this article is to propose a method for constructing Dice similarity measures. Through this approach, we can not only integrate existing Dice similarity measures into a unified framework, but also generate novel similarity measures. Firstly, we introduce a function that satisfies specific conditions as the basis for constructing Dice similarity measures. Secondly, by selecting different functions, we obtain various similarity measures. Finally, we propose the pattern recognition algorithm, the face recognition algorithm and the cluster analysis algorithm, all based on the proposed similarity measures. These algorithms are then applied to various fields, including pattern recognition, face recognition, and clustering analysis. Experimental results demonstrate that the proposed similarity measures not only compensate for the shortcomings of existing similarity measures but also exhibit high reliability and practicality, offering a powerful new tool for research and applications in related fields.

A novel colorimetric and fluorometric dual-signal identification of crude baijiu based on La-CDs

The correct classification of strong-flavored crude baijiu affects its quality and overall standard and is crucial for the intelligent development of the baijiu industry. In this work, we developed a novel optical sensing array using lanthanum-doped carbon dots (La-CDs). Using La-CDs with three metal complex dyes—chromium black T, alizarin red, and dimethylphenol orange—we were able to detect organic acids and tannic acid (TA) in crude baijiu in a way that was both colorimetric and fluorescent for the first time. Based on the indicator displacement (IDA) principle, organic acids competitively replace the dyes' binding sites on La3+, causing the dye colors to change to varying degrees. TA quenches the fluorescence of quantum dots through an internal filtering effect. We analyzed the data using pattern recognition algorithms such as HCA, PCA, and LDA, successfully classifying and identifying 16 types of strong-flavored crude baijiu, which included 10 types of carboxylic acids and various grades. In blind tests of 32 crude baijiu samples, the colorimetric method achieved a 94 % accuracy rate, while the fluorescence method achieved 100 %. The sensor demonstrates significant advantages in response speed.

Separated spacecraft attitude algorithm research based on star sensor multi-information fusion

Faced with the problem of poor anti-interference ability of star map recognition technology, in order to improve its anti-interference ability and achieve higher attitude measurement accuracy, this article proposes a star map recognition method based on binary search trees. Star sensors and gyroscopes are combined to solve the problem of the poor real-time performance of star sensors, optimizing the Extended Kalman Filtering (EKF) algorithm. This improves attitude output accuracy and formulates corresponding attitude measurement schemes considering the situation where the star sensor is not visible. The results showed that the improved star pattern recognition algorithm had better adaptability and robustness than the rasterization algorithm, with a high recognition success rate of 95.6% when the star sensor field of view was 3°*3°, while the rasterization algorithm cannot recognize small fields of view. The recognition success rate of the algorithm was 98.6% when the standard deviation of magnitude noise was 2.0 pixels, which was 15.4% higher than the rasterization algorithm. Unlike the EKF algorithm, the optimized EKF algorithm had a higher attitude output accuracy, with an average error of about 0.000 211°; compared to before optimization, it has increased by 93.43%. In the attitude measurement scheme, the relative attitude angle measurement error was small, and the maximum attitude measurement error in the rolling angle direction was 1.8 × 10−3°. This paper adopts a method to achieve high-precision satellite attitude measurement with good adaptability, which can be applied in complex space missions.

A Review of Advanced Sensor Technologies for Aquatic Products Freshness Assessment in Cold Chain Logistics.

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

Harnessing the Power of AI in Salesforce: An Empirical Study on Enhanced Data Insights and Organizational Efficiency

Abstract: This article examines the integration of Artificial Intelligence (AI) in Salesforce platforms to enhance business analytics and decision-making processes. We explore how AI-powered solutions are revolutionizing data analysis, providing organizations with actionable insights, and improving market responsiveness. The article investigates key AI techniques implemented in Salesforce, including machine learning algorithms for pattern recognition and natural language processing for unstructured data analysis. We analyze the impact of these technologies on identifying trends, forecasting, threat detection, and opportunity identification for sustainable growth. Through case studies and empirical analysis, we demonstrate the effectiveness of AI-driven Salesforce solutions in streamlining data processing, enabling real-time market adaptation, and enhancing organizational efficiency. The article also addresses the technological infrastructure supporting these AI solutions, discussing cloud-based computing resources, integration challenges, and data security considerations. Our findings suggest that AIpowered Salesforce solutions significantly improve business analytics capabilities, allowing companies to make more informed decisions and maintain a competitive edge in rapidly evolving markets. This article contributes to the growing body of knowledge on AI applications in customer relationship management and business intelligence systems

Thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) with distance-probability joint decision SVM algorithm: A novel system for Daqu Grade identification

Electronic nose is a bionic technology that uses sensor arrays and pattern recognition algorithms to mimic the human olfactory system. This study developed a thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) system, employing thermal desorption for direct sampling and humidity control, with a photoionization ion mobility tube as virtual sensor array for component separation and detection, and pattern recognition algorithms for signal processing to differentiate and identify samples. Furthermore, it was applied to assess four quality grades of Daqu samples (“Excellent+”, “Excellent”, “Grade I”, and “Grade II”) determined by the Check-All-That-Apply (CATA) method. Characteristic compound differences among these grades were identified using fingerprint spectra and reduced mobility values. A distance-probability joint decision support vector machine (SVM) algorithm model was established, validated against sensory CATA standards. Results showed identification accuracies: 90 %, 90 %, 96.88 %, and 100 % for respective grades. These findings demonstrated the promising potential of the TD-PIM-Nose system in Daqu quality grading.

Rapid identification of white tea based on colorimetric indicator displacement assay (IDA) sensor array

The identification of white tea mainly relies on sensory evaluation and mass spectrometry methods, which have drawbacks such as strong subjectivity and complex analysis. Thus, this study established a novel approach based on colorimetric indicator displacement assay (IDA) sensor to achieve rapid and accurate white tea identification. Cost-effective dyes and phenylboronic acids with different substituents were selected as the indicator and receptor, respectively. A color-signal-responsive IDA sensor array was constructed to enable the rapid and sensitive identification of white tea. Additionally, three supervised pattern recognition algorithms were utilized to process image data. The results demonstrate that combining the IDA sensor with the random forest algorithm accurately differentiates white tea samples by origin and type, achieving a 100% recognition accuracy in both training set (n = 84) and prediction set (n = 56) when ntree = 1000. This work offers an economical and efficient method for the rapid identification of white tea products.

Evaluating the cost of classifier discrimination choices for IoT sensor attack detection

The intrusion detection of IoT devices through the classification of malicious traffic packets have become more complex and resource intensive as algorithm design and the scope of the problems have changed. In this research, we compare the cost of a traditional supervised pattern recognition algorithm (k-Nearest Neighbor (KNN)), with the cost of a current deep learning (DL) unsupervised algorithm (Convolutional Neural Network (CNN)) in their simplest forms. The classifier costs are calculated based on the attributes of design, computation, scope, training, use, and retirement. We find that the DL algorithm is applicable to a wider range of problem-solving tasks, but it costs more to implement and operate than a traditional classifier. This research proposes an economic classifier model for deploying suitable AI-based intrusion detection classifiers in IoT environments. The model was empirically validated on the IoT-23 dataset using KNN and CNN. This study closes a gap in prior research that mostly concentrated on technical elements by incorporating economic factors into the evaluation of AI algorithms for IoT intrusion detection. This research thus evaluated the economic implications of deploying AI-based intrusion detection systems in IoT environments, considering performance metrics, implementation costs, and the cost of classifier discrimination choices. Researchers and practitioners should focus on the cost–benefit trade-offs of any artificial intelligence application for intrusion detection, recommending an economic evaluation and task fit assessment before adopting automated solutions or classifiers for IoT intrusion detection, particularly in large-scale industrial settings that involve active attacks.

An extended group decision-making algorithm with intuitionistic fuzzy set information distance measures and their applications

The intuitionistic fuzzy set is a generalization of the fuzzy set that performs noticeably better in expressing and managing uncertainty. The amount that one intuitionistic fuzzy set differs from the others is given by the distance measure. Certain distance measures that have been suggested by the various researchers do not satisfy the axioms of distance measures and also be counter-intuitive circumstances. In this paper we present a novel distance measure for intuitionistic fuzzy sets that is based on the difference between the cross-evaluation factor’s minimum and maximum, the membership degree and non-membership degree, respectively. The proposed measure satisfies all the axiomatic properties and also resolves the counter-intuitive cases. Consequently, this study provides an efficient symmetric distance formula for determining the distance between the information contained by intuitionistic fuzzy sets. By using numerical examples, it is shown that the new measurement is reliable. Also, we provide pattern recognition algorithms and employ them to solve diagnostic-related problems in medicine.

Elemental tracer determination and modeling for geographical origin designation of sweet oranges

Sweet oranges have long been an integral part of global health and culinary practices, offering a wealth of nutrients and bioactive compounds. Ensuring the authenticity of these citrus fruits is essential for maintaining consumer confidence, promoting transparency in sourcing, and protecting producers' reputations in the marketplace. In this study, we explored the feasibility of using multi-element profiling combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this, we employed an optimized microwave plasma atomic emission spectroscopy (MP-AES) method to analyze the elemental composition (Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Sr, and Zn) of 183 orange samples from four production regions in northeastern Argentina. Support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to identify elemental tracer's indicative of origin. Based on a comprehensive evaluation of overall accuracy, receiver operating characteristic (ROC) curves, and area under the curve (AUC), the GBT model demonstrated the best classification performance, achieving a 96.5 % correct prediction rate on test samples, as confirmed by the ROC curve (AUC = 0.973). Consequently, this approach provides compelling evidence for the potential utility of MP-AES combined with supervised modeling to determine the geographic origin of sweet oranges produced in Argentina, thereby contributing to consumer protection against fraud.

Climate Change Prediction Model using MCDM Technique based on Neutrosophic Soft Functions with Aggregate Operators

The increasing impact of climate change necessitates innovative approaches in modeling and prediction to mitigate its adverse effects. This paper introduces a novel methodology integrating Neutrosophic Soft Functions (NSFs) into climate change prediction frameworks. NSFs, a hybrid of Neutrosophic Set Theory and Soft Set Theory, provide a flexible framework for handling uncertain and imprecise information inherent in climate data. This study explores the application of NSFs in capturing the complex interplay of various climatic variables, including temperature, precipitation, humidity, and atmospheric pressure, thereby enhancing the accuracy and reliability of climate change predictions. By incorporating NSFs into existing predictive models, such as neural networks and fuzzy systems, this research demonstrates significant improvements in forecast precision, particularly in scenarios with limited or noisy data. Additionally, the paper discusses the integration of NSFs with advanced machine learning algorithms for climate pattern recognition and anomaly detection, enabling timely identification of climate change indicators and facilitating proactive measures for adaptation and mitigation. Through empirical validation using real-world climate datasets, this study underscores the efficacy of NSFs in enhancing the predictive capabilities of climate change models, thereby contributing to more informed decision-making in climate-related policies and strategies.

Data-Driven Multi-Fault Detection in Pipelines Utilizing Frequency Response Function and Artificial Neural Networks

This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an artificial neural network (ANN) algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088×10−3to0.3062×10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model's effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems.

Abstract P367: Cross-Species Epigenomic Maps of Intergenic Loci for Hypertension and Blood Pressure

Many non-coding SNPs identified in Genome-Wide Association Studies (GWAS) likely affect BP-related gene expression through epigenetic mechanisms. This study analyzes the comparative genomic and epigenomic landscapes in human and rat kidney tissues, including kidney proximal tubule (PT) and medullary thick ascending limb (mTAL), focusing on the major challenge of identifying conserved regulatory elements in intergenic regions. The purpose of our study is to identify BP gene regulatory elements in intergenic regions that are conserved from human to rat for in vivo validation and mechanistic studies. Our ultimate goal is to generate high-resolution, genome-wide epigenomic maps of key BP-relevant tissues, using ATAC-seq, Hi-C, CUT&Tag, DNA methylation profiling and RNA-seq. We are creating visualization hubs for human and rat, integrating our data within the UCSC Genome Browser environment. UCSC contains extensive data for humans but very little for rats, particularly for the updated genome assemblies. To address this gap, we integrated our results with data from the Rat Genome Database (RGD). To date, we harmonized ATAC-seq data from human and rat PT and mTAL and displayed these data in RGD’s JBrowse2 genome browser for unique comparative genome views. Based on our initial analysis of data density and intensity, we are developing a novel pattern recognition algorithm to identify epigenomic marks that are conserved across rat and human genomes. Existing algorithms predominantly focus on the conservation of coding genes, gene families, and genomic sequences. Here, average normalized peak intensities for ATAC-seq data across tissues were subject to canonical correlation between human and rat to identify conserved epigenomic patterns within the BP GWAS loci. Here we present an example of a human BP locus in the intergenic region between NPR3 and TARS1 on chromosome 5 which has a region of conserved synteny with rat chromosome 2. Comparative analysis and visualization of this region show significant correlation between ATAC-seq peaks from rat and human PT and mTAL at this locus, with predicted conserved regulatory elements. These visualizations can uncover novel biological insights and identify potential regulatory targets for intervention. This work underscores the importance of advanced visualization techniques in understanding hypertension pathogenesis, facilitating cross-species comparisons, and enhancing the utility of genomic databases.