Novel Scalable Deep Learning Approaches for Big Data Analytics Applied to ECG Processing

2020 ◽  
pp. 633-653
Author(s):  
Rostom Mennour ◽  
Mohamed Batouche
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Learning Approaches ◽  
Ecg Signals ◽  
Research Areas ◽  
Gradient Descent Algorithm ◽  
Big Data Technologies ◽  
Active Research

Big data analytics and deep learning are nowadays two of the most active research areas in computer science. As the data is becoming bigger and bigger, deep learning has a very important role to play in data analytics, and big data technologies will give it huge opportunities for different sectors. Deep learning brings new challenges especially when it comes to large amounts of data, the volume of datasets has to be processed and managed, also data in various applications come in a streaming way and deep learning approaches have to deal with this kind of applications. In this paper, the authors propose two novel approaches for discriminative deep learning, namely LS-DSN, and StreamDSN that are inspired from the deep stacking network algorithm. Two versions of the gradient descent algorithm were used to train the proposed algorithms. The experiment results have shown that the algorithms gave satisfying accuracy results and scale well when the size of data increases. In addition, StreamDSN algorithm have been applied to classify beats of ECG signals and provided good promising results.

Novel Scalable Deep Learning Approaches for Big Data Analytics Applied to ECG Processing

2018 ◽  
Vol 9 (4) ◽  
pp. 33-51
Author(s):  
Rostom Mennour ◽  
Mohamed Batouche
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Learning Approaches ◽  
Ecg Signals ◽  
Research Areas ◽  
Gradient Descent Algorithm ◽  
Big Data Technologies ◽  
Active Research

Big data analytics and deep learning are nowadays two of the most active research areas in computer science. As the data is becoming bigger and bigger, deep learning has a very important role to play in data analytics, and big data technologies will give it huge opportunities for different sectors. Deep learning brings new challenges especially when it comes to large amounts of data, the volume of datasets has to be processed and managed, also data in various applications come in a streaming way and deep learning approaches have to deal with this kind of applications. In this paper, the authors propose two novel approaches for discriminative deep learning, namely LS-DSN, and StreamDSN that are inspired from the deep stacking network algorithm. Two versions of the gradient descent algorithm were used to train the proposed algorithms. The experiment results have shown that the algorithms gave satisfying accuracy results and scale well when the size of data increases. In addition, StreamDSN algorithm have been applied to classify beats of ECG signals and provided good promising results.

Investigation on Deep Learning Approach for Big Data

2018 ◽  
pp. 25-38
Author(s):  
Dharmendra Singh Rajput ◽  
T. Sunil Kumar Reddy ◽  
Dasari Naga Raju
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Probabilistic Models ◽  
Big Data Analytics ◽  
Data Sets ◽  
Semantic Indexing ◽  
Learning Approaches ◽  
Data Sampling ◽  
Learning Mechanisms

In recent years, big data analytics is the major research area where the researchers are focused. Complex structures are trained at each level to simplify the data abstractions. Deep learning algorithms are one of the promising researches for automation of complex data extraction from large data sets. Deep learning mechanisms produce better results in machine learning, such as computer vision, improved classification modelling, probabilistic models of data samples, and invariant data sets. The challenges handled by the big data are fast information retrieval, semantic indexing, extracting complex patterns, and data tagging. Some investigations are concentrated on integration of deep learning approaches with big data analytics which pose some severe challenges like scalability, high dimensionality, data streaming, and distributed computing. Finally, the chapter concludes by posing some questions to develop the future work in semantic indexing, active learning, semi-supervised learning, domain adaptation modelling, data sampling, and data abstractions.

Optimal deep learning approaches and healthcare big data analytics for mobile networks toward 5G

2021 ◽  
Vol 95 ◽  
pp. 107376
Author(s):  
Denis A. Pustokhin ◽  
Irina V. Pustokhina ◽  
Poonam Rani ◽  
Vineet Kansal ◽  
Mohamed Elhoseny ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Mobile Networks ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Learning Approaches

Investigation on Deep Learning Approach for Big Data

2020 ◽  
pp. 1016-1029
Author(s):  
Dharmendra Singh Rajput ◽  
T. Sunil Kumar Reddy ◽  
Dasari Naga Raju
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Probabilistic Models ◽  
Big Data Analytics ◽  
Data Sets ◽  
Semantic Indexing ◽  
Learning Approaches ◽  
Data Sampling ◽  
Learning Mechanisms

In recent years, big data analytics is the major research area where the researchers are focused. Complex structures are trained at each level to simplify the data abstractions. Deep learning algorithms are one of the promising researches for automation of complex data extraction from large data sets. Deep learning mechanisms produce better results in machine learning, such as computer vision, improved classification modelling, probabilistic models of data samples, and invariant data sets. The challenges handled by the big data are fast information retrieval, semantic indexing, extracting complex patterns, and data tagging. Some investigations are concentrated on integration of deep learning approaches with big data analytics which pose some severe challenges like scalability, high dimensionality, data streaming, and distributed computing. Finally, the chapter concludes by posing some questions to develop the future work in semantic indexing, active learning, semi-supervised learning, domain adaptation modelling, data sampling, and data abstractions.

Advanced Threat Detection Based on Big Data Technologies

2018 ◽  
pp. 1-19
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

Advanced Threat Detection Based on Big Data Technologies

2020 ◽  
pp. 808-822
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

Advanced Deep Learning Applications in Big Data Analytics

2021 ◽  
pp. 1-28
Author(s):  
Venkatesan Manian ◽  
Vadivel P.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Internet ◽  
Infrastructure Development ◽  
Learning Approaches ◽  
The Internet Of Things

This chapter analyzes the Internet of Things (IoT), its history, and its tools in brief. This chapter also explores the contribution of IoT towards the recent development in infrastructure development of nations represented as smart world. This chapter also discuss the contribution of IoT towards big data analytics era. This chapter also briefly introduce the smart bio world and how it is made possible with the internet of things. This chapter also introduces the machine learning approaches and also discusses the contribution of Internet of Thing for this machine learning. This chapter also briefly introduces some tools used for IoT developments.

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