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Applied Sciences
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Deep Learning for Data Analysis
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Deep Learning for Data Analysis

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 4574

Special Issue Editors

Dr. Andres Alvarez-Meza

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Computer Engineering, Universidad Nacional de Colombia, Manizales 17001, Colombia
Interests: machine learning; deep leaerning; signal processing; neuro-engineering; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. David Cárdenas-Peña

E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira 660001, Colombia
Interests: signal processing; computer vision; deep learning; pattern recognition

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on Deep Learning for Data Analysis.

Nowadays, Deep Learning is one of the central topics on widespread applications regarding data processing and analysis. In particular, the representation capability of deep learning approaches and their optimization frameworks, mainly based on automatic differentiation and parallel computing, yields sophisticated tools to extract relevant information from raw data, attracting more and more interest from the research community in several fields, such as computer vision, neuro-engineering, big data, business intelligence, time-series forecasting, natural language processing, artificial intelligence, among others.

In this Special Issue, we invite submissions exploring theoretical or applied research concerning recent advances in deep learning methods. Also, comprehensive review and survey papers are welcome.

Dr. Andres Alvarez-Meza
Dr. David Cárdenas-Peña
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • data analysis
  • machine learning
  • representation learning
  • computer vision
  • neuroengineering
  • big data
  • time series forecasting
  • natural language processing
  • ariticial intelligence

Published Papers (4 papers)

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Research

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19 pages, 1358 KiB  
Article
Advanced Dual Reversible Data Hiding: A Focus on Modification Direction and Enhanced Least Significant Bit (LSB) Approaches
by Cheonshik Kim, Luis Cavazos Quero, Ki-Hyun Jung and Lu Leng
Appl. Sci. 2024, 14(6), 2437; https://doi.org/10.3390/app14062437 - 14 Mar 2024
Viewed by 435
Abstract
In this study, we investigate advances in reversible data hiding (RDH), a critical area in the era of widespread digital data sharing. Recognizing the inherent vulnerabilities such as unauthorized access and data corruption during data transmission, we introduce an innovative dual approach to [...] Read more.
In this study, we investigate advances in reversible data hiding (RDH), a critical area in the era of widespread digital data sharing. Recognizing the inherent vulnerabilities such as unauthorized access and data corruption during data transmission, we introduce an innovative dual approach to RDH. We use the EMD (Exploiting Modification Direction) method along with an optimized LSB (Least Significant Bit) replacement strategy. This dual method, applied to grayscale images, has been carefully developed to improve data hiding by focusing on modifying pixel pairs. Our approach sets new standards for achieving a balance between high data embedding rates and the integrity of visual quality. The EMD method ensures that each secret digit in a 5-ary notational system is hidden by 2 cover pixels. Meanwhile, our LSB strategy finely adjusts the pixels selected by EMD to minimize data errors. Despite its simplicity, this approach has been proven to outperform existing technologies. It offers a high embedding rate (ER) while maintaining the high visual quality of the stego images. Moreover, it significantly improves data hiding capacity. This enables the full recovery of the original image without increasing file size or adding unnecessary data, marking a significant breakthrough in data security. Full article
(This article belongs to the Special Issue Deep Learning for Data Analysis)
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17 pages, 1234 KiB  
Article
Optimizing Single DGX-A100 System: Overcoming GPU Limitations via Efficient Parallelism and Scheduling for Large Language Models
by Kyeong-Hwan Kim and Chang-Sung Jeong
Appl. Sci. 2023, 13(16), 9306; https://doi.org/10.3390/app13169306 - 16 Aug 2023
Viewed by 1128
Abstract
In this study, we introduce a novel training algorithm specifically designed to overcome the limitations of GPU memory on a single DGX-A100 system. By utilizing the CPU and main memory in the training process and applying a strategy of division and parallelization, our [...] Read more.
In this study, we introduce a novel training algorithm specifically designed to overcome the limitations of GPU memory on a single DGX-A100 system. By utilizing the CPU and main memory in the training process and applying a strategy of division and parallelization, our algorithm enhances the size of the trainable language model and the batch size. In addition, we developed a comprehensive management system to effectively manage the execution of the algorithm. This system systematically controls the training process and resource usage, while also enabling the asynchronous deployment of tasks. Finally, we proposed a scheduling technique integrated into the management system, promoting efficient task scheduling in a complex, heterogeneous training environment. These advancements equip researchers with the ability to work with larger models and batch sizes, even when faced with limited GPU memory. Full article
(This article belongs to the Special Issue Deep Learning for Data Analysis)
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22 pages, 7548 KiB  
Article
A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer
by Wen-Jie Liu, Yu-Ting Bai, Xue-Bo Jin, Jian-Lei Kong and Ting-Li Su
Appl. Sci. 2022, 12(13), 6396; https://doi.org/10.3390/app12136396 - 23 Jun 2022
Cited by 2 | Viewed by 1247
Abstract
Time series prediction is crucial for advanced control and management of complex systems, while the actual data are usually highly nonlinear and nonstationary. A novel broad echo state network is proposed herein for the prediction problem of complex time series data. Firstly, the [...] Read more.
Time series prediction is crucial for advanced control and management of complex systems, while the actual data are usually highly nonlinear and nonstationary. A novel broad echo state network is proposed herein for the prediction problem of complex time series data. Firstly, the framework of the broad echo state network with cascade of mapping nodes (CMBESN) is designed by embedding the echo state network units into the broad learning system. Secondly, the number of enhancement layer nodes of the CMBESN is determined by proposing an incremental algorithm. It can obtain the optimal network structure parameters. Meanwhile, an optimization method is proposed based on the nonstationary statistic metrics to determine the enhancement layer. Finally, experiments are conducted both on the simulated and actual datasets. The results show that the proposed CMBESN and its optimization have good prediction capability for nonstationary time series data. Full article
(This article belongs to the Special Issue Deep Learning for Data Analysis)
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Review

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22 pages, 1804 KiB  
Review
Singular and Multimodal Techniques of 3D Object Detection: Constraints, Advancements and Research Direction
by Tajbia Karim, Zainal Rasyid Mahayuddin and Mohammad Kamrul Hasan
Appl. Sci. 2023, 13(24), 13267; https://doi.org/10.3390/app132413267 - 15 Dec 2023
Viewed by 1084
Abstract
Two-dimensional object detection techniques can detect multiscale objects in images. However, they lack depth information. Three-dimensional object detection provides the location of the object in the image along with depth information. To provide depth information, 3D object detection involves the application of depth-perceiving [...] Read more.
Two-dimensional object detection techniques can detect multiscale objects in images. However, they lack depth information. Three-dimensional object detection provides the location of the object in the image along with depth information. To provide depth information, 3D object detection involves the application of depth-perceiving sensors such as LiDAR, stereo cameras, RGB-D, RADAR, etc. The existing review articles on 3D object detection techniques are found to be focusing on either a singular modality (e.g., only LiDAR point cloud-based) or a singular application field (e.g., autonomous vehicle navigation). However, to the best of our knowledge, there is no review paper that discusses the applicability of 3D object detection techniques in other fields such as agriculture, robot vision or human activity detection. This study analyzes both singular and multimodal techniques of 3D object detection techniques applied in different fields. A critical analysis comprising strengths and weaknesses of the 3D object detection techniques is presented. The aim of this study is to facilitate future researchers and practitioners to provide a holistic view of 3D object detection techniques. The critical analysis of the singular and multimodal techniques is expected to help the practitioners find the appropriate techniques based on their requirement. Full article
(This article belongs to the Special Issue Deep Learning for Data Analysis)
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