Pattern Extraction Research Articles

Assessing Groundwater Use Efficiency and Productivity across Punjab Agriculture: District and Farm Size Perspectives

While previous studies have focused on the technical aspects of groundwater extraction and optimal cropping patterns, there is a notable lack of research on the socio-economic factors affecting groundwater access and agricultural productivity, especially at a micro-level in Punjab, India. This study, therefore, investigates the water use efficiency (WUE) and economic water productivity (EWP) of paddy and wheat crops across diverse farm sizes and districts in Punjab, offering critical insights into agricultural water management. The study is based on primary data from Punjab, India, with Sangrur, Jalandhar, Pathankot, and Bathinda districts selected for groundwater profile analysis via multistage random sampling of 246 farmers. Notably, Punjab exhibits high EWP for wheat, surpassing the national average. However, disparities exist across districts, emphasizing the importance of localized interventions. Larger farms consistently demonstrate higher WUE and EWP, underlining the significance of scale in optimizing water inputs. Factors such as farm size, crop variety, and regional variations significantly influence WUE and EWP. Tailored approaches for marginal farmers and districts with lower efficiency are crucial for promoting sustainable agricultural practices. The findings underscore the need for targeted policy interventions to enhance water use efficiency and productivity in Punjab’s agriculture sector.

Age estimation using medial clavicle by histomorphometry method with artificial intelligence: A review.

This review research critically assesses the evolving landscape of age estimation methodologies, with a particular focus on the innovative integration of histomorphometry and artificial intelligence (AI) in the analysis of the medial clavicle. The medial clavicle emerges as a crucial skeletal feature for predicting age, offering valuable insights into the morphological changes occurring throughout an individual's lifespan. Through an in-depth exploration of histological complexities, including variations in osteons, trabecular structures, and cortical thickness, this review elucidates their utility as viable indicators for age-related evaluations. This framework is augmented by the incorporation of AI technology, which enables automatic picture identification, feature extraction, and complicated pattern analysis. Our review of previous research highlights the promise of AI in improving prediction models for nuanced age estimates, highlighting the importance of large-scale, diversified datasets and thorough cross-validation. This thorough study, which addresses ethical concerns as well as the influence of population-specific characteristics, moves the debate around age estimate ahead, presenting insights with consequences for forensic anthropology, clinical diagnoses, and future research avenues.

Introducing new node prediction in graph mining: Predicting all links from isolated nodes with Graph Neural Networks

This paper introduces new node prediction, a fresh problem in the field of graph mining and social network analysis. This task can be categorized as zero-shot out-of-graph all-links prediction, and aims to predict all links coming from or going to a new, isolated, and unobserved node that was previously disconnected from the graph. In comparison with classic approaches to link prediction (including few-shot out-of-graph link prediction), this problem bears two key differences: (1) the new node has no existing links from which to extract patterns for new predictions; and (2) the goal is to predict not just one, but all the links of this new node, or, at least, a significant part of them. Experiments used an architecture based on Deep Graph Neural Networks, and were carried out on two datasets: a bibliographic citation graph and a drug–drug interaction graph. Results demonstrate that this challenging problem can be solved satisfactorily by using state-of-the-art Deep Learning techniques.

Developing a consensus-based motivational care pathway for individuals with lower limb fractures: a Delphi protocol.

Older adults with lower limb fractures often harbor concerns about losing their mobility, fearing a loss of independence. It is vital to develop strategies that foster their active engagement in the rehabilitation process. The present protocol aims to create a care pathway tailored to motivate individuals with lower limb fractures to adhere to rehabilitation. We will develop an observational, cross-sectional, and descriptive study using the Delphi data-gathering approach. Purposive sampling will recruit a panel of healthcare professionals and experts who care for patients with lower limb fractures. Aligned with the Delphi method, a series of iterative rounds will be developed to gather consensus around the motivational strategies used by health professionals in the rehabilitation of people with lower limb fractures. We will employ the Qualtrics platform for data collection and analysis, and a consensus target of 75% has been predetermined. For quantitative data analysis, we will use descriptive statistics encompassing a range of measures, including count, mean, standard deviation, median, minimum, maximum, and range. An inductive thematic analysis procedure will be employed to extract meaningful themes and patterns from qualitative data. The study results are expected to significantly impact clinical practice by creating a specialized care pathway to motivate individuals with lower limb fractures to adhere to rehabilitation. Adopting these explicit standards by professionals will ensure uniform and high-quality care.

Conscious points and patterns extraction: a high-performance computing model for knowledge discovery in cognitive IoT

Conscious points and patterns extraction: a high-performance computing model for knowledge discovery in cognitive IoT

Extraction of sleep information from clinical notes of Alzheimer's disease patients using natural language processing.

Alzheimer's disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. We aim to automate the extraction of specific sleep-related patterns, such as snoring, napping, poor sleep quality, daytime sleepiness, night wakings, other sleep problems, and sleep duration, from clinical notes of AD patients. These sleep patterns are hypothesized to play a role in the incidence of AD, providing insight into the relationship between sleep and AD onset and progression. A gold standard dataset is created from manual annotation of 570 randomly sampled clinical note documents from the adSLEEP, a corpus of 192000 de-identified clinical notes of 7266 AD patients retrieved from the University of Pittsburgh Medical Center (UPMC). We developed a rule-based natural language processing (NLP) algorithm, machine learning models, and large language model (LLM)-based NLP algorithms to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the gold standard dataset. The annotated dataset of 482 patients comprised a predominantly White (89.2%), older adult population with an average age of 84.7 years, where females represented 64.1%, and a vast majority were non-Hispanic or Latino (94.6%). Rule-based NLP algorithm achieved the best performance of F1 across all sleep-related concepts. In terms of positive predictive value (PPV), the rule-based NLP algorithm achieved the highest PPV scores for daytime sleepiness (1.00) and sleep duration (1.00), while the machine learning models had the highest PPV for napping (0.95) and bad sleep quality (0.86), and LLAMA2 with finetuning had the highest PPV for night wakings (0.93) and sleep problem (0.89). Although sleep information is infrequently documented in the clinical notes, the proposed rule-based NLP algorithm and LLM-based NLP algorithms still achieved promising results. In comparison, the machine learning-based approaches did not achieve good results, which is due to the small size of sleep information in the training data. The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts. This study focused on the clinical notes of patients with AD but could be extended to general sleep information extraction for other diseases.

An adaptive convolutional neural network based on transmissibility grayscale image for online identification of offshore platform damage pattern

In response to the challenges in offshore platform damage pattern recognition, where traditional methods lack adaptability owing to relying on feature extraction and expert knowledge, a novel method for online recognition of offshore platform damage patterns using an improved convolutional neural network (CNN) based on transmissibility grayscale image is proposed. The original transmissibility function is converted into a two-dimensional grayscale image to capture the essential features in the original signal, which is then used as the input for the deep learning model. Subsequently, an attention mechanism is introduced to enable the CNN to focus on key areas of the image, emphasize important feature channels, dynamically adjust feature representations, and enhance its overall performance. Meanwhile, the model is optimized by virtue of variational Bayesian algorithm and early stopping criteria, and finally, the trained offline model is applied to online damage pattern recognition of offshore platforms. Through on indoor vibration monitoring experiments, seven different damage patterns of the platform model are recognized accurately, which show that the accuracy of the improved CNN model is increased by an average of 8.5 % as compared to traditional intelligent methods. Furthermore, the proposed method is applied to the online damage identification of offshore platforms in the field, and the corresponding prediction accuracy reaches 98.9 %, demonstrating the feasibility and generalizability of the proposed method in adaptive feature extraction and precise identification of damage patterns.

Sea clutter suppression using neural network

Sea clutter suppression is a critical task in radar systems to enhance target detection performance in complex naval environments and at coastlines. This paper discusses the use of neural networks for marine clutter suppression and coastal surveillance radar clutter suppression. Effective maritime clutter suppression is made possible by the Feed Forward Neural Network (FFNN) and Principal Component Analysis (PCA) based clutter reduction method, which uses neural network deep learning capabilities to automatically identify and extract features and patterns from raw radar data. Support Vector Machine (SVM) is utilized for clutter suppression along the shoreline. To train and test the network model, a sizable collection of radar measurements, including clutter and target echoes, is gathered. After pre-processing, the gathered data is used in a specially created model, which uses its underlying patterns to distinguish between target echoes and clutter. Then, clutter in real-time radar signals is suppressed using the learned neural network models, improving the detection of targets on the sea and at the coastline. Performance measures Structural Similarity (SSIM) and Signal to Noise Ratio (SNR) shows that the proposed method provides improved clutter reduction. Highlights Maritime clutter suppression is required for better visibility of targets Neural Networks provides better clutter suppression Clutter reduction using PPI images helps in Surveillance applications Objective performance measures SNR and SSI are used for performance analysis Better training and good dataset helps in improved clutter reduction for neural network approaches

Bokeh-based target tracking for structural dynamic monitoring: A novel approach in variable lighting conditions

The application of Computer Vision (CV) techniques in Structural Dynamic Monitoring (SDM) eliminates the need for sensor installation and calibration, providing reliable monitoring results. However, conventional CV methods typically require time-intensive supervised pre-processing steps such as pattern extraction and machine learning. This paper introduces a novel Bokeh-effect-based target object tracking method for SDM that forgoes the need for such pre-processing, allowing for unsupervised, real-time, non-contact monitoring. This method, adaptable for diverse lighting conditions and employing consumer-grade cameras and computers, creates circles around target objects positioned beyond the depth of field of the camera lens, then detects and tracks the movement of these circles to facilitate structural dynamic monitoring. The proposed method is applied to a wind tunnel experiment on bridge girder sections to verify its accuracy and reliability, and the result is validated with the measurement from LASER transducers in the same wind tunnel experiment. This innovative approach advances efficient SDM by offering rapid response and real-time non-contact tracking capability with a high tolerance for target objects under regular and low-light conditions while avoiding the complexities linked with the pre-processing inherent in other supervised CV detection methods.

Psychosocial and emotional management of work experience in palliative care nurses: A qualitative exploration.

Palliative care, a crucial aspect of healthcare, faces challenges in psychosocial and emotional management among nurses. With an increasing need for palliative services globally, addressing the emotional well-being of nurses becomes pivotal. To explore nurses' psychosocial and emotional work experiences in a palliative care department. The focus is on understanding the challenges, identifying coping strategies, and assessing the impact on professional and private life when facing those experiences. A phenomenologic-hermeneutical study involving semi-structured interviews was conducted to comprehend the lived experiences of ten nurses working in a hospital's palliative care department in Spain. Hermeneutic analysis was employed to extract patterns and insights from their experiences. The COREQ checklist was used to report this study. Palliative care nurses perceived insufficient preparation in emotional management, grappling with complex family interactions and unique work dynamics. They highlighted the significance of self-protection strategies, experience, clinical sessions, and external resources. Limited training in emotional resilience and challenges posed by the COVID-19 pandemic emerged as organisational barriers. Nurses expressed a desire for enhanced training and resources. The study uncovered a deficiency in the emotional preparedness of palliative care nurses, impacting their professional and personal lives. Despite positive aspects, the emotional toll necessitates attention and intervention. Comprehensive emotional training for palliative care nurses, addressing barriers, providing resources, and acknowledging emotional labour is necessary. Reinstating paused training sessions and considering specific challenges like those posed by the pandemic are vital. Supporting nurses in their professional and private lives is crucial for sustaining quality care in palliative care departments.

Effects of Conversion of Rubber to Oil Palm Plantations on Soil Properties and Hydrological Dynamics in the Low Country Wet Zone of Sri Lanka

A study was conducted to investigate the impacts of converting rubber plantations into oil palm plantations on soil properties and soil hydrology. Soil organic carbon (SOC), bulk density (BD), aggregate stability (AS), saturated hydraulic conductivity (Ks), soil water retention, texture, thermal properties, and pH were determined using soil samples collected from different depths of a twelve-year-old oil palm and rubber cultivated fields located in low country wet zone of Sri Lanka. In each field, volumetric water content (VWC) of soil was continuously measured at four soil depths (0-25, 25-50, 50-75, and 75-100 cm) over a seven-month period. While the study revealed a 40% lower SOC in 0-25 cm soil layer of the oil palm field compared to the rubber field, no significant changes were observed in BD, porosity, pore size distribution, AS, and Ks for the two fields. However, the volumetric heat capacity of rubber grown soil was significantly higher than that of the oil palm grown soil. Oil palm utilized the most water from 25-75 cm soil layer; whereas, rubber extracted more water from deeper soil layers (75-100 cm). Soil water depletion in oil palm field was faster during dry periods than in rubber fields highlighting the need to examine the soil water extraction patterns of oil palm during extended dry spells in future studies. Overall, the conversion of rubber into oil palm plantations showed no significant impact on most of the soil properties and soil hydrology after twelve years of conversion.

Correlation analysis of wind power and meteorology based on the ramp detection method with adaptive sliding window

Abstract Since the penetration of wind power rises rapidly, the coupling between wind power and meteorology strengthens the dependence of the new power system on meteorology. The fluctuation of wind power and the ramp events become key challenges that affect the safe and stable operation of the power system. To balance the accuracy and efficiency of wind power ramp event detection, an adaptive sliding time window-based ramp event detection method is proposed, which includes three major steps: dataset preprocessing and compression, wind power ramp trend pattern extraction considering bump event screening, and wind power ramp detection based on adaptive sliding time window. The correlation between the ramp event of wind power and meteorological factors like temperature, wind speed and direction are analyzed, and the feasibility of the proposed method is verified. The results show that wind speed is the decisive meteorological factor influencing ramp events, and wind power is also affected by wind direction. However, the correlation between wind power and temperature is weak, which only works in a few ramp events.

OPTIMIZATION OF CYBER-PHYSICAL SYSTEM PARAMETERS BASED ON INTELLIGENT IOT SENSORS DATA

The optimization of cyber-physical systems (CPS) parameters is researched with focusing on the integration of computation, networking, and physical processes with the Internet of Things (IoT). The use of intelligent IoT sensors is crucial for collecting real-time data, which is necessary for enhancing the efficiency, reliability, and performance of CPS. Various methods of CPS parameters optimization are analyzed and categorized into model-based approaches, data-driven approaches, and hybrid approaches. The model-based approaches rely on mathematical models to describe CPS behavior and use optimization algorithms like linear programming and evolutionary algorithms to predict system responses and optimize parameters. But, the limitations of model-based approaches are related to complex systems with uncertain or dynamic behavior. The data-driven approaches are more suitable for complex cyber-physical systems. These approaches utilize machine learning and data analytics techniques to extract patterns from sensor data, which are then used to adjust system parameters. The hybrid approaches combine elements of both model-based and data-driven methods. The method of cyber-physical system parameters optimization based on intelligent IoT sensors data processing is developed with using of distributed neural network. The optimization problem is formulated with constraints for the system parameters. The neural network mathematical model and learning algorithm are proposed. The performed research shows the importance of developing optimization methods for CPS parameters based on intelligent IoT sensor data, considering the evolving nature of IoT technology. The integrating intelligent sensors into CPS offers new opportunities for optimizing system performance but also presents challenges in data management and security that should be addressed in future.

Electrophysiological markers of orthographic pattern learning in school-aged children with reading challenges: An ERP investigation

BackgroundPrevious studies suggested that children with reading difficulty have impaired statistical learning ability in extracting distributional orthographic regularities. However, the neural mechanisms underlying have not been fully investigated. AimsThe current study aimed to identify the electrophysiological markers and to examine the neural underpinnings of statistical learning of orthographic regularities in children with reading difficulties. Methods and proceduresUsing the event-related potentials (ERPs) and the orthographic learning task, 157 children were exposed to a sequence of artificial pseudocharacters with varying levels of positional and semantic consistency (low at 60 %, moderate at 80 %, and high at 100 %). Outcomes and resultsPoor readers elicited an increased N170 response in the low consistency and a lack of left-lateralized P300 effect when learning positional regularities of radicals. Similarly, larger N170 effects were observed in poor readers, while similar N400 effects were found in both poor and average readers when learning semantic regularities of radicals. Conclusions and implicationsOur findings indicate that poor readers may have trouble using statistical information for early-stage orthographic pattern extraction, yet they can identify semantic inconsistencies after sufficient exposure. These results deepen our understanding of the neural mechanisms involved in statistical learning for poor readers and aid in improving criteria for differentiating between typically developing children and those with reading challenges.

Cryptographic Algorithm Designed by Extracting Brainwave Patterns

A new authentication method based on EEG signal is proposed here. Biometric features such as fingerprint scanning, facial recognition, iris scanning, voice recognition, and even brainwave patterns can be used for authentication methods. Brainwave patterns, also known as brain biometrics, can be captured using technologies like electroencephalography (EEG) to authenticate a user based on their unique brain activity. This method is still in the research phase and is not yet commonly used for authentication purposes. Extracting EEG features for authentication typically involves signal processing techniques to analyze the brainwave patterns. Here, a method based on statistics for extracting EEG features is designed to extract meaningful information and patterns from the brainwave data for various applications, including authentication, brain–computer interface systems, and neurofeedback training.

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Objective: An essential aspect of computer vision is content-based image retrieval (CBIR), which enables users to search for images based on their visual content instead of created annotations. Advances in technology have resulted in a significant rise in the complexity of multimedia content and the emergence of new research fields centered on similar multimedia material retrieval. The efficacy of retrieval is impacted by the limits of the present CBIR systems, which result from overlooked algorithms and computing restrictions. Methods: This research introduces a novel approach employing the Siamese Edge Attention Layered Convonet (SEAL Convonet) for Image Retrieval. We utilize the CBIR image dataset through Gaussian smoothing to enhance image quality for data preprocessing and the Canny Edge Detector (CED) for edge detection, following pre-processing. The Histogram of Oriented Gradients (HOG) is used for feature extraction to extract complex textures and patterns from the images. Findings: This approach is implemented and tested through simulations as well as the results indicate a substantial positive deviation in the performance and retrieval of the images compared to existing methods. The performance metrics are accuracy (97 %), precision (94 %), recall (91 %), F1-Score (97 %), False Positive Rate (FNR) (0.0013), Matthew's correlation coefficient (MCC) (0.85), and False Negative Rate (FPR) (0.0036) show the measurements of this proposed model. Application: The state of the art in this work is researching the influence of optimizers on the accuracy process, as indicated by the findings. Keywords: CBIR, Gaussian smoothing, Canny Edge Detector, Histogram of oriented gradients (HOG), Siamese Edge Attention Layered convonet (SEAL Convonet), Database

Deep learning on medical image analysis

AbstractMedical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring various diseases. Convolutional neural networks (CNNs) have become popular as they can extract intricate features and patterns from extensive datasets. The paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre‐trained models. The paper also discusses how transfer learning has been applied to different areas within medical image analysis. This comprehensive overview aims to assist researchers, clinicians, and policymakers by providing detailed insights, helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.

Research on the Design of Zhuang Brocade Patterns Based on Automatic Pattern Generation

To promote the inheritance of Zhuang brocade culture and the rapid extraction of features and automatic generation of patterns, this paper constructs a feature dataset of Zhuang brocade patterns and proposes an automatic generation technology using relative coordinates and regional content replacement. Firstly, by sorting through a large number of cases, a feature dataset of Zhuang brocade patterns is constructed. For the significant features of Zhuang brocade patterns, intelligent extraction algorithms and processes are used to effectively extract the color matching, patterns, and organizational forms of the patterns into the feature dataset. Secondly, to generate Zhuang brocade patterns quickly, an automatic generation technology based on genotype encoding and regional replacement algorithms is proposed, which encodes these pattern elements into a format that can be interpreted by computer algorithms. Finally, through similarity evaluation, the method’s feasibility for rapid extraction and generation of Zhuang brocade patterns is effectively verified. This method is significant for the inheritance of Zhuang brocade patterns and the development of the intangible cultural heritage industry.

Enabling CMF estimation in data-constrained scenarios: A semantic-encoding knowledge mining model

Availability of more accurate Crash Modification Factors (CMFs) is crucial for evaluating the effectiveness of various road safety treatments and prioritizing infrastructure investment accordingly. While customized study for each countermeasure scenario is desired, the conventional CMF estimation approaches rely heavily on the availability of crash data at specific sites. This dependency may hinder the development of CMFs when it is impractical to collect data for recent implementations. Additionally, the transferability of CMF knowledge faces challenges, as the intrinsic similarities between different safety countermeasure scenarios are not fully explored. Aiming to fill these gaps, this study introduces a novel knowledge-mining framework for CMF prediction. This framework delves into the connections of existing countermeasure scenarios and reduces the reliance of CMF estimation on crash data availability and manual data collection. Specifically, it draws inspiration from human comprehension processes and introduces advanced Natural Language Processing (NLP) techniques to extract intricate variations and patterns from existing CMF knowledge. It effectively encodes unstructured countermeasure scenarios into machine-readable representations and models the complex relationships between scenarios and CMF values. This new data-driven framework provides a cost-effective and adaptable solution that complements the case-specific approaches for CMF estimation, which is particularly beneficial when availability of crash data imposes constraints. Experimental validation using real-world CMF Clearinghouse data demonstrates the effectiveness of this new approach, which shows significant accuracy improvements compared to the baseline methods. This approach provides insights into new possibilities of harnessing accumulated transportation knowledge in various applications.

Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique

Privacy is a significant issue that requires consideration in all applications. Data collected from various individuals and organizations must be disclosed to the public or private parties for analysis and research purposes. The collected data are studied and analyzed digitally for the extraction of various useful patterns for decision-making research purposes. Privacy-preserving data publishing is significant as privacy violations in the patient’s data may have an adverse effect on the individual positive reputation. An efficient Cluster Based anonymity model has been proposed to anonymizes the 1:1 dataset with a single sensitive attribute through the introduction of a concept named “Semi-sensitive attribute.” Based on correlation, the attributes are categorized as quasi-identifier and semi-sensitive attributes. The k-anonymity is implemented on the quasi-identifier with the semi-sensitive attribute table and Fuzzy c-means clustering has been implemented to fix a range of values for anonymizing the semi-sensitive attributes. The disease is considered a sensitive attribute as the research work focuses on the medical dataset. The proposed model is demonstrated to resist the three privacy attacks such as, i)Identity Disclosure, ii) Attribute Disclosure, and iii) Membership Disclosure. The utility loss is calculated for each row and utility loss of each record are aggregated and considered as the total information loss for each attribute. Cluster Based anonymity model measured the utility loss for all the attributes and the average utility loss for the anonymized patient dataset is 3.78%.