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Advances in Deep Learning-Based Information Processing for Big Data Analytics...
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Advances in Deep Learning-Based Information Processing for Big Data Analytics and Digital Transformation

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 (20 October 2022) | Viewed by 18924

Special Issue Editors

Prof. Dr. Xiao Chen

E-Mail Website
Guest Editor
Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, Building 101, Room S04, 4000 Roskilde, Denmark
Interests: advanced testing and high-performance modeling of large-scale composite structures and offshore steel structures with a focus on structural integrity and digitalization using Industry 4.0 technologies
Prof. Dr. ASM Shihavuddin

E-Mail Website
Guest Editor
CSE department, Independent University, Bangladesh(IUB), Dhaka 1229, Bangladesh
Interests: computer vision; machine learning; data science
Prof. Dr. Dan Zheng

E-Mail Website
Guest Editor
School of Economics and Management, University of Chinese Academy of Sciences, Haidian District, Zhongguancun Dong Road 80, Beijing 100090, China
Interests: regional economics and urban economics; new economic geography (NEG); regional income disparity; regional economic growth and regional environmental governance focusing on China

Special Issue Information

Dear Colleagues,

Digitalization brings tremendous opportunities and unprecedented challenges to humanity. It constantly transforms our understanding of our world, the way we interact with each other, as well as the way the world itself operates. Data and analytics are key to digital transformation. With the recent advances in Artificial intelligence and Machine learning, we are starting to move into the phase where we can automate to make use of these vast amounts of data generated every day. Deep learning technologies had been revolutionary in learning from structured and unstructured data; however, constant innovations and research in this direction would ensure higher reliability and more comprehensibility into the domain.

This Special Issue calls for the latest research contributions in the area of big data analytics and digital transformation facilitated by deep-learning-based neural information processing. In this issue, contributions could cover state-of-the-art deep learning techniques for addressing big data; novel approaches for adapting deep learning for applications in other fields; innovative methods to address digital transformation through machine learning; the brilliant ideas to address challenges and identify possible solutions using big data analytics; and the new concepts that potentially lead to disruptive technological and societal innovation through digital transformation.  

The articles of this Special Issue will contribute to natural sciences and engineering facilitated by computer sciences such as artificial intelligence, data analytics, machine learning, pattern recognition, big data manipulation, data fusion, as well as digital twin technologies in various applications. The scope of the expected contributions is also extended to social sciences with data-driven approaches applied to economics, business, finance, management, and sociology in order to analyze complex resources for better decision making, collaboration, and value creation.

Prof. Dr. Xiao Chen
Prof. Dr. ASM Shihavuddin
Prof. Dr. Dan Zheng
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

  • digitalization
  • machine learning
  • big data
  • data analysis
  • data management
  • computer vision
  • digital twin
  • social science
  • data management
  • deep learning
  • artificial intelligence
  • big data analytics
  • data science

Published Papers (5 papers)

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Research

14 pages, 1637 KiB  
Article
A Unified Knowledge Extraction Method Based on BERT and Handshaking Tagging Scheme
by Ning Yang, Sio Hang Pun, Mang I Vai, Yifan Yang and Qingliang Miao
Appl. Sci. 2022, 12(13), 6543; https://doi.org/10.3390/app12136543 - 28 Jun 2022
Cited by 3 | Viewed by 2095
Abstract
In the actual knowledge extraction system, different applications have different entity classes and relationship schema, so the generalization and migration ability of knowledge extraction are very important. By training a knowledge extraction model in the source domain and applying the model to an [...] Read more.
In the actual knowledge extraction system, different applications have different entity classes and relationship schema, so the generalization and migration ability of knowledge extraction are very important. By training a knowledge extraction model in the source domain and applying the model to an arbitrary target domain directly, open domain knowledge extraction technology becomes crucial to mitigate the generalization and migration ability issues. Traditional knowledge extraction models cannot be directly transferred to new domains and also cannot extract undefined relation types. In order to deal with the above issues, in this paper, we proposed an end-to-end Chinese open-domain knowledge extraction model, TPORE (Extract Open-domain Relations through Token Pair linking), which combined BERT with a handshaking tagging scheme. TPORE can alleviate the nested entities and nested relations issues. Additionally, a new loss function that conducts a pairwise comparison of target category score and non-target category score to automatically balance the weight was adopted, and the experiment results indicate that the loss function can bring speed and performance improvements. The extensive experiments demonstrate that the proposed method can significantly surpass strong baselines. Specifically, our approach can achieve new state-of-the-art Chinese open Relation Extraction (ORE) benchmarks (COER and SAOKE). In the COER dataset, F1 increased from 66.36% to 79.63%, and in the SpanSAOKE dataset, F1 increased from 46.0% to 54.91%. In the medical domain, our method can obtain close performance compared with the SOTA method in the CMeIE and CMeEE datasets. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Information Processing for Big Data Analytics and Digital Transformation)
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18 pages, 1844 KiB  
Article
A Systematic Approach to Healthcare Knowledge Management Systems in the Era of Big Data and Artificial Intelligence
by Anh-Cang Phan, Thuong-Cang Phan and Thanh-Ngoan Trieu
Appl. Sci. 2022, 12(9), 4455; https://doi.org/10.3390/app12094455 - 28 Apr 2022
Cited by 16 | Viewed by 4057
Abstract
Big data in healthcare contain a huge amount of tacit knowledge that brings great value to healthcare activities such as diagnosis, decision support, and treatment. However, effectively exploring and exploiting knowledge on such big data sources exposes many challenges for both managers and [...] Read more.
Big data in healthcare contain a huge amount of tacit knowledge that brings great value to healthcare activities such as diagnosis, decision support, and treatment. However, effectively exploring and exploiting knowledge on such big data sources exposes many challenges for both managers and technologists. In this study, we therefore propose a healthcare knowledge management system that ensures the systematic knowledge development process on various data in hospitals. It leverages big data technologies to capture, organize, transfer, and manage large volumes of medical knowledge, which cannot be handled with traditional data-processing technologies. In addition, machine-learning algorithms are used to derive knowledge at a higher level in supporting diagnosis and treatment. The orchestration of a knowledge system, big data, and artificial intelligence brings many advances to healthcare. Our research results show that the system fully ensures the knowledge development process, serving for knowledge exploration and exploitation to improve decision-making in healthcare. The knowledge system is illustrated for the detection and classification of high blood pressure and brain hemorrhage in text and CT/MRI image formats, respectively, from medical records of hospitals. It can support doctors to accurately diagnose the diseases to give appropriate treatment regimens. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Information Processing for Big Data Analytics and Digital Transformation)
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13 pages, 570 KiB  
Article
Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
by Bo Jiang, Xinya Li, Shuhao Yang, Yaqi Kong, Wei Cheng, Chuanyan Hao and Qiaomin Lin
Appl. Sci. 2022, 12(8), 3982; https://doi.org/10.3390/app12083982 - 14 Apr 2022
Cited by 9 | Viewed by 5978
Abstract
Personalized learning paths aim to save learning time and improve learning achievements by providing the most appropriate learning sequence for heterogeneous students. Most existing methods that construct personalized learning paths focus on students’ characteristics or knowledge structure, while ignoring the critical roles of [...] Read more.
Personalized learning paths aim to save learning time and improve learning achievements by providing the most appropriate learning sequence for heterogeneous students. Most existing methods that construct personalized learning paths focus on students’ characteristics or knowledge structure, while ignoring the critical roles of learning states. This study describes a dynamic personalized learning path planning algorithm to recommend appropriate knowledge points for online students based on their learning states and the difficulty of each knowledge point. The proposed method first calculates the difficulty of knowledge points automatically and constructs a knowledge difficulty model. Then, a dynamic knowledge mastery model is built based on learning behavior and normalized test scores. Finally, a path that satisfies students’ personalized changing states is generated. To achieve the aforementioned goal, a novel method that calculates the difficulty of knowledge points automatically is proposed. Moreover, the personalized learning path planning method proposed in this research is not limited to a particular course. To evaluate the method, we use a series of approaches to verify the impact of the personalized path on student learning. The experimental results demonstrate that the proposed algorithm can effectively generate personalized learning paths. Results demonstrate that the personalized path proposed by the algorithm can improve effective behavior rates, course completion rates and learning efficiency. Results also show that the personalized learning paths based on student states would help students to master knowledge. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Information Processing for Big Data Analytics and Digital Transformation)
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17 pages, 2625 KiB  
Article
An Operational Image-Based Digital Twin for Large-Scale Structures
by Hans-Henrik Benzon, Xiao Chen, Lewis Belcher, Oscar Castro, Kim Branner and Jesper Smit
Appl. Sci. 2022, 12(7), 3216; https://doi.org/10.3390/app12073216 - 22 Mar 2022
Cited by 18 | Viewed by 3456
Abstract
This study presents a novel methodology to create an operational Digital Twin for large-scale structures based on drone inspection images. The Digital Twin is primarily used as a virtualized representation of the structure, which will be updated according to physical changes during the [...] Read more.
This study presents a novel methodology to create an operational Digital Twin for large-scale structures based on drone inspection images. The Digital Twin is primarily used as a virtualized representation of the structure, which will be updated according to physical changes during the life cycle of the structure. The methodology is demonstrated on a wind turbine transition piece. A three-dimensional geometry reconstruction of a transition piece as manufactured is created using a large number (>500) of RGB images collected from a drone and/or several LiDAR scans. Comparing the reconstruction to the original design will locate and quantify geometric deviations and production tolerances. An artificial intelligence algorithm is used to detect and classify paint defects/damages from images. The detected and classified paint defects/damages are subsequently digitalized and mapped to the three-dimensional geometric reconstruction of the structure. These developed functionalities allow the Digital Twin of the structure to be updated with manufacturing-induced geometric deviations and paint defects/damages using inspection images at regular time intervals. The key enabling technologies to realize the Digital Twin are presented in this study. The proposed methodology can be used in different industrial sectors, such as the wind energy, oil, and gas industries, aerospace, the marine and transport sector, and other large infrastructures. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Information Processing for Big Data Analytics and Digital Transformation)
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20 pages, 5831 KiB  
Article
RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound
by Shahriar Mahmud Kabir, Mohammed I. H. Bhuiyan, Md Sayed Tanveer and ASM Shihavuddin
Appl. Sci. 2021, 11(24), 12138; https://doi.org/10.3390/app112412138 - 20 Dec 2021
Cited by 6 | Viewed by 2061
Abstract
This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. [...] Read more.
This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Information Processing for Big Data Analytics and Digital Transformation)
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