Automatic Extraction Algorithm Research Articles

Three-dimensional dynamic monitoring of crevasses based on deep learning and surface elevation reconstruction methods

The evolution of crevasses on ice shelves plays a pivotal role in maintaining their stability. Investigating the developmental pattern of crevasses on ice shelf surfaces necessitates long-term dynamic monitoring, high-precision automatic extraction algorithms, and quantitative analysis tools. Accordingly, based on characteristics such as the long span of crevasses and their meandering distribution, we proposed Strip_Unet for the identification of crevasses. Within this, the Strip_block module utilizes strip convolution in conjunction with traditional convolution to capture the contextual information from the surrounding area, and the ASPP module is used to enhance the receptive field. Leveraging this model, we successfully identified crevasses within the damaged area of the Amery Ice Shelf from 2003 through 2023, utilizing 15-meter resolution Landsat imagery. Subsequently, we retrieved the depth details of these crevasses by applying ATL06 data, allowing us to quantitatively assess the evolution of crevasses. Our study represents the first investigation into the development of the damage area on the Amery Ice Shelf through long-term time-series analysis. Our findings indicate that crevasses originate from specific areas and follow recurring propagation cycles, exhibiting a consistent annual increase in total length. Additionally, changes in the width of these crevasses coincide with alterations in their depth. Notably, the downstream regions of the damaged area demonstrate higher crevasses density and the fastest movement speed. The proposed automatic identification of crevasses and three-dimensional dynamic monitoring hold significant potential for application throughout the Antarctic region, providing valuable insights into the long-term stability of ice shelves.

Algorithm for automatic extraction of tectonic fault planes from the resulting probability cubes of machine learning models

Seismic exploration is an integral part of the oil and gas industry when studying the geological structure of deposits. Extracting planes of tectonic disruptions is one of the most challenging tasks in seismic exploration, involving the interpretation of seismic information and lacking universal solutions. This problem underscores the relevance of developing and researching corresponding methods. The article introduces a new algorithm for the automatic extraction of such planes from probability cubes obtained as a result of machine learning model predictions. The algorithm’s features include: 1) data smoothing to reduce noise, 2) clustering points based on their characteristics, 3) extraction of contour points and determination of plane boundaries. The algorithm was implemented and tested on synthetic and real seismic exploration data. The test results confirmed the high efficiency of the algorithm compared to existing approaches implemented in specialized industry software packages. The proposed solution allows automating the interpretation process of seismic data, aimed at obtaining information about the shapes and orientations of planes of tectonic disruptions. This, in turn, aids in well drilling planning and determining the strategy for the extraction and development of deposits.

Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset

Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.

A real-time automatic rail extraction algorithm for low-density mobile laser scanning data

Automatic detection of railroad infrastructure using Mobile Laser Scanning systems is a key technology for both advanced rail driver assistance and intelligent track maintenance. The recent research into railway facility extraction or condition monitoring usually relies on high-density point cloud dataset with known sensor parameters but ignores the processing performance in actual deployment and has high requirements for the acquisition device. To address these limitations, a novel rail extraction algorithm is proposed for processing Mobile Laser Scanning data in real time during acquisition, which could extract rail features by a hierarchical coarse-to-fine method with basic structural parameters. Using the geometric and global statistical characteristics of rail in raw data, a new rail descriptor is defined based on the quantitative statistics of points satisfying height difference in generalized local neighborhood. The approach is evaluated experimentally by a simulated real-time acquisition data and compared with a reference method. The experimental results show that the proposed algorithm has a finer extraction effect and good real-time performance.

Unsupervised corneal contour extraction algorithm with shared model for dynamic deformation videos: improving accuracy and noise resistance

BackgroundIn this study, an automatic corneal contour extraction algorithm with a shared model is developed to extract contours from dynamic corneal videos containing noise, which improves the accuracy of corneal biomechanical evaluation and clinical diagnoses. The algorithm does not require manual labeling and completes the unsupervised semantic segmentation of each frame in corneal dynamic deformation videos based on a fully convolutional deep-learning network using corneal geometry and texture information.ResultsWe included 1027 corneal videos at Tianjin Eye Hospital (Nankai University Affiliated Eye Hospital) from May 2020 to November 2021. The videos were obtained by the ultra-high-speed Scheimpflug camera, and then we used the shared model mechanism to accelerate the segmentation of corneal regions in videos, effectively resist noise, determine corneal regions based on shape factors, and finally achieve automatic and accurate extraction of corneal region contours. The Intersection over Union (IoU) of the extracted and real corneal contours using this algorithm reached 95%, and the average overlap error was 0.05, implying that the extracted corneal contour overlapped almost completely with the real contour.ConclusionsCompared to other algorithms, the method introduced in this study does not require manual annotation of corneal contour data in advance and can still extract accurate corneal contours from noisy corneal videos with good repeatability.

Development system for coordination of activities of experts in the formation of machineschetable standards in the field of military and space activities based on ontological engineering: a case study

Standardization and technical regulation involve the digitization of international and regional standards into "smart" standards, introducing new requirements for the presentation of complex industrial manuals. The introduction of artificial intelligence and machine learning into the process of standardization, improving process automation, data analysis will provide an opportunity to create innovative standards. Creation of machine-readable standards and their presentation in the form of a database will contribute to better interoperability between systems. The article demonstrates the method of ontological engineering for the automation of partial methodological examination of documents in accordance with the National Standardization Plan. As part of the experiment, an independent tool was developed, based on the extraction of qualitative data, using an uncontrolled automatic keyword extraction algorithm. The algorithm does not require the creation of a learning corpus, can be applied to any text and language, and does not have limitations on the amount of processed data. The algorithm provides adjustment of the parameters of the repetition of keywords, with the possibility of taking into account less important keywords. The work presents the following components: model classification standards, essence extraction module, categorization, thesaurus formation, monitoring and analysis. Calculations were carried out using formalization, determination of the target function, establishment of similarity measures, and description of document features. The work presents the theoretical aspect of application, design and description of the functional system.

BibRank: Automatic Keyphrase Extraction Platform Using Metadata

Automatic Keyphrase Extraction involves identifying essential phrases in a document. These keyphrases are crucial in various tasks, such as document classification, clustering, recommendation, indexing, searching, summarization, and text simplification. This paper introduces a platform that integrates keyphrase datasets and facilitates the evaluation of keyphrase extraction algorithms. The platform includes BibRank, an automatic keyphrase extraction algorithm that leverages a rich dataset obtained by parsing bibliographic data in BibTeX format. BibRank combines innovative weighting techniques with positional, statistical, and word co-occurrence information to extract keyphrases from documents. The platform proves valuable for researchers and developers seeking to enhance their keyphrase extraction algorithms and advance the field of natural language processing.

Meso‐Skeleton Guided Hexahedral Mesh Design

AbstractWe present a novel approach for the generation of hexahedral meshes in a volume domain given its meso‐skeleton. This compact representation of the topology and geometry, composed of both curve and surface parts, is used to produce a raw decomposition of the domain into hexahedral blocks. Analysis of the different local configurations of the skeleton leads to the construction of a set of connection surfaces that are used as a scaffold onto which the hexahedral blocks are assembled. These local configurations of the skeleton completely determine the singularities of the final mesh, and by following the skeleton, the geometry of the produced mesh naturally follows the geometry of the domain. Depending on the end user needs, the obtained mesh can be further adapted, refined or optimized, for example to better fit the boundary of the domain. Our algorithm does not involve the resolution of any global problem, most decisions are taken locally and it is thus highly suitable for parallel processing. This efficiency allows the user to stay in the loop for the correction or edition of the meso‐skeleton for which a first sketch can be given by an existing automatic extraction algorithm.

Statement of Retraction: A Novel QACS Automatic Extraction Algorithm for Extracting Information in Blockchain-Based Systems

Statement of Retraction: A Novel QACS Automatic Extraction Algorithm for Extracting Information in Blockchain-Based Systems

Scale-preserving automatic concept extraction (SPACE)

Convolutional Neural Networks (CNN) have become a common choice for industrial quality control, as well as other critical applications in the Industry 4.0. When these CNNs behave in ways unexpected to human users or developers, severe consequences can arise, such as economic losses or an increased risk to human life. Concept extraction techniques can be applied to increase the reliability and transparency of CNNs through generating global explanations for trained neural network models. The decisive features of image datasets in quality control often depend on the feature’s scale; for example, the size of a hole or an edge. However, existing concept extraction methods do not correctly represent scale, which leads to problems interpreting these models as we show herein. To address this issue, we introduce the Scale-Preserving Automatic Concept Extraction (SPACE) algorithm, as a state-of-the-art alternative concept extraction technique for CNNs, focused on industrial applications. SPACE is specifically designed to overcome the aforementioned problems by avoiding scale changes throughout the concept extraction process. SPACE proposes an approach based on square slices of input images, which are selected and then tiled before being clustered into concepts. Our method provides explanations of the models’ decision-making process in the form of human-understandable concepts. We evaluate SPACE on three image classification datasets in the context of industrial quality control. Through experimental results, we illustrate how SPACE outperforms other methods and provides actionable insights on the decision mechanisms of CNNs. Finally, code for the implementation of SPACE is provided.

Retracted: Effect of Rehabilitation Training Based on Automatic Extraction Algorithm on Knee Anterior Cruciate Ligament Injury Caused by Exercise.

[This retracts the article DOI: 10.1155/2022/8304071.].

Dual Deep CNN for Tumor Brain Classification.

Brain tumor (BT) is a serious issue and potentially deadly disease that receives much attention. However, early detection and identification of tumor type and location are crucial for effective treatment and saving lives. Manual diagnoses are time-consuming and depend on radiologist experts; the increasing number of new cases of brain tumors makes it difficult to process massive and large amounts of data rapidly, as time is a critical factor in patients' lives. Hence, artificial intelligence (AI) is vital for understanding disease and its various types. Several studies proposed different techniques for BT detection and classification. These studies are on machine learning (ML) and deep learning (DL). The ML-based method requires handcrafted or automatic feature extraction algorithms; however, DL becomes superior in self-learning and robust in classification and recognition tasks. This research focuses on classifying three types of tumors using MRI imaging: meningioma, glioma, and pituitary tumors. The proposed DCTN model depends on dual convolutional neural networks with VGG-16 architecture concatenated with custom CNN (convolutional neural networks) architecture. After conducting approximately 22 experiments with different architectures and models, our model reached 100% accuracy during training and 99% during testing. The proposed methodology obtained the highest possible improvement over existing research studies. The solution provides a revolution for healthcare providers that can be used as a different disease classification in the future and save human lives.

Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory

Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensionality of the problem is directly related to the performance of machine learning models. The dimensionality problem can be addressed through feature selection and feature extraction. In this sense, the evaluation of a model with manual feature extraction and automatic feature extraction was proposed. The manual feature extraction was performed using the statistical functions of central tendency, while the automatic extraction was performed by means of a CNN and BiLSTM. These features were also evaluated in classifiers such as Softmax, ANN, and SVM. The best-performing model was the combination of BiLSTM and ANN (BiLSTM-ANN), with an accuracy of 99.9912%.

Research on parameter extraction of thin‐film transistors based on swarm intelligence

AbstractThe development of integrated circuits for displays and other applications requires semiconductor device models and appropriate parameter extraction techniques to predict and understand the circuit behavior. These techniques are paramount in reducing design errors and shortening the product development cycle. This paper presents an algorithm that employed swarm intelligence in exploring an automated and accurate parameter extraction technology. First, an automatic parameter extraction of Rensselaer Polytechnic Institute (RPI) Model for polysilicon thin‐film transistor (Poly‐Si TFT) is achieved by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Compared with the best solution of the GA algorithm for automatic parameter extraction, the PSO outperformed the GA. However, it still prematurely converges to the suboptimal solution henceforth cannot obtain the expected solution accuracy. Second, the mutual learning particle swarm optimization (MLPSO) algorithm is proposed that introduces the concept of “mutual learning.” The new algorithm aims to find the global optimum in getting suitable trade‐off between exploration and exploitation. In addition, the MLPSO algorithm implemented the novel random initialization and fitness function in simplifying the complex manual processes and the empirical calibration, and it led to achieving automatic and accurate parameters extraction.

Automatic Keyword Extraction Algorithm for Chinese Text based on Word Clustering

There are some problems in automatic keyword extraction of Chinese text, such as large feature extraction error, low precision of extracted keywords, and poor real-time performance. Therefore, an automatic keyword extraction algorithm for Chinese text based on word clustering is designed. Calculate keyword frequency, document frequency and inverse document frequency features through statistical algorithm, measure the degree of interdependence between keywords with the help of point mutual information, and construct keyword feature item quantification matrix with the help of vector space model corresponding to keywords and feature items to complete keyword feature quantification and realize keyword feature extraction of Chinese text. Calculate the average semantic similarity of keyword words, determine the similarity of keyword features, and eliminate the keyword features with high similarity; Set the comprehensive feature value of the importance of single word words in Chinese text, determine the importance of single word words in the text, remove the single word words with low importance, and use Bayesian framework to reduce the dimension of high-dimensional keyword feature data to realize preprocessing research. The mapping results of keyword vector space model are determined by word clustering algorithm, the text clusters of keyword space clustering results are calculated by clustering algorithm, and the keywords are classified by DBN method. On this basis, the automatic keyword extraction model of Chinese text is designed to realize the automatic keyword extraction of Chinese text. The experimental results show that the design algorithm can effectively reduce the feature extraction error and improve the extraction efficiency.

Excavation pits—progress estimation and cause of delay identification

This work presents a method for automated excavation speed and progress estimation. First, a measure for the progress speed of an excavation pit is taken from the literature and evaluated regarding the possibility of automation. For each possible parameter, an automated extraction algorithm is presented. The used system is an autonomous excavator arm of a backhoe loader where the used hardware and software system is described. An experimental evaluation of the presented approach has been done with the autonomous system for a small trench, including multiple digging cycles. The resulting measurements seem to include some systematic errors which could be identified and suitable sanity checks could be implemented, removing the erroneous measurements. The remaining measurements were used to determine the excavation speed of the autonomous excavator arm and compared to the values of experienced and amateur operators. The resulting speed and the relevant parameters are then sent to a web application called “DiBa” to notice a potential delay. If a measurement is smaller or larger than expected a fault identification is done in the DiBa.

Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study.

Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports. This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification. The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard. A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97). Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA. .

Extracting Semantic Relations from the Texts of Scientifc Articles

Nowadays, the number of scientifc publications existing in the form of electronic text is constantly growing. As a result, the tasks related to the text processing of scientifc articles become especially actual. This paper is dedicated to the task of extracting semantic relations between entities from the texts of scientifc articles in Russian, where we consider scientifc terms as entities. Relation extraction can be useful in some specialized areas, such as searching and question-answering systems, as well as in the compilation of ontologies. In our work, we have created a corpus of scientifc texts consisting of 136 abstracts of scientifc articles in Russian, in which 353 relations of the following types were highlighted: USAGE, ISA, TOOL, SYNONYMS, PART_OF, CAUSE. This corpus was used to train the machine learning models. In addition, we have implemented the automatic semantic relation extraction algorithm and tested it on the already existing corpus RuSERRC. The neural network model BERT was used to implement the algorithm. We’ve done a number of experiments using vectors derived from different language models, as well as two neural network architectures. The developed tool and the annotated corpus are publicly available and can be useful for other researchers.

Abstract 106: Novel Imaging-based Morphological Markers For Improved Prediction Of Stroke Outcomes: A Machine Learning Approach

Patient selection for acute reperfusion therapies of acute ischemic stroke is based on assessment of the core and tissue-at-risk and the time from stroke onset. Response to treatment and clinical outcomes, however, vary significantly since patient-specific cerebrovascular anatomy plays a vital role in governing flow and reperfusion. We developed an end-to-end automated machine learning approach to extract advanced cerebrovascular morphological features, including tortuosity and collateral index, to improve outcome prediction, and patient selection. Methods: Using our validated automatic cerebrovascular segmentation and feature extraction algorithm, we obtained the morphological properties of the brain vasculature, including vessel tortuosity, length, diameter, and bifurcation patterns, in MR angiography scans of 100 anonymized stroke patients. Each patient’s collateral index was also automatically graded using a previously established probabilistic atlas of healthy cerebrovasculature. To predict 90-day functional outcomes (mRS), a statistical model was trained using patients' clinical features (demographics, comorbidities, and baseline NIHSS), imaging features (initial ASPECTS, perfusion mismatch volume, and collateral index), and the extracted morphologic features (length, diameter, number of branches, vessel tortuosity, fractal dimension, and total volume). The predictions were compared against ground truth mRS to assess model accuracy. We also compared the model’s performance against conventional outcome predictors with only the clinical and imaging features. Results: The conventional model including the common clinical and imaging features excluding the collateral index gave an area under the curve (AUC) of 0.63, which is comparable to previously published results. Including the automatically scored collateral index improved the accuracy to 0.74. We found that further including the morphological features to the predictor model significantly improved AUC accuracy to 0.83. Conclusion: Including automatically extracted cerebrovascular morphologic features in traditional clinical and imaging markers significantly improves outcome prediction and response to treatment in stroke patients.

A global view on star formation: The GLOSTAR Galactic plane survey

As part of the Global View on Star Formation (GLOSTAR) survey we have used the Karl G. Jansky Very Large Array (VLA) in its B-configuration to observe the part of the Galactic plane between longitudes of 28° and 36° and latitudes from −1° to +1° at the C-band (4–8 GHz). To reduce the contamination of extended sources that are not well recovered by our coverage of the (u, υ)-plane, we discarded short baselines that are sensitive to emission on angular scales >4″. The resulting radio continuum images have an angular resolution of 1.″0 and a sensitivity of ~60 µJy beam−1, making it the most sensitive radio survey covering a large area of the Galactic plane with this angular resolution. An automatic source extraction algorithm was used in combination with visual inspection to identify a total of 3325 radio sources. A total of 1457 radio sources are ≥7σ and comprise our highly reliable catalog; 72 of these are grouped as 22 fragmented sources, for example, multiple components of an extended and resolved source To explore the nature of the cataloged radio sources, we searched for counterparts at millimeter and infrared wavelengths. Our classification attempts resulted in 93 H II region candidates, 104 radio stars, and 64 planetary nebulae, while it is suggested that most of the remaining radio sources are extragalactic sources. We investigated the spectral indices (α, Sv ∝ vα) of radio sources classified as H II region candidates and found that many have negative values. This may imply that these radio sources represent young stellar objects that are members of the star clusters around the high-mass stars that excite the H II regions, but not these H II regions themselves. By comparing the peak flux densities from the GLOSTAR and CORNISH surveys, we have identified 49 variable radio sources, most of them with an unknown nature. Additionally, we provide a list of 1866 radio sources detected within 5 to 7σ levels.