scholarly journals HMBI: A New Hybrid Deep Model Based on Behavior Information for Fake News Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jian Xing ◽  
Shupeng Wang ◽  
Xiaoyu Zhang ◽  
Yu Ding
Keyword(s):  
Social Behavior ◽  
Real World ◽  
Detection Accuracy ◽  
Challenging Problem ◽  
Fake News ◽  
Real World Data ◽  
Model Based ◽  
Deep Model ◽  
The Social ◽  
First Time

Fake news can cause widespread and tremendous political and social influence in the real world. The intentional misleading of fake news makes the automatic detection of fake news an important and challenging problem, which has not been well understood at present. Meanwhile, fake news can contain true evidence imitating the true news and present different degrees of falsity, which further aggravates the difficulty of detection. On the other hand, the fake news speaker himself provides rich social behavior information, which provides unprecedented opportunities for advanced fake news detection. In this study, we propose a new hybrid deep model based on behavior information (HMBI), which uses the social behavior information of the speaker to detect fake news more accurately. Specifically, we model news content and social behavior information simultaneously to detect the degrees of falsity of news. The experimental analysis on real-world data shows that the detection accuracy of HMBI is increased by 10.41% on average, which is the highest of the existing model. The detection accuracy of fake news exceeds 50% for the first time.

Abstract P505: Real-world Data Validation Of A Novel P-wave Based Automatic Atrial Fibrillation Detection Algorithm

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Åke Olsson ◽  
Magnus Samulesson
Keyword(s):  
Real World ◽  
False Negative ◽  
Detection Algorithm ◽  
P Wave ◽  
Detection Accuracy ◽  
The Novel ◽  
Real World Data ◽  
P Waves ◽  
World Data ◽  
Rr Variability

Background: Automatic ECG algorithms using only RR-variability in ECG to detect AF have shown high false positive rates. By including P-wave presence in the algorithm, research has shown that it can increase detection accuracy for AF. Methods: A novel RR- and P-wave based automatic detection algorithm implemented in the Coala Heart Monitor ("Coala", Coala Life AB, Sweden) was evaluated for detection accuracy by the comparison to blinded manual ECG interpretation based on real-world data. Evaluation was conducted on 100 consecutive anonymous printouts of chest- and thumb-ECG waveforms, where the algorithm had detected both irregular RR-rhythms and strong P-waves in either chest or thumb recording (non-AF episodes classified by algorithm as Category 12).The recordings, without exclusions, were generated from 5,512 real-world data recordings from actual Coala users in Sweden (both OTC and Rx users) during the period of March 5 to March 22, 2019, with no control or influence by the researchers or any other organization or individual. The prevalence of cardiac conditions in the user population was unknown.The blinded recordings were each manually interpreted by a trained cardiologist. The manual interpretation was compared with the automatic analysis performed by the detection algorithm to determine the number of additional false negative indications for AF as presented to the user. Results: The trained cardiologist manually interpreted 0 of the 100 recordings as AF. Manual interpretation showed that the novel automatic AF algorithm yielded 0 % False Negative error and 100 % Negative Predictive Value (NPV) for detection of AF. Irregular RR-rhythms were detected in 569 recordings (10 % of a total of 5,512 recordings). The 100 non-AF recordings containing both irregular RR-rhythms and strong P-waves constituted 18% of all recordings with irregular RR-rhythms. Respiratory sinus arrhythmia was the single most prevalent condition and was found in 47% of irregular RR-rhythms with strong P-waves. Conclusion: The novel, P-wave based automatic ECG algorithm used in the Coala, showed a zero percent False Negative error rate for AF detection in ECG recordings with RR-variability but presence of P-waves, as compared to manual interpretation by a cardiologist.

Mining Geospatial Knowledge on the Social Web

2013 ◽  
pp. 98-112
Author(s):  
Suradej Intagorn ◽  
Kristina Lerman
Keyword(s):  
Real World ◽  
Web Sites ◽  
Information Integration ◽  
Knowledge Bases ◽  
Google Earth ◽  
Social Web ◽  
Information Discovery ◽  
Real World Data ◽  
Geospatial Information ◽  
The Social

Up-to-date geospatial information can help crisis management community to coordinate its response. In addition to data that is created and curated by experts, there is an abundance of user-generated, user-curated data on Social Web sites such as Flickr, Twitter, and Google Earth. User-generated data and metadata can be used to harvest knowledge, including geospatial knowledge that will help solve real-world problems including information discovery, geospatial information integration and data management. This paper proposes a method for acquiring geospatial knowledge in the form of places and relations between them from the user-generated data and metadata on the Social Web. The key to acquiring geospatial knowledge from social metadata is the ability to accurately represent places. The authors describe a simple, efficient algorithm for finding a non-convex boundary of a region from a sample of points from that region. Used within a procedure that learns part-of relations between places from real-world data extracted from the social photo-sharing site Flickr, the proposed algorithm leads to more precise relations than the earlier method and helps uncover knowledge not contained in expert-curated geospatial knowledge bases.

scholarly journals Median age at HPV infection among women in the United States: A model-based analysis informed by real-world data

2021 ◽  
Author(s):  
Vimalanand S Prabhu ◽  
Craig S Roberts ◽  
Smita Kothari ◽  
Linda Niccolai
Keyword(s):  
Natural History ◽  
Real World ◽  
Clinical Decision Making ◽  
Age Distribution ◽  
The United States ◽  
Hpv Infection ◽  
Real World Data ◽  
World Data ◽  
Model Based ◽  
Model Based Analysis

Abstract Background The US Advisory Committee for Immunization Practices (ACIP) recommended shared clinical decision-making for HPV vaccination of individuals aged 27 to 45 years (mid-adults) in June 2019. Determining the median age at causal HPV infection and CIN2+ diagnosis based on the natural history of HPV disease can help better understand the incidence of HPV infections and the potential benefits of vaccination in mid-adults. Methods Real-world data on CIN2+ diagnosis from the pre-vaccine era were sourced from a statewide surveillance registry in Connecticut. Age distribution of CIN2+ diagnosis in 2008 and 2009 was estimated. A discrete-event simulation model was developed to predict the age distribution of causal HPV infection. The optimal age distribution of causal HPV infection provided the best goodness-of-fit statistic to compare the predicted vs real-world age distribution of CIN2+ diagnosis. Results The median age at CIN2+ diagnosis from 2008 through 2009 in Connecticut was 28 years. The predicted median age at causal HPV infection was estimated to be 23.9 years. There was a difference of 5.2 years in the median age at acquisition of causal HPV infection and the median age at CIN2+ diagnosis. Conclusions Real-world data on CIN2+ diagnosis and model-based analysis indicate a substantial burden of infection and disease among women aged 27 years or older, which supports the ACIP recommendation to vaccinate some mid-adults . When natural history is known, this novel approach can also help determine the timing of causal infections for other commonly asymptomatic infectious diseases.

Abstract P504: Quality Validation Of Different ECG Leads, And An Automatic P-wave Detection Algorithm For Af, Based On Real-world Data

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Magnus Samuelsson ◽  
Åke Olsson
Keyword(s):  
Respiratory Sinus Arrhythmia ◽  
Real World ◽  
Detection Algorithm ◽  
P Wave ◽  
Detection Accuracy ◽  
Real World Data ◽  
P Waves ◽  
Sinus Arrhythmia ◽  
Ecg Leads

Background: Single-lead ECG has shown in research to be affected by artifacts leading to lower diagnostic yield of Atrial Fibrillation (AF). Use of multiple ECG leads and algorithms for detection of AF has shown to increase detection accuracy and reduce false positives. Methods: A novel RR- and P-wave based automatic algorithm implemented in the 2-lead Coala Heart Monitor (Coala) was evaluated for detection accuracy and quality by the comparison to blinded manual ECG interpretation. Evaluation was conducted on 100 consecutive anonymous printouts of chest- and thumb-ECG waveforms, where both an irregular RR-rhythm and strong P-waves in either chest or thumb recording were detected.The recordings, without exclusions, were generated from 5,512 real-world data recordings from actual Coala users in Sweden (both OTC and Rx users) during the period of March 5 to March 22, 2019, with no control or influence by the researchers or any other organization or individual. The prevalence of cardiac conditions in the user population was unknown. The blinded recordings were each manually interpreted and assessed for quality by a trained cardiologist. The manual interpretation was compared with the automatic analysis performed by the cloud-based detection algorithm to determine the detection quality of the respective ECG leads. Results: Strong P-waves were detected more often in the chest ECG as compared to the thumb ECG (90 vs 32 recordings). The assessed quality of the ECG tracings was higher in the chest ECGs as compared to the thumb ECGs (4.61 vs 3.88). Irregular RR-rhythms were detected in 569 recordings (10 % of a total of 5,512 recordings), the 100 non-AF recordings containing both irregular RR-rhythms and strong P-waves thus constituted 18% of all recordings with irregular RR-rhythms. Non-pathological rhythm (normal) was present in 84% of the recordings although all of these recordings contained irregular rhythm disturbances (respiratory sinus arrhythmia, PAC/PVC etc). Respiratory sinus arrhythmia was the single most prevalent condition and found in 47% of the recordings with irregular RR-rhythms with strong detected P-waves. Conclusion: The combination of chest and thumb ECG for detection of AF by an automatic P-wave based algorithm is shown to be more than 300% superior to thumb ECG alone with the majority of automatically detected P-waves and highest assessed ECG quality in the chest recordings.

scholarly journals L*a*b*Fruits: A Rapid and Robust Outdoor Fruit Detection System Combining Bio-Inspired Features with One-Stage Deep Learning Networks

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 275 ◽  
Author(s):  
Raymond Kirk ◽  
Grzegorz Cielniak ◽  
Michael Mangan
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Model Complexity ◽  
Detection Accuracy ◽  
Learning Networks ◽  
Real World Data ◽  
Data Set ◽  
World Data ◽  
One Stage ◽  
Controlled Conditions

Automation of agricultural processes requires systems that can accurately detect and classify produce in real industrial environments that include variation in fruit appearance due to illumination, occlusion, seasons, weather conditions, etc. In this paper we combine a visual processing approach inspired by colour-opponent theory in humans with recent advancements in one-stage deep learning networks to accurately, rapidly and robustly detect ripe soft fruits (strawberries) in real industrial settings and using standard (RGB) camera input. The resultant system was tested on an existent data-set captured in controlled conditions as well our new real-world data-set captured on a real strawberry farm over two months. We utilise F 1 score, the harmonic mean of precision and recall, to show our system matches the state-of-the-art detection accuracy ( F 1 : 0.793 vs. 0.799) in controlled conditions; has greater generalisation and robustness to variation of spatial parameters (camera viewpoint) in the real-world data-set ( F 1 : 0.744); and at a fraction of the computational cost allowing classification at almost 30fps. We propose that the L*a*b*Fruits system addresses some of the most pressing limitations of current fruit detection systems and is well-suited to application in areas such as yield forecasting and harvesting. Beyond the target application in agriculture this work also provides a proof-of-principle whereby increased performance is achieved through analysis of the domain data, capturing features at the input level rather than simply increasing model complexity.

scholarly journals Model‐Based Approach to Improve Clinical Outcomes in Neonates With Opioid Withdrawal Syndrome Using Real‐World Data

2020 ◽  
Vol 109 (1) ◽  
pp. 243-252
Author(s):  
Nadeesri Wijekoon ◽  
Oluwatobi Aduroja ◽  
Jessica M. Biggs ◽  
Dina El‐Metwally ◽  
Mathangi Gopalakrishnan
Keyword(s):  
Clinical Outcomes ◽  
Real World ◽  
Withdrawal Syndrome ◽  
Opioid Withdrawal ◽  
Real World Data ◽  
World Data ◽  
Model Based ◽  
Opioid Withdrawal Syndrome
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