scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI

2021 ◽  
Author(s):  
Marcel Bengs ◽  
Finn Behrendt ◽  
Julia Krüger ◽  
Roland Opfer ◽  
Alexander Schlaefer
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Magnetic Resonance Images ◽  
Spatial Context ◽  
Data Sets ◽  
Learning Methods ◽  
Data Set ◽  
Performance Improvements ◽  
Wide Range ◽  
The Impact

Abstract Purpose Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis of neurological diseases. Recently, deep learning methods for unsupervised anomaly detection (UAD) have been proposed for the analysis of brain MRI. These methods rely on healthy brain MRIs and eliminate the requirement of pixel-wise annotated data compared to supervised deep learning. While a wide range of methods for UAD have been proposed, these methods are mostly 2D and only learn from MRI slices, disregarding that brain lesions are inherently 3D and the spatial context of MRI volumes remains unexploited. Methods We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance compared to learning from slices. We evaluate and compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Results Using two publicly available segmentation data sets for evaluation, 3D VAEs outperform their 2D counterpart, highlighting the advantage of volumetric context. Also, our 3D erasing methods allow for further performance improvements. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE. Conclusions We propose 3D deep learning methods for UAD in brain MRI combined with 3D erasing and demonstrate that 3D methods clearly outperform their 2D counterpart for anomaly segmentation. Also, our spatial erasing method allows for further performance improvements and reduces the requirement for large data sets.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals Embed2Detect: temporally clustered embedded words for event detection in social media

2021 ◽  
Author(s):  
Hansi Hettiarachchi ◽  
Mariam Adedoyin-Olowe ◽  
Jagdev Bhogal ◽  
Mohamed Medhat Gaber
Keyword(s):  
Social Media ◽  
Event Detection ◽  
High Volume ◽  
Detection Methods ◽  
Word Embeddings ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

scholarly journals The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

2015 ◽  
Vol 8 (1) ◽  
pp. 421-434 ◽  
Author(s):  
M. P. Jensen ◽  
T. Toto ◽  
D. Troyan ◽  
P. E. Ciesielski ◽  
D. Holdridge ◽  
...  
Keyword(s):  
Large Scale ◽  
Scale Model ◽  
Data Sets ◽  
Central Plains ◽  
Data Set ◽  
Convective Systems ◽  
Convective Clouds ◽  
Quality Checks ◽  
Network Operations ◽  
The Impact

Abstract. The Midlatitude Continental Convective Clouds Experiment (MC3E) took place during the spring of 2011 centered in north-central Oklahoma, USA. The main goal of this field campaign was to capture the dynamical and microphysical characteristics of precipitating convective systems in the US Central Plains. A major component of the campaign was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state with the intent of deriving model forcing data sets. Over the course of the 46-day MC3E campaign, a total of 1362 radiosondes were launched from the enhanced sonde network. This manuscript provides details on the instrumentation used as part of the sounding array, the data processing activities including quality checks and humidity bias corrections and an analysis of the impacts of bias correction and algorithm assumptions on the determination of convective levels and indices. It is found that corrections for known radiosonde humidity biases and assumptions regarding the characteristics of the surface convective parcel result in significant differences in the derived values of convective levels and indices in many soundings. In addition, the impact of including the humidity corrections and quality controls on the thermodynamic profiles that are used in the derivation of a large-scale model forcing data set are investigated. The results show a significant impact on the derived large-scale vertical velocity field illustrating the importance of addressing these humidity biases.

Improving SAR Altimeter processing over the coastal zone and inland waters - the ESA HYDROCOASTAL project

2021 ◽  
Author(s):  
David Cotton ◽  
Keyword(s):  
Coastal Zone ◽  
Test Data ◽  
River Discharge ◽  
Altimeter Data ◽  
Inland Waters ◽  
Data Sets ◽  
Data Set ◽  
Discharge Data ◽  
Processing Algorithms ◽  
The Impact

<p><strong>Introduction</strong></p><p>HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products.</p><p>New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets.</p><p>A series of case studies will assess these products in terms of their scientific impacts.</p><p>All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided</p><p> </p><p><strong>Objectives</strong></p><p>The scientific objectives of HYDROCOASTAL are to enhance our understanding  of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes</p><p>The technical objectives are to develop and evaluate  new SAR  and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated.</p><p><strong>Project  Outline</strong></p><p>There are four tasks to the project</p><ul><li>Scientific Review and Requirements Consolidation: Review the current state of the art in SAR and SARin altimeter data processing as applied to the coastal zone and to inland waters</li> <li>Implementation and Validation: New processing algorithms with be implemented to generate a Test Data sets, which will be validated against models, in-situ data, and other satellite data sets. Selected algorithms will then be used to generate global coastal zone and river discharge data sets</li> <li>Impacts Assessment: The impact of these global products will be assess in a series of Case Studies</li> <li>Outreach and Roadmap: Outreach material will be prepared and distributed to engage with the wider scientific community and provide recommendations for development of future missions and future research.</li> </ul><p> </p><p><strong>Presentation</strong></p><p>The presentation will provide an overview to the project, present the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and early results from the evaluation of the initial test data set.</p><p> </p>

Automatic Deep Learning-Based Consolidation/Collapse Classification in Lung Ultrasound Images for COVID-19 Induced Pneumonia

2022 ◽  
Author(s):  
Nabeel Durrani ◽  
Damjan Vukovic ◽  
Maria Antico ◽  
Jeroen van der Burgt ◽  
Ruud JG van van Sloun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Class Imbalance ◽  
Ultrasound Images ◽  
Pleural Effusions ◽  
Data Set ◽  
Label Noise ◽  
Set Size ◽  
Video Frames ◽  
Two Factors ◽  
The Impact

<div>Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the diagnosis of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos on a frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method of our previous work on pleural effusions. More surprisingly, this method outperformed the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score that are suitable for the class imbalance scenario of our dataset despite being a form of inaccurate learning. This may be due to the combination of a significantly smaller data set size compared to our previous work and the higher complexity of consolidation/collapse compared to pleural effusion, two factors which contribute to label noise and overfitting; specifically, we argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. Using clinical expert feedback, separate criteria were developed to exclude data from the training and test sets respectively for our ten-fold cross validation results, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method must be verified on a larger consolidation/collapse dataset, when considering the complexity of the pathology, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained experts and improves over the video-based method of our previous work on pleural effusions.</div>

scholarly journals Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Jose M. Castillo T. ◽  
Muhammad Arif ◽  
Martijn P. A. Starmans ◽  
Wiro J. Niessen ◽  
Chris H. Bangma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Model Development ◽  
Learning Model ◽  
Multiparametric Mri ◽  
Data Sets ◽  
Data Set ◽  
Test Sets ◽  
Deep Learning Model

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

scholarly journals Six years of total ozone column measurements from SCIAMACHY nadir observations

2009 ◽  
Vol 2 (1) ◽  
pp. 87-98 ◽  
Author(s):  
C. Lerot ◽  
M. Van Roozendael ◽  
J. van Geffen ◽  
J. van Gent ◽  
C. Fayt ◽  
...  
Keyword(s):  
Cross Sections ◽  
Total Ozone ◽  
Large Scale ◽  
European Space Agency ◽  
Data Sets ◽  
Data Set ◽  
Ozone Data ◽  
Space Agency ◽  
German Aerospace ◽  
The Impact

Abstract. Total O3 columns have been retrieved from six years of SCIAMACHY nadir UV radiance measurements using SDOAS, an adaptation of the GDOAS algorithm previously developed at BIRA-IASB for the GOME instrument. GDOAS and SDOAS have been implemented by the German Aerospace Center (DLR) in the version 4 of the GOME Data Processor (GDP) and in version 3 of the SCIAMACHY Ground Processor (SGP), respectively. The processors are being run at the DLR processing centre on behalf of the European Space Agency (ESA). We first focus on the description of the SDOAS algorithm with particular attention to the impact of uncertainties on the reference O3 absorption cross-sections. Second, the resulting SCIAMACHY total ozone data set is globally evaluated through large-scale comparisons with results from GOME and OMI as well as with ground-based correlative measurements. The various total ozone data sets are found to agree within 2% on average. However, a negative trend of 0.2–0.4%/year has been identified in the SCIAMACHY O3 columns; this probably originates from instrumental degradation effects that have not yet been fully characterized.

scholarly journals What Your Tweets Tell Us About You: Identity, Ownership and Privacy of Twitter Data

2012 ◽  
Vol 7 (1) ◽  
pp. 174-197 ◽  
Author(s):  
Heather Small ◽  
Kristine Kasianovitz ◽  
Ronald Blanford ◽  
Ina Celaya
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Data Sets ◽  
Data Set ◽  
Social Media Data ◽  
Twitter Data ◽  
Other Information ◽  
Rich Data ◽  
Additional Value ◽  
Media Data

Social networking sites and other social media have enabled new forms of collaborative communication and participation for users, and created additional value as rich data sets for research. Research based on accessing, mining, and analyzing social media data has risen steadily over the last several years and is increasingly multidisciplinary; researchers from the social sciences, humanities, computer science and other domains have used social media data as the basis of their studies. The broad use of this form of data has implications for how curators address preservation, access and reuse for an audience with divergent disciplinary norms related to privacy, ownership, authenticity and reliability.In this paper, we explore how the characteristics of the Twitter platform, coupled with an ambiguous and evolving understanding of privacy in networked communication, and divergent disciplinary understandings of the resulting data, combine to create complex issues for curators trying to ensure broad-based and ethical reuse of Twitter data. We provide a case study of a specific data set to illustrate how data curators can engage with the topics and questions raised in the paper. While some initial suggestions are offered to librarians and other information professionals who are beginning to receive social media data from researchers, our larger goal is to stimulate discussion and prompt additional research on the curation and preservation of social media data.

Sign in / Sign up

Export Citation Format

Share Document