Application of Deep Learning and Big Data Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 412

Special Issue Editor


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Guest Editor
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: cloud storage; storage for AI; HPC; decentralized storage

Special Issue Information

Dear Colleagues,

In today's digital age, deep learning and big data processing has drawn significant attention in the scientific community. Deep learning exhibits remarkable capabilities in handling complex tasks such as image and speech recognition, natural language processing, and decision-making. Its ability to autonomously learn intricate patterns from data empowers applications in diverse domains, revolutionizing fields like healthcare, finance, and autonomous systems. Big data processing involves efficiently managing and analyzing vast datasets, unlocking valuable insights for informed decision-making and driving innovations across industries.

This Special Issue mainly focuses on the application of deep learning and big data processing techniques in various scientific fields. We welcome original papers and review papers related to the topics below. Authors are encouraged to delve into real-world case studies, offering insights into challenges in deploying deep learning models on large-scale datasets. We are also interested in papers on scalable, efficient big data processing frameworks that enable the seamless integration of deep learning technologies. The topics of interest include but are not limited to the following:

  • Computer Vision.
  • Speech, Natural Language Processing and Understanding.
  • Data Mining and Data Science.
  • Distributed Computing.
  • Big Data Infrastructure.
  • Social and Economic Aspects of Deep Learning

Prof. Dr. Yuchong Hu
Guest Editor

Manuscript Submission Information

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Keywords

  • computer vision
  • natural language processing
  • machine learning
  • big data
  • data processing
  • AI for big data
  • data system for AI
  • distributed data management
  • large-scale systems for data analysis

Published Papers (1 paper)

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Research

23 pages, 5464 KiB  
Article
Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis
by Edgar Ek-Chacón, Erik Molino-Minero-Re, Paul Erick Méndez-Monroy, Antonio Neme and Hector Ángeles-Hernández
Appl. Sci. 2024, 14(10), 4175; https://doi.org/10.3390/app14104175 - 15 May 2024
Viewed by 277
Abstract
A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts [...] Read more.
A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems. Full article
(This article belongs to the Special Issue Application of Deep Learning and Big Data Processing)
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