A creative approach to understanding the hidden information within the business data using Deep Learning

Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

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Application of Deep Learning in Biological Big Data Analysis

Large-Scale Data Streaming, Processing, and Blockchain Security - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-3444-1.ch006 ◽

2021 ◽

pp. 117-148

Author(s):

Rohit Shukla ◽

Arvind Kumar Yadav ◽

Tiratha Raj Singh

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analysis ◽

Data Extraction ◽

Basic Structure ◽

Big Data Analysis ◽

Biological Data ◽

Omics Data ◽

Hidden Information ◽

Continuous Development

The meaningful data extraction from the biological big data or omics data is a remaining challenge in bioinformatics. The deep learning methods, which can be used for the prediction of hidden information from the biological data, are widely used in the industry and academia. The authors have discussed the similarity and differences in the widely utilized models in deep learning studies. They first discussed the basic structure of various models followed by their applications in biological perspective. They have also discussed the suggestions and limitations of deep learning. They expect that this chapter can serve as significant perspective for continuous development of its theory, algorithm, and application in the established bioinformatics domain.

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Digital Image Classification Techniques

Advances in Social Networking and Online Communities - Hidden Link Prediction in Stochastic Social Networks ◽

10.4018/978-1-5225-9096-5.ch009 ◽

2019 ◽

pp. 162-187

Author(s):

Utkarsh Shrivastav ◽

Sanjay Kumar Singh

Keyword(s):

Remote Sensing ◽

Image Processing ◽

Deep Learning ◽

Ct Scan ◽

Image Classification ◽

Digital Image ◽

Digital Cameras ◽

Learning Networks ◽

Hidden Information ◽

Classification Techniques

Image classification is a technique to categorize an image in to given classes on the basis of hidden characteristics or features extracted using image processing. With rapidly growing technology, the size of images is growing. Different categories of images may contain different types of hidden information such as x-ray, CT scan, MRI, pathologies images, remote sensing images, satellite images, and natural scene image captured via digital cameras. In this chapter, the authors have surveyed various articles and books and summarized image classification techniques. There are supervised techniques like KNN and SVM, which classify an image into given classes and unsupervised techniques like K-means and ISODATA for classifying image into a group of clusters. For big images, deep learning networks can be employed that are fast and efficient and also compute hidden features automatically.

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Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2019.00305 ◽

2019 ◽

Vol 7 ◽

Cited By ~ 6

Author(s):

Nguyen Quoc Khanh Le ◽

Edward Kien Yee Yapp ◽

N. Nagasundaram ◽

Hui-Yuan Yeh

Keyword(s):

Deep Learning ◽

Dna Sequences ◽

Hidden Information

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A Research on Discourse Access

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1135.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 827-830

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Discourse Analysis ◽

Semantic Analysis ◽

Complex Problem ◽

Hidden Information ◽

Intelligent Machines ◽

Learning Techniques ◽

Work Done ◽

The Given

— We are living in the era of intelligent machines where everything is replacing by machine, interpretation of hidden meaning of text/speech becomes necessary to understands by machine. The last few years have seen an emergence of new perspectives in understanding the meaning of the given text. Researchers are working hard for this kind of complex problem. Discourse comes under this theme of understanding hidden information from the text. Syntactical and semantic analysis plays important role in discourse analysis. This paper focuses on the work done by various researchers in understanding discourse using various methods of machine learning and deep learning techniques.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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