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Advances in Physical Activity and Nutrition Environment Assessment Tools and Applications

Karen Glanz, James F. Sallis, Brian E. Saelens
2015 American Journal of Preventive Medicine  
Additional recommendations include (1) improving multidisciplinary collaborations; (2) engaging stakeholders across sectors; (3) centralized data resource centers; (4) increased use of emerging technologies  ...  Participants proposed and voted on recommended future directions in two categories: "big ideas" and additional recommendations.  ...  A proposal for funding a next-generation BEAT Institute training was developed (though it has not yet been funded), which focused on better researcher-practitioner collaboration around environmental measurement  ... 
doi:10.1016/j.amepre.2015.01.023 pmid:25891061 fatcat:jcvx3nywcndrnk56qtx6yu4tmm

Big Data and Situation-Aware Technology for Smarter Healthcare

Mu-Yen Chen, Edwin David Lughofer, Robi Polikar
2018 Journal of Medical and Biological Engineering  
Fewer Research Questions, Diverse Fields The first field covers Big Data Analysis. Liu et al.  ...  Wearable health sensors provide many advantages for location-aware, situation-aware, mobile and ubiquitous data collection and real-time processing of big data.  ... 
doi:10.1007/s40846-018-0452-4 fatcat:xvpv65u5yjh3pkrtfgig3opznu

Analysis and classification of heart diseases using heartbeat features and machine learning algorithms

Fajr Ibrahem Alarsan, Mamoon Younes
2019 Journal of Big Data  
Availability of data and materials The data set is available to public and can be found in https ://physi onet.org/physi obank /, it is simple to extract the data in many formats using physionet ATM Bank  ...  MY took on a supervisory role and oversaw the completion of the work, Proposed data set for implementation and validation. Both authors read and approved the final manuscript.  ...  Therefore, a Big data technique is introduced in this work to meet the challenges faced by classify the ECG beats.  ... 
doi:10.1186/s40537-019-0244-x fatcat:4ta5xxvgvngnvocdxpd6chkewu

Applications of Machine Learning in Ambulatory ECG

Joel Xue, Long Yu
2021 Hearts  
At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections.  ...  The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction.  ...  The rapid development of DL models goes side-by-side with the availability of big data and big computation power.  ... 
doi:10.3390/hearts2040037 fatcat:c2up34ys7bejbofaj3doxokepu

Market efficiency analysis using AI models based on Investors' Mood

Gómez Martínez Raúl, Paola Plaza Casado, Miguel Prado Román
2021 Revista Perspectiva Empresarial  
Este estudio utiliza modelos de inteligencia artificial diseñados para predecir las tendencias del IBEX con base en el estado de ánimo de los inversores, extrayendo información del big data y utilizando  ...  If the market were efficient according to the EMH, a big data model using artificial intelligence would not be able to beat the market systematically.  ...  The purpose of this study is to analyze different artificial intelligence big data IBEX 35 asset management models and to determine whether they are capable of systematically beating the market and under  ... 
doi:10.16967/23898186.649 fatcat:fi2c2y5u4jgjbeq7hefeq7chmu

Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming

Sadegh Ilbeigipour, Amir Albadvi, Elham Akhondzadeh Noughabi
2021 Journal of Healthcare Engineering  
The ECG data collected from the MIT/BIH database for the detection of three class labels: normal beats, RBBB, and atrial fibrillation arrhythmias.  ...  We also developed three decision trees, random forest, and logistic regression multiclass classifiers for data classification where the random forest classifier showed better performance in classification  ...  Also, big data can lead to more accurate decisions and changes in the providing of health care services [1] .  ... 
doi:10.1155/2021/6624829 pmid:33968352 pmcid:PMC8084659 fatcat:ibnrgyaatrezxprqbwt364ga3e

An interpretable classifier for detection of cardiac arrhythmias by using the fuzzy decision tree

Omar Behadada, M. A Chikh
2013 Artificial intelligence research  
It could lead to increase understanding the cause of various abnormal beats in cardiac activity, leading to a better medical diagnosis.  ...  In the last part we discuss the activity of fuzzy decision rules extracted from cardiological data analyzing.  ...  Our data extracted from the MIT-BIH is mainly composed of, beats taken from derivation DII which is a major handicap in the classification.  ... 
doi:10.5430/air.v2n3p45 fatcat:gorpgkaexveafdojos7twh5n64

Sparse and dense coding of natural stimuli by distinct midbrain neuron subpopulations in weakly electric fish

Katrin Vonderschen, Maurice J. Chacron
2011 Journal of Neurophysiology  
On the other hand, sparse coding TS neurons were better detectors of whether their preferred stimulus occurred compared with either dense coding TS or ELL neurons.  ...  We thank Ana Catarina Casari Giassi for the processing of brain tissue and help with the interpretation of the histological data.  ...  ., small, big chirps, moving objects). This selectivity was the basis for these neurons showing better chirp detection than either ELL or dense TS neurons.  ... 
doi:10.1152/jn.00588.2011 pmid:21940609 pmcid:PMC4535167 fatcat:xwm3sytow5av5fuc22opqsb4wa

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

Aurora Gonzalez-Vidal, Payam Barnaghi, Antonio F. Skarmeta
2018 IEEE Transactions on Knowledge and Data Engineering  
BEATS is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources.  ...  The algorithm, called BEATS, is designed to tackle dynamic IoT streams, whose distribution changes over time.  ...  Regarding the available Big Data Tools, we have considered Hadoop 5 and Spark 6 Big Data frameworks. Hadoop was designed for batch processing.  ... 
doi:10.1109/tkde.2018.2817229 fatcat:qt6qe5j5gnhkdg4jn3ie5bxyoy

Using music and motion analysis to construct 3D animations and visualisations

Kuen-Meau Chen, Siu-Tsen Shen, Stephen D. Prior
2008 Digital Creativity  
Motion capture data is extracted to generate a motion library; this places the digital motion model at a fixed posture.  ...  Acknowledgements We are grateful to CMU Graphic Lab and AXIS 3D Co. for providing us with character models and captured motion data.  ...  (C) After the search, there may be a big action with some small actions, or just a big action, or all small actions, but no big action between two beats.  ... 
doi:10.1080/14626260802037403 fatcat:xj72pe7efravlf7mjfsakc4bom

Does attitude towards wife beating determine infant feeding practices during diarrheal illness in sub-Saharan Africa?

Betregiorgis Zegeye, Nicholas Kofi Adjei, Bright Opoku Ahinkorah, Edward Kwabena Ameyaw, Abdul-Aziz Seidu, Comfort Z. Olorunsaiye, Sanni Yaya
2021 Tropical Medicine and Health  
Methods We analyzed data from the Demographic and Health Survey on 40,720 children under 5 years.  ...  Women who disagreed with wife-beating practices had higher odds of proper child feeding practices during childhood diarrhea compared to those who justified wife-beating practices (aOR = 2.02, 95% CI; 1.17  ...  Acknowledgements The authors thank the MEASURE DHS project for their support and for free access to the original data.  ... 
doi:10.1186/s41182-021-00369-1 pmid:34627410 fatcat:jkf44nbrxjab7hi6qw3abhkhm4

AN ANALYTICAL REVIEW STUDY ON BIG DATA ANALYSIS USING R STUDIO

Anita Kumari, Neeraj Verma
2020 International Journal of Engineering Technologies and Management Research  
It was said and proved through study cases that "More data usually beats better algorithms".  ...  A larger amount of data gives a better output but also working with it can become a challenge due to processing limitations.  ...  That is why this articol presents the Big Data concept and the R technologies associated in order to understand better the multiple benefices of this new concept ant technology.  ... 
doi:10.29121/ijetmr.v6.i6.2019.399 fatcat:ahhiovrnzjaolnlehwyhgmtmey

BPM calibration independent LHC optics correction

R. Calaga, R. Tomas, F. Zimmermann
2007 2007 IEEE Particle Accelerator Conference (PAC)  
The tight mechanical aperture for the LHC imposes severe constraints on both the beta and dispersion beating.  ...  The tight mechanical aperture for the LHC imposes severe constraints on both the beta and dispersion beating.  ...  Data cleaning: Different filters are run to spot and remove the faulty BPMs. Data analysis: A refined Fourier Transform is ran to obtain the phase at all the BPMs.  ... 
doi:10.1109/pac.2007.4440536 fatcat:unugxleuozaxrk6fvbkmdg52ym

Testing the quality of 12 lead holter analysis algorithms

R. Fischer, M.F. Sinner, R. Petrovic, E. Tarita, S. Kaab, T.K. Zywietz
2008 2008 Computers in Cardiology  
While there are several data bases to evaluate the performance of 2-lead Holter algorithms, e.g., MIT-BIH, AHA etc., to our knowledge, there is no annotated data base for real 12lead Holter algorithms.  ...  We have therefore created a new 12 lead ECG data base containing 50 Holter ECGs from  ...  The reports of the developed software tools are in text format for better readability and simple data import/export. 3.  ... 
doi:10.1109/cic.2008.4749076 fatcat:lij55fytknab3d33vyvxourybi

The Impact of Body Language Use of a Conductor on Musical Quality

Bilgen Özcan Çoşkunsoy, Bahar Güdek
2019 Journal of Education and Training Studies  
Audial and imagery data records obtained from each conduction method have been converted to Mpeg-4 format and evaluated by 9 expert conductors by using musical sub-dimensions such as Note, Tone Quality  ...  The study carries an important role as it indicates what affective impacts there can be depending on how the conductor leads "Pulse", "Measure Beat", "Left Hand" and "Body Language" and whether this conductive  ...  be better.  ... 
doi:10.11114/jets.v7i9.4271 fatcat:yaaubzpnajgv3fwlqfvhrqi4ne
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