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Electronics
Special Issues
Big Data Analytics, Emerging Technologies and Its Applications
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Big Data Analytics, Emerging Technologies and Its Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 9315

Special Issue Editors

Dr. Abdul Rehman

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Kyungpook National University, 41566, Daegu, South Korea
Interests: IoT & SIoT; small world problem; big data; artificial intelligence; networking and security
Dr. Anand Paul

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Interests: machine learning; digital twinning; self-supervised learning; smart city; data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Malik Junaid Jami Gul

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea
Interests: artificial intelligence; federated learning; security and privacy; data analytics; image processing; blockchain
Dr. Ateeq Ur Rehman

E-Mail Website
Guest Editor
Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
Interests: social Internet of Things; AI; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Big data analytics (BDA) and its applications are emerging research fields that gather all analytical approaches for processing massive data by mining hidden insights that would not be achievable using traditional methods. Big data analytics has been attracting significant attention from diverse disciplines, such as computer science, information technology, and social sciences. The key use of big data analytics by these industries is to acquire substantial information that allows them to better evaluate, predict, and encounter undiscovered patterns and then improve their competitiveness. Using big data tools and software enables an organization to process gigantic volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future. This Special issue is open to submitting high-quality research contributions from a wide range of professions, including scholars, researchers, academicians, and industry people. The Special Issue aims to focus on original research papers of high quality, providing practical applications to real-world problems and bridging the gap between industry and academia. Topics of interests include, but are not limited to, the following:

  • Big data applications;
  • Big data analytics and social media;
  • Machine learning and AI for big data;
  • IoT for big data;
  • Smart computing models, tools, and devices;
  • Retrieval and generation of big data;
  • Tools and systems for big data;
  • Techniques, models and algorithms for big data;
  • Smart computing models, tools, and devices;
  • Security and privacy for big data;
  • Infrastructure and platform for big data and smart computing;
  • Data and information quality;
  • Big data and Cloud Computing;
  • Big data analytics and stock market;
  • Big algorithms and software development;
  • Big data analytics for smart cities;
  • Big data analytics for e-healthcare;
  • Big data analytics for water management;
  • Big data analytics for waste management;
  • Medical big data analytics;
  • Internet of Medical Things;
  • Remote pain/patient monitoring;
  • Big data analytics for urban traffic management;
  • Application of big data analytics;
  • Smart grid, smart home, connected car, connected health, smart farming, etc.;
  • Deployment of big data for security, privacy, and trust for smart future networks;
  • Big data analytics for mitigating traffic accidents, congestion, environmental pollution, etc.;
  • Traffic accident detection and analysis based on big data.

Dr. Abdul Rehman
Dr. Anand Paul
Dr. Malik Junaid Jami Gul
Dr. Ateeq Ur Rehman
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. Electronics 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

  • big data analytics
  • machine learning
  • artificial intelligence (AI)
  • IoT
  • security and privacy

Published Papers (4 papers)

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Research

22 pages, 6276 KiB  
Article
Leveraging State-of-the-Art Topic Modeling for News Impact Analysis on Financial Markets: A Comparative Study
by Weisi Chen, Fethi Rabhi, Wenqi Liao and Islam Al-Qudah
Electronics 2023, 12(12), 2605; https://doi.org/10.3390/electronics12122605 - 9 Jun 2023
Cited by 10 | Viewed by 4201
Abstract
News impact analysis has become a common task conducted by finance researchers, which involves reading and selecting news articles based on themes and sentiments, pairing news events and relevant stocks, and measuring the impact of selected news on stock prices. To facilitate more [...] Read more.
News impact analysis has become a common task conducted by finance researchers, which involves reading and selecting news articles based on themes and sentiments, pairing news events and relevant stocks, and measuring the impact of selected news on stock prices. To facilitate more efficient news selection, topic modeling can be applied to generate topics out of a large number of news documents. However, there is very limited existing literature comparing topic models in the context of finance-related news impact analysis. In this paper, we compare three state-of-the-art topic models, namely Latent Dirichlet allocation (LDA), Top2Vec, and BERTopic, in a defined scenario of news impact analysis on financial markets, where 38,240 news articles with an average length of 590 words are analyzed. A service-oriented framework for news impact analysis called “News Impact Analysis” (NIA) is advocated to leverage multiple topic models and provide an automated and seamless news impact analysis process for finance researchers. Experimental results have shown that BERTopic performed best in this scenario, with minimal data preprocessing, the highest coherence score, the best interpretability, and reasonable computing time. In addition, a finance researcher was able to conduct the entire news impact analysis process, which validated the feasibility and usability of the NIA framework. Full article
(This article belongs to the Special Issue Big Data Analytics, Emerging Technologies and Its Applications)
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14 pages, 677 KiB  
Article
Attentional Interactive Encoder Network Focused on Aspect for Sentiment Classification
by Bin Yang, Haoling Li, Sikai Teng, Yuze Sun and Ying Xing
Electronics 2023, 12(6), 1329; https://doi.org/10.3390/electronics12061329 - 10 Mar 2023
Cited by 2 | Viewed by 1003
Abstract
Aspect-based sentiment analysis (ABSA) plays a significant role in the field of big data and aims to distinguish the sentiment polarity of specific aspects in given sentences; however, the previous works on ABSA had two limitations. They mainly considered semantic features, rather than [...] Read more.
Aspect-based sentiment analysis (ABSA) plays a significant role in the field of big data and aims to distinguish the sentiment polarity of specific aspects in given sentences; however, the previous works on ABSA had two limitations. They mainly considered semantic features, rather than syntactic dependency features, and paid too much attention to the context words, while ignoring the high-level interaction of multiple representations of aspects themselves. To cope with these limitations, we propose a new method based on the graph convolutional network (GCN) and the multi-head attention mechanism, called the attention interactive encoder network (AIEN). The GCN was used to obtain the syntactic information that has the greatest syntactic impact on the aspect based on the syntax dependency tree. The multi-head attention mechanism can not only obtain the context-aware information of the given aspects, but also the interaction information between multiple representations of the aspect itself. The high-level information generated by the interaction of multi-dimensional features can produce a stronger representation ability for the aspect. Our experiments with the proposed model on five benchmark datasets showed that our model outperformed other works significantly. The experimental results further demonstrated the feasibility and applicability of our proposed model in the ABSA task. Full article
(This article belongs to the Special Issue Big Data Analytics, Emerging Technologies and Its Applications)
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17 pages, 4552 KiB  
Article
Digital Twin-Based Zero-Touch Management for IoT
by Jiali Ma, Yuanbo Guo, Chen Fang and Qi Zhang
Electronics 2022, 11(24), 4104; https://doi.org/10.3390/electronics11244104 - 9 Dec 2022
Cited by 5 | Viewed by 1591
Abstract
The rapid development of the Internet of Things (IoT) requires network automation, to improve management efficiency and reduce manual operations. Zero-touch network is a promising technology for empowering network management automation by creating virtualized networks for software-based solutions. However, the traditional software-defined network [...] Read more.
The rapid development of the Internet of Things (IoT) requires network automation, to improve management efficiency and reduce manual operations. Zero-touch network is a promising technology for empowering network management automation by creating virtualized networks for software-based solutions. However, the traditional software-defined network (SDN) technology is not suitable for IoT devices, due to its massive, heterogeneous, and distributed characteristics. In this paper, we introduce digital twin technology (DT) into the IoT, and propose a DT modeling method through ontology and knowledge graph technologies, which maps IoT elements in the digital space and provides the advantages of centralized control, device abstraction, and flexible control of management. Then, referring to the conceptual architecture of a zero-touch network, a DT-based zero-touch management framework suitable for IoT is established. Finally, aiming at specific device management and network optimization problems in the IoT, a zero-touch management scheme with digital twin technology as the core and intention as the driver is proposed, and the effectiveness of the proposed method is demonstrated using an example. Full article
(This article belongs to the Special Issue Big Data Analytics, Emerging Technologies and Its Applications)
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19 pages, 4142 KiB  
Article
Deep Learning Architecture for Flight Flow Spatiotemporal Prediction in Airport Network
by Haipei Zang, Jinfu Zhu and Qiang Gao
Electronics 2022, 11(23), 4058; https://doi.org/10.3390/electronics11234058 - 6 Dec 2022
Cited by 5 | Viewed by 1358
Abstract
Traffic flow prediction is a significant component for the new generation intelligent transportation. In the field of air transportation, accurate prediction of airport flight flow can help airlines schedule flights and provide a decision-making basis for airport resource allocation. With the help of [...] Read more.
Traffic flow prediction is a significant component for the new generation intelligent transportation. In the field of air transportation, accurate prediction of airport flight flow can help airlines schedule flights and provide a decision-making basis for airport resource allocation. With the help of Deep Learning technology, this paper focuses on the characteristics of flight flow easily disturbed by environmental factors, studies the spatiotemporal dependence between flight flows, and predicts the spatiotemporal distribution of flight flows from the airport network level. We proposed a deep learning architecture named ATFSTNP, which combining the residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM). Based big data analytics of air traffic management, this method takes the spatiotemporal causal relationship between weather impact and flight flow as the core, and deeply mines the complex spatiotemporal relationship of flight flow. The model’s methodologies are improved from the practical application level, and extensive experiments conducted on the China’s flight operation dataset. The results illustrate that the improved model has significant advantages in predicting the flight flow under weather affect. Even in the complex and variable external environment, the model can still accurately predict the spatiotemporal distribution of the airport network flight flow, with strong robustness. Full article
(This article belongs to the Special Issue Big Data Analytics, Emerging Technologies and Its Applications)
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