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A Spark ML driven preprocessing approach for deep learning based scholarly data applications [article]

Samiya Khan, Xiufeng Liu, Mansaf Alam
2019 arXiv   pre-print
With the advent of advanced machine and deep learning techniques, the accuracy and novelty of such applications have risen manifold.  ...  However, the biggest challenge in the development of deep learning models for scholarly applications in cloud based environment is the underutilization of resources because of the excessive time taken  ...  The constituents of these structures are essentially unstructured with text, images and tables present. Different structures make use of data from different or all sections.  ... 
arXiv:1911.07763v1 fatcat:6rj64p3gavfdfk3o6w56r2wgi4

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

Halid Kaplan, Kambiz Tehrani, Mo Jamshidi
2021 Energies  
An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software.  ...  This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning.  ...  Deep learning methods can successfully simulate highly nonlinear time series such as structured or unstructured output prediction.  ... 
doi:10.3390/en14206599 fatcat:mt7smmzpybck7dttapjpm5lbmq

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

Emmanuel Mutabazi, Jianjun Ni, Guangyi Tang, Weidong Cao
2021 Applied Sciences  
A considerable amount of research work has focused on open domain QASs based on deep learning techniques due to the availability of data sources.  ...  Therefore, in this study, the medical textual question-answering systems based on deep learning approaches were reviewed, and recent architectures of MQA systems were thoroughly explored.  ...  Deep Learning Based MQA Approaches Deep-learning-based models have been widely applied in various research domains, such as natural language processing [17] , computer vision [58] , etc.  ... 
doi:10.3390/app11125456 fatcat:mmnhrkhr4fa7zc6zcfw3anjrkq

A Frame Work for Hospital Readmission Based on Deep Learning Approach and Naive Bayes Classification Model

Thalakola Rao, Bhanu Battula
2019 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
But EHR data is a heterogeneous in nature, in order to process heterogeneous data we use deep learning based feature extraction method, and for predation we use navie basian classifier to make prediction  ...  Here in this paper we propose a frame work based on VAR and Skipgram method to take features using those feature we use basian classification to predict the readmission into hospital.  ...  But EHR data is a heterogeneous in nature, in order to process heterogeneous data we use deep learning based feature extraction method, and for predation we use navie-basian classifier to make prediction  ... 
doi:10.18280/ria.330112 fatcat:5han7ko3i5dmpnihpkqbzdjh5m

BIG DATA ANALYTICS: A PRIMER

Matthew Sadiku, Justin Foreman, Sarhan Musa
2020 International Journal of Engineering Technologies and Management Research  
The use of digital devices and systems such smart phones, computers, the Internet, and social media has resulted in a massive volume of data which is exponentially increasing daily.  ...  Analyzing big data enables organizations and businesses to make better and faster decisions. This paper briefly presents the fundamental concepts of big data analytics and its tools.  ...  Deep learning algorithms are used to train deep networks with large amounts of data.  ... 
doi:10.29121/ijetmr.v5.i9.2018.287 fatcat:gi2swpl2rbbd3dt337mxzqpmhy

Heterogeneous Data and Big Data Analytics

Lidong Wang
2017 Automatic Control and Information Sciences  
Deep learning and its potential in Big Data analytics are analysed.  ...  Challenges of dealing with heterogeneous data and Big Data analytics are also discussed.  ...  Deep learning and HPC working with Big Data improve computation intelligence and success; deep learning and heterogeneous computing (HC) working with Big Data increase success [39] .  ... 
doi:10.12691/acis-3-1-3 fatcat:t3yzrk4r2bfornki34khobe4su

NLP-Based Prediction of Medical Specialties at Hospital Admission Using Triage Notes [article]

Mahmoud Elbattah
2021 figshare.com  
Our approach aims to integrate structured data with unstructured textual notes recorded at the triage stage. On one hand, a standard MLP model is used against the typical set of features.  ...  On the other hand, a Convolutional Neural Network is used to operate over the textual data. While both learning components are conducted independently in parallel.  ...  Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text. In Proceedings of the IEEE International Conference on Big Data. IEEE.  ... 
doi:10.6084/m9.figshare.16920829.v1 fatcat:szxlq47yuzb2xnftqe5lqx4cv4

Big Data in Smart Energy Systems: A Critical Review

Keziban Seçkin Codal, İzzet Arı, H. Kemal İlter
2020 AJIT-e Online Academic Journal of Information Technology  
This study reviews the literature for aligning big data and smart energy systems and criticized according to regional perspective, period, disciplines, big data characteristics, and used data analytics  ...  approaches.  ...  Khan, Ali, and Mahmud (2014) suggest a model for prediction of the power generation of a wind-based power plant from a single hour up to a year (Khan, Ali, and Mahmud, 2014) .  ... 
doi:10.5824/ajite.2020.02.001.x fatcat:3veekoxil5brffxcrk3h6jfzfy

Construing the big data based on taxonomy, analytics and approaches

Ajeet Ram Pathak, Manjusha Pandey, Siddharth Rautaray
2018 Iran Journal of Computer Science  
Every organization is inundated with colossal amount of data generated with high speed, requiring high-performance resources for storage and processing, special skills and technologies to get value out  ...  Big data have become an important asset due to its immense power hidden in analytics.  ...  techniques based on machine learning, deep learning, statistics, predictive analytics, and natural language processing.  ... 
doi:10.1007/s42044-018-0024-3 fatcat:teiovluolngepjyebzz2wnwjxu

Primer on machine learning

Parisa Rashidi, David A. Edwards, Patrick J. Tighe
2019 Current Opinion in Anaesthesiology  
This review provides a summary of key machine learning principles, as well as applications to both structured and unstructured datasets.  ...  Aside from increasing use in the analysis of electronic health record data, machine and deep learning algorithms are now key tools in the analyses of neuroimaging and facial expression recognition data  ...  .), and NSF CAREER 1750192 (P.R.). All authors have contributed and reviewed this work and report no commercial conflicts of interest to this submission.  ... 
doi:10.1097/aco.0000000000000779 pmid:31408024 pmcid:PMC6785021 fatcat:7j7vvvfoezgzfotrvyks5irxne

A Review on Intelligent Process Automation

Yuvaraja Devarajan
2019 International Journal of Computer Applications  
Predictive Analytics Predictive analytics is a subset of Machine learning that utilizes statistical algorithms to determine patterns and predict future outcomes based on the existing historical data sets  ...  , Speech recognition, Computer Vision, Image analytics, Predictive Analytics, Semantic Intelligence etc. allowing automation around Structured, Semi-Structured and Unstructured data.  ... 
doi:10.5120/ijca2019918374 fatcat:kq7q6vzw5naadiw3wrloh23gxa

Hybrid Text Feature Modeling for Disease Group Prediction using Unstructured Physician Notes [article]

Gokul S Krishnan, Sowmya Kamath S
2019 arXiv   pre-print
These word embeddings are used to train a deep neural network for effectively predicting ICD9 disease groups.  ...  Experimental evaluation showed that the proposed approach outperformed the state-of-the-art disease group prediction model built on structured EHRs by 15% in terms of AUROC and 40% in terms of AUPRC, thus  ...  In this paper, a deep neural network based model for predicting ICD9 disease groups from physician notes in the form of unstructured text is discussed.  ... 
arXiv:1911.11657v1 fatcat:5qnq2evywrcuhm5mhrhlotwhei

Deep neural network: Recognize Data Management of Artificial Intelligence in Retail

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Deep neural networks with the artificial intelligence on Machine Learning (ML) algorithms constitute the best design specifically to deal with vast amount of data for retail business.  ...  Finally, system precede and follow a NoSQL layers of a model employs in-memory database compression techniques and executes data management challenges with large datasets successfully.  ...  Herewith, ML is used as a new way to program to deal with structures as well as unstructured data.  ... 
doi:10.35940/ijitee.j9779.0881019 fatcat:cp4x2k5gjvgsjc3sdsh2thkpp4

Hybrid Text Feature Modeling for Disease Group Prediction Using Unstructured Physician Notes [chapter]

Gokul S. Krishnan, S. Sowmya Kamath
2020 Lecture Notes in Computer Science  
These word embeddings are used to train a deep neural network for effectively predicting ICD9 disease groups.  ...  Experimental evaluation showed that the proposed approach outperformed the state-of-the-art disease group prediction model built on structured EHRs by 15% in terms of AUROC and 40% in terms of AUPRC, thus  ...  Conclusion and Future Work In this article, a deep neural network based model for predicting ICD9 disease groups from physician notes in the form of unstructured text is discussed.  ... 
doi:10.1007/978-3-030-50423-6_24 fatcat:kxmtti3cmfaenboeu3cb4vxh3e

An Analysis of Data Processing for Big Data Analytics

Steve Blair, Jon Cotter
2021 Journal of Computing and Natural Science  
Data Analytics and coping with a wide range of data are discussed.  ...  The use of deep learning to Data Analytics is investigated. The benefits of integrating BDA, deep learning, HPC (High Performance Computing), and HC are highlighted.  ...  A large quantity of unstructured data is used in deep learning algorithms to derive complicated representations. Deep learning systems allow for global and non-local generalization.  ... 
doi:10.53759/181x/jcns202101019 fatcat:z5wjv3y2prfthk3l34xaz3hx6e
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