Machine Learning

2017 ◽  
Vol 6 (3) ◽  
pp. 1-16
Author(s):  
Bertrand Cauvin ◽  
Pierre Benning
Keyword(s):  
Machine Learning ◽  
Information Exchange ◽  
Data Dictionary ◽  
Term List ◽  
Software Packages ◽  
Common Term ◽  
Exhaustive List ◽  
Over Time

A Bridge Data Dictionary contains an exhaustive list of terms used in the field of bridges. These terms are classified in systems in order to avoid any lacks, to identify all the expected object attributes, and to allow machines to understand the associated concepts. The main objectives of a Bridge Data Dictionary are many: ensure the sustainability of information over time; facilitate information exchange between the actors of the same project; ensure interoperability between the software packages. Other objectives have been reached during the process: to test a working methodology to be applied by other infrastructure domains (Roads, Rails, Tunnels, etc.); to check the current functions and capabilities of a buildingSMART Data Dictionary platform; and to define a common term list, in order to facilitate standardization and IFC-Bridge classes' development.

Machine learning based accident prediction in secure IoT enable transportation system

2021 ◽  
pp. 1-13 ◽  
Author(s):  
Bhabendu Kumar Mohanta ◽  
Debasish Jena ◽  
Niva Mohapatra ◽  
Somula Ramasubbareddy ◽  
Bharat S. Rawal
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Information Exchange ◽  
Secure Communication ◽  
Intelligent Transportation System ◽  
Transportation System ◽  
Information And Communications Technology ◽  
Classification Models ◽  
Architecture Model ◽  
Accident Severity

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

scholarly journals An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

2021 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Ninghan Chen ◽  
Zhiqiang Zhong ◽  
Jun Pang
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Exploratory Study ◽  
Representation Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Twitter Users ◽  
Over Time

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

scholarly journals T157. THE COURSE OF SCHIZOPHRENIA-RELATED NEURAL FINGERPRINTS OVER NINE YEARS - A LONGITUDINAL POPULATION-BASED MACHINE LEARNING STUDY

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S290-S291
Author(s):  
Johannes Lieslehto ◽  
Erika Jääskeläinen ◽  
Jouko Miettunen ◽  
Matti Isohanni ◽  
Dominic Dwyer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity And Specificity ◽  
Verbal Learning ◽  
Clinical Variable ◽  
Support Vector ◽  
Cohen’S D ◽  
Low Degree ◽  
Cohen's D ◽  
Over Time

Abstract Background Previous machine learning studies using structural MRI (sMRI) have been able to separate schizophrenia from controls with relatively high (about 80%) sensitivity and specificity (Kambeitz et al. Neuropsychopharmacology 2015). Interestingly, prediction accuracy in first-episode psychosis is lower compared to older and probably more chronic patients. One possibility is that the appearance of the neurodiagnostic fingerprints (NF) originated from the schizophrenia vs. controls classifier become more visible over time in schizophrenia due to the progressive nature of the disorder. Methods Using the Cobre sample (70 schizophrenia and 74 controls), we trained support vector machine (SVM) to differentiate schizophrenia from controls using sMRI. Next, we utilized the Northern Finland Birth Cohort 1966 (NFBC 1966) sample of 29 schizophrenia and 61 non-psychotic controls who participated in the nine-year follow-up. We applied the Cobre-trained SVM models at the baseline (participants 34 years old) and the follow-up (participants 43 years old) using out of sample cross-validation without any in-between retraining. Two independent schizophrenia datasets (the Neuromorphometry by Computer Algorithm Chicago [NMorphCH] and the Consortium for Neuropsychiatric Phenomics [CNP]) were utilized for replication analyses of the SVM generalizability. To address the possibility that the NF mainly capture some general psychopathology, we tested whether the NF generalize to depression using two independent MDD samples from Munich and Münster, Germany. Results Using the Cobre-trained SVM models for schizophrenia vs. controls differentiation in the NFBC 1966, we found balanced accuracy (i.e. mean of sensitivity and specificity, [BAC]) of 72.8% (sensitivity=58.6%, specificity=86.9%) at the baseline and BAC of 79.7% (sensitivity=75.9%, specificity=83.6%) at the follow-up. In the NFBC 1966 schizophrenia patients, we found that SVM decision scores varied as a function of timepoint into the direction of more schizophrenia-likeness at the follow-up (paired T-test, Cohen’s d=0.58, P=0.004). The same was not true in controls (Cohen’s d=0.09, P=0.49). The SVM decision score difference*timepoint interaction related to the decrease of hippocampus and medial prefrontal cortex. The SVM models’ performance was also validated at the two replication samples (BAC of 77.5% in the CNP and BAC of 69.1% in the NMorphCH). In the NFBC 1966 the strongest clinical variable correlating with the trajectory of SVM decision scores over the follow-up was poor performance in the California Verbal Learning Test. This finding was also replicated in the CNP dataset. Further, in the NFBC 1966, those schizophrenia patients with a low degree of SVM decision scores had a higher probability of being in remission, being able to work, and being without antipsychotic medication at the follow-up. The generalization of the SVM models to MDD was worse compared to schizophrenia classification (DeLong’s tests for the two ROC curves: P<0.001). Discussion The degree of schizophrenia-related neurodiagnostic fingerprints appear to magnify over time in schizophrenia. By contrast, the discernibility of these fingerprints in controls does not change over time. This indicates that the NF captures some schizophrenia-related progressive neural changes, and not, e.g., normal aging-related brain volume loss. The fingerprints were also generalizable to other schizophrenia samples. Further, the fingerprints seem to have some disorder specificity as the SVM models do not generalize to depression. Lastly, it appears that a low degree of schizophrenia-related NF in schizophrenia might possess some value in predicting patients’ future remission and recovery-related factors.

scholarly journals OP0287 A MACHINE LEARNING APPROACH FOR PRECISION STRATIFICATION OF JUVENILE-ONSET SLE

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 179.2-179
Author(s):  
G. Robinson ◽  
J. Peng ◽  
P. Dönnes ◽  
L. Coelewij ◽  
M. Naja ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
T Cell ◽  
B Cell ◽  
T Cell Subsets ◽  
Healthy Controls ◽  
Juvenile Onset ◽  
Immune Signature ◽  
Immune Phenotyping ◽  
Over Time

Background:Juvenile-onset systemic lupus erythematosus (JSLE) is a complex and heterogeneous disease characterised by diagnosis and treatment delays. An unmet need exists to better characterise the immunological profile of JSLE patients and investigate its links with the disease trajectory over time.Objectives:A machine learning (ML) approach was applied to explore new diagnostic signatures for JSLE based on immune-phenotyping data and stratify patients by specific immune characteristics to investigate longitudinal clinical outcome.Methods:Immune-phenotyping of 28 T-cell, B-cell and myeloid-cell subsets in 67 age and sex-matched JSLE patients and 39 healthy controls (HCs) was performed by flow cytometry. A balanced random forest (BRF) ML predictive model was developed (10,000 decision trees). 10-fold cross validation, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) and logistic regression was used to validate the model. Longitudinal clinical data were related to the immunological features identified by ML analysis.Results:The BRF-model discriminated JSLE patients from healthy controls with 91% prediction accuracy suggesting that JSLE patients could be distinguished from HCs with high confidence using immunological parameters. The top-ranked immunological features from the BRF-model were confirmed using sPLS-DA and logistic regression and included CD19+ unswitched memory B-cells, naïve B-cells, CD14+monocytes and total CD4+, CD8+and memory T-cell subsets.K-mean clustering was applied to stratify patients using the validated signature. Four groups were identified, each with a distinct immune and clinical profile. Notably, CD8+T-cell subsets were important in driving patient stratification while B-cell markers were similarly expressed across the JSLE cohort. JSLE patients with elevated effector memory CD8+T-cell frequencies had more persistently active disease over time, and this was associated with increased treatment burden and prevalence of lupus nephritis. Finally, network analysis identified specific clinical features associated with each of the top JSLE immune-signature variables.Conclusion:Using a combined ML approach, a distinct immune signature was identified that discriminated between JSLE patients and HCs and further stratified patients. This signature could have diagnostic and therapeutic implications. Further immunological association studies are warranted to develop data-driven personalised medicine approaches for JSLE.Acknowledgments:Lupus UK, Rosetrees Trust, Versus ArthritisDisclosure of Interests:George Robinson: None declared, Junjie Peng: None declared, Pierre Dönnes: None declared, Leda Coelewij: None declared, Meena Naja: None declared, Anna Radziszewska: None declared, Chris Wincup: None declared, Hannah Peckham: None declared, David Isenberg Consultant of: Study Investigator and Consultant to Genentech, Yiannis Ioannou: None declared, Ines Pineda Torra: None declared, Coziana Ciurtin Grant/research support from: Pfizer, Consultant of: Roche, Modern Biosciences, Elizabeth Jury: None declared

scholarly journals Dynamic Detection of Delayed Cerebral Ischemia Using Machine Learning

2020 ◽  
Author(s):  
Murad Megjhani ◽  
Kalijah Terilli ◽  
Ayham Alkhachroum ◽  
David J. Roh ◽  
Sachin Agarwal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Ischemia ◽  
Characteristic Curve ◽  
Delayed Cerebral Ischemia ◽  
Risk Scores ◽  
Support Vector ◽  
Model Parameters ◽  
Learning Approaches ◽  
Physiologic Data ◽  
Over Time

AbstractObjectiveTo develop a machine learning based tool, using routine vital signs, to assess delayed cerebral ischemia (DCI) risk over time.MethodsIn this retrospective analysis, physiologic data for 540 consecutive acute subarachnoid hemorrhage patients were collected and annotated as part of a prospective observational cohort study between May 2006 and December 2014. Patients were excluded if (i) no physiologic data was available, (ii) they expired prior to the DCI onset window (< post bleed day 3) or (iii) early angiographic vasospasm was detected on admitting angiogram. DCI was prospectively labeled by consensus of treating physicians. Occurrence of DCI was classified using various machine learning approaches including logistic regression, random forest, support vector machine (linear and kernel), and an ensemble classifier, trained on vitals and subject characteristic features. Hourly risk scores were generated as the posterior probability at time t. We performed five-fold nested cross validation to tune the model parameters and to report the accuracy. All classifiers were evaluated for good discrimination using the area under the receiver operating characteristic curve (AU-ROC) and confusion matrices.ResultsOf 310 patients included in our final analysis, 101 (32.6%) patients developed DCI. We achieved maximal classification of 0.81 [0.75-0.82] AU-ROC. We also predicted 74.7 % of all DCI events 12 hours before typical clinical detection with a ratio of 3 true alerts for every 2 false alerts.ConclusionA data-driven machine learning based detection tool offered hourly assessments of DCI risk and incorporated new physiologic information over time.

scholarly journals The Open Data Challenge: An Analysis of 124,000 Data Availability Statements and an Ironic Lesson about Data Management Plans

2020 ◽  
Vol 2 (4) ◽  
pp. 554-568
Author(s):  
Chris Graf ◽  
Dave Flanagan ◽  
Lisa Wylie ◽  
Deirdre Silver
Keyword(s):  
Machine Learning ◽  
Data Management ◽  
Open Data ◽  
Research Data ◽  
Data Availability ◽  
Policy Changes ◽  
Management Plans ◽  
Trends Over Time ◽  
New Research ◽  
Over Time

Data availability statements can provide useful information about how researchers actually share research data. We used unsupervised machine learning to analyze 124,000 data availability statements submitted by research authors to 176 Wiley journals between 2013 and 2019. We categorized the data availability statements, and looked at trends over time. We found expected increases in the number of data availability statements submitted over time, and marked increases that correlate with policy changes made by journals. Our open data challenge becomes to use what we have learned to present researchers with relevant and easy options that help them to share and make an impact with new research data.

scholarly journals Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Johannes Allgaier ◽  
Winfried Schlee ◽  
Berthold Langguth ◽  
Thomas Probst ◽  
Rüdiger Pryss
Keyword(s):  
Machine Learning ◽  
Gender Differences ◽  
Daily Life ◽  
Sound Stimulation ◽  
Life Data ◽  
Symptom Profiles ◽  
Symptom Profile ◽  
The Family ◽  
Gender Impacts ◽  
Over Time

AbstractTinnitus is an auditory phantom perception in the absence of an external sound stimulation. People with tinnitus often report severe constraints in their daily life. Interestingly, indications exist on gender differences between women and men both in the symptom profile as well as in the response to specific tinnitus treatments. In this paper, data of the TrackYourTinnitus platform (TYT) were analyzed to investigate whether the gender of users can be predicted. In general, the TYT mobile Health crowdsensing platform was developed to demystify the daily and momentary variations of tinnitus symptoms over time. The goal of the presented investigation is a better understanding of gender-related differences in the symptom profiles of users from TYT. Based on two questionnaires of TYT, four machine learning based classifiers were trained and analyzed. With respect to the provided daily answers, the gender of TYT users can be predicted with an accuracy of 81.7%. In this context, worries, difficulties in concentration, and irritability towards the family are the three most important characteristics for predicting the gender. Note that in contrast to existing studies on TYT, daily answers to the worst symptom question were firstly investigated in more detail. It was found that results of this question significantly contribute to the prediction of the gender of TYT users. Overall, our findings indicate gender-related differences in tinnitus and tinnitus-related symptoms. Based on evidence that gender impacts the development of tinnitus, the gathered insights can be considered relevant and justify further investigations in this direction.

scholarly journals Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics

2020 ◽  
Author(s):  
Malik Magdon-Ismail
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Early Infection ◽  
Data Driven ◽  
Infection Dynamics ◽  
Serious Infection ◽  
Specific Virus ◽  
Different Populations ◽  
Learning Analysis ◽  
Over Time

AbstractWe present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal is to extract actionable public health insights. These insights include the infectious force, the rate of a mild infection becoming serious, estimates for asymtomatic infections and predictions of new infections over time. We focus on USA data starting from the first confirmed infection on January 20 2020. Our methods reveal significant asymptomatic (hidden) infection, a lag of about 10 days, and we quantitatively confirm that the infectious force is strong with about a 0.14% transition from mild to serious infection. Our methods are efficient, robust and general, being agnostic to the specific virus and applicable to different populations or cohorts.

scholarly journals Detection of FAKE NEWS on SOCIAL MEDIA using CLASSIFICATION Data Mining Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3132-3138
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Information Exchange ◽  
Learning Algorithm ◽  
Daily Life ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Fake News ◽  
Other Information

In today’s world social media is one of the most important tool for communication that helps people to interact with each other and share their thoughts, knowledge or any other information. Some of the most popular social media websites are Facebook, Twitter, Whatsapp and Wechat etc. Since, it has a large impact on people’s daily life it can be used a source for any fake or misinformation. So it is important that any information presented on social media should be evaluated for its genuineness and originality in terms of the probability of correctness and reliability to trust the information exchange. In this work we have identified the features that can be helpful in predicting whether a given Tweet is Rumor or Information. Two machine learning algorithm are executed using WEKA tool for the classification that is Decision Tree and Support Vector Machine.

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