Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes

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.

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

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch035 ◽

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.

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As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

Yearbook of Medical Informatics ◽

10.1055/s-0038-1667078 ◽

2018 ◽

Vol 27 (01) ◽

pp. 140-145 ◽

Cited By ~ 3

Author(s):

Ferdinand Dhombres ◽

Jean Charlet ◽

Keyword(s):

Knowledge Representation ◽

Medical Informatics ◽

Semantic Representation ◽

Semantic Annotation ◽

Knowledge Bases ◽

Medical Data ◽

Added Value ◽

Association Mining ◽

Clinical Practices ◽

New Challenges

Objectives: To select, present, and summarize the best papers published in 2017 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2017, based on a PubMed query. Results: In direct line with the research on data integration presented in the KRM section of the 2017 edition of the International Medical Informatics Association (IMIA) Yearbook, the five best papers for 2018 demonstrate even further the added-value of ontology-based integration approaches for phenotype-genotype association mining. Additionally, among the 15 preselected papers, two aspects of KRM are in the spotlight: the design of knowledge bases and new challenges in using ontologies. Conclusions: Ontologies are demonstrating their maturity to integrate medical data and begin to support clinical practices. New challenges have emerged: the query on distributed semantically annotated datasets, the efficiency of semantic annotation processes, the semantic representation of large textual datasets, the control of biases associated with semantic annotations, and the computation of Bayesian indicators on data annotated with ontologies.

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Deep Learning Approaches for Big Data Analysis

Proceeding of the Electrical Engineering Computer Science and Informatics ◽

10.11591/eecsi.v6.2008 ◽

2019 ◽

Vol 6 (1) ◽

Author(s):

Naomie Salim

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analysis ◽

Big Data Analysis ◽

Learning Approaches

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Investigation on Deep Learning Approach for Big Data

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Handbook of Research on Pattern Engineering System Development for Big Data Analytics ◽

10.4018/978-1-5225-3870-7.ch002 ◽

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.

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

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2021.107376 ◽

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

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Evaluation and Construction of College Students’ Growth and Development Index System Based on Data Association Mining and Deep Learning Model

Security and Communication Networks ◽

10.1155/2021/7415129 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Yanjie Li ◽

He Mao

Keyword(s):

College Students ◽

Big Data ◽

Deep Learning ◽

Growth And Development ◽

Language Model ◽

Association Mining ◽

Student Management ◽

Passive Response ◽

Point Data

The rise of big data in the field of education provides an opportunity to solve college students’ growth and development. The establishment of a personalized student management mode based on big data in universities will promote the change of personalized student management from the empirical mode to the scientific mode, from passive response to active warning, from reliance on point data to holistic data, and thus improve the efficiency and quality of personalized student management. In this paper, using the latest ideas and techniques in deep learning such as self-supervised learning and multitask learning, we propose an open-source educational big data pretrained language model F-BERT based on the BERT model architecture. Based on the BERT architecture, F-BERT can effectively and automatically extract knowledge from educational big data and memorize it in the model without modifying the model structure specific to educational big data tasks so that it can be directly applied to various educational big data domain tasks downstream. The experiment demonstrates that Vanilla F-BERT outperformed the two Vanilla BERT-based models, Vanilla BERT and BERT tasks, by 0.0.6 and 0.03 percent, respectively, in terms of accuracy.

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Investigation on Deep Learning Approach for Big Data

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch056 ◽

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.

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