Pattern Recognition Research Articles

Bidirectional Photovoltage-Driven Oxide Transistors for Neuromorphic Visual Sensors.

Biological vision is one of the most important parts of the human perception system. However, emulating biological visuals is challenging because it requires complementary photoexcitation and photoinhibition. Here, the study presents a bidirectional photovoltage-driven neuromorphic visual sensor (BPNVS) that is constructed by monolithically integrating two perovskite solar cells (PSCs) with dual-gate ion-gel-gated oxide transistors. PSCs act as photoreceptors, converting external visual stimuli into electrical signals, whereas oxide transistors generate neuromorphic signal outputs that can be adjusted to produce positive and negative photoresponses. This device mimics the human vision system's ability to recognize colored and color-mixed patterns. The device achieves a static color recognition accuracy of 96% by utilizing the reservoir computing system for feature extraction. The BPNVS mem-reservoir chip is also proposed for handing object movement and dynamic color recognition. This work is a significant step forward in neuromorphic sensing and complex pattern recognition.

Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns

Understanding the landscape determinants that escalate Lyme disease (LD) risk through various times and regions is vital for appraising disease susceptibility and shaping precise intervention and prevention strategies. This research introduces a novel data-driven framework to identify potential indicators from an extensive array of potential variables. We then deployed an advanced spatiotemporal weighted regression (STWR) model to investigate how landscape fragmentation metrics correlate with the spatiotemporal variability of LD incidence rate in Connecticut towns. We proposed a data-driven filtering framework to select five variables from a large data pool. The analysis unveils that LD incidence rates exhibit heightened sensitivity to proportional or exponential shifts in landscape fragmentation; logarithmic and squared transformations of landscape metrics shed light on lesser effects and venue for potential parabolic relationships. Observations also disclose significant spatial trends, showing elevated LD incidence rates in locales with vast, uninterrupted deciduous forests, alongside contributions from wetland ecosystem-related variables to the rise in disease occurrence. Compared with Geographically Weighted Regression (GWR), the STWR model proved more potent and reliable with higher R2 and lower estimated standard errors (SE). The STWR model is highly flexible in terms of spatiotemporal variations in data. The STWR results further reversely indicate the changes made by the Center for Disease and Prevention (CDC) in the case classification of LD in 2008. The integration of data-driven and model-driven approaches in this study delivers a robust framework that combines empirical pattern detection with theoretical insight, enhancing the robustness and predictive power of ecological studies.

Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}

Objective: Machine learning techniques have emerged in response to the desire for automatic pattern detection withindatasets in fields such as statistics, mathematics, and data analytics. They allow for the extraction of relevant informationfrom datasets of significantly large volumes, providing the possibility of making predictions. This paper presents an application focused on decision trees, linear regression, and random forest regression algorithms to predict final data fromconsecutive dependent data of type {[(a, b) → c] → D}. Methodology: The study adopts a quantitative research design, which takes as input datasets based on interval data. It utilizes a correlational research model by implementing Python and its Scikit-Learn library, which includes various algorithms for prediction. Specifically, we compare the application of decision trees, linear regression, and random forest regression on the same set of datasets, but with a characteristic of dependency between them. Results: Upon application of the proposed model, it yields an estimated prediction score, which indicates the accuracy of the model concerning the data provided. Conclusions: The application of a complex algorithm does not inherently guarantee a higher rate of accuracy. Conversely, configuring the model correctly, training multiple trees, or adjusting parameter values can significantly enhance the obtained results

A hydrogel sensor based on conic baston structure

AbstractResistive flexible pressure sensors are extensively employed in wearable devices owing to their wide operational range and straightforward construction. This study presents a conic bastion‐structured sensor microunit to improve the sensor sensitivity. The base hydrogel is synthesized using acrylamide (AM), with Mg2+ and Na+ acting as conductive ions. The sensor is fabricated using digital light processing (DLP) 3D printing technology and is subjected to experimental evaluation. The findings indicate that the hydrogel sensor with a 50 wt% AM composition demonstrates enhanced mechanical strength and conductive properties, achieving a peak sensitivity of 0.534 kPa−1 within a pressure range of 0–0.8 kPa. Furthermore, the sensor exhibits favorable response characteristics (30 ms) and recovery characteristics (40 ms), along with stability. The proposed sensor is suitable for wearable devices and live joint angle detection. Additionally, the “handwriting fingerprint” pattern recognition and document verification proposed in this article make it applicable in scenarios, such as banking, notarization, and other handwriting and seal verification contexts.

An insight into the role of innate immune cells in breast tumor microenvironment.

The immune background of breast cancer is highly heterogeneous and the immune system of the human body plays a dual role by both promoting and suppressing its progression. Innate immune cells are the first line of defense in the immune system and impart protection by identifying and interacting with foreign pathogens and cancer cells. Different innate immune cells like natural killer cells, macrophages, dendritic cells, and myeloid suppressor cells take part in hosting the cancer cells. Autophagy is another key component inside the tumor microenvironment and is linked to the disintegration and recycling of cellular components. Within the tumor microenvironment autophagy is involved with Pattern Recognition Receptors and inflammation. Various clinical studies have shown prominent results where innate immune cells and autophagy in combination are used for pathogen as well as cancer cell clearance. However, it is necessary to comprehend the complex tumor microenvironment so that different therapeutic approaches can be developed to enhance the suppressive actions of the cells toward breast cancer cells. In this review article, the complex interaction between immune cells and breast cancer cells and their role in developing effective immunotherapies to improve patient outcomes are discussed in detail.

Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

The increasing global demand for renewable energy has propelled the adoption of photovoltaic systems as a key component of sustainable energy infrastructure. Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.

Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.

Enhanced salience of edge frequencies in auditory pattern recognition.

Within musical scenes or textures, sounds from certain instruments capture attention more prominently than others, hinting at biases in the perception of multisource mixtures. Besides musical factors, these effects might be related to frequency biases in auditory perception. Using an auditory pattern-recognition task, we studied the existence of such frequency biases. Mixtures of pure tone melodies were presented in six frequency bands. Listeners were instructed to assess whether the target melody was part of the mixture or not, with the target melody presented either before or after the mixture. In Experiment 1, the mixture always contained melodies in five out of the six bands. In Experiment 2, the mixture contained three bands that stemmed from the lower or the higher part of the range. As expected, Experiments 1 and 2 both highlighted strong effects of presentation order, with higher accuracies for the target presented before the mixture. Notably, Experiment 1 showed that edge frequencies yielded superior accuracies compared with center frequencies. Experiment 2 corroborated this finding by yielding enhanced accuracies for edge frequencies irrespective of the absolute frequency region. Our results highlight the salience of sound elements located at spectral edges within complex musical scenes. Overall, this implies that neither the high voice superiority effect nor the insensitivity to bass instruments observed by previous research can be explained by absolute frequency biases in auditory perception.

An overview of machine learning applications in elastic power systems

Abstract. With the development of economy and the access of renewable energy, the power system is facing new challenges. Machine learning provides a new way for the intelligent management of power system through big data analysis, pattern recognition and predictive modeling. This paper reviews the application of machine learning technology in elastic power system, including load forecasting, transient stability assessment, intelligent scheduling and fault diagnosis. Machine learning will better improve the stability, reliability and economic benefit of the system. Future research will focus on algorithm optimization, model adaptability improvement and integration with traditional power system knowledge to promote the development of power system to a higher level of intelligence and automation.

A review on depth perception techniques in organoid images

Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.

Water flow pattern recognition and fracture characterization in near-surface chalks using time-lapse ground-penetrating radar: An experiment

Predicting subsurface water flow is inherently complex due to the heterogeneity of geological formations. Water infiltration occurs through various pathways, such as pore throats, fractures, or faults, depending on the sediment type. In an effort to enhance our understanding of water flow within heterogeneous, fractured chalks formations, we integrate ground penetrating radar (GPR), acoustic measurements, and photogrammetric surveys to examine the flow patterns. We first conduct a field experiment of injecting water into chalks and monitor the water flow through a time-lapse GPR reflection survey. Then we identify the water-affected zone by analyzing the GPR data, and correlate the water-affected zone with the identified fractures on the exposed strata. Finally, we verify the water effect through measured acoustic velocities of acquired samples. Our analysis of the GPR data encompasses direct subtraction, traveltime delay, electromagnetic (EM) velocity changes, and water saturation variations. These analyses consistently indicate a predominant southeastward flow direction. This finding is corroborated by the fractures identified through photogrammetry, which closely match the water-affected zone, suggesting that water flow is indeed fracture-driven. The measured acoustic velocities are utilized to estimate water saturation using an iso-frame rock physics model, which shows consistency with the saturation estimated through EM velocity, further validating the monitoring results. These findings advance the understanding of fluid flow and its interaction with fracture system in heterogeneous chalks, and provide a reference for future studies by demonstrating the efficacy of integrating photogrammetry-identified fractures, EM velocity, and acoustic velocity to verify monitoring outcomes.

Recognition of Street Landscape Patterns in Kunming City Based on Intelligent Decision Algorithm and Regional Cultural Expression

The integration of intelligent decision-making algorithms with urban cultural expression is becoming a hot topic in both academic and practical fields for exploring urban street landscapes. Exploring the application strategies of intelligent decision-making algorithms and regional cultural expression in street landscape pattern recognition and innovative design is a key step. The single layout of urban street construction, cultural deficiency, ecological imbalance, and low resident participation seriously constrain the overall quality improvement of the city. To address this dilemma, this study delved into Kunming City and selected the ten “most beautiful streets”, such as Dianchi Road, for research. By using the Analytic Hierarchy Process, a comprehensive evaluation system covering multiple dimensions, such as the street layout, plant landscape, and historical culture, was constructed to analyze the street landscape of Kunming. The research results indicate that the top four roads in terms of weight evaluation scores are Cuihu Ring Road, Jiaochang Middle Road, Qingnian Road, and Beijing Road, with values of 0.2076, 0.1531, 0.1274, and 0.1173. The weight reveals that each street has its unique landscape factors, such as the profound cultural heritage of Cuihu Ring Road and the beautiful plant landscape of Jiaochang Middle Road. Further analysis also reveals the close relationship between various factors in the evaluation model, emphasizing the importance of supplementing material and cultural elements in street landscape design. The significance of this study goes beyond a single analysis of the street landscape in Kunming City. Drawing a regional street landscape pattern map sets an example for other cities to build distinctive, eco-friendly, culturally rich, and highly humanized street spaces, providing reference and inspiration. More importantly, this study promotes the application and development of intelligent decision-making algorithms in the field of urban landscapes. Future research will further optimize algorithms to improve their adaptability and accuracy in complex environments.

CriteriaMapper: establishing the automatic identification of clinical trial cohorts from electronic health records by matching normalized eligibility criteria and patient clinical characteristics

The use of electronic health records (EHRs) holds the potential to enhance clinical trial activities. However, the identification of eligible patients within EHRs presents considerable challenges. We aimed to develop a CriteriaMapper system for phenotyping eligibility criteria, enabling the identification of patients from EHRs with clinical characteristics that match those criteria. We utilized clinical trial eligibility criteria and patient EHRs from the Mount Sinai Database. The CriteriaMapper system was developed to normalize the criteria using national standard terminologies and in-house databases, facilitating computability and queryability to bridge clinical trial criteria and EHRs. The system employed rule-based pattern recognition and manual annotation. Our system normalized 367 out of 640 unique eligibility criteria attributes, covering various medical conditions including non-small cell lung cancer, small cell lung cancer, prostate cancer, breast cancer, multiple myeloma, ulcerative colitis, Crohn’s disease, non-alcoholic steatohepatitis, and sickle cell anemia. About 174 criteria were encoded with standard terminologies and 193 were normalized using the in-house reference tables. The agreement between automated and manual normalization was high (Cohen’s Kappa = 0.82), and patient matching demonstrated a 0.94 F1 score. Our system has proven effective on EHRs from multiple institutions, showing broad applicability and promising improved clinical trial processes, leading to better patient selection, and enhanced clinical research outcomes.

On the decision-making framework for policing: A proposal for improving police decision-making

This article introduces the Decision-Making Framework for Policing (DMFP), a comprehensive tool designed to enhance the decision-making understanding of police officers. The DMFP considers the principles of heuristic, naturalistic, and rational decision-making along a fluid cognitive continuum to create a framework that addresses the limitations of the existing police National Decision Model (NDM). It achieves this by including 10 proposed typologies of police decision-making including: Routine, Tactical, Operational, Crisis, Investigative, Ethical, Interpersonal, Administrative, Managerial, and Strategic. These are integrated alongside existing and adapted decision-making models which are presented using a mnemonic letter strategy. Although the DMFP is theoretical, and its utility is presently untested in comparison to the existing NDM, it is presented to provide a tool to help improve officers' tacit knowledge, pattern recognition, and experiential learning through provision of easily recallable mnemonic decision-models. Thereby fostering a deeper understanding of cognitive processes and the factors influencing police decisions, potentially increasing consistency in reasoning, reducing decision errors, and enhancing policing outcomes.

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.

Feature Extraction and Identification of Rheumatoid Nodules Using Advanced Image Processing Techniques

Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class prediction in nodule detection, with a specific focus on rheumatoid nodules, by employing a comprehensive approach to feature extraction and classification. We utilized a diverse dataset of nodules, including rheumatoid nodules sourced from the DermNet dataset and local rheumatologists. Method: This study integrates 62 features, combining traditional image characteristics with advanced graph-based features derived from a superpixel graph constructed through Delaunay triangulation. The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. Vascular pattern recognition, crucial for identifying rheumatoid nodules, was enhanced using the Frangi filter. A Hybrid CNN–Transformer model was employed for feature fusion, and feature selection and hyperparameter tuning were optimized using Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Feature importance was assessed using SHAP values. Results: The proposed methodology achieved an accuracy of 85%, with a precision of 0.85, a recall of 0.89, and an F1 measure of 0.87, demonstrating the effectiveness of the approach in detecting and classifying rheumatoid nodules in both binary and multi-class classification scenarios. Conclusions: This study presents a robust tool for the detection and classification of nodules, particularly rheumatoid nodules, in medical imaging, offering significant potential for improving diagnostic accuracy and aiding in the early identification of rheumatoid conditions.

Creation of an Anomaly Detection System for Blockchain-based Cloud Deployments that Consider Privacy and Delay.

Cloud-based deployments are increasingly vulnerable to many kinds of assaults; hence powerful anomaly detection systems are needed to prevent security breaches. While effective to some degree, current methods like RSSI, GTM, and APG have drawbacks in terms of scalability, precision, and accuracy. In order to improve the forecast accuracy of cloud assaults and optimize blockchain-based cloud installations, this study suggests a novel anomaly detection system that combines multimodal feature analysis, deep learning models, and QoS-aware sidechains. The suggested strategy considerably surpasses traditional approaches in terms of precision, accuracy, recall, and AUC performance by optimizing feature variance across various sample types and utilizing cutting-edge deep learning algorithms. Additionally, the framework shows increased efficiency in terms of throughput, energy usage, and block mining latency, makingcombining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), the system analyses system logs to find anomalous patterns of activity linked to various threats. The architecture guarantees the transparency and integrity of system records through the use of Blockchain technology, while Deep Learning models offer accurate and quick anomaly detection. The advantages of both Deep Learning and Blockchain technology support the choice to merge them. Blockchain technology ensures the accuracy of anomaly detection and prevents tampering with system records by providing a distributed, immutable ledger. On the other hand, deep learning models produce high precision, accuracy, recall, and AUC measures because of their remarkable pattern recognition skills and ability to adjust to shifting attack strategies. Experimental data analyses show the effectiveness of the suggested framework.

Enhancing Smart Grid Stability through a Hybrid Biometric Pattern Recognition and Long Short-Term Memory Approach

The stability of power grids is critical in ensuring the consistent and efficient delivery of electricity. However, traditional predictive models often fall short in addressing grid data's intricate and ever-changing nature, making it challenging to maintain grid reliability. This paper introduces a novel hybrid approach that combines Biometric Pattern Recognition (BPR) with Long Short-Term Memory (LSTM) networks to enhance the prediction of smart grid stability. This approach employs BPR techniques to extract essential features from smart grid data by leveraging the pattern recognition capabilities typically used in biometric systems. These techniques are particularly effective in identifying and isolating the most relevant patterns within the complex datasets generated by smart grids. On the other hand, the LSTM-based model is designed to handle the temporal dependencies and nonlinear patterns characteristic of grid data. LSTMs, known for their proficiency in time-series analysis, are well-suited for capturing the sequential nature of grid data, enabling more accurate predictions over time. Integrating BPR and LSTM in this hybrid model addresses several limitations in existing predictive methods. By combining the strengths of both techniques, the model enhances the accuracy of predictions and improves the overall reliability of grid stability assessments. Extensive experiments were conducted using real-world datasets to validate the effectiveness of the proposed hybrid model. The results demonstrate a significant improvement in prediction accuracy, with the BPR-LSTM model achieving a 98.25% increase in accuracy compared to traditional prediction methods. This improvement underscores the potential of the BPR-LSTM hybrid approach to play a pivotal role in advancing the stability and reliability of smart grid systems.

Dynamics and Detection of Pulsed Tremor at Whakaari (White Island), Aotearoa New Zealand

AbstractVolcanic tremor is a crucial indicator for assessing the state and hazard potential of volcanic systems. At Whakaari (White Island volcano, Aotearoa New Zealand), a pulsed tremor signal emerged after a hydrothermal explosion in August 2012. The tremor accompanied the extrusion of a lava dome, before gradually disappearing prior to the onset of renewed hydrothermal activity in January 2013. We interpret this seismic signal to represent discrete gas transfers from a magmatic intrusion toward a permeable cap—possibly a hydrothermal seal—in the upper layers of Whakaari's hydrothermal system. Such tremor may thus be associated with heightened potential for hazardous explosive activity but is difficult to detect using conventional seismic monitoring parameters. To highlight the emergence of subtle periodic signals, we experiment with Lomb‐Scargle periodograms (LS). LS detect the tremor 5 days before it becomes visible in seismograms, thus facilitating the recognition of such elusive seismic patterns.

MdSVWC1, a new pattern recognition receptor triggers multiple defense mechanisms against invading bacteria in Musca domestica

BackgroundSingle-domain von Willebrand factor type C (SVWC) constitute a protein family predominantly identified in arthropods, characterized by a SVWC domain and involved in diverse physiological processes such as host defense, stress resistance, and nutrient metabolism. Nevertheless, the physiological mechanisms underlying these functions remain inadequately comprehended.ResultsA massive expansion of the SVWC gene family in Musca domestica (MdSVWC) was discovered, with a count of 35. MdSVWC1 was selected as the representative of the SVWC family for functional analysis, which led to the identification of the immune function of MdSVWC1 as a novel pattern recognition receptor. MdSVWC1 is highly expressed in both the fat body and intestines and displays acute induction upon bacterial infection. Recombinant MdSVWC1 binds to surfaces of both bacteria and yeast through the recognition of multiple pathogen-associated molecular patterns and exhibits Ca2+-dependent agglutination activity. MdSVWC1 mutant flies exhibited elevated mortality and hindered bacterial elimination following bacterial infection as a result of reduced hemocyte phagocytic capability and weakened expression of antimicrobial peptide (AMP) genes. In contrast, administration of recombinant MdSVWC1 provided protection to flies from bacterial challenges by promoting phagocytosis and AMP genes expression, thereby preventing bacterial colonization. MdSPN16, a serine protease inhibitor, was identified as a target protein of MdSVWC1. It was postulated that the interaction of MdSVWC1 with MdSPN16 would result in the activation of an extracellular proteolytic cascade, which would then initiate the Toll signaling pathway and facilitate the expression of AMP genes.ConclusionsMdSVWC1 displays activity as a soluble pattern recognition receptor that regulates cellular and humoral immunity by recognizing microbial components and facilitating host defense.