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Applied Sciences
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Advances in Applied Deep Learning Based Methods and Architectures for...
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Advances in Applied Deep Learning Based Methods and Architectures for Data Analytics

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 (30 November 2022) | Viewed by 4530

Special Issue Editors

Dr. Daniel Ortiz-Arroyo

E-Mail Website
Guest Editor
Energy Department, Aalborg University, 6700 Esbjerg, Denmark
Interests: artificial intelligence; machine learning; data science; data analytics; deep learning; deep neural networks; big data; transformer neural networks; probabilistic deep neural networks; long short term memory networks; end to end deep learning systems; autoencoders; convolutional neural networks; capsul neural networks; temporal convolutional neural networks; recurrent deep neural networks; deep reinforcement learning
Dr. Petar Durdevic Løhndorf

E-Mail Website
Guest Editor
Energy Department, Aalborg University, 6700 Esbjerg, Denmark
Interests: control, artificial intelligence; machine learning; deep reinforcement learning; deep learning; transformer neural networks; probabilistic deep neural networks; long short term memory networks; end to end deep learning systems; autoencoders; convolutional neural networks; temporal convolutional neural networks

Special Issue Information

Dear Colleagues,

Deep learning has become the predominant technology in machine learning. Supervised, unsupervised, reinforcement, and hybrid approaches in deep learning have been successfully applied in various domains, from computer vision to natural language processing, robotics, and data science and analytics. Deep learning techniques require large amounts of data, from which knowledge about hidden patterns, trends, and correlations can be extracted. Today, these data are generated by companies and public organizations at unprecedented rates. The synergy between deep learning and its use of large amounts of data is exploited in data analytics to extract increasingly complex data patterns and abstractions to gain higher-level knowledge about the data.

This Special Issue of Applied Sciences focuses on recent research work on deep learning techniques that may be tailored to perform data analytics, including big data. Of special interest is state-of-the-art research on theoretical and applied methods in deep-learning-based data analytics within science and engineering. The topics of interest also include, but are not limited to: new deep-neural-network-based (DNNs) architectures and novel applications of ensembles of DNNs for data analytics; efficient processing methods in real-time with deep learning algorithms, novel frameworks, architectures, and pipelines of distributed-cloud-based DNNs; emerging applications of deep learning with probabilistic deep neural networks, temporal convolutional networks, transformer deep learning models for data analytics, variational methods, recurrent neural networks for predictive analytics, and reinforcement learning approaches for prescriptive data analytics. We invite authors to contribute original research work in this peer-reviewed Special Issue of Applied Sciences.

Dr. Daniel Ortiz-Arroyo
Dr. Petar Durdevic Løhndorf
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 analytics
  • big data
  • deep neural networks
  • cloud-based machine learning
  • theory and applications of deep learning

Published Papers (3 papers)

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Research

17 pages, 626 KiB  
Article
HDL-ODPRs: A Hybrid Deep Learning Technique Based Optimal Duplication Detection for Pull-Requests in Open-Source Repositories
by Saud S. Alotaibi
Appl. Sci. 2022, 12(24), 12594; https://0-doi-org.brum.beds.ac.uk/10.3390/app122412594 - 08 Dec 2022
Viewed by 1004
Abstract
Recently, open-source repositories have grown rapidly due to volunteer contributions worldwide. Collaboration software platforms have gained popularity as thousands of external contributors have contributed to open-source repositories. Although data de-duplication decreases the size of backup workloads, this causes poor data locality (fragmentation) and [...] Read more.
Recently, open-source repositories have grown rapidly due to volunteer contributions worldwide. Collaboration software platforms have gained popularity as thousands of external contributors have contributed to open-source repositories. Although data de-duplication decreases the size of backup workloads, this causes poor data locality (fragmentation) and redundant review time and effort. Deep learning and machine learning techniques have recently been applied to identify complex bugs and duplicate issue reports. It is difficult to use, but it increases the risk of developers submitting duplicate pull requests, resulting in additional maintenance costs. We propose a hybrid deep learning technique in this work on the basis of an optimal duplication detection is for pull requests (HDL-ODPRs) in open-source repositories. An algorithm used to extract textual data from pull requests is hybrid leader-based optimization (HLBO), which increases the accuracy of duplicate detection. Following that, we compute the similarities between pull requests by utilizing the multiobjective alpine skiing optimization (MASO) algorithm, which provides textual, file-change, and code-change similarities. For pull request duplicate detection, a hybrid deep learning technique (named GAN-GS) is introduced, in which the global search (GS) algorithm is used to optimize the design metrics of the generative adversarial network (GAN). The proposed HDL-ODPR model is validated against the public standard benchmark datasets, such as DupPR-basic and DupPR-complementary data. According to the simulation results, the proposed HDL-ODPR model can achieve promising results in comparison with existing state-of-the-art models. Full article
(This article belongs to the Special Issue Advances in Applied Deep Learning Based Methods and Architectures for Data Analytics)
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17 pages, 4022 KiB  
Article
A Novel Approach to Classify Telescopic Sensors Data Using Bidirectional-Gated Recurrent Neural Networks
by Ali Raza, Kashif Munir, Mubarak Almutairi, Faizan Younas, Mian Muhammad Sadiq Fareed and Gulnaz Ahmed
Appl. Sci. 2022, 12(20), 10268; https://0-doi-org.brum.beds.ac.uk/10.3390/app122010268 - 12 Oct 2022
Cited by 9 | Viewed by 1312
Abstract
Asteroseismology studies the physical structure of stars by analyzing their solar-type oscillations as seismic waves and frequency spectra. The physical processes in stars and oscillations are similar to the Sun, which is more evolved to the red-giant branch (RGB), representing the Sun’s future. [...] Read more.
Asteroseismology studies the physical structure of stars by analyzing their solar-type oscillations as seismic waves and frequency spectra. The physical processes in stars and oscillations are similar to the Sun, which is more evolved to the red-giant branch (RGB), representing the Sun’s future. In stellar astrophysics, the RGB is a crucial problem to determine. An RGB is formed when a star expands and fuses all the hydrogen in its core into helium which starts burning, resulting in helium burning (HeB). According to a recent state by NASA Kepler mission, 7000 HeB and RGB were observed. A study based on an advanced system needs to be implemented to classify RGB and HeB, which helps astronomers. The main aim of this research study is to classify the RGB and HeB in asteroseismology using a deep learning approach. Novel bidirectional-gated recurrent units and a recurrent neural network (BiGR)-based deep learning approach are proposed. The proposed model achieved a 93% accuracy score for asteroseismology classification. The proposed technique outperforms other state-of-the-art studies. The analyzed fundamental properties of RGB and HeB are based on the frequency separation of modes in consecutive order with the same degree, maximum oscillation power frequency, and mode location. Asteroseismology Exploratory Data Analysis (AEDA) is applied to find critical fundamental parameters and patterns that accurately infer from the asteroseismology dataset. Our key findings from the research are based on a novel classification model and analysis of root causes for the formation of HeB and RGB. The study analysis identified that the cause of HeB increases when the value of feature Numax is high and feature Epsilon is low. Our research study helps astronomers and space star oscillations analyzers meet their astronomy findings. Full article
(This article belongs to the Special Issue Advances in Applied Deep Learning Based Methods and Architectures for Data Analytics)
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12 pages, 1888 KiB  
Article
Aircraft Rotation Detection in Remote Sensing Image Based on Multi-Feature Fusion and Rotation-Aware Anchor
by Feifan Tang, Wei Wang, Jian Li, Jiang Cao, Deli Chen, Xin Jiang, Huifang Xu and Yanling Du
Appl. Sci. 2022, 12(3), 1291; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031291 - 26 Jan 2022
Cited by 3 | Viewed by 1404
Abstract
Due to the variations of aircraft types, sizes, orientations, and complexity of remote sensing images, it is still difficult to effectively obtain accurate position and type by aircraft detection, which plays an important role in intelligent air transportation and digital battlefield. Current aircraft [...] Read more.
Due to the variations of aircraft types, sizes, orientations, and complexity of remote sensing images, it is still difficult to effectively obtain accurate position and type by aircraft detection, which plays an important role in intelligent air transportation and digital battlefield. Current aircraft detection methods often use horizontal detectors, which produce significant redundancy, nesting, and overlap of detection areas and negatively affect the detection performance. To address these difficulties, a framework based on RetinaNet that combines a multi-feature fusion module and a rotating anchors generation mechanism is proposed. Firstly, the multi-feature fusion module mainly realizes feature fusion in two ways. One is to extract multi-scale features by the feature pyramid, and the other is to obtain corner features for each layer of feature map, thereby enriching the feature expression of aircraft. Then, we add a rotating anchor generation mechanism in the middle of the framework to realize the arbitrary orientation detection of aircraft. In the last, the framework connects two sub-networks, one for classifying anchor boxes and the other for regressing anchor boxes to ground-truth aircraft boxes. Compared with state-of-the-art methods by conducting comprehensive experiments on a publicly available dataset to validate the proposed method performance of aircraft detection. The detection precision (P) of proposed method achieves 97.06% on the public dataset, which demonstrates the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances in Applied Deep Learning Based Methods and Architectures for Data Analytics)
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