Approach For Automatic Detection Research Articles

Monitoring event data extracted from online news for outbreak detection

Abstract Introduction Digital disease surveillance (DDS) detects public health events from internet-based data e.g., online news. Event features depicting epidemiological and social characteristics of health events can be extracted from news using the natural language process techniques. However, few studies have leveraged the event features to support anomaly detection in DDS. We aimed to understand the distribution of the event features and explore anomaly detection using the frequency of these features. Methods We collected event data from COVID-19-related news collected from October 1 to December 31, 2021, sourced from BioCaster, an infectious-disease-focused DDS system. The predefined event features in BioCaster include disease, pathogen, location and 14 binary features, such as if an event was caused by an unclassified virus. We described the distribution of the features and detected changes in the frequency of event features using a Bayesian online change point detection. We compared the change points with the number of new cases and of genomic samples collected. Results We included 170,168 news articles reporting COVID-19 in 155 countries. The event feature indicating that an event was caused by an unclassified virus was identified as positive among 3831 (2.25%) news and 12.91% of news had positive value for the feature indicating cases who had travelled across international borders. The change points detected from these two features were temporally correlated to the emergence of the Omicron variant in corresponding countries, which was more significant in countries with at least 300 news articles. Conversely, event features irrelevant to this case study, e.g., if the cases were military workers, were identified as negative in all news and no change points were detected. Conclusions Our study highlights the potential of monitoring the frequency of event features extracted from online news for anomaly detection in DDS, which relies on sufficient news coverage. Key messages • Monitoring the event features extracted from online news provide is useful approach for automatic anomaly detection in digital disease surveillance. • Increasing media coverage is fundamental for improving the early detection in a digital disease surveillance system.

Retraction Note: An approach for automatic detection of fetal gestational age at the third trimester using kidney length and biparietal diameter

Retraction Note: An approach for automatic detection of fetal gestational age at the third trimester using kidney length and biparietal diameter

Automatic Detection of Concrete Surface Defects Using PRE-TRAINED CNN and Laser Ultrasonic Visualization Testing

In recent years, nondestructive testing for civil engineering structures has become increasingly important. Ultrasonic testing is one of nondestructive inspection methods for civil structures. However, the inspection of civil engineering structures takes much time because of the extensive scope of the inspection. Moreover, in the field of nondestructive testing, there are also concerns about a future shortage of inspectors, so that an innovative effective nondestructive method needs to be developed. This study proposes an automatic defect detection approach using pre-trained convolutional neural network for laser ultrasonic visualization testing. The effectiveness of the proposed method is confirmed by applying it to a concrete structure with a surface defect. Grad-CAM demonstrates that the created CNN model in this study accurately predicts the position of a surface defect of concrete specimens.

Long Short-Term Memory Autoencoder for Anomaly Detection in Rails Using Laser Doppler Vibrometer Measurements

Abstract This study presents an application of Long Short-Term Memory Autoencoder (LSTM AE) for the detection of broken rails based on laser Doppler Vibrometer (LDV) measurements. This work is part of an ongoing project aimed at developing a non-contact damage detection system using LDV measurements. The damage detection system consists of two laser Doppler vibrometers (LDV) mounted on a moving rail car to measure vibrations induced on the rail head. Field tests were carried out at the Transportation Technology Center (TTC) in Pueblo, CO, to collect the vibrational data. This study focused on the detection of broken rails. To simulate the reflected and transmitted waves induced by the broken rail, a welded joint was used. The data was collected from moving LDV measurements, in which the train was operating at three different speeds: 16km/h (10mph), 32km/h (20mph), and 48km/h (30mph). After obtaining the data, filtering and signal processing were applied to obtain the signal features in time and frequency domains. Next, correlation analysis and principal component analysis were carried out for feature selection and dimension reduction to determine the input used to train and test the LSTM AE model. In this study, the LSTM AE models were trained based on different data sets for anomaly detection. Consequently, an automatic anomaly detection approach for anomaly detection based on the LSTM AE model was evaluated. The results show that the LSTM AE model can efficiently detect the anomaly based on the selected features at three different speeds.

Introducing Methods for Analyzing and Detecting Concrete Cracks at the No. 3 Huaiyin Pumping Station in the South-to-North Water Diversion Project in China

Concrete cracks pose significant threats to concrete structures, causing immediate strength loss and leading to gradual erosion that compromises structural integrity. Therefore, accurate and automatic detection and classification of concrete cracks, along with the evaluation of their effects on target structures, are critically important. This study focuses on the No. 3 Huaiyin pumping station, a large-scale hydraulic structure on the Eastern Route of the South-to-North Water Diversion Project in Jiangsu, China. First, relevant field test literature is reviewed, and the finite element method is applied to investigate the effects of an existing crack on the No. 2 supporting wall. Using thermomechanically coupled numerical simulations, the distribution of tensile stress in the supporting wall is reported in two cases: without a crack and with an existing crack. The findings indicate that the increase in tensile stress due to the existing crack is relatively small and can be considered negligible for the No. 2 supporting wall. Next, the pretrained YOLOX network for the detection and classification of three types of cracks is proposed and retrained using collected concrete crack datasets. The mean average precision of the retrained YOLOX network for all three types of cracks reaches 80%. Finally, the retrained YOLOX network is applied to detect and classify cracks at the No. 3 Huaiyin pumping station. This automatic detection and classification approach will enhance the high-quality management of the pumping station because it is labor-saving and easy to deploy.

Improving treatment completion for young adults with substance use disorder: Machine learning-based prediction algorithms

Substance use disorder (SUD) treatment completion was intertwined with various factors. However, few studies have explored the intersections of psychosocial and system-related factors with SUD treatment completion, particularly for individuals receiving publicly funded SUD treatment services. This study aimed to examine the intersections of these factors with treatment completion. We analyzed the psycho-social assessment data of 2909 young adults who participated in publicly funded outpatient-based substance use treatments in 2021. The Chi-square Automatic Interaction Detection (CHAID) approach was employed to examine intersections for SUD treatment completion. The analysis highlights the significance of multiple factors and their interactions in predicting SUD treatment completion. The results indicate that SUD treatment outcomes varied based on the level of improvement rates in total actionable items (TAI) improvement rates, underscoring the importance of monitoring individual progress in treatment. Specifically, among young adults with the highest TAI, those residing in rural communities were less likely to complete treatment compared to their urban counterparts. For individuals with TAI improvement rates at the middle level, there was a significant intersection with criminal justice involvement. Within this subgroup, individuals who had both justice system involvement and opioid use disorders had a relatively low SUD treatment completion rate, while those with non-opioid-related SUD exhibited a higher completion rate. The study illustrates the importance of considering multiple factors and their interactions, including TAI improvement rates, family strengths, demographic characteristics, and social determinants, in predicting SUD treatment completion among young adults.

Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica

AbstractAs seismic data collection continues to grow, advanced automated processing techniques for robust phase identification and event detection are becoming increasingly important. However, the performance, benefits, and limitations of different automated detection approaches have not been fully evaluated. Our study examines how the performance of conventional techniques, including the Short‐Term Average/Long‐Term Average method and cross‐correlation approaches, compares to that of various deep learning models. We also evaluate the added benefits that transfer learning may provide to machine learning‐based seismic applications. Each detection approach has been applied to 3 years of data recorded by stations in East Antarctica, which contain both cryospheric and tectonic‐related seismic events. Our results demonstrate that by integrating different event detection approaches with transfer learning, the strengths of each approach can maximize seismic detection accuracy and reliability while also enhancing the completeness of associated event catalogs. That said, model performance can vary, depending on the quality of the data set as well as the data augmentation schemes and training strategies employed. Our results in East Antarctica provide new insight into polar seismicity and demonstrate how automated event detection approaches can be optimized to investigate seismic activity in challenging environments.

A Robust Automatic Epilepsy Seizure Detection Algorithm Based on Interpretable Features and Machine Learning

Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach.

DIABETIC FOOT ULCER CLASSIFICATION USING SEQUENTIAL CNN ALGORITHM

The occurrence of diabetic foot ulcers (DFUs) and their possible consequences provide a major healthcare challenge. For prompt care and the avoidance of serious problems, early and accurate detection of DFUs is essential. This study proposes a novel approach for automatic DFU detection utilizing Convolutional Neural Networks (CNNs), a powerful deep learning technique proven effective in image analysis. A large dataset of foot photos covering a variety of DFU types, phases, and circumstances is used to train the suggested CNN model. The training process involves learning intricate patterns and features indicative of DFUs, enabling the model to generalize well to unseen data. The CNN algorithm's effectiveness in feature extraction and spatial hierarchy learning is harnessed to identify subtle visual cues associated with DFUs, enhancing diagnostic accuracy. The proposed system is designed to operate on medical images, particularly those obtained through various imaging modalities such as digital photography or thermal imaging. Through rigorous validation and performance evaluation, the CNN model exhibits promising results, showcasing its potential as a reliable tool for automated DFU detection.The integration of this technology into clinical practice holds the promise of expediting the diagnostic process, facilitating timely medical interventions, and ultimately improving patient outcomes. This research contributes to the ongoing efforts in leveraging advanced technologies to address critical healthcare challenges, particularly in the realm of diabetic care and wound management.

An AutoML-based approach for automatic traffic incident detection in smart cities

In the realm of modern urban mobility, automatic incident detection is a critical element of intelligent transportation systems (ITS), since the ability to promptly identify unexpected events allows for quick implementation of preventive measures and efficient response to the situations as they arise. With the growing availability of traffic data, Machine Learning (ML) has become a vital tool for enhancing traditional incident detection methods. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process; however the application of AutoML for incident detection has not been widely explored in scientific research In this paper, we propose and apply an AutoML-based methodology for traffic incident detection and compare it with state-ofthe-art ML approaches. Our approach integrates data preprocessing with AutoML, and uses Tree-based Pipeline Optimization Tool (TPOT) to refine the process from raw data to prediction. We have tested the efficiency of our approach in two major European cities, Athens and Antwerp. Finally, we present the limitations of our work and outline recommendations for application of AutoML in the incident detection task and potentially in other domains.

Interval type-2 fuzzy approach for retinopathy detection in fundus images

In the manuscript, an automatic approach for analysis and detection of various stages of retinopathy defects in human eyes has been proposed. The approach consists of a robust preprocessing technique of the retina fundus image to mitigate the effects of noise and poor lightening in the image. To realize a compressive analysis of the defects, methods for extracting blood vessels and optic disc in the fundus image has also been developed. Adaptive Histogram Equalization (AHE), median filtering and Connected Component Analysis techniques were used in separating blood vessels and optic disc from each fundus image. The pre-processing utilizes canny edge detection and Morphological Closing on the fundus image. An interval type-2 fuzzy (IT2F) clustering is applied to segments an input image into four clusters. These four clusters from the fuzzy segmentation are further analyzed to extract various stages of retinopathy abnormalities (e.g., Hemorrhage, hard exudates etc.). The extracted blood vessels and optic disc are removed from the analysis to enhance the defects detection process. Experiments were conducted on DIARETDB1 database. The experimental results obtained are validated using the ground-truth images contained in DIARETDB1 database. Impressive results are recorded throughout the experiment. Hard-Exudates and Hemorrhage were detected from the fundus images and results from similarity indexes such as, accuracy (94.11%) sensitivity (93.03%) and specificity (98.45%) were recorded.

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks.

The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of -10 dB using the LSTM network and fused features.

An efficient approach for automatic crack detection using deep learning

PurposeAutomation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.Design/methodology/approachIn this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.FindingsA comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.Originality/valueThe proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

Automatic approach for breast cancer detection based on deep belief network using histopathology images

Automatic approach for breast cancer detection based on deep belief network using histopathology images

Landet: an efficient physics-informed deep learning approach for automatic detection of anatomical landmarks and measurement of spinopelvic alignment

Purpose:An efficient physics-informed deep learning approach for extracting spinopelvic measures from X-ray images is introduced and its performance is evaluated against manual annotations.Methods:Two datasets, comprising a total of 1470 images, were collected to evaluate the model’s performance. We propose a novel method of detecting landmarks as objects, incorporating their relationships as constraints (LanDet). Using this approach, we trained our deep learning model to extract five spine and pelvis measures: Sacrum Slope (SS), Pelvic Tilt (PT), Pelvic Incidence (PI), Lumbar Lordosis (LL), and Sagittal Vertical Axis (SVA). The results were compared to manually labelled test dataset (GT) as well as measures annotated separately by three surgeons.Results:The LanDet model was evaluated on the two datasets separately and on an extended dataset combining both. The final accuracy for each measure is reported in terms of Mean Absolute Error (MAE), Standard Deviation (SD), and R Pearson correlation coefficient as follows: [SS∘:3.7(2.7),R=0.89]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[SS^\\circ : 3.7 (2.7), R = 0.89]$$\\end{document}, [PT∘:1.3(1.1),R=0.98],[PI∘:4.2(3.1),R=0.93],[LL∘:5.1(6.4),R=0.83],[SVA(mm):2.1(1.9),R=0.96]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[PT^\\circ : 1.3 (1.1), R = 0.98], [PI^\\circ : 4.2 (3.1), R = 0.93], [LL^\\circ : 5.1 (6.4), R=0.83], [SVA(mm): 2.1 (1.9), R = 0.96]$$\\end{document}. To assess model reliability and compare it against surgeons, the intraclass correlation coefficient (ICC) metric is used. The model demonstrated better consistency with surgeons with all values over 0.88 compared to what was previously reported in the literature.Conclusion:The LanDet model exhibits competitive performance compared to existing literature. The effectiveness of the physics-informed constraint method, utilized in our landmark detection as object algorithm, is highlighted. Furthermore, we addressed the limitations of heatmap-based methods for anatomical landmark detection and tackled issues related to mis-identifying of similar or adjacent landmarks instead of intended landmark using this novel approach.

Navigating misinformation in voice messages: Identification of user‐centered features for digital interventions

AbstractMisinformation presents a challenge to democracies, particularly in times of crisis. One way in which misinformation is spread is through voice messages sent via messenger groups, which enable members to share information on a larger scale. Gaining user perspectives on digital misinformation interventions as countermeasure after detection is crucial. In this paper, we extract potential features of misinformation in voice messages from literature, implement them within a program that automatically processes voice messages, and evaluate their perceived usefulness and comprehensibility as user‐centered indicators. We propose 35 features extracted from audio files at the character, word, sentence, audio, and creator levels to assist (1) private individuals in conducting credibility assessments, (2) government agencies faced with data overload during crises, and (3) researchers seeking to gather features for automatic detection approaches. We conducted a think‐aloud study with laypersons () to provide initial insight into how individuals autonomously assess the credibility of voice messages, as well as which automatically extracted features they find to be clear and convincing indicators of misinformation. Our study provides qualitative and quantitative insights into valuable indicators, particularly when they relate directly to the content or its creator, and uncovers challenges in user interface design.

MRC-VulLoc: Software source code vulnerability localization based on multi-choice reading comprehension

Recently, automatic vulnerability detection approaches based on machine learning (ML) have outperformed traditional rule-based approaches in terms of detection performance. Existing ML-based approaches typically concentrate on function or line granularity, which fail to realize accurate vulnerability localization and are insufficient to support effective root cause analysis of vulnerability. To address this issue, we propose a new approach that maps the multi-choice reading comprehension (MRC) task to the vulnerability localization task at the granularity of vulnerability triggering path named MRC-VulLoc. Initially, we design six large datasets (including C/C++ and Java languages) in the form of MRC. Subsequently, we introduce a novel pre-trained vulnerability localization model, combining the effective code semantic comprehension ability of pre-trained model with the advantages of Bidirectional Short-Term Memory Network (Bi-LSTM) and Convolutional Neural Network (CNN) models. Lastly, we conduct experiments to evaluate the vulnerability localization with several state-of-the-art MRC approaches and vulnerability detectors. Experimental results demonstrate the effectiveness of the proposed datasets in evaluating MRC approaches for vulnerability localization. Furthermore, MRC-VulLoc achieves higher precision on vulnerability localization compared to comparative vulnerability detectors.

Deep Learning Approach for automatic detection of split defects on sheet metal stamping parts

Sheet metal stamping processes are used primarily for high-volume products produced for a range of sectors, from white goods manufacturing to the automotive and aerospace sectors. However, the process is susceptible to defects. Due to the numerous potential defects that may arise in the stamping product, human inspectors are often deployed for their detection. However, they are unreliable and expensive, especially when operating at production speeds equivalent to the stamping rate. This study investigate CNN-based automatic inspection for stamping defects. The study carried out two sets of experiments. All the Experiments yielded high classification accuracy, recall and precision demonstrating the viability of the CNN method for defect detection in the sheet metal stamping process. Additionally, this study revealed that in limited data confounding factors can be a challenge. The second experiment further explored the impact of small neck defects, harsh lighting and reflections on defect detection. The observations indicated that the model struggled to identify defects occluded by reflections, particularly small neck defects.

Cardiorespiratory fitness and muscular fitness correlates in youth: A hierarchy of behavioral, contextual, and health-related outcomes

ObjectivesDespite the health importance of identifying correlates of physical fitness in youth, no investigation to date has explored the influence of behavioral, health-related, and contextual correlates simultaneously. We investigated the hierarchical relationship of multiple modifiable correlates favoring or diminishing cardiorespiratory and muscular fitness in youth. DesignCross-sectional investigation. MethodsIn a sample of 5174 children and adolescents, 31 correlates were hierarchized according to their impact on cardiorespiratory and muscular fitness assessed using the FITESCOLA® fitness battery. A Chi-squared Automatic Interaction Detection approach was employed and measures of correlation and association were used to investigate the relationship between physical fitness and correlates. ResultsIn children, body mass index was the most relevant factor to discriminate between high and low cardiorespiratory and muscular fitness of the upper, middle, and lower body. While body mass index was more important than any other correlate to differentiate levels of upper and lower body muscular fitness during adolescence, specific characteristics of sports participation emerged as key factors to discriminate between high and low cardiorespiratory fitness and middle body muscular fitness. Other correlates, including the self-report of active recess time, active commuting to school, favorable neighborhood conditions, and limited time on screens and cellphones, were demonstrative of favorable physical fitness levels. ConclusionsBoth body composition and sports-related characteristics emerged as the two most relevant factors of physical fitness in youth. Additional health benefits may be obtained from building supportive environments for sports and healthy exercise habits within the household and at different school education levels.

Disinformation detection using graph neural networks: a survey

The creation and propagation of disinformation on social media is a growing concern. The widespread dissemination of disinformation can have destructive effects on people’s attitudes and behavior. So, it is essential to detect disinformation as soon as possible. Therefore, the interest in effective detection techniques has grown rapidly in recent years. Major social media and social networking sites are trying to develop robust strategies to detect disinformation and prevent its spread. Machine learning techniques and especially neural networks, have an essential role in this task. In this paper, we review different approaches for automatic disinformation detection, with a focus on methods that leverage graph neural networks (GNNs). GNNs are very suitable tools for detecting disinformation in social networks. Because on the one hand, graphs are the most comprehensive way to model social networks and on the other hand, GNNs are the best tool for processing graph data. We define different forms of disinformation, and examine the features used and the methods presented from different perspectives. We also discuss relevant research areas, open problems, and future research directions for disinformation detection in social media.